v3.25.4
Risk management
 12 Months Ended
Dec. 31, 2025
Risk management [abstract]  
Risk management
Basis of disclosures (*)
The risk management section contains information relating to the
nature and extent of the risks of financial instruments as required
by International Financial Reporting Standards (IFRS) 7 ‘Financial
Instruments: Disclosures’. These disclosures are an integral part of
the ING Group Consolidated financial statements and are indicated
by the symbol (*). This is applicable for the chapters, paragraphs,
graphs, or tables within the risk management section that are
indicated with this symbol in the respective headings or table
header.
This risk management section includes additional disclosures
beyond those required by IFRS, such as certain legal and regulatory
disclosures. Not all information in this section can be reconciled
back to the primary financial statements and corresponding notes,
as it has been prepared using risk data that differs from the
accounting basis of measurement. Disclosures in accordance with
Part Eight of the CRR3 and CRD VI, and as required by the
supervisory authority, are published in our ‘Additional Pillar III
Report’, which can be found on our corporate website ing.com.
Credit & counterparty risk categories (*)
In the table below, we describe the different types of credit and
counterparty risk categories and include a reconciliation with the notes in
the financial statements:
Credit risk appetite and concentration risk framework (*)
The credit risk appetite and concentration risk framework is designed to
prevent undesired high levels of credit risk and credit concentrations within
various levels of the ING portfolio. It is derived from the concepts of
boundaries and instruments as described in the ING Risk Appetite Framework.
Credit risk appetite is the maximum level of credit risk ING is willing to
accept for growth and value creation. The credit risk appetite is linked to
the overall bank-wide framework and is expressed in quantitative and
qualitative measures.
The credit risk appetite is set at different levels and dimensions within ING.
The credit risk appetite framework specifies the scope and focus of the
credit risk ING is willing to take, as well the composition of the credit
portfolio, including the concentration and diversification objectives across
business lines, locations, sectors, and products.
Reconciliation between credit & counterparty risk categories and financial position (*)
Credit risk categories
Notes in the financial statements
Lending risk: The risk that the client (counterparty, corporate, or individual)
does not pay the principal interest or fees on a loan when they are due, or
on demand for letters of credit (LCs) and guarantees provided by ING.
2
Cash and balances with central banks
3
Loans and advances to banks
4
Financial assets at fair value through profit or loss
5
Financial assets at fair value through other comprehensive income
7
Loans and advances to customers
41
Commitments
Investment risk: The credit default and risk rating migration risk that is
associated with ING’s investments in bonds, commercial paper, equities,
securitisations, and other similar publicly traded securities. This can be
viewed as the potential loss that ING may incur from holding a position in
underlying securities whose issuer's credit quality deteriorates or defaults.
4
Financial assets at fair value through profit or loss
5
Financial assets at fair value through other comprehensive income
6
Debt securities
Money market risk: This arises when ING places short-term deposits with a
counterparty in order to manage excess liquidity. In the event of a
counterparty default, ING may lose the deposit placed.
2
Cash and balances with central banks
3
Loans and advances to banks
7
Loans and advances to customers
Pre-settlement risk: This arises when a client defaults on a transaction before
settlement and ING must replace the contract by a trade with another
counterparty at the then prevailing (possibly unfavourable) market price.
This credit risk category is associated with derivatives transactions
(exchange-traded derivatives, over-the-counter (OTC) derivatives and
securities financing transactions).
4
Financial assets at fair value through profit or loss
14
Financial liabilities at fair value through profit or loss
40
Offsetting financial assets and liabilities
Settlement risk: This arises when there is an exchange of value (funds or
instruments) and receipt from its counterparty is not verified or expected
until after ING has given irrevocable instructions to pay or has paid or
delivered its side of the trade. The risk is that ING delivers but does not
receive delivery from its counterparty.
4
Financial assets at fair value through profit or loss
11
Other assets
14
Financial liabilities at fair value through profit or loss
16
Other liabilities
The credit concentration risk framework is composed of:
§Country risk concentration:  Country risk is the risk that arises due to
events in a specific country (or group of countries). To manage the
maximum country loss ING is willing to accept, boundaries are
approved by the SB. The estimated level is correlated to the risk rating
assigned to a given country. Actual country limits are set by means of
country instruments, which are monitored monthly and updated when
needed. For countries with elevated levels of geopolitical or severe
economic cycle risk, monitoring is performed on a more frequent basis,
with strict pipeline and exposure management.
§Single name concentration (including secondary risk): ING has an
established credit concentration risk framework to identify, measure,
and monitor single name concentration, including secondary risk. The
same concept of boundaries and instruments is applicable.
§Sector and product concentration  risk are managed via the credit risk
appetite framework.
Credit risk portfolio (*)
Within ING, credit risk exposures are classified into four sources: 1)
Consumer Lending (to private individuals), 2) Business Lending, 3)
Investment and Money Market and 4) Pre-settlement. ING’s credit
exposure is mainly related to lending to individuals (also referred to as
consumer lending, all Retail) and businesses (referred to as business
lending, both in Retail and Wholesale), followed by investments in bonds
and securitised assets, and money market (Wholesale). Loans to
individuals are mainly mortgage loans secured by residential property.
Loans (including guarantees issued) to businesses are often collateralised,
but may be unsecured based on the internal analysis of the borrower’s
creditworthiness. Bonds in the investment portfolio are generally
unsecured, but predominantly consist of bonds issued by central
governments and EU and/or OECD-based financial institutions. Secured
bonds, such as mortgage-backed securities and asset-backed securities,
are secured by the underlying diversified pool of assets (commercial or
residential mortgages, car loans and/or other assets) held by the securities
issuer. For money market, exposure is mainly deposits to central banks.
The last major credit risk source involves pre-settlement exposures which
arise from trading activities, including derivatives, repurchase transactions
and securities lending/borrowing transactions. This is also commonly
referred to as counterparty credit risk.
Overall portfolio (*)
During 2025, ING’s portfolio size increased by €41.1 billion (4.3%) to
€1,002.9 billion outstanding. Foreign exchange rate changes had a €22.0
billion negative impact on the portfolio size, mainly driven by the
depreciation of the US dollar which had an impact of €16.6 billion mainly in
Wholesale Banking. Following this, net growth in Wholesale Banking was
€4.4 billion. Retail Banking outstandings increased by €38.4 billion mainly
due to underlying growth in residential mortgages.
Rating distribution (*)
Overall, the rating class distribution remained stable in 2025. The share of
investment grade rating classes increased from 78.1% to 79.3%, while the
share of non-investment grade decreased from 19.9% to 18.7%.
Performing restructuring outstandings remained constant at 0.6% of the
total portfolio in 2025, whereas non-performing loans (calculated
including Investment and Pre-Settlement exposures) decreased from 1.4%
to 1.3%.
With respect to the rating distribution within the business lines, in
Wholesale Banking, investment grade increased to 84.3% from 83.7%,
while non-investment grade exposures decreased to 13.9% from 14.6%
compared to 2024. Performing restructuring assets increased from 0.6%
to 0.7% of total Wholesale Banking assets where non-performing loans for
Wholesale Banking decreased from 1.2% to 1.1%.
For Retail Banking, investment grade increased to 75.4% from 73.3%,
while non-investment grade exposures decreased to 22.5% from 24.4% as
compared to 2024. Performing restructuring decreased to 0.6% from 0.7%
whereas NPL remained constant at 1.5% in 2025.
Industry (*)
The industry breakdown is presented in accordance with the NAICS
definition. The increase of €41.1 billion in total volume during 2025 was
mainly due to the increase in Private Individuals (€28.7 billion) and Central
Governments (€13.4 billion) which was partially offset by the decrease in
Central Banks (€ 21.9 billion). The share of Private Individuals increased
from 39.5% last year to 40.7%.
Portfolio analysis per business line (*)
Outstandings per line of business (*) 1, 2, 3
in EUR million
Wholesale Banking
Retail Banking
Corporate line
Total
Rating class
2025
2024
2025
2024
2025
2024
2025
2024
Investment grade
1 (AAA)
43,030
53,363
34,022
29,151
27
1,790
77,079
84,304
2-4 (AA)
70,691
72,462
68,484
63,187
1
4
139,175
135,653
5-7 (A)
130,280
101,766
149,694
140,479
555
154
280,528
242,400
8-10 (BBB)
119,661
129,429
175,823
155,375
3,240
3,662
298,724
288,466
Non-investment grade
11-13 (BB)
49,980
53,757
90,916
94,753
140,896
148,510
14-16 (B)
8,900
7,396
33,663
31,165
42,563
38,561
17 (CCC)
1,224
1,037
3,147
3,345
147
170
4,518
4,552
Performing Restructuring loans
18 (CC)
2,209
1,792
1,945
2,001
4,154
3,794
19 (C)
637
560
1,678
1,760
2,315
2,321
Non-performing loans
20-22 (D)
4,602
5,204
8,393
8,100
12,995
13,303
Total
431,214
426,767
567,764
529,317
3,970
5,779
1,002,947
961,863
Industry
Private Individuals
1,893
2,116
406,589
377,712
408,482
379,827
Central Banks
40,270
61,091
15,713
15,044
1,785
55,983
77,919
Natural Resources
39,229
39,974
1,757
1,925
40,985
41,899
Real Estate
25,024
24,643
29,665
28,738
54,689
53,381
Commercial Banks
44,512
40,962
6,351
6,662
3,638
3,616
54,501
51,240
Non-Bank Financial Institutions
65,037
64,217
1,973
2,212
262
290
67,271
66,719
Central Governments
59,600
48,389
10,280
8,107
1
1
69,880
56,497
Transportation & Logistics
25,427
27,499
6,507
6,037
31,934
33,536
Utilities
26,378
25,517
2,431
2,196
28,809
27,713
Food, Beverages & Personal Care
13,798
13,827
11,298
10,419
25,096
24,246
Services
9,489
8,844
13,756
13,442
19
27
23,265
22,312
General Industries
13,235
10,512
9,655
8,812
22,890
19,324
Lower Public Administration
8,904
6,959
22,529
19,598
31,433
26,557
Other
58,418
52,218
29,261
28,412
50
62
87,730
80,691
Total
431,214
426,767
567,764
529,317
3,970
5,779
1,002,947
961,863
Outstandings per line of business (*) - continued 1, 2, 3
in EUR million
Wholesale Banking
Retail Banking
Corporate line
Total
Region
2025
2024
2025
2024
2025
2024
2025
2024
Europe
Netherlands
35,165
44,421
180,030
164,590
526
1,903
215,720
210,913
Belgium
25,786
26,506
99,055
95,584
124,841
122,091
Germany
29,699
27,443
136,951
128,598
22
30
166,672
156,071
Poland
23,740
21,190
34,223
30,946
3
57,966
52,136
United Kingdom
30,475
28,257
252
265
78
91
30,805
28,613
France
26,462
24,351
3,742
3,122
3
3
30,207
27,476
Spain
15,568
11,990
31,015
28,507
38
36
46,620
40,533
Luxembourg
28,844
26,176
4,587
5,139
33,431
31,314
Rest of Europe
70,498
72,860
26,384
23,203
37
14
96,919
96,076
America
83,269
86,402
2,304
2,402
201
232
85,775
89,037
Asia
47,881
44,136
257
215
3,060
3,464
51,198
47,815
Australia
11,673
10,887
48,938
46,723
1
8
60,613
57,618
Africa
2,154
2,148
27
22
2,181
2,170
Total
431,214
426,767
567,764
529,317
3,970
5,779
1,002,947
961,863
1Based on credit risk measurement contained in lending, pre-settlement, money market and investment activities.
2Based on the total amount of credit risk in the respective column using ING’s internal credit risk measurement methodologies. Economic sectors (industry) below 2% are not shown separately but grouped in Other.
3Geographical areas are based on country of residence, except for private individuals, for which the geographical areas are based on the primary country of risk.
Portfolio analysis per geographical area (*)
The portfolio analysis per geographical area re-emphasises the
international distribution of ING’s credit portfolio. The Netherlands
maintains the largest portfolio share in a single country with 21.5%
(2024: 21.9%) of the total amount, followed by Germany with 16.6%
(2024: 16.2%), and Belgium with 12.4% (2024: 12.7%).
In terms of region, the majority of the portfolio balance remained in
Europe with 80.1% (2024: 79.6%), followed by the Americas with 8.6%
(2024: 9.3%), and Australia with 6.0% (2024: 6.0%).
The top five countries within Rest of Europe based on outstandings were
Italy (€25.2 billion), Romania (€14.0 billion), Switzerland (€10.9 billion),
Türkiye (€9.7 billion) and Ireland (€5.2 billion).
The main contributors for the overall increase in outstanding are Germany
(€10.6 billion), Spain (€ 6.1 billion), Poland (€ 5.8 billion), and the
Netherlands (€4.8 billion).
Private Individuals remained the largest composition of portfolio balances
for the Netherlands at 62.0% (2024: 58.3%), Belgium at 37.7%
(2024: 37.4%), Germany at 68.5% (2024: 68.8%), and Australia at 68.1%
(2024: 66.1%).The decrease in Central Banks is mainly attributed to  the
Netherlands (€13.8 billion), Belgium (€3.6 billion), and Asia (€3.1 billion).
In individual countries, the total share of investment grade/non-
investment grade remains substantial for the Netherlands at 98.5%
(2024: 98.5%), Germany at 98.7% (2024: 98.9%), and Belgium 96.8%
(2024: 96.6%).
In Europe, the increase in investment grade outstandings was mainly
observed in  Germany (€11.9 billion), Spain (€ 8.4 billion), Poland (€ 5.2
billion), and France (€3.0 billion).
The net decrease in non-investment grade outstandings was mainly
observed in Spain (€ 2.4 billion), America (€1.9 billion), and Germany (€1.7
billion).
Outstandings by economic sectors and geographical area (*)  1
in EUR million
Region
Total
Industry
Netherlands
Belgium
Germany
Poland
Spain
United Kingdom
Luxembourg
France
Rest of Europe
America
Asia
Australia
Africa
2025
Private Individuals
133,644
47,047
114,235
18,871
29,412
117
2,734
2,038
18,761
183
128
41,294
20
408,482
Central Banks
8,742
6,621
13,938
1,499
577
3,791
6,088
6,912
6,426
1,388
55,983
Natural Resources
2,220
1,257
869
909
156
2,848
2,254
261
10,730
8,974
8,830
1,396
281
40,985
Real Estate
17,212
14,082
1,184
2,356
1,749
679
3,257
3,055
3,805
2,591
1,409
3,309
54,689
Commercial Banks
1,482
504
5,605
493
754
4,917
6,808
5,228
8,205
9,182
10,762
398
164
54,501
Non-Bank Financial Institutions
3,577
1,536
5,717
2,295
880
8,040
6,819
6,255
5,738
21,294
3,451
1,659
10
67,271
Central Governments
3,297
14,525
374
11,768
6,692
31
186
5,485
11,463
14,146
935
400
580
69,880
Transportation & Logistics
4,652
2,132
1,239
1,759
698
2,384
967
839
6,048
3,665
6,196
663
692
31,934
Utilities
1,678
1,868
3,890
1,127
1,932
3,184
539
593
4,291
6,144
1,376
2,067
121
28,809
Food, Beverages & Personal Care
8,415
3,675
604
2,425
340
261
1,472
801
2,998
2,358
1,269
476
1
25,096
Services
5,322
8,420
1,906
1,639
127
1,123
306
394
1,464
1,106
873
585
1
23,265
General Industries
5,118
3,232
1,304
2,957
208
262
476
550
4,399
2,575
1,461
322
25
22,890
Lower Public Administration
2,236
7,362
10,121
815
682
235
3,432
520
1,462
38
4,528
31,433
Other
18,125
12,579
5,685
9,053
2,413
3,169
1,290
1,277
11,584
12,097
8,044
2,127
287
87,730
Total
215,720
124,841
166,672
57,966
46,620
30,805
33,431
30,207
96,919
85,775
51,198
60,613
2,181
1,002,947
Rating class
Investment grade
171,615
74,908
148,033
41,249
41,114
25,930
29,551
24,917
71,116
71,271
45,214
50,441
147
795,507
Non-investment grade
40,889
45,899
16,505
14,477
4,771
4,664
3,586
5,048
22,521
12,841
5,430
9,527
1,821
187,976
Performing Restructuring
1,528
928
680
694
265
30
53
134
1,486
406
66
162
39
6,469
Non-performing loans
1,689
3,107
1,454
1,546
470
181
242
108
1,795
1,257
488
483
175
12,995
Total
215,720
124,841
166,672
57,966
46,620
30,805
33,431
30,207
96,919
85,775
51,198
60,613
2,181
1,002,947
1Geographical areas are based on country of residence, except for Private Individuals for which the geographical areas are based on the primary country of risk.
Outstandings by economic sectors and geographical area (*)  1
in EUR million
Region
Total
Industry
Netherlands
Belgium
Germany
Poland
Spain
United Kingdom
Luxembourg
France
Rest of Europe
America
Asia
Australia
Africa
2024
Private Individuals
122,914
45,611
107,415
16,525
27,083
122
3,058
2,260
16,391
198
129
38,106
16
379,827
Central Banks
22,529
10,196
13,966
1,729
510
1,935
5,737
10,913
9,525
879
77,919
Natural Resources
2,197
1,531
881
778
152
3,021
2,503
405
11,212
8,475
8,989
1,593
159
41,899
Real Estate
16,749
13,387
1,218
2,085
1,595
552
3,446
2,713
3,707
3,220
1,066
3,642
53,381
Commercial Banks
1,285
314
4,129
695
376
4,733
5,268
5,074
7,779
10,700
9,394
1,336
157
51,240
Non-Bank Financial Institutions
2,872
1,766
5,147
2,874
249
8,479
6,031
5,932
5,174
23,367
3,518
1,255
55
66,719
Central Governments
1,416
11,009
51
9,435
5,308
48
82
3,202
9,203
15,377
288
488
589
56,497
Transportation & Logistics
4,290
2,076
1,426
1,623
679
2,262
828
765
7,407
3,983
6,912
504
781
33,536
Utilities
1,805
1,843
3,920
814
1,971
2,826
395
712
3,951
5,886
1,187
2,253
152
27,713
Food, Beverages & Personal Care
7,377
3,690
695
2,215
351
328
1,393
1,102
3,008
2,498
1,168
406
14
24,246
Services
4,919
8,431
1,852
1,538
122
869
540
310
1,271
1,265
516
680
22,312
General Industries
4,568
2,690
1,059
2,824
219
301
539
484
3,862
2,039
708
23
8
19,324
Lower Public Administration
782
6,824
7,435
608
557
246
3,091
476
1,554
44
4,941
26,557
Other
17,208
12,722
6,876
8,394
1,361
3,137
1,248
1,426
11,722
10,475
4,372
1,513
238
80,691
Total
210,913
122,091
156,071
52,136
40,533
28,613
31,314
27,476
96,076
89,037
47,815
57,618
2,170
961,863
Rating class
Investment grade
170,093
74,882
136,096
36,029
32,741
23,844
27,235
21,886
67,110
72,686
41,203
46,959
59
750,822
Non-investment grade
37,689
43,059
18,238
13,948
7,126
4,388
3,858
5,229
25,679
14,763
5,859
9,889
1,898
191,623
Performing Restructuring
1,579
1,078
305
701
234
59
56
54
1,369
443
30
203
4
6,114
Non-performing loans
1,552
3,071
1,432
1,458
432
322
166
307
1,918
1,145
723
568
210
13,303
Total
210,913
122,091
156,071
52,136
40,533
28,613
31,314
27,476
96,076
89,037
47,815
57,618
2,170
961,863
1Geographical areas are based on country of residence, except for Private Individuals for which the geographical areas are based on the primary country of risk.
Credit risk mitigation (*)
ING uses various techniques and instruments to mitigate the credit risk
associated with an exposure and to reduce the losses incurred subsequent
to a default by a customer. The most common terminology used in ING for
credit risk protection is ‘cover’. While a cover may be an important
mitigant of credit risk and an alternative source of repayment, generally it
is ING’s practice to lend on the basis of the customer’s creditworthiness
rather than relying on the value of the cover.
Cover forms (*)
Within ING, there are two distinct forms of covers. First, where the asset
has been pledged to ING as collateral or security, ING has the right to
liquidate it should the customer be unable to fulfil its financial obligation.
As such, the proceeds can be applied towards full or partial compensation
of the customer's outstanding exposure. This may be tangible (such as
cash, securities, receivables, inventory, plant and machinery, and
mortgages on real estate properties) or intangible (such as patents,
trademarks, contract rights, and licences). Second, where there is a third-
party obligation, indemnification or undertaking (either by contract and/or
by law), ING has the right to claim from that third party an amount if the
customer fails in its obligations. The most common examples are
guarantees, such as parent guarantees, export credit insurances, or third-
party pledged mortgages. Insurance or reinsurance covers, including
comprehensive private risk insurance (CPRI) may be recognised as
guarantees and effectively function in an equivalent manner. ING accepts
credit risk insurance companies and export credit agencies (ECAs) as cover
providers.
Cover valuation methodology (*)
General guidelines for cover valuation are established with the objective of
ensuring consistent application within ING. These also require that the
value of the cover is monitored on a regular basis. Covers are revalued
periodically and whenever there is reason to believe that the market is
subject to significant changes in conditions. The frequency of monitoring
and revaluation depends on the type of cover.
The valuation method also depends on the type of covers. For asset
collateral, the valuation sources can be the customer’s balance sheet (e.g.
inventory, machinery, and equipment), nominal value (e.g. cash and
receivables), market value (e.g. securities and commodities), independent
valuations (e.g. commercial real estate) and market indices (e.g. residential
real estate). For third-party obligations, the valuation is based on the value
that is attributed to the contract between ING and that third party.
Where collateral values are used in the calculation of Stage 3 individual
loan loss provisions, haircuts may be applied to the valuation in specific
circumstances to sufficiently include all relevant factors impacting future
cash flows. ING applies haircuts to the collateral values of real estate,
shipping and aviation assets that are used in the calculation of the loss-
given-default in recovery scenarios. The haircut reflects the risks of
adverse price developments between the moment of valuation of an asset
and the actual settlement/cash receipt.
Cover values (*)
This section provides insight into the types of cover and the extent to
which exposures benefit from collateral or guarantees. The disclosure
differentiates between risk categories (lending, investment, money market
and pre-settlement). The most relevant types of cover include mortgages
(market values), financial collateral (cash and securities), guarantees, and
other covers (mainly pledges). The scope of covers in this overview has
been revised due to CRR3 implementation to align with regulatory
reporting. This explains the decrease in the values of 'other covers', which
had only a limited effect on the fully covered part of the portfolio, as ING's
portfolio remains well collateralised. Collateral covering financial market
transactions is valued on a daily basis (margining), and is as such not
included in the following tables. To mitigate the credit risk arising from
financial markets transactions, the bank enters into legal agreements
governing the exchange of financial collateral (high-quality government
bonds and cash).
The cover values are presented for the total portfolio of ING, both the
performing and non-performing portfolio.
Cover values including guarantees received (*)
in EUR million
Cover type and value
Collateralisation
2025
Outstandings
Mortgages
Financial Collateral
Guarantees
Other covers
No cover
Partially covered
Fully covered
Consumer lending
407,552
905,028
5,317
37,473
842
7.8%
2.0%
90.2%
Business lending
383,536
186,276
31,834
166,953
215,177
42.1%
15.8%
42.1%
Investment and money market
151,639
—
—
1,506
—
99.6%
—%
0.4%
Total lending, investment and money market
942,727
1,091,304
37,151
205,932
216,019
36.6%
7.3%
56.1%
of which NPL
12,981
9,827
196
2,771
3,591
33.0%
18.5%
48.5%
Pre-settlement
60,220
Total Group
1,002,947
Cover values including guarantees received (*)
in EUR million
Cover type and value
Collateralisation
2024
Outstandings
Mortgages
Financial Collateral
Guarantees
Other covers
No cover
Partially covered
Fully covered
Consumer lending
378,832
865,466
6,257
25,428
55,115
6.5%
2.0%
91.5%
Business lending
368,570
163,143
24,838
119,410
484,148
34.1%
23.7%
42.2%
Investment and money market
153,493
—
—
1,115
95
99.3%
—%
0.7%
Total lending, investment and money market
900,894
1,028,609
31,095
145,953
539,357
33.6%
10.5%
55.9%
of which NPL
13,295
10,427
194
3,093
11,109
27.6%
27.7%
44.7%
Pre-settlement
60,968
Total
961,863
The above tables gives an overview of the collateralisation of ING’s total
portfolio. Excluding the pre-settlement portfolio, 56.1% (2024: 55.9%) of
ING’s outstandings were fully collateralised in 2025. Since investments
traditionally do not require covers, the ‘no covers’ percentage in this
portfolio is over 99.6%.
Consumer lending portfolio (*)
The consumer lending portfolio accounts for 40.6% (2024: 39.4%) of ING’s
total outstanding, primarily consisting of residential mortgage loans and
consequently most collateral consists of mortgages. Mortgage values are
collected in an internal central database and in most cases external data is
used to index the market value.
A significant part of ING’s residential mortgage portfolio is in the
Netherlands (2025: 35.0%, 2024: 35.0% ), Germany (2025: 27.0%,
2024: 27.3%), Belgium (including Luxembourg) (2025: 12.0%, 2024: 12.2%)
and Australia (2025: 10.9%, 2024: 10.6%).
Business lending portfolio (*)
Business lending accounts for 38.2% (2024: 38.3%) of ING’s total
outstanding. Business lending presented in this section does not include
pre-settlement, investment and money market exposures.
Credit quality (*)
ING uses three distinct statuses to categorise the management of clients
with (perceived) deteriorating credit risk profiles. ING makes use of Early
Warning Indicators (EWIs) in daily credit risk management processes,
which relate to a change in (internal and/or external) circumstances or
outlook of the specific obligor, the sector or the portfolio. ING usually
classifies a client with a 'watch list' status (Business lending only) when
there are early warning indicators triggered that detect an increased risk
profile. Watch list status requires more than usual attention, increased
monitoring and quarterly reviews. Some clients with a watch list or EWI
status may develop into a performing restructuring status or a non-
performing status. When there is increasing doubt as to the performance
and collectability of the client’s contractual obligations, the loans are
managed by Global Credit Restructuring (GCR) or by restructuring units in
the various regions and business units. The statuses and links with rating
grades are illustrated in the table below.
Credit quality outstandings  (*)
in EUR million
2025
2024
Performing not past due
879,378
823,478
Business lending performing past due
8,071
9,174
Consumer lending performing past due
895
802
Non-performing
12,981
13,295
Total lending and investment
901,325
846,749
Money market
41,402
54,145
Pre-settlement
60,220
60,968
Total
1,002,947
961,863
Past due obligations (*)
Retail Banking measures its portfolio in terms of payment arrears and
determines on a monthly basis if there are any significant changes in the
level of arrears. This methodology applies to Private Individuals as well as
Business lending. An obligation is considered ‘past due’ if it is more than
one day late, subject to materiality thresholds in line with Definition of
Default Regulation. ING aims to help its customers as soon as they are past
due by reminding them of their payment obligations. In its contact with
customers, ING aims to solve the (potential) financial difficulties by offering
a range of measures (e.g. payment arrangements, restructuring). If the
issues cannot be resolved because the customer is unable or unwilling to
pay, for example, the contract is sent to the recovery unit. The facility is
downgraded to risk rating 20 (non-performing) when the facility or obligor
– depending on the level at which the non-performing status is applied – is
more than 90 days past due and to risk rating 21 or 22 in case of an exit
scenario.
The table below represents the breakdown of lending and investment
credit risk outstandings that are performing by age and geographic area.
The past due but performing consumer lending outstanding increased by
€93 million, due to the increase in 1-30 days (€90 million) and 31-60 days
(€3 million).
The largest single country increase was in Belgium (€42 million), followed
by the Netherlands (€22 million), mainly in the 1-30 days bucket.           
The largest decrease was seen in Australia (€21 million).
Ageing analysis (past due but performing): Consumer lending portfolio by geographic area, outstandings (*)
in EUR million
2025
2024
Region
Past due for 1–30 days
Past due for 31–60 days
Past due for 61–90 days
Total
Past due for 1–30 days
Past due for 31–60 days
Past due for 61–90 days
Total
Europe
Netherlands
84
32
6
122
62
35
4
101
Belgium
219
60
26
305
185
49
29
263
Germany
70
37
22
129
65
37
24
125
Poland
58
6
3
68
61
9
4
74
Spain
22
17
9
48
12
16
8
36
Luxembourg
25
5
4
34
22
6
3
32
Rest of Europe
121
23
6
150
93
15
4
112
America
0
1
0
1
0
0
0
1
Asia
0
1
0
1
0
0
0
0
Australia
29
8
1
38
38
19
2
59
Africa
0
0
0
0
0
0
0
0
Total
628
189
78
895
538
186
78
802
Ageing analysis (past due but performing): Business lending portfolio by geographic area, outstandings (*)
in EUR million
2025
2024
Region
Past due for 1–30 days
Past due for 31–60 days
Past due for 61–90 days
Total
Past due for 1–30 days
Past due for 31–60 days
Past due for 61–90 days
Total
Europe
Netherlands
586
31
9
626
929
14
943
Belgium
476
22
10
508
1,187
17
13
1,217
Germany
415
2
2
419
215
3
2
220
Poland
403
31
15
449
173
17
19
209
United Kingdom
516
531
1,046
830
8
838
France
73
73
194
194
Spain
9
9
26
26
Luxembourg
212
9
1
222
367
51
5
423
Rest of Europe
899
17
3
919
630
4
46
681
America
2,946
68
3,013
3,504
95
3,599
Asia
133
133
310
310
Australia
636
10
7
654
469
6
475
Africa
39
39
Total
7,303
720
47
8,071
8,873
215
86
9,174
Total past due but performing outstanding of business lending decreased
by €1.1 billion. The decrease was mainly witnessed in the 1–30 days past
due bucket (€1.6 billion) and was offset by the increase in 31-60 days (€0.5
billion) past due bucket, fully in the United Kingdom related to overdue
fees. The largest decreases were in Belgium  (€0.7 billion) and America
(€0.6 billion), while the largest increase were in Australia (€0.2 billion),
Germany (€0.2 billion), Poland (€0.2 billion), and the United Kingdom (€0.2
billion).
Forbearance (*)
Forbearance occurs when a client is unable to meet their financial
commitments due to financial difficulties they are facing or are about to
face and ING grants them concessions. Forborne assets are assets for
which forbearance measures have been granted.
Forbearance may enable clients experiencing financial difficulties to
continue repaying their debt.
For business clients, ING mainly applies forbearance measures to support
clients with fundamentally sound business models that are experiencing
temporary difficulties. The aim is to maximise the client’s repayment
ability, thereby avoiding a default situation, and help the client to return to
a performing situation.
For ING Retail units, clear criteria have been established to determine
whether a client is eligible for the forbearance process. Specific approval
mandates are in place to approve the measures, as well as procedures to
manage, monitor, and report the forbearance activities.
ING reviews the performance of forborne exposures at least quarterly,
either on a case-by-case (Business) or portfolio (Retail) basis.
Both performing (risk ratings 1-19) and non-performing (risk ratings 20-22)
exposures are eligible for Forbearance measures. ING uses specific criteria
to move forborne exposures from non-performing to performing or to
remove the forbearance statuses that are consistent with the
corresponding European Banking Authority (EBA) standards. An exposure
is reported as forborne for a minimum of two years. An additional one-
year probation period is applied to forborne exposures that move from
non-performing back to performing.
Summary Forborne portfolio (*)
in EUR million
2025
2024
Business line
Outstandings
Of which: performing
Of which: non-performing
% of total portfolio(1)
Outstandings
Of which: performing
Of which: non-performing
% of total portfolio(1)
Wholesale Banking
5,910
3,194
2,716
1.7%
5,934
3,191
2,743
1.9%
Retail Banking
7,551
4,538
3,013
1.4%
6,883
3,987
2,897
1.3%
Total
13,461
7,732
5,729
1.5%
12,817
7,178
5,640
1.5%
Summary Forborne portfolio by forbearance type (*)
in EUR million
2025
2024
Forbearance type
Outstandings
Of which: performing
Of which: non-performing
% of total portfolio(1)
Outstandings
Of which: performing
Of which: non-performing
% of total portfolio(1)
Loan modification
11,883
6,581
5,302
1.3%
11,726
6,734
4,993
1.4%
Refinancing
1,578
1,151
427
0.2%
1,091
444
647
0.1%
Total
13,461
7,732
5,729
1.5%
12,817
7,178
5,640
1.5%
% of total portfolio is based on lending and investment outstandings.
Forborne assets increased by €644 million, mainly due to the increase of
€667 million in Retail Banking, which was offset by a €24 million decrease
in Wholesale Banking. The rise in Retail Banking forborne assets was
primarily driven by residential mortgages in the Netherlands, which
contributed €608 million. This increase was largely the result of the
implementation of an updated framework, under which more clients are
classified as forborne, while outflow is only allowed after a two-year
probation period.
In terms of forbearance type, the increase is mainly witnessed in
refinancing type (€487 million) driven by a few larger files in Wholesale
Banking.
Modification of financial assets (*)
The following table shows:
§Financial assets that were modified during the year (i.e. qualified as
forborne) while they had a loss allowance measured at an amount
equal to lifetime ECL; and
§Financial assets that were reclassified to Stage 1 during the period
while being modified before.
§
Financial assets modified (*)
in EUR million
2025
2024
Financial assets modified during the period
Amortised cost before modification
1,746
1,888
Net modification results
-72
-107
Financial assets modified since initial recognition
Gross carrying amount at 31 December of financial assets for which loss
allowance has changed to 12-month measurement during the period
1,027
1,506
Wholesale Banking (*)
Wholesale Banking forborne assets amounted to €5.9 billion (2024:
€5.9 billion), which represented 1.7% (2024: 1.9%) of the total Wholesale
Banking portfolio.
The net decrease in Wholesale Banking forborne assets was driven by a 
€27 million reduction in non-performing forborne exposure and partially
offset by a €3 million rise in performing forborne exposures. 
Wholesale Bankings' forborne assets show an increase in Telecom due to
refinancing, which was offset by a decrease in other sectors, such as real
estate, food, beverages & personal care, chemicals, health &
pharmaceuticals and natural resources. These five sectors accounted for
69.6% of the total Wholesale Banking forborne outstandings.
Wholesale Banking: Forborne portfolio by geographical area (*)
in EUR million
2025
2024
Region
Outstandings
Of which:
performing
Of which:
non-
performing
Outstandings
Of which:
performing
Of which:
non-
performing
Europe
Netherlands
316
191
124
217
69
148
Belgium
3
3
0
172
165
7
Germany
336
29
307
372
62
310
Poland
662
305
357
630
284
346
United Kingdom
676
516
160
444
266
178
Italy
547
519
28
389
353
36
Rest of Europe
1,081
558
523
1,339
940
399
America
1,313
545
768
1,586
867
719
Asia
711
313
398
652
111
541
Australia
212
177
35
79
34
44
Africa
53
37
15
54
40
15
Total
5,910
3,194
2,716
5,934
3,191
2,743
Wholesale Banking: Forborne portfolio by economic sector (*)
in EUR million
2025
2024
Industry
Outstandings
Of which:
performing
Of which:
non-
performing
Outstandings
Of which:
performing
Of which:
non-
performing
Natural Resources
605
308
297
781
424
356
Real Estate
945
375
570
1,115
703
412
Transportation & Logistics
145
48
97
214
83
131
Food, Beverages & Personal Care
629
289
340
810
415
395
Services
172
149
24
211
176
34
Automotive
263
139
124
332
183
149
Utilities
497
218
279
677
301
376
General Industries
197
42
155
127
70
58
Retail
111
41
70
149
21
128
Chemicals, Health & Pharmaceuticals
619
74
545
668
136
532
Builders & Contractors
179
174
4
122
118
5
Telecom1
1,314
1,166
148
486
383
103
Other
234
172
62
243
178
65
Total
5,910
3,194
2,716
5,934
3,191
2,743
1The presentation has been revised to separately disclose Telecom. Comparative figures have been adjusted accordingly.
Retail Banking (*)
As of year end, Retail Banking forborne assets amounted to €7.6 billion
(2024: €6.9 billion), which represented 1.4% (2024: 1.3%) of the total Retail
Banking portfolio. Retail Banking performing forborne exposure increased
by €0.6 billion.
The main concentration of forborne assets in a single country was in
Belgium with 28.5% (2024: 28.2%) of total Retail Banking forborne assets
and 39.0% (2024: 39.4%) of the non-performing forborne assets. The other
significant single country concentration is in the Netherlands with 28.3%
(2024: 22.5%), which increased during 2025 because of the
implementation of a new framework for mortgages, and Germany having
17.3% (2024: 20.0%) of the total Retail forborne assets.
Retail Banking: Forborne portfolio by geographical area (*)
in EUR million
2025
2024
Region
Outstandings
Of which: performing
Of which: non-
performing
Outstandings
Of which: performing
Of which: non-
performing
Europe
Netherlands
2,136
1,711
424
1,548
1,134
414
Belgium
2,153
978
1,175
1,942
800
1,142
Germany
1,304
965
338
1,379
1,052
327
Poland
793
361
432
777
403
374
Spain
92
55
37
159
127
31
Italy
104
33
70
122
43
79
Türkiye
22
16
6
13
9
4
Romania
216
106
110
173
72
101
Rest of
Europe
91
32
59
102
50
52
America
5
5
22
17
6
Asia
1
1
1
Australia
635
279
357
646
279
367
Africa
Total
7,551
4,538
3,013
6,883
3,987
2,897
Non-performing loans (*)
ING has aligned the regulatory concept of non-performing with that of the
definition of default. Hence, borrowers are classified as non-performing
when a default trigger occurs (non-exhaustive list):
§ING believes the borrower is unlikely to pay. The borrower has
evidenced significant financial difficulty, to the extent that it will have a
negative impact on the future cash flows of the financial asset. The
following events could be seen as indicators of financial difficulty:
–The borrower (or third party) has started insolvency proceedings;
–A group company/co-borrower has NPL status;
–Indication of fraud (affecting the company’s ability to service its
debt);
–There is doubt as to the borrower’s ability to generate stable and
sufficient cash flows to service its debt;
–Restructuring of debt; and
–ING has granted concessions relating to the borrower’s financial
difficulty, the effect of which is a reduction in expected future cash
flows of the financial asset below current carrying amount.
§The obligor has failed in the payment of principal, interest, or fees; the
total past due amount is above the materiality threshold, and this
remains the case for more than 90 consecutive days.
Furthermore, Wholesale Banking has an individual name approach, using
early warning indicators to signal possible future issues in debt service.
Also in Retail Banking early warning indicator frameworks have been
implemented.
The table below represents the breakdown of credit risk outstandings that
have been classified as non-performing by sector and business line.
Non-performing Loans: Outstandings by economic sector and business lines (*) 1
in EUR million
Wholesale Banking
Retail Banking
Total
Industry
2025
2024
2025
2024
2025
2024
Private Individuals
3
4
4,899
4,766
4,902
4,769
Natural Resources
945
965
115
99
1,061
1,064
Food, Beverages & Personal Care
372
452
325
357
697
809
Transportation & Logistics
251
347
227
157
478
504
Services
94
102
375
394
469
495
Real Estate
586
831
656
603
1,241
1,434
General Industries
243
236
471
451
713
687
Builders & Contractors
31
51
541
445
571
496
Retail
184
157
189
224
373
381
Utilities
336
582
26
19
362
600
Chemicals, Health & Pharmaceuticals
602
654
150
185
752
839
Telecom2
170
151
36
12
206
163
Other
771
666
383
387
1,154
1,052
Total
4,589
5,196
8,392
8,099
12,981
13,295
1Based on lending and investment outstandings.
2The presentation has been revised to separately disclose Telecom. Comparative figures have been adjusted accordingly.
Non-performing loans: Outstandings by economic sectors and geographical area (*)
in EUR million
Region
Total
Industry
Netherlands
Belgium
Germany
Poland
Spain
United Kingdom
France
Luxembourg
Rest of Europe
America
Asia
Australia
Africa
2025
Private Individuals
697
1,481
1,113
220
357
10
8
39
574
2
2
399
1
4,902
Natural Resources
137
84
28
14
584
23
191
1,061
Food, Beverages & Personal Care
167
94
1
94
19
6
126
59
131
697
Transportation & Logistics
87
40
136
47
22
72
22
10
1
41
478
Services
68
262
75
1
3
4
16
26
13
469
Real Estate
24
419
59
115
45
47
101
8
377
48
1,241
General Industries
137
138
38
196
20
13
130
10
31
713
Builders & Contractors
89
219
7
182
8
66
571
Retail
80
79
33
56
1
4
15
105
373
Utilities
14
10
19
8
151
1
12
80
33
35
362
Chemicals, Health & Pharmaceuticals
47
65
112
346
43
25
64
49
752
Telecom
37
4
3
15
146
2
206
Other
102
209
72
88
2
1
10
105
358
75
132
1,154
Total
1,686
3,105
1,454
1,546
470
181
108
242
1,786
1,257
488
483
175
12,981
Non-performing loans: Outstandings by economic sectors and geographical area (*)
in EUR million
Region
Total
Industry
Netherlands
Belgium
Germany
Poland
Spain
United Kingdom
France
Luxembourg
Rest of Europe
America
Asia
Australia
Africa
2024
Private Individuals
646
1,461
1,066
210
304
10
8
44
545
2
2
469
4,769
Natural Resources
13
54
33
569
31
343
21
1,064
Food, Beverages & Personal Care
196
154
1
93
23
5
158
51
127
809
Transportation & Logistics
93
40
3
124
47
1
136
1
59
504
Services
57
293
5
87
2
1
3
5
10
34
495
Real Estate
12
374
63
114
59
59
90
6
606
52
1,434
General Industries
153
123
24
147
20
2
1
170
17
30
687
Builders & Contractors
68
175
5
162
7
78
496
Retail
53
97
39
62
3
14
97
15
1
381
Utilities
13
8
25
21
285
12
128
109
600
Chemicals, Health & Pharmaceuticals
37
94
84
340
1
110
113
36
24
839
Telecom
7
1
3
44
14
90
4
163
Other
202
198
117
60
2
72
17
90
54
92
150
1,052
Total
1,549
3,071
1,432
1,457
432
322
307
166
1,916
1,145
723
567
210
13,295
In 2025, the NPL portfolio decreased to €13.0 billion (2024: €13.3 billion),
mainly driven by the decrease of €0.6 billion in Wholesale Banking. The
decrease was offset by the increase in Retail Banking (€0.3 billion) in line
with strong portfolio growth, mainly in residential mortgages.
Total net decrease was mainly seen in sectors Utilities, Real Estate and
Food, Beverage & Personal Care while the largest increase was in Private
Individuals.
Loan loss provisioning (*)
ING recognises loss allowances based on the expected credit loss (ECL)
model of IFRS 9, which is designed to be forward-looking. The IFRS 9
impairment requirements are applicable to on-balance-sheet financial
assets measured at amortised cost or fair value through other
comprehensive income (FVOCI), such as loans, debt securities, and lease
receivables, as well as off-balance-sheet items such as undrawn loan
commitments and financial- and non-financial guarantees issued.
ING distinguishes between two types of calculation methods for credit loss
allowances:
§Collective 12-month ECL (Stage 1) and collective lifetime ECL (Stage 2)
for portfolios of financial instruments, as well as collective lifetime ECL
for credit-impaired exposures (Stage 3) below €1 million; and
§Individual lifetime ECL for credit-impaired (Stage 3) financial
instruments with exposures above €1 million.
IFRS 9 models (*)
ING’s IFRS 9 models leverage on the internal rating-based (IRB) models (PD,
LGD, EAD), which include certain required conservatism. To include IFRS 9
requirements, such regulatory conservatism is removed from the ECL
parameters (PD, LGD and EAD). The IFRS 9 models apply two other types of
adjustments to the IRB ECL parameters: (i) to the economic outlook and (ii)
for Stage 2 and Stage 3 assets only, to the lifetime horizon. The IFRS 9
model parameters are estimated based on statistical techniques and
supported by expert judgement.
ING has aligned the definition of default for regulatory purposes with the
definition of ‘credit-impaired’ financial assets under IFRS 9 (Stage 3). ING
has also aligned its definition of default between IFRS 9 and the regulatory
technical standards (RTS) and EBA guidelines.
Climate and environmental risks in IFRS 9 models (*)
Climate risk drivers (physical and transition risks) can reduce the ability of
businesses and households to fulfil their obligations due, under existing
lending contracts. These climate risks are partly reflected in IFRS9 ECL
through regular credit risk transmission channels such as the
macroeconomic forecast and relevant risk parameters.
On the other hand, future physical or transition risks from climate change
are not yet fully captured within the current ECL models. In our
assessment of physical risks, it is generally expected that these risks will
only become financially significant on the long-term horizon.
Consequently, for short- and medium-term loans, such risks are not
expected to lead to significant credit deterioration over the exposure’s
lifetime. For the long term, the probability and timing of physical risk
events remain highly uncertain. ING is however actively enhancing the
integration of physical and transition risks into collateral valuations,
ensuring that effects on property values are appropriately captured.
In case of risk events that have already occurred (e.g. floods, stranded
assets), the impact of such events is assessed in the calculation of Stage 3
individual provisions, collective SICR or management adjustments to ECL
models. For example, we consider whether affected assets have suffered
from a significant increase in credit risk (or are credit impaired) and
whether the ECL is appropriate.
Additionally, a management adjustment to ECL models for business
clients was introduced to specifically cover for medium- to long-term
transition risk on high greenhouse gas-emitting sectors. The management
adjustment reflects the risk of financial loss foreseen on the longer horizon
for lenders with higher transition risk in these high greenhouse gas-
emitting sectors as consequence of future carbon taxation. ‘See
Management adjustments applied this reporting period’.
ING is continuously improving on climate risk data, which will enable us to
further embed climate risks into the IFRS 9 ECL models. For more
information on ESG risk management, see ‘ESG risk’.
Portfolio quality (*)
The table below describes the portfolio composition over the different
IFRS 9 stages and rating classes. The Stage 1 portfolio represents  92.0%
(2024: 91.1%) of the total gross carrying amounts, mainly composed of
investment grade, while Stage 2 makes up 6.8% (2024: 7.6%) and Stage 3
makes up 1.2% (2024: 1.3%) of the total gross carrying amounts,
respectively.
Gross carrying amount per IFRS 9 stage and rating class (*)  1,2
in EUR million
12-month ECL  (Stage 1)
Lifetime ECL not credit impaired (Stage 2)
Lifetime ECL credit impaired (Stage 3)
Total
Rating class
Gross carrying amount
Provisions
Gross carrying amount
Provisions
Gross carrying amount
Provisions
Gross carrying amount
Provisions
2025
2024
2025
2024
2025
2024
2025
2024
2025
2024
2025
2024
2025
2024
2025
2024
Investment grade
1 (AAA)
79,938
79,076
2
1
126
281
80,064
79,357
2
1
2-4 (AA)
146,878
140,671
11
10
1,437
1,579
1
1
148,315
142,250
13
11
5-7 (A)
299,699
244,241
39
21
5,394
6,908
6
8
305,093
251,149
45
29
8-10 (BBB)
334,845
310,324
77
55
19,168
24,683
43
55
354,012
335,008
119
110
Non-investment grade
11-13 (BB)
156,373
154,348
165
190
17,217
18,479
89
91
173,589
172,827
254
281
14-16 (B)
26,187
25,377
147
124
22,668
17,433
357
366
48,855
42,811
504
490
17 (CCC)
412
905
5
8
4,682
3,992
224
173
5,094
4,897
228
181
Performing
Restructuring
18 (CC)
4,349
4,059
262
233
4,349
4,060
262
233
19 (C)
2,447
2,474
198
203
2,447
2,474
198
203
Non-performing loans
20-22 (D)
13,590
13,742
4,476
4,509
13,590
13,742
4,476
4,509
Total
1,044,332
954,943
446
409
77,487
79,888
1,179
1,130
13,590
13,742
4,476
4,509
1,135,409
1,048,574
6,101
6,049
1Compared to the credit risk portfolio, the differences are due to undrawn committed amounts (€200 billion; 2024: €156 billion) and other positions (2025: nil; 2024:€6 billion) not included in credit outstandings and non-IFRS 9 eligible assets (€ 68 billion; 2024: €75 billion) included in credit outstandings but not in the
gross carrying amounts.
2Stage 3 lifetime credit impaired provision includes €29 million (2024: €21 million) on purchased or
originated credit impaired.
Changes in gross carrying amounts and loan loss provisions (*)
The table below provides a reconciliation by stage of the gross carrying
amount and allowances for loans and advances to banks and customers,
including loan commitments and financial guarantees. The transfers of
financial instruments represent the impact of stage transfers upon the
gross carrying/nominal amount and associated allowance for ECL. This
includes the net-remeasurement of ECL arising from stage transfers, for
example, moving from a 12-month (Stage 1) to a lifetime (Stage 2) ECL
measurement basis.
The net-remeasurement line represents the changes in provisions for
facilities that remain in the same stage.
Please note the following comments with respect to the movements
observed in the table below:
§Stage 3 gross carrying amount decreased by €0.2 billion from €13.7
billion as at 31 December 2024 to €13.6 billion as at 31 December 2025,
mainly as a result of €3.5 billion net inflow into NPL (credit impaired) in
2025, which is offset by €2.3 billion derecognitions and repayments
and €1.5 billion write-offs and disposals. Following the decrease in
carrying amount, Stage 3 provisions slightly decreased by €33 million.
§Stage 2 gross carrying amounts decreased by €2.4 billion from €79.9
billion as at 31 December 2024 to €77.5 billion as at 31 December 2025,
largely driven by €15.7 billion net transfers from Stage 1 into Stage 2,
including the impact of changes in risk drivers (including updated
macro-economic forecasts), model redevelopments mainly for
Wholesale Banking models, and new Stage 2 overlays. This was offset
by a decrease of exposure by €18.1 billion due to derecognised
financial assets (including sales and repayments) and €1.2 billion
exposure moving to Stage 3. Stage 2 provisions increased by € 49.3
million to €1.2 billion as of 31 December 2025.
Changes in gross carrying amounts and loan loss provisions (*)  1, 2
in EUR million
12-month ECL  (Stage
1)
Lifetime ECL not
credit impaired
(Stage 2)
Lifetime ECL credit
impaired (Stage 3)
Total
12-month ECL 
(Stage 1)
Lifetime ECL not
credit impaired
(Stage 2)
Lifetime ECL credit
impaired (Stage 3)
Total
Gross
carrying
amount
Provisions
Gross
carrying
amount
Provisions
Gross
carrying
amount
Provisions
Gross
carrying
amount
Provisions
Gross
carrying
amount
Provisions
Gross
carrying
amount
Provisions
Gross
carrying
amount
Provisions
Gross
carrying
amount
Provisions
2025
2024
Opening balance
954,943
409
79,888
1,130
13,742
4,509
1,048,574
6,049
937,633
517
75,454
1,435
11,956
3,887
1,025,043
5,839
Transfer into 12-month ECL (Stage 1)
21,773
21
-21,555
-199
-218
-35
-212
20,486
22
-20,236
-195
-249
-34
-207
Transfer into lifetime ECL not credit impaired (Stage 2)
-37,208
-50
38,174
533
-966
-115
368
-43,155
-49
43,900
429
-745
-96
285
Transfer into lifetime ECL credit impaired (Stage 3)
-2,534
-14
-2,172
-138
4,705
1,259
1,108
-2,980
-18
-2,856
-235
5,836
1,802
1,548
Net remeasurement of loan loss provisions
7
-53
349
302
-181
-137
185
-133
New financial assets originated or purchased
263,371
189
22
2
263,393
191
212,516
192
212,516
192
Financial assets that have been derecognised
-119,098
-78
-12,295
-147
-1,422
-240
-132,814
-465
-126,858
-76
-11,840
-153
-1,450
-257
-140,148
-485
Net drawdowns and repayments
-57,493
-5,739
-853
-64,085
-41,763
-4,393
-309
-46,465
Changes in models/risk parameters
-33
71
-35
3
8
-6
-22
-20
Increase in loan loss provisions
43
68
1,185
1,296
-102
-297
1,578
1,179
Write-offs
-937
-937
-937
-937
-1,017
-1,017
-1,017
-1,017
Disposals
-666
-3
-84
-3
-564
-286
-1,314
-291
-935
-1
-141
-8
-279
-215
-1,355
-225
Recoveries of amounts previously written off
58
58
69
69
Other movements
21,242
-4
1,269
-16
80
-53
22,591
-73
-5
208
203
Closing balance
1,044,332
446
77,487
1,179
13,590
4,476
1,135,409
6,101
954,943
409
79,888
1,130
13,742
4,509
1,048,574
6,049
1Stage 3 lifetime credit impaired provision includes €29 million (2024: €21 million) on purchased or originated credit impaired.
2The addition to the loan provision (in the consolidated statement of profit or loss) amounts to €1,304 million (2024: €1,194 million) of which €1,301 million (2024: €1,170 million) related to IFRS 9 eligible financial assets, €-5 million (2024: €9 million) to IFRS eligible commitments and €8 million (2024: €15 million) to
modification gains and losses on restructured financial assets.
Macroeconomic scenarios and sensitivity analysis of key sources
of estimation uncertainty (*)
Methodology (*)
This section outlines our methodology for adopting and generating
macroeconomic scenarios. We apply this methodology when producing
our probability-weighted ECL, incorporating alternative scenarios and
management adjustments where necessary. These adjustments are made
when management believes the consensus forecast does not fully capture
the extent of recent credit or economic events. The macroeconomic
scenarios are applicable to the whole ING portfolio in the scope of IFRS 9
ECLs.
The IFRS 9 standard, with its inherent complexities and potential impact on
the carrying amounts of our assets and liabilities, represents a key source
of estimation uncertainty. In particular, ING’s reportable ECL numbers are
sensitive to the forward-looking macroeconomic forecasts used as model
inputs, the probability weights applied to each of the three scenarios, and
the criteria for identifying a significant increase in credit risk. As such,
these crucial components require consultation and management
judgement, and are subject to extensive governance.
Baseline scenario (*)
As a baseline for IFRS 9, ING has adopted a market-neutral view combining
consensus forecasts for economic variables (GDP, unemployment) with
market forwards (for interest rates, exchange rates, and oil prices). Input
from a leading third-party service provider is used to complement the
consensus with consistent projections for variables for which there are no
consensus estimates available (most notably house prices and – for some
countries – unemployment), to generate alternative scenarios, to convert
annual consensus information to a quarterly frequency, and to ensure
general consistency of the scenarios. As the baseline scenario is consistent
with the consensus view, it can be considered as free from any bias.
The relevance and selection of macroeconomic variables is defined by the
ECL models under credit risk model governance. The scenarios are
reviewed and challenged by a panels of ING experts.
Alternative scenarios and probability weights (*)
Two alternative scenarios are taken into account: an upside and a
downside scenario. The alternative scenarios have statistical
characteristics as they are based on the forecast deviations of the leading
third-party service provider.
To understand the baseline level of uncertainty around any forecast, the
leading third-party service provider keeps track of all its deviations (so-
called forecast errors) of the past 30 years. The distribution of forecast
errors for GDP, unemployment, house prices, and share prices is applied to
the baseline forecast creating a broad range of alternative outcomes. In
addition, to understand the balance of risks facing the economy in an
unbiased way, the leading third-party service provider runs a survey with
respondents from around the world and across a broad range of
industries. In this survey, respondents put forward their views of key risks.
Following the survey results, the distribution of forecast errors (that is
being used for determining the scenarios) may be skewed.
For the downside scenario, ING has chosen the 90th percentile of that
distribution because this corresponds with the way risk management
earnings-at-risk is defined within the Group. The upside scenario is
represented by the 10th percentile of the distribution. The applicable
percentiles of the distribution imply a 20 percent probability for each
alternative scenario. Consequently, the baseline scenario has a 60 percent
probability weighting. Please note that, given their technical nature, the
downside and upside scenarios are not based on an explicit specific
narrative and have not changed compared to previous year.
Macroeconomic scenarios applied (*)
The macroeconomic scenarios applied in the calculation of loan loss
provisions are based on the consensus forecasts.
Baseline assumptions (*)
The general picture that the consensus conveys is that global economic
growth is slowing compared to recent years. US growth is expected to
continue to outpace European markets, but the gap is narrowing down.
China is expected to continue a declining growth trend, but still at higher
rates than seen in advanced markets. Inflation is expected to remain near
target for most advanced economies, but a bit above for the United States.
With interest rates moderating, monetary conditions are turning more
favourable for growth. For the housing market, continued price growth is
expected for almost all main markets.
The December 2025 consensus expects global output (as measured by the
weighted average GDP growth rate of ING’s 25 main markets) to remain at
a constant 2.4% from 2025-2027.
The eurozone economy has shown more resilience than expected despite
global headwinds, with sluggish but positive growth through 2025. The
coming years are expected to see modest growth rates as well. The trade
environment continues to be plagued by higher US tariffs and a stronger
euro. Expectations of a pickup in growth over the course of 2026 come
from stronger consumer spending and investment. German infrastructure
spending is expected to add to this momentum. Southern European
economies continue to benefit from European Recovery Fund investments.
Consensus expects the eurozone to have grown by 1.4% in 2025, before
slowing to 1.1% in 2026 and then recovering to 1.3% in 2027.
Elsewhere in Europe, the growth outlook is stronger. Poland’s growth
momentum remains solid, growing faster than its European peers. The key
growth drivers over the near-term forecast are consumption and public
investments, such as those carried out under the National Recovery Plan
(KPO). The economy is expected to grow by 3.6% in 2025, picking up to
3.7% in 2026 and settling at 3.2% in 2027. The consensus expectation for
Türkiye is to see stable growth of 3.5% and 3.4% in 2025 and 2026, as
domestic demand remains resilient and fixed investments and inventories
see an uptick. For 2027, a recovery to 3.7% is expected.
The American economy has held its ground despite the tariff war and
slowing employment growth. Its characterisation by a K-shaped economy
where most of the growth comes from high-income consumers and tech
investments is likely to continue in 2026. Tariffs are not feeding into
inflation as much as expected earlier in the year, and inflation
expectations remain benign, although still above the 2% target. This has
prompted the Federal Reserve to resume its rate cutting cycle. Inflation is
expected to remain around 2.8% and 2.9% in 2025 and 2026 before
steadying down at 2.4% in 2027.The US economy is expected to
experience steady growth of 2.0% in 2025-2027.
For China, economic underperformance continues as it still struggles with
the impact of the real estate correction and weak domestic demand.
However, the economy has come out resilient to the tariff war with a trade
surplus reaching record levels, making external demand the key driver of
2025. This enables the economy to reach its short-term growth target, but
medium-term consensus continues to be downbeat for the moment
because of its weak domestic economy. For 2025, consensus expects 
4.9% growth, down to 4.5% in 2026 and 4.3% in 2027.
Economic momentum in Australia is expected to be soft, as much of the
growth in 2025 came from government consumption. The economy is
lacking a clear growth engine, with the private sector struggling against
restrictive policy settings and consumers facing a tough outlook with
weaker employment growth and real wage gains moderating. Growth is
expected to come in at 1.8% in 2025, with a pick-up expected for 2026 to
2.2% and 2.3% for 2027.
When compared to the June 2025 consensus forecast, the December 2025
forecast has improved slightly because of better than expected growth in
major economies. Global GDP is expected to increase by  2.4% in both
2025 and 2026 (compared to 2.0% and 2.1% assumed before). The global
economy showed quite some resilience in 2025 despite trade and
geopolitical tensions, as GDP growth has continued despite political
disruptions. Markets, too, have adjusted to the new world of volatility.
Alternative scenarios and risks (*)
The baseline scenario assumes steady economic growth. However, further
escalation of geopolitical tensions and global trade uncertainty are seen as
key downside risks to the global economy. The balance of risks to the
baseline outlook is negative, and the alternative scenarios have a
downward skew.
The downside scenario sees a recession in 2026 and 2027 for most
countries. Unemployment increases strongly in this scenario and house
prices in most countries show outright falls. The downside scenario
captures the possible impact of geopolitical tensions, global trade policy
and heightened tariff uncertainty.
The upside scenario reflects the possibility of a better economic out-turn
resulting from the avoidance of further trade escalation, an AI-driven
productivity rebound and stronger European growth supported by defence
spending.
Management adjustments applied this reporting period (*)
In times of volatility and uncertainty where portfolio quality and the
economic environment are changing rapidly, models alone may not be
able to accurately predict losses. In these cases, management
adjustments can be applied to appropriately reflect ECL. Management
adjustments may also be applied when the impact of the updated
macroeconomic scenarios is over- or under-estimated by the IFRS 9
models, or to account for model redevelopment, recalibration, and periodic
assessment procedures that have not yet been incorporated into the IFRS9
models.
ING has an internal governance framework and controls in place to assess
the appropriateness of all management adjustments.
Management adjustments to ECL models (*)
in EUR million
2025
2024
Commercial Real Estate/ Inflation and interest rate
increases
50
Economic sector / portfolio based adjustments
9
38
Mortgage portfolio adjustments
121
112
Climate transition risk
47
29
Other Post Model Adjustments
7
-27
Total management adjustments
183
203
The management adjustment of €50 million for the Commercial Real
Estate portfolio, reported at 31 December 2024 due to prevailing risks in
this sector, is released in 2025 due to improved risk profile and partially
replaced with a management adjustment for a recent model update,
which corrects for the effects of the aforementioned risks on the model
outcome. As the model update has not been implemented in production
yet, a management adjustment of €10 million is reported under the Other
Post Model Adjustments category at 31 December 2025.
As at 31 December 2025, an economic sector / portfolio-based adjustment
is reported in the Mortgages portfolio in Australia (€9 million) to address
emerging risk from inflation, interest rate increases and overall cost of
living. The management adjustment reported at 31 December 2024 for the
increased credit risk in the Mortgages and Consumer Lending portfolios in
Spain – due to the effects of floods – has been released, as the remaining
affected performing customers are reported in Stage 2. Also the
adjustment taken in the Business Banking portfolio in Germany at 31
December 2024 to cover for the increased uncertainty in the German
economy has been released due to the agreed sale of the affected
portfolio.
As of 31 December 2025, the overall mortgage portfolio adjustment
amounts to €121 million (31 December 2024: €112 million) and fully
related to the management adjustment in Stage 2 for the risk
segmentation model that captures affordability, repayment, and
refinancing risk on performing mortgage customers with a bullet loan in
the Netherlands.
As of 31 December 2025, an adjustment of €47 million is in place to
address the impact of climate transition risk in Wholesale Banking (€31
million) and in Business Banking (€16 million). Climate transition risk is
expected to lead to a structural change in credit risk, which means specific
business activities will become structurally riskier due to environmental
policies, technological progress or changes in market sentiment and
preferences. The current IFRS 9 models do not capture this (novel) risk. The
management adjustment to ECL models for business clients was made to
specifically cover for the medium- to long-term transition risk on high
greenhouse gas-emitting sectors and is reported in Stage 2. The sectors
included in the overlay represent approximately 20% of the exposure
within Wholesale and Business Banking.
Other post-model adjustments mainly relate to the impact of model
redevelopment or recalibration and periodic model assessment
procedures that have not been incorporated in the ECL models yet. The
impact on total ECL can be positive or negative. These adjustments will be
removed once updates to the specific models have been implemented.
The change in balance compared to previous reporting date is due to i)
released negative overlays because of model updates that have been
implemented and ii) new overlays recognised for new recalibrations and
periodic model assessment procedures that have not been incorporated
yet.
Analysis on sensitivity (*)
The table below presents the analysis on the sensitivity of key forward-
looking macroeconomic inputs used in the ECL collective-assessment
modelling process and the probability weights applied to each of the three
scenarios. The countries included in the analysis are the most significant
geographic regions in ING. For Wholesale Banking, the US is the most
significant in terms of both gross contribution to reportable ECL and
sensitivity of ECL to forward-looking macroeconomics. Accordingly, ING
considers these portfolios to present the most significant risk of resulting in
a material adjustment to the carrying amount of financial assets within
the next financial year.
The purpose of the sensitivity analysis is to enable the reader to
understand the extent of the impact from the upside and downside
scenario on model-based reportable ECL.
In the table below, the real GDP is presented in percentage year-on-year
change, the unemployment in percentage of total labour force, and the
house price index (HPI) in percentage year-on-year change.
Sensitivity analysis as at December 2025 (*)
2026
2027
2028
Unweighted ECL (€ mln)
Probability-weighting
Reportable ECL (€ mln)1
Netherlands
Upside scenario
Real GDP
2.6
2.9
2.3
288
20%
389
Unemployment
3.5
3.2
3.2
HPI
8.2
8.5
7.0
Baseline scenario
Real GDP
1.1
1.4
1.5
364
60%
Unemployment
4.0
3.9
4.1
HPI
4.4
3.0
2.9
Downside scenario
Real GDP
-1.2
-0.9
0.2
566
20%
Unemployment
5.5
6.5
7.4
HPI
-0.8
-5.1
-5.1
Germany
Upside scenario
Real GDP
2.8
3.0
1.8
584
20%
633
Unemployment
3.0
2.4
2.2
HPI
8.2
9.8
9.4
Baseline scenario
Real GDP
1.1
1.5
1.3
623
60%
Unemployment
3.5
3.2
3.1
HPI
5.0
6.2
6.3
Downside scenario
Real GDP
-1.7
-1.1
0.4
713
20%
Unemployment
4.7
5.3
5.6
HPI
0.9
0.4
2.7
Belgium
Upside scenario
Real GDP
2.7
2.3
1.9
544
20%
596
Unemployment
5.1
4.6
4.5
HPI
5.2
4.9
5.4
Baseline scenario
Real GDP
1.1
1.4
1.6
585
60%
Unemployment
5.9
5.6
5.5
HPI
3.7
4.2
4.4
Downside scenario
Real GDP
-1.5
0.1
1.4
682
20%
Unemployment
7.0
7.7
7.9
HPI
1.8
2.6
2.6
United States
Upside scenario
Real GDP
3.2
2.9
2.7
64
20%
105
Unemployment
3.2
2.5
2.6
HPI
4.8
9.4
10.9
Baseline scenario
Real GDP
2.0
2.0
2.0
92
60%
Unemployment
4.3
4.1
4.1
HPI
1.4
2.7
3.6
Downside scenario
Real GDP
-0.4
-0.1
0.8
184
20%
Unemployment
6.7
7.2
7.5
HPI
-5.1
-7.9
-5.5
1Excluding management adjustments.
Sensitivity analysis as at December 2024 (*)
2025
2026
2027
Unweighted ECL (€ mln)
Probability-weighting
Reportable ECL (€ mln)1
Netherlands
Upside scenario
Real GDP
2.6
3.0
2.5
193
20%
270
Unemployment
3.5
3.3
3.3
HPI
18.9
11.7
2.5
Baseline scenario
Real GDP
1.5
1.4
1.5
249
60%
Unemployment
4.0
4.1
4.3
HPI
9.1
3.5
2.4
Downside scenario
Real GDP
-0.4
-1.4
-0.2
411
20%
Unemployment
5.7
7.2
8.1
HPI
-3.7
-7.2
2.2
Germany
Upside scenario
Real GDP
2.0
2.8
1.6
510
20%
548
Unemployment
2.9
2.4
2.0
HPI
5.4
8.9
9.9
Baseline scenario
Real GDP
0.5
1.1
1.2
540
60%
Unemployment
3.4
3.3
3.2
HPI
2.6
5.6
6.3
Downside scenario
Real GDP
-1.7
-1.7
0.3
609
20%
Unemployment
4.7
5.6
5.9
HPI
-1.7
1.3
2.2
Belgium
Upside scenario
Real GDP
2.2
2.6
2.1
534
20%
579
Unemployment
5.1
5.0
4.9
HPI
4.8
4.5
4.4
Baseline scenario
Real GDP
1.1
1.5
1.6
569
60%
Unemployment
5.7
5.7
5.6
HPI
3.2
4.1
3.8
Downside scenario
Real GDP
-0.6
-0.2
1.1
654
20%
Unemployment
7.0
8.0
8.0
HPI
1.2
2.9
2.5
United States
Upside scenario
Real GDP
3.1
3.5
3.2
74
20%
113
Unemployment
3.4
2.4
2.3
HPI
4.3
8.4
9.4
Baseline scenario
Real GDP
2.0
2.0
2.0
101
60%
Unemployment
4.2
4.1
4.0
HPI
3.3
3.7
3.9
Downside scenario
Real GDP
-0.1
-1.1
-0.4
187
20%
Unemployment
5.9
7.3
8.0
HPI
-0.7
-3.0
-2.5
1Excluding management adjustments.
When compared to the sensitivity analysis of 2024, the baseline
macroeconomic inputs have become slightly more positive overall,
underpinned by the resilience of the global economy in the face of trade
and geopolitical tensions. House price growth expectations are lower for
the Netherlands, given a rise in supply from the sale of rental properties
and slowdown of wage growth. In the US, a weak labour and housing
market both signal underlying economic challenges due to policy and
trade uncertainty, and persistent affordability issues. The upside and the
downside scenarios are not directly comparable with those of 2024 due to
an update in the basis of their derivation.
On a total ING level, the unweighted ECL for all collective provisioned
clients in the upside scenario was €2,938 million, in the baseline scenario
€3,232 million and in the downside scenario €3,993 million compared to
€3,326 million reportable model ECL as at 31 December 2025 (excluding all
management adjustments). To perform the sensitivity analysis, a point in
time reportable ECL is used as input, which slightly deviates from the total
reportable collective provisions as presented below:
Reconciliation of reportable collective ECL to total ECL (*)
in EUR million
2025
2024
Total reportable collective provisions
3,168
2,975
ECL from individually assessed impairments
2,750
2,871
ECL from management adjustments
183
203
Total ECL
6,101
6,049
Criteria for identifying a significant increase in credit risk (SICR) (*)
All assets and off-balance-sheet items that are in scope of IFRS 9
impairment and which are subject to collective ECL assessment are
allocated a 12-month ECL if deemed to belong in Stage 1, or a lifetime ECL
if deemed to belong in Stages 2 or 3. An asset belongs in Stage 2 if it is
considered to have experienced a significant increase in credit risk (SICR)
since initial origination or purchase.
The main determinant of SICR is a quantitative test, whereby the lifetime
PD of an asset at each reporting date is compared against its lifetime PD
determined at the date of initial recognition. If either a threshold for
absolute change in lifetime PD or a threshold for relative change in lifetime
PD is reached, the item is considered to have experienced an SICR (for more
details on absolute and relative thresholds, see the following sections).
Furthermore, any facility which shows an increase of 200 percent between
the PD at the date of initial recognition and the lifetime PD at the reporting
date (i.e. threefold increase in PD) must be classified as Stage 2. This is
considered a backstop within the quantitative assessment of SICR.
In Wholesale Banking, a significant increase in lifetime PD is not considered
plausible for assets of obligors with a credit rating at the reporting date in
the top range of investment grade. The assets of these Wholesale Banking
obligors are excluded from the assessment of significant increase in credit
risk triggers. For these obligors the qualitative significant increases in credit
risk triggers remain applicable (see the section below on Qualitative SICR
triggers). These are, for example, the watch list and/or forbearance
triggers. Finally, the 30 days past due backstop also remains applicable for
the top range of investment grade exposures to ensure a significant
increase in credit risk recognition.
Absolute lifetime PD threshold
The absolute threshold is a fixed value calibrated per portfolio/segment
and provides a fixed threshold that, if exceeded by the difference between
lifetime PD at reporting date and lifetime PD at origination, triggers Stage 2
classification. The absolute threshold is calibrated during model
development.
Relative lifetime PD threshold
The relative threshold defines a relative increase of the lifetime PD, beyond
which a given facility is classified in Stage 2 because of a significant
increase in credit risk. The relative threshold is dependent on the individual
PD assigned to each facility at the moment of origination, and a scaling
factor calibrated in the model development phase.
Ultimately, the relative threshold provides a criterion to assess whether
the ratio (i.e. increase) between lifetime PD at reporting date and lifetime
PD at origination date is deemed a significant increase in credit risk. If the
threshold is breached, SICR is identified and Stage 2 is assigned to the
given facility.
The threshold for the relative change in lifetime PD is inversely correlated
with the PD at origination; the higher the PD at origination, the lower the
threshold. The logic behind this is to allow facilities originated in very
favourable ratings to downgrade for longer without the need of a Stage 2
classification. In fact, it is likely that such facilities will still be in favourable
ratings even after a downgrade of a few notches. On the contrary, facilities
originated in already unfavourable ratings grades are riskier and even a
single-notch downgrade might represent a significant increase in credit
risk and thus a tighter threshold will be in place. Still, the relative threshold
is relatively sensitive for investment-grade assets while the absolute
threshold primarily affects non-investment grade assets.
Average threshold ratio
The table below shows the average increase in PD at origination needed to
be classified in Stage 2 is reported, taking into account the PD at
origination of the facilities included in each combination of asset class and
rating quality. In terms of rating quality, assets are divided into
'investment grade' and 'non-investment grade' facilities. Rating 18 and 19
are not included in the table, since facilities are not originated in these
ratings and they constitute a staging trigger of their own (i.e. if a facility is
ever to reach rating 18 or 19 at reporting date, it is classified in Stage 2). In
the table, values are weighted by IFRS 9 exposure and shown for both
year-end 2024 and year-end 2025.
To represent the thresholds as a ratio (i.e. how much should the PD at
origination increase in relative terms to trigger Stage 2 classification) the
absolute threshold is recalculated as a relative threshold for disclosure
purposes. Since breaching only relative or absolute threshold triggers
Stage 2 classification, the minimum between the relative and recalculated
absolute threshold is taken as value of reference for each facility.
Quantitative SICR thresholds  (*)
2025
2024
Average threshold ratio
Investment
grade (rating
grade 1-10)
Non-investment
grade (rating
grade 11-17)
Investment
grade (rating
grade 1-10)
Non-investment
grade (rating
grade 11-17)
Asset class category
Mortgages
2.9
2.4
2.9
2.4
Consumer lending
2.9
2.3
2.8
2.1
Business lending
2.7
2.0
2.7
2.1
Governments and financial institutions
2.9
1.8
2.9
1.9
Other Wholesale Banking
2.7
1.9
2.7
1.9
As it is apparent from the disclosures above, as per ING’s methodology, the
threshold is tighter the higher the riskiness at origination of the assets,
illustrated by the difference between the average threshold applied to
investment grade facilities and non-investment grade facilities.
Sensitivity of ECL to PD lifetime PD thresholds
The setting of PD threshold bands requires management judgement and is
a key source of estimation uncertainty. On Group level, the total model
ECL on performing assets, which is the ECL collective assessment without
taking management adjustments into account, was €1,501 million as at
31 December 2025 (31 December 2024: €1,328 million). To demonstrate
the sensitivity of the ECL to these PD threshold bands, hypothetically
solely applying the upside scenario would result in total model ECL on
performing assets of €1,159 million and a decrease in the Stage 2 ratio by
0.3%-point, while solely applying the downside scenario would result in
total model ECL on performing assets of €2,193 million and an increase in
the Stage 2 ratio by 1.6%-point.
Qualitative SICR thresholds
It should be noted that the lifetime PD thresholds are not the only drivers
of stage allocation as ING Group also relies on a number of qualitative
indicators to identify and assess SICR. An asset can also change stages as
a result of other triggers, such as having over 30 days arrears (used as a
backstop), the occurrence of an early warning indicator, collective SICR
assessment, being on a watch list, being under intensive care
management, having a substandard internal rating, or being forborne.
Market risk
Introduction (*)
Market risk is the risk that movements in market variables, such as interest
rates, equity prices, foreign exchange rates, credit spreads, and real-estate
prices negatively impact the bank’s earnings, capital, market value, or
liquidity position. Market risk either arises through positions in banking
books or trading books.
The banking book positions are intended to be held for the long term (or
until maturity) or for the purpose of hedging other banking book positions.
The trading book positions are typically held with the intention of short-
term trading or to hedge other positions in the trading book. Policies and
processes are in place to monitor the inclusion of positions in either the
trading or banking book as well as to monitor the transfer of risk between
the trading and banking books.
The following sections elaborate on the various elements of the risk
management framework for:
§Market risk in banking books;
§Market risk in trading books; and
§Market risk capital.
Market risk in banking books (*)
ING distinguishes between the trading and banking (non-trading) books.
Positions in banking books originate from the market risks inherent in
commercial products that are sold to clients, Group Treasury exposures, and
from the investment of our own funds (core capital). Both the commercial
products and the products used to hedge related market-risk exposures are
intended to be held until maturity, or at least for the long term.
Risk transfer (*)
Market risks in the banking book are managed via the risk transfer process.
In this process the interest rate, FX, funding, and liquidity risks are
transferred from the commercial books through matched funding or
replication to Group Treasury, where they are centrally managed. The
scheme below presents the transfer and management process of market
risks in the banking books.
market-risk.jpg
Risk measurement (*)
The main concepts and metrics used for measuring market risk in the
banking book are described below per risk type.
Interest rate risk in banking book (*)
Interest rate risk in the banking book is defined as the exposure of a bank’s
earnings, capital, and market value to adverse movements in interest
rates originated from positions in the banking book.
ING centralises interest rate risk management from commercial books
(that capture the products sold to clients) to globally managed interest
rate risk books. This enables a clear demarcation between commercial
business results and results based on unhedged interest rate positions.
ING distinguishes between three types of activities that generate interest
rate risk in the banking book:
§Investment of own funds;
§Commercial business; and
§Group Treasury exposures, including strategic interest rate positions.
Group Treasury is responsible for managing the investment of own funds
(core capital). Capital is invested for longer periods to contribute to stable
earnings within the risk appetite boundaries set by ALCO Bank.
Commercial activities can result in linear interest rate risk due to different
re-pricing properties of assets and liabilities. Also, interest rate risk can
arise from customer behaviour and/or convexity risk, depending on the
nature of the underlying product characteristics.
To determine interest rate risk in specific products (such as savings or
mortgages) certain assumptions may need to be applied. Customer
behaviour risk is defined as the potential future loss (value) arising from
deviations between actual client behaviour and the modelled behaviour
regarding embedded options within commercial products. General sources
of customer behaviour risk include, among other things, the state of the
economy, competition, changes in regulation, legislation and tax regime,
developments in the housing market, and interest rate developments.
From an interest rate risk perspective, commercial activities can typically
be divided into the following main product types: savings and current
accounts (funds entrusted), demand deposits, mortgages, and loans.
Savings and demand deposits are generally invested in such a way that
both the value is hedged and the sensitivity of the margin to market
interest rates is minimised. This is achieved by creating the investment
profile distributed from short term to long term, which dampens the
immediate impact from changes in the market rates and stabilises margin
in the longer horizon. Interest rate risk is modelled based on the stability of
deposits and the pass-through rate. This takes account of different
elements, such as pricing strategies, volume developments, and the level
and shape of the yield curve.
In determining the interest rate profile of mortgages, prepayment or other
embedded optionality can be an important risk driver. In modelling this
risk, both interest-rate-dependent prepayments and constant
prepayments are considered. Next to a dependence on interest rates,
modelled prepayments may include other effects such as loan-to-value,
seasonality, and the reset date of the loan. In addition, the interest
sensitivity of embedded offered rate options is typically considered. 
Wholesale Banking loans typically do not experience interest-rate-
dependent prepayment behaviour, as these are mostly floating rate
products. These portfolios are match-funded, taking the constant
prepayment model into account, and typically do not contain significant
convexity risk. Wholesale Banking loans can have an all-in rate floor or a
floor on a reference rate.
Customer behaviour in relation to mortgages, loans, savings, and demand
deposits is modelled based on extensive analysis of historical data.
However, the substantial fluctuations in the interest rate environment in
recent years make the analysis more challenging than before and may
increase model risk. Models are backtested and updated when deemed
necessary, at least annually. Model parameters and the resulting risk
measures are approved by (local) ALCO, and are closely monitored on a
monthly basis.
Linear risk transfers take place from commercial business books to the
treasury book (Group Treasury), if necessary, by using estimations of
customer behaviour. The originating commercial business is ultimately
responsible for estimating this customer behaviour, leaving convexity risk
and (unexpected) customer behaviour risk with the commercial business.
Risk measurement and the risk transfer process take place at least
monthly. If deemed necessary, additional risk transfers can take place.
The commercial business manages the convexity risk that is the result of
products that contain embedded options, like mortgages. Here the
convexity risk is defined as the optionality effects in the value due to
interest rate changes, excluding the first-order effects. In some cases,
convexity risk is hedged by treasury using cap/floor contracts and
swaptions.
NII at Risk (*)
The NII-at-Risk measures the impact of changing interest rates on the
forecasted net interest income (before tax) of the banking book, excluding
the impacts of credit spread sensitivity, fees, and fair value impact. Future
projected balance sheet developments (dynamic plan) are included in this
risk metric. NII-at-Risk provides insight into the sensitivity of ING’s NII under
shocked interest rate scenarios against what is projected in a base case
scenario.
In its risk management, ING monitors the NII-at-Risk under a three-year
time frame. Interest rates are shocked during the first year of analysis
through the gradual application of shock. The rate changes considered
encompass both upward and downward scenarios, as well as both parallel
(equal movements across the yield curve) and non-parallel scenarios.
The impact of changing interest rates on ING’s NII is predominantly caused
by the following factors:
§Change in returns of (re)investments of client deposits;
§Change in client deposit rates (mainly savings), (partially) tracking
changes in market interest rates;
§Change in the amortisation profile of mortgages, due to an increase or
decrease in expected prepayments;
§Higher/lower returns of (re-)investments of core capital investment;
§Open interest rate positions, leading to changes in return because of
different market rates; and
§Assumed volume development of the balance sheet in line with ING’s
dynamic plan.
For projecting the change in client deposit rates, ING uses a client rate
model that describes the relation between market interest rates and client
deposit rates. The model is calibrated under a range of interest rate
scenarios. Per scenario, the actual change in client deposit rates may
deviate from this calibrated model. The actual NII development of
customer deposits may, indeed, differ from the provided scenarios,
depending on, among other things, actual interest rate and savings client
rate evolution, as well as changes to ING’s balance sheet composition,
such as net deposit growth and relative share of savings deposits and non-
remunerated current accounts.
The NII-at-Risk figures in the table below represent a parallel, linear
interest rate shift over one year (‘ramped’) based on the assumption that
balance sheet developments follow ING’s dynamic plan within a one-year
horizon.
The NII-at-Risk is mainly influenced by the difference in the sensitivity
between client liabilities and client assets and investments to rate
changes. The primary factor of NII-at-Risk are the investments of current
accounts, while the investments of own funds have a marginal effect, as
only a relatively small portion needs to be (re)invested within a one-year
period.
NII-at-Risk banking book per currency - year one (*)
in EUR million
2025
2024
Ramped, floored
Ramped, floored
parallel ▼
parallel ▲
parallel ▼
parallel ▲
By currency
Euro
-182
205
-146
160
US dollar
1
-1
-4
5
Other
-74
94
-2
21
Total
-254
298
-153
186
EUR ramped (floored at -100bps) is at +/- 120bps in 1 year
USD ramped (floored at -100bps) is at +/- 120bps in 1 year
The change in NII under declining and upward interest rate scenarios may
not be equal. This is due to different expected reactions in prepayment
behaviour of mortgages and different pricing developments of commercial
loans and deposits products (mainly savings). This is caused by embedded
options, explicit or implicit pricing floors, and other (assumed) pricing
factors.
The metrics mentioned above are internal metrics, which therefore deviate
from the regulatory NII SOT metric. Both internal and regulatory measures
are part of the bank's risk appetite framework and are actively managed.
Year-on-year variance analysis (*)
In 2025, ING maintained its dynamic hedging strategy, transferring
interest rate risk from business units to Group Treasury, where it was
subsequently hedged in financial markets, resulting in overall limited
sensitivity of NII at Risk (NIIaR) compared to ING’s total net interest income.
Year-on-year NIIaR increase by €100 million under a parallel down (worst
case) scenario, is primarily driven by revised repricing assumptions and
positions taken by treasury.
Net present value (NPV) at Risk (*)
NPV-at-Risk measures the impact of changing interest rates on the value
of the positions in the banking book and it is defined as the outcome of an
instantaneous increase or decrease in interest rates from applying
currency-specific scenarios. The metric primarily reflects the impact of
interest rate fluctuations on the bank’s strategic interest rate positions
(e.g. investments of own funds), as well as on the savings and mortgage
portfolios. While strategic positions typically adjust proportionally with
changing rates, the savings and mortgage products exhibit more complex
behavior due to embedded optionalities. Customers can prepay or
reallocate their balances, resulting in non-linear value changes. As a result,
these portfolios may incur value losses both when interest rates increase
and decrease, contributing to the asymmetric NPV-at-Risk pattern
highlighted in the annual report.
The full value impact cannot be directly linked to the financial position or
profit or loss account, as fair value movements in banking books are not
necessarily reported through the profit or loss account or through other
comprehensive income (OCI). The changes in value are expected to
materialise over time in the profit and loss account if interest rates develop
according to forward rates throughout the remaining maturity of the
portfolio. The majority of the risk comes from the investments of own
funds and from positions exhibiting negative convexity due to embedded
optionality (most notably variable rate savings and fixed rate mortgages).
The metrics mentioned above are internal metrics, which therefore deviate
from the regulatory EVE SOT metric. Both internal and regulatory
measures are part of the bank’s risk appetite framework and are actively
managed.
NPV-at-Risk banking books per currency (*)
in EUR million
2025
2024
floored
floored
parallel ▼
parallel ▲
parallel ▼
parallel ▲
By currency
Euro
1,319
-2,593
154
-1,613
US dollar
273
-265
274
-266
Other
269
-267
321
-329
Total
1,862
-3,125
749
-2,208
EUR (floored at -100bps) is at +/- 120bps
USD (floored at -100bps) is at +/- 120bps
Year-on-year variance analysis (*)
The overall NPV sensitivity increased considerably compared to the
previous year. The parallel up scenario continues to represent the worst-
case outcome, and the calculation shock for the primary currencies (EUR
and USD) was maintained at 120 basis points, consistent with the level
used in 2024. Year-over-year variations are explained by the evolving
balance sheet composition, movements in interest rates, and shifting
market expectations.
The impact of the benchmark rate reform (*)
In line with the recommendations of the Financial Stability Board, a
fundamental review of important interest rates benchmarks has been
undertaken. Some interest rate benchmarks have been reformed, while
others have or will be replaced by risk-free rates and discontinued.
In 2025, the benchmark rate reform of only one reference rate, to which
ING has significant exposures as at 31 December 2025, was continuing (i.e.
WIBOR in Poland).
On 24 January 2025, the Steering Committee of the National Working
Group (NWG SC) in Poland selected POLSTR (Polish Short Term Rate) as the
ultimate interest rate benchmark to replace WIBOR. POLSTR is calculated
based on unsecured deposits of Credit and Financial Institutions and is
expected to replace WIBOR by 31 December 2027.
In April 2025, the NWG SC published the updated transition roadmap of the
replacing process of WIBOR and announced in June 2025 that the official
determination of the POLSTR has started.
During the second half of 2025, the NWG SC approved several
recommendations for products based on POLSTR, and informed that
POLSTR has been applied for the first time in the domestic financial market
on 1 September 2025. Therefore, POLSTR gained a status of a benchmark
in accordance with the requirements of the EU Benchmark Regulation.
On 30 September 2025, the benchmark administrator GPW Benchmark
S.A, published the decision to cease providing the WIBOR reference rate for
various tenors in phases, between the end of 2025 and the end of 2026.
On 21 November 2025, the Ministry of Finance (of Poland) conducted the
first pilot issuance of treasury bonds linked to the POLSTR benchmark
during a sale auction.
Other important milestones of the process are planned in subsequent
years, including the construction of a market for financial products based
on the new benchmark and achieving regulatory and operational
readiness of all market participants to offer and operate these financial
products.
Due to the discontinuation of WIBOR, ING, its customers, and in general
those market participants with exposure to this benchmark rate will be
faced with a number of risks. These risks include legal, financial,
operational, reputational, and conduct risk. The WIBOR rates are used in
several of our lending and derivative products, and hence a project team
has been established to manage the transition. The WIBOR transition is
especially important for our Polish subsidiary (ING Bank Śląski S.A.) with a
significant amount of Polish zloty-denominated assets and liabilities
including derivatives that are continuously rebalanced to hedge the risk
exposures.
The tables below summarise the approximate gross exposure of ING that
has yet to transition related to WIBOR, excluding exposures expiring before
the transition date of 31 December 2027.
Non-derivative financial instruments to transition from WIBOR to POLSTR (*)
Financial assets
non-derivative
Financial
liabilities  non-
derivative
Off-balance
sheet
commitments
in EUR million
Carrying value
Carrying value
Nominal value
31 December 2025
25,333
360
2,301
31 December 2024
19,202
134
1,544
Derivative financial instruments to transition from WIBOR to POLSTR (*)
31 December 2025
31 December 2024
in EUR million
Nominal value
Nominal value
WIBOR
196,133
110,189
See sections 1.5.4 of Note 1 ‘Basis of preparation and material accounting
policy information’ for information on the Phase 1 amendments to IFRS.
As at 31 December 2025, Phase 1 reliefs are applicable to WIBOR indexed
fair value and cash flow hedge accounting relationships as there is
uncertainty arising from the WIBOR reform with respect to the timing and
the amount of the underlying cash flows that the Group is exposed to.
Therefore, for WIBOR financial instruments designated in hedge
accounting the applicable Phase 1 reliefs will continue to apply until the
relevant contract is modified. At that point in time, the reliefs of Phase 2
amendments to IFRS will become applicable.
Following the Phase 1 reliefs that allow ING to continue applying hedge
accounting, ING has to assume that the WIBOR-based cash flows from the
hedging instrument and hedged item will remain unaffected for the
affected fair value and cash flow hedge relationships. In addition, for the
forecast transactions that are subject to cash flow hedge accounting,
under the Phase 1 reliefs ING has to assume that the WIBOR benchmark on
which the hedged cash flows are based is not altered as a result of the
reform and, therefore, the forecast transactions still meet the highly
probable requirement.
The total gross notional amounts of hedging instruments that are used in
ING’s hedge accounting relationships for which the Phase 1 amendments
to IAS 39 were applied are:
Notional amounts of hedging instruments from WIBOR to POLSTR (*)
31 December 2025
31 December 2024
in EUR million
Nominal value
Nominal value
WIBOR
97,492
99,663
As at 31 December 2025, 50% (31 December 2024: 32%) of the notional
amounts have a maturity date beyond 31 December 2027. The notional
amounts of the derivative hedging instruments provide a close
approximation of the extent of the risk exposure ING manages through
these hedging relationships.
Credit spread risk in banking books (CSRBB) (*)
Credit spread risk is defined as risk driven by the changes of the market
price for credit risk, for liquidity and potentially other characteristics of
credit-risky instruments, which is not captured by another existing
prudential framework such as Interest Rate Risk in Banking Book or by
expected credit/(jump-to-) default risk. The CSRBB framework is
implemented based on EBA Guidelines. Metrics used are NPV-at-Risk, NII-
at-Risk and Market Value Changes-at Risk and view the positions across
different accounting treatments.
Credit spread risk is not part of the internal risk transfer towards Group
Treasury and therefore remains in the business unit it originated in. Group
Treasury itself is also an important driver of credit spread risk via its high-
quality liquid assets (HQLA) investment portfolio and issuance activities.
Risk appetite limits are set on a combination of metrics and accounting
scopes and are cascaded to local ALCOs depending on the type of limit
and materiality. Metrics and limits are monitored and reported monthly to
global and local risk committees, and various stakeholders.
Foreign exchange (FX) risk in banking books (*)
FX exposures in banking books result from core banking business activities
(business units doing business in currencies other than their base
currency), foreign currency investments in subsidiaries (including realised
net profit and loss), and strategic equity stakes in foreign currencies. The
policy regarding these exposures is briefly explained below.
Core banking business (*)
Every business unit hedges the FX risk resulting from core banking
business activities into its base currency to prevent volatility in profit and
loss. Consequently, assets and liabilities are matched in terms of currency,
within certain friction limits.
FX translation (*)
ING’s strategy is to protect the CET1 ratio against adverse impact from FX
rate fluctuations, while limiting the volatility in the profit and loss account
due to this CET1 hedging and limiting the RWA impact under the
regulatory framework. Currency exposure of Return on Equity is also
considered. Hedge accounting is applied to the largest extent possible.
Taking this into account, the CET1 ratio hedge can be achieved by
deliberately taking foreign currency positions equal to certain target
positions, such that the CET1 capital and risk-weighted assets are equally
sensitive in relative terms to changing FX rates.
Risk profile – FX translation (*)
The following table presents the currency exposures in the banking books
for the most important currencies for the FX translation result. Positive
figures indicate long positions in the respective currency. As a result of the
strategy to hedge the CET1 ratio, an open structural FX exposure exists.
To measure the volatility of the CET1 ratio from FX rate fluctuations,
different metrics are used, including the CET1 Ratio-at-Risk. The impact is
controlled via the Solvency and Financial Risk RAS.
Foreign currency exposures banking books (*)
in EUR million
Foreign Investments
Hedges
Net exposures
2025
2024
2025
2024
2025
2024
US Dollar
9,698
11,251
-4,500
-4,823
5,198
6,429
Pound Sterling
1,584
1,674
-515
-484
1,069
1,190
Polish Zloty
4,652
4,292
-2,009
-1,616
2,643
2,677
Australian Dollar
3,307
3,373
-1,933
-2,161
1,374
1,212
Turkish Lira
469
557
0
0
469
557
Chinese Yuan
2,015
2,439
1
-830
2,016
1,609
Russian Rouble
597
396
0
0
597
396
Romanian Leu
913
913
-228
-176
685
736
Thai Baht
1,307
1,266
-829
-838
478
428
Other currency
3,032
3,346
-1,995
-2,748
1,036
599
Total
27,573
29,509
-12,008
-13,675
15,565
15,834
1.The FX sensitivity is expressed as the FX spot equivalent position.
EBA Structural FX guidelines
In line with the EBA guidelines on Structural FX, upon permission from the
competent authorities, certain currency positions are being excluded from
the calculation of net open currency positions under CRR article 352(2).
The resulting impact is presented in the Pillar 3 disclosure.
Equity price risk in banking books (*)
ING maintains a portfolio with substantial equity exposure in its banking
books.
Risk profile (*)
Equity price risk arises from the possibility that an equity security’s price
will fluctuate, affecting the values of the equity security itself as well as
other instruments whose values react similarly to the particular security, a
defined basket of securities, or a securities index. ING’s equity exposure
mainly consists of the investments in associates and joint ventures of
€1,607 million (2024: €1,679 million) and equity securities held at fair value
through other comprehensive income (FVOCI) of €2,607 million (2024:
€2,562 million). The value of equity securities held at FVOCI is directly
linked to equity security prices with increases/decreases being recognised
in the revaluation reserve.
Investments in associates and joint ventures are measured in accordance
with the equity method of accounting, and the balance sheet value is
therefore not directly linked to equity security prices. The equity sensitivity
is expressed as the equity position.
Year-on-year variance analysis (*)
In 2025, the revaluation reserve equity securities decreased by €372
million from €1,816 million to €1,444 million due to revaluation of the
shares in Bank of Beijing with €-403 million. In 2025, the equity securities
at fair value through OCI increased by €45 million mainly due to increased
stake in Van Lanschot Kempen. 
Revaluation reserve equity securities at fair value through other
comprehensive income (*)
in EUR million
2025
2024
Positive remeasurement
1,456
1,820
Negative remeasurement
-12
-4
Total
1,444
1,816
Market risk in trading books (*)
Within the trading portfolios, the positions are maintained in the financial
markets. These positions are often a result of transactions with clients and
may benefit from short-term price movements. In 2025, ING continued its
strategy of undertaking trading activities to develop its client-driven
franchise and deliver a differentiating experience by offering multiple
market and trading products.
With respect to the trading portfolios, Trading Risk Management (TRM)
focuses on the management of market risks of Wholesale Banking (mainly
Financial Markets) as this is the only business line within ING where trading
activities take place. Trading activities include facilitation of client business
and market making. TRM is responsible for the development and
implementation of trading risk policies and risk measurement
methodologies, and for reporting and monitoring risk exposures against
approved trading limits. TRM also reviews trading mandates and global
limits, and performs the gatekeeper role in the product review process
(PARP).
Risk measurement (*)
ING uses a comprehensive set of methodologies and techniques to
measure market risk in trading books: Value at Risk (VaR) and Stressed
Value at Risk (SVaR), Incremental Risk Charge (IRC), and stress testing.
Systematic validation processes are in place to validate the accuracy and
internal consistency of data and parameters used for the internal models
and modelling processes.
Value at Risk (*)
TRM uses the historical simulation VaR methodology (HVaR) as its primary
risk measure. The HVaR for market risk quantifies, with a one-sided
confidence level of 99 percent, the maximum overnight loss that could
occur in the trading portfolio of ING due to changes in risk factors (e.g.
interest rates, equity prices, foreign exchange rates, credit spreads, implied
volatilities), considering the positions remain unchanged for a time period
of one day.
Next to general market movements in these risk factors, HVaR also takes
into account market data movements for specific moves in, for example,
the underlying issuer or securities. A single model which diversifies general
and specific risk is used. In general, a full revaluation approach is applied,
while for a limited number of linear trading positions and risk factors in
commodity and equity risk classes, a sensitivity-based approach is applied.
The potential impact of historical market movements on today’s portfolio
is estimated, based on equally weighted observed market movements of
the previous year (260 business days). When simulating potential
movements in risk factors, depending on the risk factor type, either an
absolute or a relative shift is used.
The data used in the computations is updated daily. ING uses HVaR with a
one-day horizon for internal risk measurement, management control, and
backtesting, and HVaR with a 10-day horizon for determining regulatory
capital. To compute HVaR with a 10-day horizon, the one-day risk factor
shifts are scaled by the square root of 10 and then used as an input for the
revaluation. The same model is used for all legal entities within ING with
market risk exposure in the trading portfolio.
Limitations (*)
HVaR has some limitations: it uses historical data to forecast future price
behaviour, but future price behaviour could differ substantially from past
behaviour. Moreover, the use of a one-day holding period (or 10 days for
regulatory capital calculations) assumes that all positions in the portfolio
can be liquidated or hedged in one day. In periods of illiquidity or market
events, this assumption may not hold. Also, the use of a 99 percent
confidence level means that HVaR does not take into account any losses
that occur beyond this confidence level.
Backtesting (*)
Backtesting is a technique for the ongoing monitoring of the plausibility of
the HVaR model in use. Although HVaR models estimate potential future
trading results, estimates are based on historical market data. In a backtest,
the actual daily trading result (excluding fees and commissions) is
compared with the one-day HVaR.
In addition to using actual results for backtesting, ING also uses hypothetical
results, which exclude the effects of intraday trading, fees, and
commissions. When an actual or a hypothetical loss exceeds the HVaR, an
‘outlier’ occurs. Based on ING’s one-sided confidence level of 99 percent, an
outlier is expected once in every 100 business days.
On an overall level in 2025, there was one outlier for hypothetical P&L and
zero outliers for actual P&L. The hypothetical outlier occurred in the first
quarter of 2025, mainly driven by higher EUR rates due to anticipated
changes in German fiscal policy.
Stressed HVaR (*)
The stressed HVaR (SVaR) is intended to replicate the HVaR calculation that
would be generated on the bank’s current portfolio with inputs calibrated to
the historical data from a continuous 12-month period of significant
financial stress relevant to the bank’s portfolio.
To calculate SVaR, ING uses the same model that is used for 1DHVaR, with a
10-day horizon. The data for the historical stress period used currently
includes the height of the credit crisis around the fall of Lehman Brothers
(2008-2009), and this stressed period selection is reviewed annually. The
historical data period is chosen so that it gives the worst-scenario loss
estimates for the current portfolio. The same SVaR model is used for
management purposes and for regulatory purposes. The same SVaR model
is used for all legal entities within ING with market risk exposure in the
trading portfolio.
Incremental risk charge (*)
The incremental risk charge (IRC) for ING is an estimate of the default and
migration risks for credit products (excluding securitisations) in the trading
book, over a one-year capital horizon, with a 99.9 percent confidence level.
Trading positions (excluding securitisations) of ING, which are subject to
specific interest rate risk included in the internal model approach for market
risk regulatory capital, are in scope of the IRC model. By model choice,
equity is excluded from the model. For the calculation of IRC, ING performs a
Monte Carlo simulation based on a multi-factor t-copula. In the multi-factor
IRC model the supervisory asset correlations are no longer applicable and
the calibration of the correlations is based on historical market data. The
rating change is simulated for all issuers over the different liquidity horizons
(i.e. time required to liquidate the position or hedge all significant risks)
within one year. Movements across different rating categories and
probabilities of default are governed by a credit-rating transition matrix. An
internal transition matrix along with internal LGDs is used, to comply with
the consistency requirement. The financial impact is then determined for
the simulated migration to default, or for the simulated migration to a
different rating category, based on LGD or credit spread changes,
respectively.
The liquidity horizon has been set to the regulatory minimum of three
months for all positions in scope. ING reviews the liquidity horizons on a
yearly basis, based on a structured assessment of the time it takes to
liquidate the positions in the trading portfolio.
Stress testing and event risk (*)
Stress testing is a valuable risk management tool. In addition to the bank-
wide stress-test framework as described in the stress-testing section,
Trading Risk Management performs stress tests specific to the trading book
with various frequencies. The trading book stress tests evaluate the impact
on the bank’s trading book under severe but plausible stress scenarios, using
a full revaluation approach. The framework is based on historical as well as
hypothetical scenarios. The stress result is an estimate of the profit and loss
caused by a potential event and its worldwide impact for ING. The results of
the stress tests are used for decision-making, aimed at maintaining a
financially healthy going-concern institution after a severe event occurs.
In stress scenarios, shocks are applied to prices (credit spreads, interest
rates, equity, commodities, and FX rates) and volatilities. Depending on the
type of the stress test, additional scenario assumptions can be made, for
example on correlations, dividends, or recovery rates. The structural
scenarios are defined to cover market moves in various directions and
capture different asset class correlations. Scenarios are calculated using a
full revaluation approach. The worst scenarios are determined for each
product line, business line, and super business line, and compared against
limits.
Sensitivities (*)
As part of the risk monitoring framework, TRM actively monitors the
sensitivities of the trading portfolios. Sensitivities measure the impact of
movements in individual market risk factors (foreign exchange rates,
interest rates, credit spreads, equity and commodity prices) on profit and
loss results of the trading positions and portfolios.
The following tables show the five largest trading positions in terms of
sensitivities to foreign exchange, interest rate, and credit spread risk factor
movements. These largest exposures also reflect concentrations of risk in
FX risk per currency, interest rate risk per currency, and credit spread risk
per country, rating, and sector. Due to the nature of the trading portfolios,
positions in the portfolios can change significantly from day to day, and
sensitivities of the portfolios can change daily accordingly.
Most important foreign exchange year-end trading positions (*)
in EUR million
2025
2024
Foreign exchange
Foreign exchange
US Dollar
-260
US Dollar
-93
Turkish Lira
229
Turkish Lira
84
Chinese Yuan
88
Korean Won
62
Japanese Yen
-49
Japanese Yen
61
Romanian Leu
42
Chinese Yuan
-37
Most important interest rate and credit spread sensitivities at year-end (*)
in EUR thousand
2025
2024
Interest rate (BPV) 1
Interest rate (BPV) 1
Euro
-312
Euro
-799
US Dollar
-161
US Dollar
-198
British Pound
-133
British Pound
-189
Japanese Yen
133
Korean Won
-54
Philippine Peso
-98
Philippine Peso
-54
Credit spread (CSO1)  2
Credit spread (CSO1)  2
France
178
United States
193
United Kingdom
111
Netherlands
-165
Netherlands
75
France
-113
Indonesia
-63
Poland
69
South Korea
63
Germany
49
1.Basis point value (BPV) measures the impact on value of a one basis point increase in interest
rates.
2.Credit spread sensitivity (CS01) measures the impact on value of a one basis point increase in
credit spreads. Exposures to supranational institutions are not assigned to a specific country.
Credit spread sensitivities per risk class and sector at year-end (*)
2025
2024
in EUR thousand
Corporate
Financial
institutions
Corporate
Financial
institutions
Credit spread (CSO1)  1
Risk classes
1 (AAA)
-2
-19
-2
-118
2–4 (AA)
26
-13
-44
-27
5–7 (A)
64
-37
49
-246
8–10 (BBB)
191
-54
93
-76
11–13 (BB)
38
-14
38
-13
14–16 (B)
56
8
23
-12
17–22 (CCC and NPL)
6
4
4
2
Total
378
-124
162
-489
1Credit Spread Sensitivity (CS01) measures the impact on value of a 1 basis point
increase in credit spreads.
Funding and liquidity risk
Introduction (*)
Funding and liquidity (F&L) risk is the risk that ING or one of its subsidiaries
cannot meet their financial obligations upon their maturity date at a
reasonable cost and in a timely manner. ING incorporates funding and
liquidity risk management in its business strategy and has established a
funding and liquidity risk framework to manage these risks within pre-
defined boundaries.
The following sections elaborate on the various elements of funding and
liquidity risk:
§Funding and liquidity risk framework;
§Funding and liquidity risk management strategy and objectives;
§Funding and liquidity adequacy and risk appetite;
§Funding and liquidity risk indicators;
§Liquidity stress testing; and
§Contingency funding planning.
Funding and liquidity risk framework (*)
Macroeconomic and market environments are important considerations in
ING’s F&L framework. The macroeconomic environment is comprised of
various exogenous factors over which ING has no control, but which may
have a material impact on ING’s F&L position. Exogenous factors analysed
on a regular basis include:
§performance of global and local macroeconomic indicators, e.g. shifts
in gross domestic product, inflation rates, unemployment rates, and
public deficit/surplus;
§developments and risks arising from geopolitical tensions and trends;
§monetary policy with a focus on the alternative monetary measures
employed by central banks in recent years as a result of the global
energy crisis and periods of high inflation; and
§regulatory requirements, e.g. understanding the changing regulatory
landscape as well as the impact of ING’s actions on existing regulatory
boundaries.
The strategic ambitions of ING, together with the design and execution of
the funding plan, are assessed under both current and projected market
conditions. An emphasis is placed on understanding overall market trends
and developments, credit rating changes, and peer comparisons.
The EB, MBB, and staff departments from the CRO and CFO domains, as
well as Group Treasury, have oversight of, and are responsible for,
managing funding and liquidity risks.
Funding and liquidity management strategy and objectives (*)
The main objective of ING’s funding and liquidity risk management is to
maintain sufficient liquidity to fund the commercial activities of ING both
under normal and stressed market circumstances across various locations,
currencies and tenors.
ING’s funding consists mainly of retail and corporate deposits contributing
to 53 percent and 22 percent of total funding, respectively. These funding
sources provide a stable funding base. The remainder of the required
funding is attracted primarily through a combination of long-term and
short-term professional funding. Group Treasury manages the professional
funding in line with the F&L risk appetite with the aim of ensuring a
sufficiently diversified and stable funding base.ING’s long-term professional funding is diversified across maturities and
currencies. The main portion of long-term professional funding is euro and
US dollar denominated, which is in line with the currency composition of
customer lending.
Funding and liquidity adequacy and risk appetite (*)
ING identifies key drivers of short-term and future liquidity and funding
needs on an ongoing basis through the periodic risk-identification process.
Taking into consideration the identified risk drivers, ING regularly assesses
its current and future liquidity adequacy and, if deemed necessary, takes
action to further improve ING’s liquidity position and maintain sufficient
counterbalancing capacity. A Liquidity Adequacy Statement is formulated
on a regular basis to substantiate and reflect the management view on
the current funding and liquidity position as well as the potential future
challenges. The Liquidity Adequacy Statement is an important part of
ING’s Internal Liquidity Adequacy Assessment Process (ILAAP).
Additionally, ING completes ad-hoc funding and liquidity assessments if
deemed necessary.   
ING assesses its F&L adequacy through three lenses – stress, economic,
and normative:
§Through the stress lens, ING evaluates its ability to withstand periods of
prolonged F&L stress for both normative and economic requirements.
Limits under idiosyncratic, market-related, combined idiosyncratic and
market-related, and climate risk scenarios, which lead to customer
deposit outflows, deterioration of access to funding markets, and lower
liquidity value of counterbalancing capacity are evaluated.
§Through the economic lens, ING assesses the extent to which its
customers, professional counterparties, and investors are comfortable
to provide deposits and funding in the tenors, currencies, and
instruments necessary to sustainably fund the business (intraday,
short-term, and long-term) in a going-concern situation.
§Through the normative lens, ING ascertains that the bank is in the
position to meet current and future domestic and host regulatory
requirements.
For each lens, ING has established a related set of risk appetite statements,
which define ING’s risk appetite, commensurate with the principles of
liquidity adequacy.
The F&L risk appetite statements are translated into metrics with
appropriate boundaries and instruments which are used to regularly
measure and manage ING’s funding and liquidity risk. The risk appetite
with respect to the stress lens aims to have sufficient counterbalancing
capacity under various internally defined stress scenarios. Regarding the
economic perspective, an internally defined stable funding to loans (SFtL)
ratio and stable funding surplus (SFS) metric (supplemented by other
metrics) is used to stimulate a diversified funding base and to prevent
overreliance on professional funding. Finally, the liquidity coverage ratio
(LCR) and the net stable funding ratio (NSFR) regulatory metrics are
monitored in terms of both ING’s risk appetite and normative
requirements.
Liquidity stress testing (*)
Funding and liquidity stress testing forms part of the overall F&L
framework. It allows ING to examine the effects of exceptional but
plausible future events on ING’s funding and liquidity position and provides
insight into which entities, business lines, or portfolios are vulnerable to
which types of risk drivers or scenarios.
The stress-testing framework encompasses the funding and liquidity risks
of the consolidated balance sheet of ING Group, including all entities,
business lines as well as on- and off-balance sheet positions. The net
liquidity position (NLP) is the main stress-testing measure and is measured
at different time buckets.
The stress-testing framework considers idiosyncratic, market-wide,
combined (idiosyncratic and market-wide) and climate-stress scenarios.
The design of the framework is based on empirical evidence supplemented
by expert judgment. The framework can be extended to additional ad-hoc
scenarios. For example, it can be used as input for firm-wide stress testing
and reverse stress testing.
Outcomes of the stress tests are considered in the key aspects of ING’s F&L
risk framework and F&L risk management, including:
§Risk Appetite Framework (through risk appetite statements);
§Risk identification and assessment;
§Monitoring of the liquidity and funding position;
§Business actions (if needed);
§Contingency funding plan; and
§Early-warning indicators.
The funding and liquidity stress-testing framework is also subject to
regular internal validation by model validation.
In line with supervisory expectations, ING’s liquidity position is stress tested
on (at minimum) a monthly basis using scenarios that form part of the
F&L risk appetite statement. The results of all internal stress scenarios are
monitored and assessed on a monthly basis. In addition, ad-hoc scenarios
based on current economic and market developments are run to
determine their potential impacts on the funding and liquidity position of
ING.  In 2025, this included stress-test scenarios assessing the impact of a
shutdown of US short- and long-term funding markets and FX markets.
The internal stress scenarios and their corresponding results serve as input
in the decision on holding additional contingency measures.
Contingency funding planning (*)
ING’s contingent F&L risks are addressed in its Contingency Capital and
Funding Plan (CCFP). The objectives of the CCFP include:
§Establishment of a monitoring framework to detect approaching
contingent events as well as their impact on ING’s F&L position;
§Provision of a plan for responding to various and increasing levels of a
bank’s liquidity and capital shortfall under adverse and stressed
conditions;
§Designation of management responsibilities, crisis communication
methods and channels, and reporting requirements;
§Identification of contingent capital and liquidity sources under
escalating adverse and stressed conditions; and
§Description of the steps needed to ensure the bank’s capital and
liquidity sources are sufficient to fund scheduled operating
requirements and meet the institution’s commitments, with minimal
costs and disruption.
The contingency funding measures are developed in conjunction with the
ING Recovery Plan and are reviewed and tested on a regular basis.