v3.25.4
Risk Report - Credit Risk Management
 12 Months Ended
Dec. 31, 2025
Credit Risk Management [Abstract]  
Risk Identification and Assessment Risk identification and assessment
Risks to Deutsche Bank’s businesses and infrastructure functions, including under stressed conditions, are regularly
identified. This assessment incorporates input from both first and second line of defense, with the identified risks
assessed for materiality based on their severity and likelihood of materialization. The assessment of risks is
complemented by a view on emerging risks applying a forward-looking perspective. This risk identification and
assessment process results in the risk inventory which captures the material risks for the Group, and where relevant,
across businesses, entities and branches.
Regular updates to the Group risk inventory are reported to the Group Risk Committee and the Management Board. The
inventory informs key risk management processes, including the development of stress scenarios tailored to Deutsche
Bank’s risk profile and informing risk appetite setting and monitoring. Risks in the inventory are also mapped to risks in
the Group risk type taxonomy, where a corresponding materiality assessment is also provided.
Credit Risk Management  
Credit Risk Framework Credit Risk Management
Credit risk framework
Credit risk arises from all transactions where actual, contingent or potential claims against any counterparty, borrower,
obligor or issuer (which Deutsche Bank refers to collectively as “counterparties”) exist, including those claims that
Deutsche Bank plans to distribute. It captures the risk of loss because of a deterioration of a counterparty’s
creditworthiness or the failure of a counterparty to meet the terms of any contract with Deutsche Bank or otherwise
perform as agreed.
Based on the Risk Type Taxonomy, credit risk is grouped into four material categories, namely default/migration risk,
transaction/settlement risk (exposure risk), mitigation risk and concentration risk. This is complemented by a regular risk
identification and materiality assessment.
–Default/migration risk as the main element of credit risk, is the risk that a counterparty defaults on its payment
obligations or experiences material credit quality deterioration increasing the likelihood of a default
–Transaction/settlement risk is the risk that arises from any existing, contingent or potential future positive exposure
–Mitigation risk is the risk of higher losses due to risk mitigation measures not performing as anticipated
–Credit concentration risk is the risk of an adverse development in a specific single counterparty, country, industry or
product leading to a disproportionate deterioration in the risk profile of Deutsche Bank’s credit exposures to that
counterparty, country, industry or product
Deutsche Bank manages its credit risk using the following principles:
–Credit Risk is only accepted:
–for adopted clients
–after completed appropriate due diligence led by the respective origination teams as 1st LoD
–New products and changes to existing products having been assessed within Deutsche Bank Group’s Product
Lifecycle Policy
–If a rating has been assigned in line with agreed and approved processes
–If all credit relevant exposures are correctly reflected in the relevant risk systems
–If plans for an orderly termination of the risk positions have been considered
–Credit Risk is assumed within the applicable Risk Appetite
–Profit and Loss responsibility for credit exposures is kept by and remains with the originating Group Division
–Risk taken needs to be adequately compensated
–Risk must be continuously monitored and managed across 1LoD and CRM/“Marktfolge” as well as 2LoD
–Credit standards are applied consistently across all Units in order to maintain a favorable risk profile in line with the
Risk Appetite
–Collateral or other risk mitigating, hedging or rating transfer instruments, which can be an alternative source of
repayment do not substitute for underwriting standards and a thorough assessment of the debt service ability of a
counterparty has to be performed during the credit process
–Deutsche Bank strives to adequately secure, guarantee or hedge outright cash risk and longer tenor-exposures; this
approach does usually not include lower risk short-term transactions and facilities supporting specific trade finance or
other lower risk products where the margin allows for adequate loss coverage
–Deutsche Bank measures and consolidates globally all exposure and facilities to the same Obligor
–Deutsche Bank has established within Credit Risk Management – where appropriate – specialized teams for deriving
internal client ratings, analyzing and approving transactions or covering workout clients; for transaction approval
purposes, structured credit risk management teams are aligned to the respective products or specific risks to
ascertain adequate product expertise
–Where required, Deutsche Bank has established processes to manage credit exposures at a legal entity or regional
level
To meet the requirements of Article 190 CRR, Deutsche Bank has allocated the various control requirements for the
credit processes to units/role holders best suited to perform such controls
Climate and environmental risks are integrated across the different stages of the credit lifecycle including transaction
approval/client onboarding, risk classification and credit ratings, portfolio analysis and monitoring and collateral
valuation.
Measuring Credit Risk  
Measuring credit risk paragraph 1 Measuring credit risk
Credit risk is measured by credit rating, regulatory and internal capital demand and other key components like credit
limits as mentioned below.
The credit rating is an essential part of the bank’s underwriting and credit process and provides – amongst others – a
cornerstone for credit limit determination on an individual counterparty level, credit decision and transaction pricing as
well as the determination of regulatory capital demand for credit risk. Each counterparty must be rated, and each rating
has to be reviewed at least annually supported by ongoing monitoring of counterparties. A credit rating is a prerequisite
for any credit limit established/approved. For each credit rating, the appropriate rating approach has to be applied and
the derived credit rating has to be established in the relevant systems. Specific rating approaches have been established
to best reflect the respective characteristics of exposure classes, including specific product types, central governments
and central banks, institutions, corporates and retail.
Counterparties in the bank’s non-retail portfolios are rated by Deutsche Bank’s independent Credit Risk Management
function partly using automated or semi-automated rating systems. Given the largely homogeneous nature of the retail
portfolio, counterparty creditworthiness and ratings are derived by utilizing an automated decision engine. Country risk-
related ratings are provided by Enterprise and Treasury Risk Management (ETRM) Risk Research.
Deutsche Bank’s rating analysis is based on a combination of qualitative and quantitative factors. When rating a
counterparty, Deutsche Bank applies in-house assessment methodologies, as well as its 21-grade rating scale.
Measuring credit risk paragraph 2 Besides the credit rating, as a key component for managing the bank’s credit portfolio, including individual transaction
approval and the setting of risk appetite, Deutsche Bank establishes credit limits for all credit exposures. Credit limits set
forth maximum credit exposures Deutsche Bank is willing to assume over specified periods. In determining the credit
limit for a counterparty, Deutsche Bank considers the counterparty’s credit quality above others by reference to its
internal credit rating. Credit limits and credit exposures are both measured on a gross and net basis where net is derived
by deducting appropriate hedges and certain collateral from respective gross figures. For derivatives, Deutsche Bank
looks at current market values and the potential future exposure over the relevant time horizon, which is based upon the
bank’s legal agreements with the counterparty.
IFRS 9 Impairment IFRS 9 Impairment
In the following chapter, the Group provides an overview of the IFRS 9 impairment framework, model updates and
methodology implemented in 2025 as well as key model assumptions and its changes. Further explanations are provided
regarding the development of management overlays applied to the credit loss allowance, how reviews of relevant
assumptions and inputs to the ECL calculation are performed and how potential model imprecisions are assessed. To
provide additional transparency on the impact of reasonable changes to the key assumptions, model sensitivities are
presented in a separate section below which concludes with the key drivers for the IFRS 9 model results.
Description of IFRS 9 model and methodology
The impairment requirements of IFRS 9 apply to all credit exposures that are measured at amortized cost or fair value
through other comprehensive income and to off balance sheet lending commitments, such as loan commitments and
financial guarantees. For purposes of the bank’s impairment approach, the Group refers to these instruments as financial
assets.
The Group determines its allowance for credit losses in accordance with IFRS 9 as follows:
–Stage 1 reflects financial assets where it is assumed that credit risk has not increased significantly after initial
recognition
–Stage 2 contains all financial assets, that are not defaulted, but have experienced a significant increase in credit risk
since initial recognition
–Stage 3 consists of financial assets which are deemed to be in default in accordance with Deutsche Bank’s policies,
which are based on the Capital Requirements Regulation (CRR) Article 178. The Group defines these financial assets
as impaired, non-performing and defaulted
–Significant increase in credit risk is determined using quantitative and qualitative information based on the Group’s
historical experience, credit risk assessment and forward-looking information
–Purchased or Originated Credit-Impaired (POCI) financial assets are assets where at the time of initial recognition,
there is objective evidence of impairment and the Group purchased at a discount
The IFRS 9 impairment approach is an integral part of the Group’s credit risk management procedures. The estimation of
ECL is either performed via the automated, parameter based ECL calculation using the Group’s ECL model or determined
by credit officers. In both cases, the calculation takes place for each financial asset individually. Similarly, the
determination of the need to transfer between stages is made on an individual asset basis. The Group’s ECL model is
used to calculate the allowance for credit losses for all financial assets in Stage 1 and Stage 2, as well as for Stage 3 in
the homogeneous portfolio (i.e., retail and small business loans with similar credit risk characteristics). For financial assets
in the bank’s non-homogeneous portfolio in Stage 3 and for POCI assets, the allowance for credit losses is determined
individually by credit officers.
The Group uses three main components to measure ECL. These are Probability of Default (PD), Loss Given Default (LGD)
and Exposure at Default (EAD). The Group leverages existing parameters used for determination of capital demand under
the Basel Internal Ratings Based Approach (IRBA) and internal risk management practices as much as possible to
calculate ECL. These parameters are adjusted where necessary to comply with IFRS 9 requirements (e.g., use of point in
time ratings and removal of downturn add-ons in the regulatory parameters). Incorporating forecasts of future economic
variables into the measurement of ECL influences the allowance for credit losses. In order to calculate lifetime ECL, the
Group’s calculation derives the corresponding lifetime PDs from migration matrices that reflect economic forecasts.
The Group regularly reviews and validates the ECL model and its components which can result in model updates,
including recalibrations and model changes, of which some may constitute a change in estimate. Any future model
updates may have an impact on ECL and therefore represent a model uncertainty which cannot be reliably quantified.
Stage determination and significant increase in credit risk
At initial recognition, financial assets are reflected in Stage 1, unless the financial assets are POCI. If there is a significant
increase in credit risk, the financial asset is transferred to Stage 2. A significant increase in credit risk is determined by
using rating-related and process-related indicators. The transfer of financial assets to Stage 3 is based on the status of
the borrower being in default. If a borrower is in default, all financial assets of the borrower are transferred to Stage 3.
Rating-related Stage 2 indicators: In the third quarter of 2025 Deutsche Bank introduced a change to the rating-related
Stage 2 indicator approach, in which the Group compares a borrower’s lifetime PD at the reporting date with lifetime PD
expectations at the date of initial recognition to determine if there has been a significant change in the borrower’s PDs
and consequently to any of the borrower’s transaction in the scope of IFRS 9 impairment. Previously the model
determined the lifetime PD distribution based on historically observed migration behavior and a sampling of different
economic scenarios. A quantile of this distribution, which was defined for each counterparty class, was chosen as the
lifetime PD threshold. If the remaining lifetime PD of a transaction according to current expectations exceeded this
threshold, the financial asset was deemed to have incurred a significant increase in credit risk and was transferred to
Stage 2. The new approach compares the annualized lifetime PD at reporting date with the annualized conditional
lifetime PD expectation at origination. A relative and an absolute threshold are used for the comparison, which
represents a key assumption in the model. Different relative thresholds are applied to low-risk assets defined by an
annualized conditional PD at origination of 0.5% or below compared to the remaining high-risk assets. The relative
threshold for high-risk assets is 63% PD increase and for low-risk assets is 131% PD increase, floored by an absolute
threshold of 5.05% PD increase. If one of the newly introduced thresholds is breached, the financial asset is deemed to
have incurred a significant increase in credit risk and is transferred to Stage 2. This change in estimate led to an increase
of the credit loss allowance in the amount of € 15 million at the time of implementation and impacted Stages 1 and 2 in
all portfolios. In addition, the Group applied a threefold annualized lifetime PD increase as additional Stage 2 trigger as a
backstop until the implementation of the new rating related approach. This new approach always identifies assets
subject to the backstop as Stage 2 already.
Process-related Stage 2 indicators are derived via the use of existing risk management indicators, which in the bank’s
view represent situations where the credit risk of financial assets has significantly increased. These include borrowers
being added to the Group’s watchlist, being transferred to workout status, payments being 30 days or more past due or
being in forbearance. As long as the condition for one or more of the process-related or rating-related indicators is
fulfilled and the borrower of the financial asset has not met the definition of default, the asset will remain in Stage 2. If
the Stage 2 indicators are no longer fulfilled and the financial asset has not defaulted, the financial asset transfers back
to Stage 1. In case of performing forborne financial assets, the probation period is two years before the financial asset is
reclassified to Stage 1, which is aligned with regulatory guidance.
If the borrower defaults, all transactions of the borrower are allocated to Stage 3. If, at a later date, the borrower is no
longer in default, the curing criteria according to regulatory guidance is applied (including probation periods), which are
at least three months or one year in case of distressed restructurings. Once the regulatory cure period or criteria has been
met, the borrower will cease to be classified as defaulted and will be transferred back to Stage 2 or Stage 1.
The ECL calculation for Stage 3 distinguishes between transactions in the homogeneous and non-homogeneous
portfolios, as well as POCI financial assets. For transactions that are in Stage 3 and in a homogeneous portfolio, the
Group uses a parameter-based automated approach to determine the credit loss allowance per transaction. For these
transactions, the LGD parameters are to a large extent modelled to be time-dependent, i.e., consider the declining
recovery expectation as time elapses after default. The allowance for credit losses for financial assets in the bank’s non-
homogeneous portfolios in Stage 3, as well as for POCI assets are determined by credit officers and have to be approved
along an established authority grid up to and including the Management Board. This allows credit officers to consider
currently available information and recovery expectations specific to the borrowers and the financial assets at the
reporting date.
Estimation techniques for key input factors
The first key input factor in the Group ECL calculation is the one-year PD for borrowers which is derived from the bank’s
internal rating systems. The Group assigns a PD to each borrower credit exposure based on a 21-grade master rating
scale for all of the Group’s exposure.
The borrower ratings assigned are derived based on internally developed rating models which specify consistent and
distinct customer-relevant criteria and assign a rating grade based on a specific set of criteria as given for a certain
customer. The set of criteria is generated from information sets relevant for the respective customer segments including
general customer behavior, financial and external data (e.g., credit bureau). The methods in use range from statistical
scoring models to expert-based models taking into account the relevant available quantitative and qualitative
information. Expert-based models are usually applied for borrowers in the exposure classes “Central governments and
central banks”, “Institutions” and “Corporates” with the exception of those “Corporates” for which a sufficient data basis
is available for statistical scoring models. For the latter, as well as for the retail segment, statistical scoring or hybrid
models combining both approaches are commonly used. Quantitative rating methodologies are developed based on
applicable statistical modelling techniques, such as logistic regression.
One-year PDs are extended to multi-year PD curves using through-the-cycle matrices and macroeconomic forecasts.
Based on economic scenarios centered around the macroeconomic baseline forecast, through-the-cycle matrices are
first transformed into point-in-time rating migration matrices, typically for a two-year period. The calculation of the
point-in-time matrices provides the link between macroeconomic variables and the default and rating behavior of
borrowers, which is derived from time series through regression techniques involving macroeconomic variables (MEVs)
and historical rating and default data. In a final step, multi-year PD curves are derived from point-in-time rating migration
matrices for periods where reasonable and supportable forecasts are available and extrapolated based on through-the-
cycle rating migration matrices beyond those periods.
The second key input factor into the ECL calculation is the LGD parameter, which is defined as the likely loss intensity in
case of a borrower’s default. It provides an estimation of the exposure that cannot be recovered in a default event and
therefore captures the severity of a loss. Conceptually, LGD estimates are independent of a borrower’s probability of
default. The LGD models applied in Stages 1 and 2, which are based on regulatory LGD models, but adjusted for IFRS 9
requirements (i.e., removal of downturn-add-on and removal of indirect costs of workout), ensure that the main drivers
for losses (i.e., different levels and quality of collateralization and customer or product types or seniority of facility) are
reflected as risk drivers in LGD estimates. In June 2025, the Group introduced a model update with regard to the Loss
Given Default (LGD) parameter used in the IFRS 9 accounting framework, primarily to align with the corresponding
models implemented in the solvency framework following regulatory guidelines. This change in estimate led to a net
reduction of the credit loss allowance in the amount of € 133 million at the time of implementation and impacted all
stages. The most pronounced reduction of the credit loss allowance was observed in the Private Bank. In the Corporate
Bank and Investment Bank the net impact was primarily in stages 1 and 2 and less pronounced. However, for certain
underlying portfolios such as CRE, a more pronounced increase in credit loss allowance was observed, which was offset
by a reduction of credit loss allowance in other underlying portfolios in these businesses.
In the third quarter of 2025 the Group continued to validate the model performance of relevant feeder and receiver
models. Three probability of default (PD) models and one LGD model were recalibrated on that basis which led to a net
increase in the credit loss allowance in the amount of € 110 million, impacting all stages, mainly in the Investment Bank
and Private Bank.
Forward-looking information (FLI) is also incorporated into LGD projections in terms of FLI LGD scaling factors based on
forecasts for key MEVs. LGD adjustments are quantified relative to long-term historical averages, which represent a
neutral reference point throughout an economic cycle.
The LGD setting for defaulted homogeneous portfolios is partially dependent on time after default and are either
calibrated based on the Group’s multi-decade loss and recovery experience using statistical methods or for less
significant portfolios certain LGD model input parameters (e.g., cure rates) are determined by expert judgement.
The third key input factor is the exposure at default over the lifetime of a financial asset which is modelled taking into
account expected repayment profiles (e.g., linear amortization, annuities, bullet loan structures). Prepayment options are
modelled for some portfolios. The bank applies specific credit conversion factors (CCFs) in order to calculate an EAD
value. Conceptually, the EAD is defined as the expected amount of the credit exposure to a borrower at the time of its
default. In instances where a transaction involves an unused limit, a percentage share of this unused limit is added to the
outstanding amount in order to appropriately reflect the expected outstanding amount in case of a borrower’s default.
This reflects the assumption that for commitments, the utilization at the time of default might be higher than the current
outstanding balance. In case a transaction involves an additional contingent component (i.e., guarantees) a further
percentage share is applied as part of the CCF model in order to estimate the amount of guarantees drawn in case of
default. The calibrations of such parameters are based on internal historical data and are either based on empirical
analysis or supported by expert judgement and consider borrower and product type specifics. Where supervisory CCF
values need to be applied for regulatory purposes, internal estimates are used for IFRS 9.
Expected lifetime
IFRS 9 requires the determination of lifetime ECL for which the expected lifetime of a financial asset is a key input factor.
Lifetime ECL represent default events over the expected life of a financial asset. The Group measures ECL considering
the risk of default over the maximum contractual period (including any borrower’s extension options) over which the
Group is exposed to credit risk.
Retail overdrafts, credit card facilities and certain corporate revolving facilities typically include both a loan and an
undrawn commitment component. The expected lifetime of such on-demand facilities exceeds their contractual life as
they are typically cancelled only when the Group becomes aware of an increase in credit risk. The expected lifetime is
estimated by taking into consideration historical information and the Group’s credit risk management actions such as
credit limit reductions and facility cancellation. Where such facilities are subject to an individual review by credit risk
management, the lifetime for calculating ECL is 12 months. For facilities not subject to individual review by credit risk
management, the bank applies a lifetime for calculating ECL of 24 months.
Interest rate used in the IFRS 9 model
In the context of the ECL calculation, the Group applies in line with IFRS 9 an approximation of the effective interest rate
(EIR), which is usually the contractual interest rate. The contractual interest rate is deemed to be an appropriate
approximation, as the interest rate is consistently used in the ECL model, interest recognition and for discounting of the
ECL and does not materially differ from the EIR.
Consideration of collateralization in IFRS 9 Expected Credit Loss calculation
The ECL model projects the level of collateralization for each point in time in the life of a financial asset. At the reporting
date, the model uses a collateral distribution process that allocates the value of each eligible collateral to relevant
financial assets. In the ECL calculation, the Group subsequently calculates the collateralization resulting from physical
collateral that enters the LGD model as a risk driver based on the allocated collateral value and the exposure value of the
financial asset.
For personal collateral (e.g., guarantees), the ECL model assumes that the relative level of collateralization remains
stable over time. In the case of an amortizing loan, the outstanding exposure and collateral values decrease together
over time. For physical collateral (e.g., real estate property), the ECL shall assume that the absolute collateral value
remains constant. In case of an amortizing loan, the collateralized part of the exposure increases over time and the loan-
to-value decreases accordingly.
Certain financial guarantee contracts are integral to the financial assets guaranteed. In such cases, the financial
guarantee is considered as collateral for the financial asset and the benefit of the guarantee is used to mitigate the ECL
of the guaranteed financial asset.
Forward looking information
Under IFRS 9, the allowance for credit losses is based on reasonable and supportable forward-looking information
available without undue cost or effort, which takes into consideration past events, current conditions and forecasts of
future economic conditions.
To incorporate forward looking information into the Group’s allowance for credit losses, the bank uses two key elements:
–As its base scenario, the Group uses external survey-based macroeconomic forecasts (e.g., consensus views on GDP
and unemployment rates); in addition, the scenario expansion model, which has been initially developed for stress
testing, is used for forecasting macroeconomic variables that are not covered by external consensus data; all
forecasts are assumed to reflect the most likely development of the respective variables; the Group regularly updates
its forecasts for macro-economic factors during the quarter and reviews aspects of potential model imprecision (e.g.,
MEV parameters outside the historic range used for model calibration, if not already included in the model) as part of
an MEV monitoring framework to assess if an overlay is required
–Statistical techniques are then applied to transform the base scenario projections into a probability distribution of the
macroeconomic variables; these scenarios specify deviations from the baseline forecasts; the scenario distribution is
then used for deriving multi-year PD curves for different rating and counterparty classes, which are applied in the ECL
calculation and in the identification of significant deterioration in credit quality of financial assets as described above
in the rating-related Stage 2 indicators
The Group's Risk and Finance Credit Loss Provision Forum monitors the impact of forward-looking information, including
the latest macroeconomic variables, on a quarterly basis and determines if any additional overlays are required. Although
interest rates and inflation are not separately included in the MEVs, the economic impact of these risks is reflected in
GDP growth rates, unemployment, equities and credit spreads as higher rates and inflation filter through these forecasts.
As of December 31, 2025, the consensus data applied in the ECL model was deemed to have reflected the latest
macroeconomic developments and after considering all relevant uncertainties in the MEVs no additional overlays were
required.
As described earlier, the Group’s approach to reflect forward-looking information into the calculation of ECL is to
incorporate forecasts for the next two to three years into PD and LGD projections. For periods beyond those covered in
terms of reasonable and supportable economic forecasts, reversion to historically observed behavior of defaults, rating
migrations and recoveries is used for ECL measurement.
The tables below contain relevant forward-looking information by macroeconomic variable included in the Group’s IFRS
9 model as of December 31, 2025, and as of December 31, 2024.
Forward-looking information applied
December 31, 20251 2
Year 1
(4 quarter avg)
Year 2
(4 quarter avg)
GDP - USA
1.87%
1.97%
GDP - Eurozone
1.16%
1.44%
GDP - Germany
0.65%
1.54%
GDP - Italy
0.60%
0.91%
GDP - Developing Asia
4.45%
4.78%
GDP - Emerging Markets
3.85%
4.19%
Unemployment - USA
4.42%
4.29%
Unemployment - Eurozone
6.30%
6.18%
Unemployment - Germany
3.75%
3.66%
Unemployment - Italy
6.14%
6.22%
Unemployment - Spain
10.37%
10.05%
Unemployment - Japan
2.49%
2.45%
Real Estate Prices - CRE Index USA
300.74
301.87
Real Estate Prices - CRE Index Eurozone
110.44
111.75
Real Estate Prices - House Price Index USA
331.21
340.69
Real Estate Prices - House Price Index Germany
157.28
158.82
Real Estate Prices - House Price Index Spain
2,213.53
2,264.16
Equity - S&P500
6,942
7,366
Equity - Eurostoxx50
5,793
6,086
Equity - DAX40
24,453
25,886
Equity - MSCI EAFE
1,288
1,351
Equity - MSCI Asia
2,068
2,160
Equity - Nikkei
50,891
53,099
Credit - High Yield Index
308.27
348.99
Credit - CDX High Yield
333.97
370.05
Credit - CDX IG
54.64
62.78
Credit - CDX Emerging Markets
149.82
179.86
Credit - ITX Europe 125
56.42
62.27
Commodity - WTI
61.07
59.01
Commodity - Gold
3,976.94
4,189.01
1 MEV as of December 8, 2025 based on Bloomberg Consensus, which barely changed until December 31, 2025
2 Year 1 equals fourth quarter of 2025 to third quarter of 2026, Year 2 equals fourth quarter of 2026 to third quarter of 2027
December 31, 20241 2
Year 1
(4 quarter avg)
Year 2
(4 quarter avg)
GDP - USA
2.23%
2.04%
GDP - Eurozone
1.04%
1.19%
GDP - Germany
0.38%
1.14%
GDP - Italy
0.74%
1.02%
GDP - Developing Asia
4.53%
4.26%
GDP - Emerging Markets
4.11%
3.81%
Unemployment - USA
4.29%
4.20%
Unemployment - Eurozone
6.46%
6.42%
Unemployment - Germany
3.46%
3.40%
Unemployment - Italy
6.50%
6.76%
Unemployment - Spain
11.12%
10.93%
Unemployment - Japan
2.48%
2.40%
Real Estate Prices - CRE Index USA
312.27
316.81
Real Estate Prices - CRE Index Eurozone
107.75
108.39
Real Estate Prices - House Price Index USA
325.05
333.47
Real Estate Prices - House Price Index Germany
152.78
158.19
Real Estate Prices - House Price Index Spain
1,959.68
2,000.70
Equity - S&P500
6,109
6,436
Equity - Eurostoxx50
4,965
5,162
Equity - DAX40
20,131
20,968
Equity - MSCI EAFE
1,069
1,112
Equity - MSCI Asia
1,602
1,630
Equity - Nikkei
38,972
39,582
Credit - High Yield Index
312.32
358.66
Credit - CDX High Yield
332.33
374.29
Credit - CDX IG
56.50
64.29
Credit - CDX Emerging Markets
177.90
202.59
Credit - ITX Europe 125
62.15
68.66
Commodity - WTI
70.46
65.85
Commodity - Gold
2,588.02
2,612.91
1 MEV as of December 5, 2024, based on Bloomberg Consensus, which barely changed until December 31, 2024
2 Year 1 equals fourth quarter of 2024 to third quarter of 2025, Year 2 equals fourth quarter of 2025 to third quarter of 2026
Model sensitivity
The Group has identified three key model assumptions included in the IFRS 9 model. These include forward looking
macroeconomic variables, the quantitative criteria for determining if a borrower has incurred a significant increase in
credit risk and is transferred to Stage 2, and the LGD setting on homogeneous portfolios in Stage 3. Below the bank
provides sensitivity analysis on the potential impact if these key assumptions applied in the ECL model were to deviate
from the bank’s base case expectations.
Macroeconomic variables
The sensitivity of the ECL model with respect to potential changes in projections for key MEVs is shown in the tables
below, which provides ECL impacts for Stages 1 and 2 from downward and upward shifts applied separately to each
group of MEV as of December 31, 2025, and December 31, 2024. The magnitude of the shifts is selected in the range of
one standard deviation, which is a statistical measure of the dispersion of the values of a random variable. Each of these
groups consists of MEVs from the same category:
–GDP growth rates: includes USA, Eurozone, Germany, Italy, Developing Asia, Emerging Markets
–Unemployment rates: includes USA, Eurozone, Germany, Italy, Japan, Spain
–Equities: S&P500, Eurostoxx50, DAX 40, Nikkei, MSCI Asia, MSCI EAFE
–Credit spreads: ITX Europe 125, High Yield Index, CDX IG, CDX High Yield, CDX Emerging Markets
–Real Estate: CRE Index Eurozone, House Price Index USA, House Price Index Germany, House Price Index Italy, House
Price Index Spain
–Commodities: WTI oil price, Gold price
Although interest rates and inflation are not included in the above set of MEVs, adverse effects associated with changes
in these risk drivers typically manifest themselves in other economic forecasts incorporated in the ECL model, such as
lower GDP growth, higher unemployment or wider credit spreads.
In addition, the sensitivity analysis only includes the impact of the aggregated MEV group (i.e., potential correlations
between different MEV groups or the impact of management overlays is not taken into consideration). ECL quantification
for Stage 3 does not follow a model-based process for various portfolios and is therefore excluded from the following
tables.
Compared to December 31, 2024, calculation of sensitivities are reflective of ECL model updates that went live in 2025,
including updates to the LGD parameter used in the IFRS9 accounting framework and the Significant Increase in Credit
Risk (SICR) model for staging assessment. The effects of both FLI impacting PDs and FLI impacting LGDs were already
reflected in the 2024 sensitivities. As of December 31, 2025, sensitivities additionally capture the extension of the FLI
LGD model to ex‑Postbank portfolios, resulting in higher sensitivities to real estate prices. Furthermore, the overall
increase in sensitivities observed for GDP, unemployment and real estate prices is also driven by a higher ECL baseline
level compared to last year.
IFRS 9 – Sensitivities of forward-looking information applied on Stage 1 and Stage 2 – Group Level
December 31, 2025
Upward sensitivity
Downward sensitivity
Upward shift
ECL impact
in € m.
Downward shift
ECL impact
in € m.
GDP growth rates
1pp
(76.3)
(1)pp
87.2
Unemployment rates
(0.5)pp
(49.4)
0.5pp
51.6
Real estate prices1
5%
(35.3)
(5%)
40.1
Equities
10%
(17.1)
(10%)
23.7
Credit spreads
(40%)
(19.5)
40%
21.6
Commodities2
10%
(6.9)
(10%)
7.4
December 31, 2024
Upward sensitivity
Downward sensitivity
Upward shift
ECL impact
in € m.
Downward shift
ECL impact
in € m.
GDP growth rates
1pp
(66.4)
(1)pp
71.8
Unemployment rates
(0.5)pp
(44.9)
0.5pp
49.0
Real estate prices1
5%
(13.9)
(5%)
16.0
Equities
10%
(14.1)
(10%)
17.8
Credit spreads
(40%)
(20.7)
40%
24.2
Commodities2
10%
(7.7)
(10%)
8.7
1 For a more severe stress test relating to the CRE portfolio that also takes into consideration existing and potential exposure in Stage 3 reference is made to the section on
Commercial Real Estate above
2Here the sign of the shift applies to oil prices changes. Gold price changes have the opposite sign. 1pp (percentage point), e.g., GDP shifts from 3% to 4% // 1%
(percentage change), e.g., Real estate price shifts from 100 to 101
At divisional level, the sensitivity analysis below was performed for the year ended December 31, 2025, and 2024,
respectively, and revealed GDP growth rates, credit spreads and commodities prices to be the dominant factors for the
Investment Bank, whereas the model sensitivity for the Corporate Bank and Private Bank is mainly associated with
changes in GDP growth rates and unemployment rates. The model sensitivity table for the Private Bank shows GDP
growth rates and unemployment rates only, as the key MEVs relevant to the underlying portfolios.
IFRS 9 – Sensitivities of forward-looking information applied on Stage 1 and Stage 2 - Corporate Bank
December 31, 2025
Upward sensitivity
Downward sensitivity
Upward shift
ECL impact
in € m.
Downward shift
ECL impact
in € m.
GDP growth rates
1pp
(16.0)
(1)pp
19.2
Unemployment rates
(0.5)pp
(11.0)
0.5pp
12.2
Real estate prices1
5%
(4.3)
(5)%
5.4
Credit spreads
(40)%
(4.1)
40%
4.5
Commodities2
10%
(2.2)
(10)%
2.4
December 31, 2024
Upward sensitivity
Downward sensitivity
Upward shift
ECL impact
in € m.
Downward shift
ECL impact
in € m.
GDP growth rates
1pp
(18.2)
(1)pp
20.3
Unemployment rates
(0.5)pp
(12.6)
0.5pp
14.2
Real estate prices1
5%
(2.1)
(5)%
2.2
Credit spreads
(40)%
(4.5)
40%
5.0
Commodities2
10%
(2.8)
(10)%
3.1
1 For a more severe stress test relating to the CRE portfolio that also takes into consideration existing and potential exposure in Stage 3 reference is made to the section on
Commercial Real Estate below
2 Here the sign of the shift applies to oil prices changes. Gold price changes have the opposite sign.
IFRS 9 – Sensitivities of forward-looking information applied on Stage 1 and Stage 2 - Investment Bank
December 31, 2025
Upward sensitivity
Downward sensitivity
Upward shift
ECL impact
in € m.
Downward shift
ECL impact
in € m.
GDP growth rates
1pp
(36.1)
(1)pp
41.5
Unemployment rates
(0.5)pp
(10.7)
0.5pp
11.6
Real estate prices1
5%
(19.4)
(5)%
21.5
Equities
10%
(5.6)
(10)%
9.3
Credit spreads
(40)%
(14.0)
40%
15.7
Commodities2
10%
(4.4)
(10)%
4.7
December 31, 2024
Upward sensitivity
Downward sensitivity
Upward shift
ECL impact
in € m.
Downward shift
ECL impact
in € m.
GDP growth rates
1pp
(26.4)
(1)pp
28.9
Unemployment rates
(0.5)pp
(11.0)
0.5pp
12.1
Real estate prices1
5%
(8.5)
(5)%
10.2
Equities
10%
(4.7)
(10)%
5.9
Credit spreads
(40)%
(13.5)
40%
16.2
Commodities2
10%
(4.6)
(10)%
5.3
1 For a more severe stress test relating to the CRE portfolio that also takes into consideration existing and potential exposure in Stage 3 reference is made to the section on
Commercial Real Estate below
2 Here the sign of the shift applies to oil prices changes. Gold price changes have the opposite sign.
IFRS 9 – Sensitivities of forward-looking information applied on Stage 1 and Stage 2 - Private Bank
December 31, 2025
Upward sensitivity
Downward sensitivity
Upward shift
ECL impact
in € m.
Downward shift
ECL impact
in € m.
GDP growth rates
1pp
(22.6)
(1)pp
24.6
Unemployment rates
(0.5)pp
(26.9)
0.5pp
26.8
December 31, 2024
Upward sensitivity
Downward sensitivity
Upward shift
ECL impact
in € m.
Downward shift
ECL impact
in € m.
GDP growth rates
1pp
(18.3)
(1)pp
19.3
Unemployment rates
(0.5)pp
(19.5)
0.5pp
20.6
Impact of lifetime expected credit losses for Stage 1 borrowers
As described above, the Group uses a mixture of quantitative and qualitative criteria to determine significant increase in
credit risk which require, for affected borrowers, a move to lifetime ECL (Stage 2). If for all Stage 1 borrowers Deutsche
Bank were to record lifetime expected credit losses, the Group’s allowance for credit losses amounting to € 6.6 billion as
of December 31, 2025 and € 6.2 billion as of December 31, 2024 which would represent an increase of approximately
28% for year end 2025 and 34% for year end 2024, respectively.
Stage 3 LGD setting
The Group’s allowance for credit losses in Stage 3 for the homogeneous portfolios amounts to € 2.4 billion as of
December 31, 2025 and € 2.3 billion as of December 31, 2024. The key driver in determining the ECL provision is the loss
given default estimate, which differs by individual portfolios. Loss given default is influenced by recovery rates, proceeds
from the sale of collateral, and cure rates. Some of the drivers for different portfolios include elements of expert
judgment in particular on expected cure rates. If the LGD for all homogeneous portfolios were to increase by 1%, then
Stage 3 ECL would increase as of December 31, 2025 by approximately € 28 million (thereof € 20 million in Germany,
€ 5 million in Italy and € 2 million in Spain), and by approximately € 26 million as of December 31, 2024 (thereof
€ 17 million in Germany, € 5 million in Italy and € 2 million in Spain).
Management overlays applied to the IFRS 9 model output
The Group regularly reviews the IFRS 9 methodology and processes, key inputs into the ECL calculation and discusses
upcoming model changes, potential model imprecisions or other estimation uncertainties, for example in the
macroeconomic environment to determine if any material overlays are required. Moreover, regular reviews for evolving or
emerging risks are performed, especially in the current geopolitical environment. Measures applied include client surveys
and interviews, along with analysis of portfolios across businesses, regions and sectors. In addition, the Group regularly
reviews and validates key model inputs and assumptions (including those in feeder models) and ensures where expert
judgement is applied, it is in line with the Group’s risk management framework. As of December 31, 2025, the Group had
two existing overlays, one existing overlay reflected a model refinement related to refinancing risk which had not yet
been implemented and a newly created overlay to reflect observations from the bank's portfolio reviews and credit risk
assessments. Apart from these known model weaknesses and overlays, the group did not identify any additional model
weaknesses which would require an overlay.
As of year end 2025, the Group’s IFRS9 management overlays amounted to €156 million, compared to €124 million for
year end 2024 (which resulted in an increase of Allowance for Credit Losses in both periods). Management overlays as of
year end 2025 encompassed two main items, as mentioned above: Expected impacts from a model refinement related to
refinancing risk and observations from the bank’s portfolio reviews and credit risk assessments. The overlay relating to
refinancing risk existed already in 2024 and has been adjusted during 2025. Additionally, at the end of 2024, the Group
recorded an overlay with regard to a PD parameter recalibration, which was released in 2025. The change to
management overlays during the year 2025 was primarily driven by dedicated tariff overlays taken in view of the impacts
from U.S. tariff announcements during the first quarter which were released during the year.
Overall Assessment of ECL’s
To ensure that Deutsche Bank’s ECL model accounted for the uncertainties in the macroeconomic environment
throughout 2025, the Group continued to review emerging risks, assessed potential baseline and downside impacts and
required actions to manage the bank’s credit strategy and risk appetite. The outcome of these reviews concluded that
the bank adequately provisioned for its expected credit losses as of December 31, 2025, and December 31, 2024.
Results from the above reviews and development of key portfolio indicators are regularly discussed at the Credit Risk
Appetite and Management Forum and Group Risk Committee and as appropriate at the Management Board and the
Audit Committee. Where necessary, actions and measures are taken to mitigate the risks. Client ratings are regularly
reviewed to reflect the latest macroeconomic developments and where potentially significant risks are identified clients
are moved to the watchlist (Stage 2), forbearance measures may be negotiated, and credit limits and collateralization are
reviewed. Overall, the Group believes that based on its day-to-day risk management activities and regular reviews of
emerging risks it has adequately provided for its ECL.
IFRS 9 model results
Provision for credit losses was €1.7 billion in 2025, down from €1.8 billion in 2024 and 35 basis points (bps) of average
loans, in line with the guidance the bank provided after the third quarter. The decrease was primarily driven by lower
Stage 3 provisions, notwithstanding persistently elevated provisions for the commercial real estate sector. This was
partially offset by higher Stage 1 and Stage 2 provisions resulting from model-related effects.
With regard to climate risks, estimates of higher transition and physical risk exposures and their impact on the ECL did not
result in any adjustment of credit loss provisions for the years ended December 31, 2025 as well as December 31, 2024.
For details of the provision for credit losses related to the segments, please refer to section “Segment results of
operations”.
For details on the Group’s accounting policy related to IFRS 9 Impairment, please refer to Note 01 “Material accounting
policies and critical accounting estimates” of the consolidated financial statements.
Managing and Mitigation of credit risk  
Managing and Mitigation of credit risk paragraph 1 Managing and mitigation of credit risk
Managing credit risk on counterparty level
Credit-relevant counterparties are principally allocated to credit officers within credit teams which are organized by type
of counterparty (such as financial institutions, corporates or private individuals), economic area (e.g., emerging markets)
or product and supported by dedicated rating analyst teams where deemed necessary, except for retail clients, which are
managed by the sales unit. The individual credit officers have the relevant expertise and experience to manage the credit
risks associated with these counterparties and their associated credit related transactions. For retail clients, credit
decision making and credit monitoring is highly automated due to standardized products and processes. Credit Risk
Management has oversight of the respective processes and tools used in these highly automated retail credit processes.
It is the responsibility of each credit authority holder or credit officer to undertake ongoing credit monitoring for their
allocated counterparties. Deutsche Bank also has procedures in place intended to identify credit exposures for which
there may be an increased risk of deteriorated risk/loss at an early stage.
In instances where Deutsche Bank has identified counterparties with emerging concern about their credit quality
deteriorating or likely to deteriorate to the point where they present a heightened risk of default/loss, the respective
counterparty is generally placed on the “Watchlist”. Deutsche Bank aims to identify those counterparties at an early
stage to ensure that credit exposures with increased risks are effectively managed, the Bank’s risk management tools are
appropriately applied aiming to minimize potential losses. The objective of this early warning system is to address
potential problems while adequate options for action are still available. This early risk detection is a tenet of Deutsche
Bank’s credit culture and is designed to raise management awareness of these positions.
Credit limits for individual counterparties are established by the Credit Risk Management function (except for retail
clients) applying credit authorities assigned to individual Credit Officers. This also applies to settlement risk that must fall
within limits pre-approved by Credit Risk Management and in a manner that reflects expected settlement patterns for
the subject counterparty. Credit approvals are documented by electronic signature under 4-eye principle by the
respective credit authority holders and are retained for future reference.
Credit authority is generally assigned as a personal credit authority according to the individual’s professional
qualification and experience. All assigned credit authorities are reviewed on a periodic basis to help ensure that they are
commensurate with the individual performance of the authority holder.
Where credit authority is insufficient to establish required credit limits, the transaction is referred to a credit authority
holder with the respective credit authority or if exceeding the highest personal authority to an appropriate credit
committee. Where personal and committee authorities are insufficient to establish appropriate limits, the case is referred
to the Management Board for approval.
Mitigation of credit risk on counterparty level
In addition to determining counterparty credit quality by assigning internal ratings and the alignment of the exposure
with the Bank’s counterparty concentration risk guidelines, Deutsche Bank also uses various credit risk mitigation and
protection techniques to optimize credit exposure and reduce potential credit losses. These techniques are applied in
the following forms:
–Comprehensive and enforceable credit documentation with adequate terms and conditions
–Collateral in its various forms to reduce losses by increasing the recovery of obligations; key principles for collateral
management include legal effectiveness and enforceability, prudent and realistic collateral valuations, risk and
regulatory capital reduction, as well as cost efficiency
–Risk transfers, which shift the risk of default of an obligor to a third-party, including significant risk transfer
instruments are executed by the bank’s Strategic Corporate Lending (SCL) business unit
–Netting and collateral arrangements which reduce the credit exposure from derivatives and securities financing
transactions (e.g., repo transactions)
–Hedging of derivatives counterparty risk including CVA, using primarily CDS contracts via the bank’s Counterparty
Portfolio Management desk
Collateral
Deutsche Bank regularly agrees on collateral to be received from customers that are subject to credit risk or to be
provided by third parties agreed by legally effective and enforceable contracts as documented by a written and
reasoned legal opinion. Collateral is credit protection in the form of (funded) assigned or pledged assets or (unfunded)
third-party obligations that serves to mitigate the inherent risk of credit loss in an exposure, by either improving
recoveries in the event of a default or substituting the counterparty default risk. Deutsche Bank generally takes all types
of eligible collateral for its respective businesses but may limit accepted collateral types for specific businesses or
regions as customary in the respective market or driven by purpose of efficiency. While collateral can be an alternative
source of repayment, it does not replace the necessity of high-quality underwriting standards and a thorough
assessment of the debt service ability of the counterparty in line with Article 194 (9) CRR.
Deutsche Bank distinguishes the following two types of credit protection approaches:
–Funded Credit Protection like financial and other collateral, which enables Deutsche Bank to recover all or part of the
outstanding exposure by liquidating the collateral/asset provided, in cases where the counterparty is unable or
unwilling to fulfill its primary obligations. Cash collateral, securities (equity, bonds), collateral pledges or assignments
of other claims or inventory, movable assets (i.e., plant, machinery, ships and aircraft) and real estate typically fall into
this category; all financial collateral is regularly, mostly daily, revalued and measured against the respective credit
exposure; the value of other collateral, including real estate, is monitored based upon established processes that
includes regular reviews or revaluations by internal and/or external experts
–Unfunded Credit Protection like Guarantees, which complement the counterparty’s ability to fulfill its obligation
under the legal contract and as such is provided by uncorrelated third parties. Letters of credit, credit insurance,
export credit insurance, guarantees, credit derivatives and (unfunded) risk participations typically fall into this
category. Guarantees and strong letters of comfort provided by correlated group members of customers (generally
the parent company) may also be accepted and considered in approved rating approaches; guarantee collateral with a
non-investment grade rating of the guarantor is limited
Deutsche Bank’s processes seek to ensure that the collateral accepted for risk mitigation purposes is of high quality. This
includes processes to generally ensure legally effective and enforceable documentation for realizable and measurable
collateral or assets which are evaluated within the on-boarding process by dedicated internal appraisers or teams with
the respective qualification, skills and experience or adequate external valuers mandated in regulated processes. The
applied valuations follow generally accepted valuation methods or models and include the identification of material
climate physical and transition risks. Ongoing correctness of values is monitored by collateral type-specific,
appropriately frequent, and event-driven reviews considering relevant risk parameters. Revaluations are applied in cases
of identified probable material deterioration and future monitoring may be adjusted respectively. The assessment of the
suitability of collateral for a specific transaction is part of the credit decision and must be undertaken in a conservative
way. Deutsche Bank strives to avoid “wrong-way” risk characteristics where the counterparty’s risk is positively correlated
with the risk of deterioration in the collateral value. If collateral with material correlation risk is accepted anyhow, a
potential impact on its value is considered conservatively in the valuation. For unfunded credit protection like
guarantees, the process for the analysis of the guarantor’s creditworthiness is aligned to the credit assessment process
for credit-relevant counterparties.
For funded collateral, the value depends on the type and quality of the respective collateral as well as its suitability for
third-party use, its lifespan and other value-influencing factors. Haircuts reflecting risks of liquidation in a default
scenario are implicitly considered in the LGD estimation. Collateral can either move in value over time (dynamic value) or
not (static value). Deutsche Bank uses value deductions to reflect i.a.:
–price fluctuations,
–insufficient third-party usability,
–limitations on liquidation/realization,
–currency mismatch between the secured exposure and the collateral,
–maturity mismatch,
–environmental risks,
–asset specific aspects (age-related discounts, encumbrances and restrictions),
–correlation between the performance of the borrower and the value of the collateral, e.g., in the case of the pledge of
a borrower’s own shares or securities (in this case generally full correlation leads to a 100% value deduction).
These value deductions are either applied within the scope of the assessment and hence directly considered in the
market value or deducted afterwards.
Managing and Mitigation of credit risk paragraph 2 In order to reduce the credit risk resulting from OTC derivative transactions, where CCP clearing is not available,
Deutsche Bank regularly seeks the execution of standard master agreements (such as master agreements for derivatives
published by the International Swaps and Derivatives Association, Inc. (ISDA) or the German Master Agreement for
Financial Derivative Transactions with the bank’s counterparties. A master agreement allows for the close-out netting of
rights and obligations arising under derivative transactions that have been entered into under such a master agreement
upon the counterparty’s default, resulting in a single net claim owed by or to the counterparty. Payment netting may be
agreed from time to time with the bank’s counterparties for multiple transactions having the same payment dates (e.g.,
foreign exchange transactions) pursuant to the terms of master agreements which can, reduce the bank’s settlement risk.
In its risk measurement and risk assessment processes Deutsche Bank applies close-out netting only to the extent
Deutsche Bank has concluded that the master agreement is legally valid and enforceable in all relevant jurisdictions and
the recognition of close-out netting has been approved in accordance with the bank’s Netting Policy.
Deutsche Bank also enters into credit support annexes (CSAs) to master agreements in order to further reduce the bank’s
derivatives-related credit risk. These annexes generally provide risk mitigation through periodic, usually daily, margining
of the covered exposure. The CSAs also provide for the right to terminate the related derivative transactions upon the
counterparty’s failure to honor a margin call. As with netting, when Deutsche Bank believes the annex is enforceable,
Deutsche Bank reflects this in its exposure measurement.
Managing and Mitigation of credit risk paragraph 3 Concentrations within credit risk mitigation
Concentrations within credit risk mitigations taken may occur if a number of guarantors and credit derivative providers
with similar economic characteristics are engaged in comparable activities with changes in economic or industry
conditions affecting their ability to meet contractual obligations. Concentration risk may also occur in collateral
portfolios (e.g., multiple claims and receivables against third parties) which are considered conservatively within the
valuation process and/or on-site inspections where applicable. Deutsche Bank uses a range of tools and metrics to
monitor its credit risk mitigating activities and potential concentrations.
Managing and Mitigation of credit risk paragraph 4 Managing credit risk on portfolio level
Enterprise and Treasury Risk Management (ETRM) sets the framework for the management of concentration risks at a
portfolio level. This includes strategically setting, monitoring, reviewing, reporting, and controlling credit risk appetites
across various dimensions such as Deutsche Bank Group, Corporate Division, Business Unit , legal entity, branch, country,
and industry level that need to be considered in the context of credit approvals. ETRM is also responsible for calibrating
and monitoring the single name counterparty concentration grid that provides guidance to credit officers on limit sizing
at counterparty level. In addition, ETRM provides a comprehensive and holistic view of the bank’s risk profile across risk
types.
On a portfolio level, significant concentrations of credit risk could result from having material exposures to a number of
counterparties with similar economic characteristics, or who are engaged in comparable activities, where these
similarities may cause their ability to meet contractual obligations to be affected in the same manner by changes in
economic or industry conditions.
Deutsche Bank’s portfolio management framework supports a comprehensive assessment of concentrations within its
credit risk portfolio in order to keep concentrations within acceptable levels.
Emerging Risks and portfolio developments are discussed at the monthly Credit Risk and Portfolio Management Forum
which includes representation from senior credit risk managers including the Head of Credit Risk Management, as well as
senior managers from ETRM.
Industry risk management
To manage industry risk, Deutsche Bank has grouped its corporate and financial institutions counterparties into various
industry sub-portfolios. Portfolios are regularly reviewed at least on an annual basis. Reviews highlight industry
developments and risks to the bank’s credit portfolio, review cross-risk concentration risks, analyze the risk/reward
profile of the portfolio and incorporate the results of an economic downside stress test. Finally, this analysis is used to
define the credit strategies and risk appetite for respective industries. The setting of industry risk appetite takes into
consideration the group-wide credit risk appetite.
In the bank’s industry risk management framework, thresholds are established by ETRM for aggregate credit limits to
counterparties within each industry sub-portfolio. For risk management purposes, the aggregation of limits across
industry sectors follows an internal risk view that does not have to be congruent with NACE (Nomenclature statistique
des activités économiques dans la Communauté européenne) code-based view applied elsewhere in this report.
Beyond credit risk, the bank’s industry risk framework comprises of thresholds for Traded Credit Positions while key
industry relevant non-financial risks are considered.
Country risk management
Avoiding undue concentrations from a regional and country perspective is also an integral part of the bank’s credit risk
management framework. In order to achieve this, country risk thresholds are set for countries in Non-Japan Asia, Central
Eastern Europe, Middle East & Africa and Latin America as well as selected Developed Markets countries (based on
internal country risk ratings). These thresholds are set for aggregate exposures to all counterparties assigned to specific
‘country of risk’. The counterparty’s ‘country of risk’ reflects its main (macro) economic risk, balance sheet earnings,
jurisdiction, or other financial dependencies. Country of risk is typically aligned with the counterparty’s 'country of
domicile’. As such, for the bank’s country risk management purposes, the aggregation of exposures across countries may
differ from the geographical exposure view applied elsewhere in this report.
Country portfolios are regularly reviewed with the frequency of review dependent on portfolio size and risk profile as well
as risk developments. Larger/riskier portfolios are reviewed at least on an annual basis. These reviews assess amongst
other factors, key macroeconomic and political risk developments and outlook; portfolio composition, quality
developments and risk concentrations under normal and stress conditions. Based on this and taking into account the
Group’s Risk Appetite and strategy, country risk appetite and strategies are set by ETRM.
In addition to country thresholds, thresholds are set to monitor country-on-country wrong-way risk exposure. Beyond
credit risk, the bank’s country risk framework comprises thresholds for trading positions that measure the aggregate
market value of traded credit risk positions. For Emerging Markets, market risk thresholds are also set to measure the
profit and loss impact under specific country stress scenarios on trading positions across the bank’s portfolio.
Furthermore, thresholds are set for capital and intra-group funding exposure of Deutsche Bank entities in above
countries given the transfer risk inherent in these cross-border positions. Key non-financial risks are considered and
factored into financial threshold setting considerations where relevant. To assess country risk, Deutsche Bank utilizes
different measures including country risk ratings that are set and monitored by the research team within ETRM. These
ratings include:
–Sovereign default ratings which measure the probability of the sovereign defaulting on its foreign or local currency
obligations
–Transfer risk ratings which measure the probability of a “transfer risk event”, i.e., the risk that an otherwise solvent
debtor is unable to meet its obligations due to inability to obtain foreign currency or to transfer assets as a result of
direct sovereign intervention
All sovereign and transfer risk ratings are reviewed, at least on an annual basis.
Climate and environmental risk management
The bank established a dedicated framework for the management of climate and environmental risks. The framework
sets out key requirements around governance, risk identification and materiality assessment, risk appetite, risk
monitoring, controls and stress testing.
Concentrations of climate and environmental risks are monitored, via dedicated reports, by key committees of the Bank
(e.g., the Group Risk Committee), and are managed through:
–Risk Appetite thresholds around the bank’s decarbonization targets, established for eight priority sectors (Upstream
Oil and Gas, Power Generation, Automotive - Light Duty, Steel, Coal Mining, Cement and Aviation) and the overall
financed emissions of the Corporate Loan Book
–Early Warning Indicators, established across different portfolios (Corporates, Sovereigns and Financial Institutions) for
climate-transition, climate-physical and nature-related risks
New transactions with a significant impact on the bank’s financed emissions and/or net zero targets are reviewed by the
Group Net Zero Forum consisting of senior representatives from the Business, Risk, and the Chief Sustainability Office.
The review of the forum’s members includes an assessment of client sustainability disclosures, transition strategies,
decarbonization targets and governance. New transactions must fit within Deutsche Bank’s internal sectoral risk appetite
aligned to net zero targets. In 2024 and continuing in 2025, the Group-level sectoral risk appetite metrics were cascaded
to the divisions, to enhance their responsibility and support their business strategies. In this context, dedicated Divisional
Net Zero Fora in the Corporate Bank and the Investment Bank have been established.
Product/Asset class specific risk management
Complementary to the bank’s counterparty, industry and country risk approach, Deutsche Bank has a framework to
manage certain asset class risk concentrations and sets limits or thresholds where required for risk management
purposes. For purposes of DB’s internal portfolio risk management, asset classes are groups of financial exposures that
exhibit similar performance and behaviors in both normal operating conditions and under severe stress. The exposures in
an asset class will typically have a common characteristic or sensitivity to the same economic and/or market factors and
business, legal and regulatory developments. When such characteristic or sensitivity is triggered, transactions in the
asset class may react and perform in a similar manner. These are portfolios which the bank’s Risk division considers as
having the potential for sizable tail risks and require additional monitoring. Group-wide credit risk appetite is considered
in the setting of asset class risk limits or thresholds.
Private Bank and certain Corporate Bank businesses are managed via product-specific strategies setting the bank’s risk
appetite for portfolios with similar credit risk characteristics, such as the retail portfolios of mortgages and consumer
finance products as well as products for business clients. Risk analyses are performed on portfolio level including further
breakdown into business units as well as countries/regions. In Wealth Management, target levels are set for global
concentrations along products as well as based on type and liquidity of collateral.
Underwriting of capital markets transactions
Specific focus is placed on transactions with underwriting risks where Deutsche Bank underwrites commitments with the
intention to sell down or distribute part of the risk to third parties. These commitments include the undertaking to
provide bank loans for syndication into the debt capital market and bridge loans for the issuance of notes. The inherent
risks of being unsuccessful in the distribution of the facilities or the placement of the notes, comprise of a delayed
distribution, funding of the underlying loans as well as a pricing risk as some underwriting commitments are additionally
exposed to market risk in the form of widening credit spreads. Where applicable, Deutsche Bank dynamically hedges this
credit spread risk to be within the approved market risk limit framework.
A major asset class, in which Deutsche Bank is active in underwriting, is leverage lending, which Deutsche Bank mainly
executes through its Leveraged Debt Capital Markets business unit. The business model is a fee-based‚ originate to
distribute approach focused on the distribution of largely unfunded underwriting commitments into the capital market.
The afore-mentioned risks regarding distribution and credit spread movement apply to this business unit, however, are
managed under a range of specific notional as well as market risk limits. The latter require the business to also hedge its
underwriting pipeline against market dislocations. The fee-based model of the bank’s Leveraged Debt Capital Markets
business unit includes a restrictive approach to single-name risk concentrations retained on Deutsche Bank’s balance
sheet, which results in a diversified overall portfolio without any material concentrations. The resulting longer-term on-
balance sheet portfolio is also subject to a comprehensive credit limit and hedging framework.
Deutsche Bank also provides material underwriting activity through its Debt Capital Markets desk which is focused on
supporting Investment Grade and cross-over rated corporate borrowers, usually in connection with M&A transaction
financing. These exposures are typically 12-24 month bridge loans, which are expected to be repaid by syndicated loans
and/or capital markets issuance by the borrower. Deutsche Bank does not bear market placement or pricing risk on these
exposures but faces funding risk and credit risk for the duration of the commitment, which are managed through notional
underwriting limits for the Group and an industry concentration framework.
Market Risk Management  
Market Risk Framework Market Risk ManagementMarket risk framework
The vast majority of Deutsche Bank’s businesses are subject to market risk, defined as the potential for change in the
market value of the Group’s trading and invested positions. Risk can arise from changes in interest rates, credit spreads,
foreign exchange rates, equity prices, commodity prices and other relevant parameters, such as market volatility and
market implied default probabilities. The market risk can affect accounting, economic and regulatory views of the
exposure.
Market Risk Management is part of Deutsche Bank’s independent Risk function and sits within the Market and Valuations
Risk Management group. One of the primary objectives of Market Risk Management is to ensure that the business units’
risk exposure is within the approved risk appetite commensurate with its defined strategy. To achieve this objective,
Market Risk Management works closely together with risk takers (“the business units”) and other control and support
groups.
The Group distinguishes between three substantially different types of market risk:
–Trading market risk arises primarily through the market-making and client facilitation activities of the Investment Bank
segment. This involves taking positions in debt, equity, foreign exchange, other securities and commodities as well as
in equivalent derivatives
–Traded default risk arising from defaults and rating migrations relating to trading instruments
–Non-trading market risk arises from market movements, primarily outside the activities of the trading units, in the
banking book and from off-balance sheet items; this includes interest rate risk, credit spread risk, investment risk and
foreign exchange risk as well as market risk arising from pension schemes, guaranteed funds and equity
compensation; non-trading market risk also includes risk from the modeling of client deposits as well as savings and
loan products
Market Risk Management governance is designed and established to promote oversight of all market risks, effective
decision-making and timely escalation to senior management.
Market Risk Management defines and implements a framework to systematically identify, assess, monitor and report the
Group’s market risk. Market risk managers identify market risks through active portfolio analysis and engagement with the
business units.
Trading Market Risk Trading market risk
The Group’s primary mechanism to manage trading market risk is the application of the bank’s risk appetite framework, of
which the limit framework is a key component. The Management Board, supported by Market Risk Management, sets
group-wide value-at-risk, economic capital and portfolio stress testing limits for market risk in the trading book. Market
Risk Management allocates this overall appetite to the business segments and their individual business units based on
established and agreed business plans. Deutsche Bank also has business aligned heads within Market Risk Management
who establish business unit limits, by allocating the limit down to individual portfolios, geographical regions and types of
market risks.
Value-at-risk, economic capital and portfolio stress testing limits are used for managing all types of market risk at an
overall portfolio level. As an additional and important complementary tool for managing certain portfolios or risk types,
Market Risk Management performs risk analysis and business specific stress testing. Limits are also set on sensitivity and
concentration/liquidity, exposure, business-level stress testing and event risk scenarios, taking into consideration
business plans and the risk versus return assessment.
Business units are responsible for adhering to the limits against which exposures are monitored and reported. The market
risk limits set by Market Risk Management are monitored on a daily, weekly and monthly basis, dependent on the risk
management tool being used.
Value-at-Risk Internally developed market risk models
Value-at-Risk (VaR)
VaR is a quantitative measure of the potential loss (in value) of Fair Value positions due to market movements that should
not be exceeded in a defined period of time and with a defined confidence level.
The Group’s value-at-risk for the trading businesses is based on historical simulation model (internal model approach)
predominantly utilizing full revaluation, although some portfolios remain on a sensitivity-based approach. The approach
is used for both Risk Management and capital requirements.
Risk management VaR is calibrated to a 99% confidence level and a one-day holding period. This means we estimate
there is a 1 in 100 chance that a mark-to-market loss from the bank´s trading positions will be at least as large as the
reported VaR. For regulatory capital purposes, the VaR model is calibrated to a 99% confidence interval and a ten-day
holding period.
The calculation employs a historical simulation technique that uses one-year of historical market data as input and
observed correlations between the risk factors during this one-year period.
The VaR model is designed to take into account a comprehensive set of risk factors across all asset classes. Key risk
factors are swap/government curves, index and issuer-specific credit curves, single equity and index prices, foreign
exchange rates, commodity prices as well as their implied volatilities. To help ensure completeness in the risk coverage,
second order risk factors, e.g., money market basis, implied dividends, option-adjusted spreads and precious metals lease
rates are also considered in the VaR calculation. The list of risk factors included in the VaR model is reviewed regularly
and enhanced as part of ongoing model performance reviews.
The model incorporates both linear and, especially for derivatives, nonlinear impacts predominantly through a full
revaluation approach but it also utilizes a sensitivity-based approach for certain portfolios. The full revaluation approach
uses the historical changes to risk factors as input to pricing functions. Whilst this approach is computationally
expensive, it does yield a more accurate view of market risk for nonlinear positions, especially under stressed scenarios.
The sensitivity-based approach uses sensitivities to underlying risk factors in combination with historical changes to
those risk factors.
For each business unit a separate VaR is calculated for each risk type, e.g., interest rate risk, credit spread risk, equity risk,
foreign exchange risk and commodity risk. “Diversification effect” reflects the fact that the total VaR on a given day will
be lower than the sum of the VaR relating to the individual risk types. Simply adding the VaR figures of the individual risk
types to arrive at an aggregate VaR would imply the assumption that the losses in all risk types occur simultaneously.
VaR enables the Group to apply a consistent measure across the fair value exposures. It allows a comparison of risk in
different businesses, and also provides a means of aggregating and netting positions within a portfolio to reflect
correlations and offsets between different asset classes. Furthermore, it facilitates comparisons of the market risk both
over time and against the daily trading results.
When using VaR results a number of considerations should be taken into account. These include:
–The use of historical market data may not be a good indicator of potential future events, particularly those that are
extreme in nature; this “backward-looking” limitation can cause VaR to understate future potential losses (as in
financial credit crisis 2008/09), but can also cause it to be overstated immediately following a period of significant
stress (as in COVID-19 pandemic)
–The one day holding period does not fully capture the market risk arising during periods of illiquidity, when positions
cannot be closed out or hedged within one day
–VaR does not indicate the potential loss beyond the 99th quantile
–Intra-day risk is not reflected in the end of day VaR calculation
–There may be risks in the trading or banking book that are not fully captured in the VaR model (either partially
captured or missing entirely)
Stressed Value-at-Risk Stressed Value-at-Risk
Stressed Value-at-Risk (SVaR) calculates a stressed value-at-risk measure based on a one year period of significant
market stress. The Group calculates a stressed value-at-risk measure using a 99% confidence level. Stressed VaR is
calculated with a holding period of ten days. The SVaR calculation utilizes the same systems, trade information and
processes as those used for the calculation of value-at-risk. The only difference is that historical market data and
observed correlations from a period of significant financial stress (i.e., characterized by high volatilities) are used as an
input for the historical simulation.
Incremental Risk Charge Incremental Risk Charge
Incremental Risk Charge captures default and credit rating migration risks for credit-sensitive positions in the trading
book. The Group uses a Monte Carlo Simulation for calculating incremental risk charge as the 99.9% quantile of the
portfolio loss distribution over a one-year capital horizon under a constant position approach and for allocating
contributory incremental risk charge to individual positions.
Nontrading Market Risk Non-trading market risk
Non-trading market risk arises primarily from activities outside of the trading units, in the banking book, including pension
schemes and guarantees, and embedding considerations of different accounting treatments of transactions. Significant
market risk factors to which the Group is exposed and are overseen by risk management groups in that area are interest
rate risk (including risk from embedded optionality and changes in behavioral patterns for certain product types), credit
spread risk, foreign exchange risk (including structural foreign exchange risk) and equity risk (including equity
compensation related risk and investments in public and private equity as well as real estate, infrastructure and fund
assets).
Nontrading Market Risk Economic Capital Non-trading market risk economic capital
Non-trading market risk economic capital is calculated either by applying the standard trading market risk economic
capital methodology or through the use of non-trading market risk models that are specific to each risk class and which
consider, among other factors, historically observed market moves, the liquidity of each asset class, and changes in
client’s behavior in relation to products with behavioral optionality.
Liquidity Risk Management  
Liquidity Risk Management Framework Liquidity Risk Management
Liquidity risk arises from Deutsche Bank Groups potential inability to meet payment obligations when they come due or
without incurring excessive costs. The Group’s risk taxonomy differentiates between two aspects of liquidity risk: Short-
term liquidity risk and structural funding risk, both embedded in an overarching liquidity and funding risk management
framework. The framework’s objective is to ensure that robust governance and controls are established within the Group
to fulfill its payment obligations (including intraday) at all times, including periods of stress, and to manage its liquidity
and funding risks within the Management Board’s approved risk appetite, when executing the strategic plan. The
framework considers all relevant and significant drivers of liquidity risk, whether on-balance sheet or off-balance sheet.
Liquidity and funding risk framework
Liquidity and funding key risk metrics are embedded in the bank’s risk appetite framework and reviewed as well as
approved by the Management Board at least on an annual basis. The risk appetite is applied at the Group level and to
internally defined Key Liquidity Entities, e.g., Deutsche Bank AG, to monitor and control liquidity risk as well as the
Group’s long-term funding and issuance plan.
The Group Asset and Liability Committee is the Group’s decision making governing body mandated by the Management
Board to optimize the sourcing and deployment of the Group’s balance sheet and financial resources in line with the
Management Board’s risk appetite and strategy. From the second line of defense perspective, the Group Risk Committee
is mandated by the Management Board with decision-making authority regarding material risk-related topics. Detailed
roles and responsibilities of the Group Asset and Liability committee as well as the Group Risk Committee are defined in
the “Risk Governance” section of this report.
The Liquidity and Funding Risk Management Framework defines the organization of the liquidity managing functions in
alignment with the three lines of defense structure, which is described in the “Risk Management principles” section of
this report, including the respective responsibilities of those functions comprising of the three lines of defense. In the
context of the Liquidity and Funding Risk Management Framework, these functions include the following:
–First Line of Defense: Corporate divisions and Treasury
–Second Line of Defense: CRO - Enterprise and Treasury Risk Management (ETRM)
–Third Line of Defense: Group Audit
The Group’s liquidity risk management principles are documented in a policy document and the framework is described
in the framework document. Both the policy and framework documents adhere to and articulate how the eight key risk
management practices are applied to liquidity risk, with such key practices including 1) risk governance, 2) risk
organization (3 lines of defense), 3) risk culture, 4) risk appetite and -strategy, 5) risk identification and -assessment,
6) risk mitigation and controls, 7) risk measurement and reporting as well as 8) stress planning and execution. The
individual roles and responsibilities relevant to each of these practices are laid out and documented in the Global
Responsibility Matrix for liquidity risk, which provides further clarity and transparency on the roles and responsibilities
across all involved stakeholders. All additional procedures and supporting documents (both global and local) issued by
the liquidity risk management functions further define the requirements specific to liquidity risk practices.
In accordance with the European Central Bank’s Supervisory Review and Evaluation Process (and revised Internal
Liquidity Adequacy Assessment Process requirement issued in November 2018), the Group has implemented an Internal
Liquidity Adequacy Assessment Process which is carried out, assessed, documented, and reviewed at least annually and
approved by the Management Board.
Risk appetite and control setting
The Group’s liquidity risk appetite, which is defined through qualitative principles and supporting quantitative metrics, is
laid out in the Risk Appetite Statement and is subject to the standards defined in the Risk Appetite Policy. This Group
Risk Appetite Statement (RAS) covers regulatory (Pillar 1) as well as internal (Pillar 2) metrics, and is further underpinned
by the liquidity risk controls framework consisting of Risk Appetite limits, as well as a suite of additional limits, thresholds
and early warning indicators.
Treasury manages liquidity and funding, in accordance with the risk appetite across all relevant metrics and implements
tools, including business level risk limits, further cascading aspects of risk appetite to divisional level, ensuring ease of
compliance at Group level. As such, Treasury works closely with Enterprise and Treasury Risk Management under its
delegated authority and the business divisions to identify, analyze and monitor underlying liquidity risk characteristics
within business portfolios. These parties are engaged in regular dialogue regarding changes in the Group’s liquidity
position arising from business activities and market events.
Furthermore, the Group ensures at the level of each Liquidity Relevant Entity that all local liquidity metrics are managed
in compliance with the defined risk appetite. Local liquidity surpluses are pooled in Deutsche Bank AG hubs and local
liquidity shortfalls can be met through support from these hubs. Transfers of liquidity capacity between entities are
subject to the Intercompany Funding approval framework involving the Group’s liquidity steering function as well as the
local liquidity managers considering the compliance with Pillar 1 metrics, including Liquidity Coverage Ratio (LCR) and
Net Stable Funding Ratio (NSFR), as well as Pillar 2 metrics, including the stressed Net Liquidity Position (sNLP) and
Funding Matrix. Any available surplus that resides in entities with restrictions on transferring liquidity to other Group
entities, for example due to regulatory lending requirements, is treated as trapped and as such not considered in the
calculation of the consolidated Group liquidity surplus.
The Management Board is informed about the Group’s performance against the key liquidity metrics, including the risk
appetite and internal and market indicators, via a weekly liquidity dashboard.
Funding Risk Management and Funding Diversification
In line with regulatory guidelines, Deutsche Bank has developed a set of internal indicators to measure its inherent
funding risks. These are considered for risk management and steering purposes in addition to the Pillar 1 requirements.
The Group relies on a diverse range of funding sources including deposits, unsecured wholesale funding, Capital Markets
Issuances and secured funding. These funding sources protect the Group’s liquidity position in two ways. First, since
stress events may impact funding markets differently, maintaining a well-diversified funding portfolio will lower the
average impact of these events. Second, when experiencing a liquidity stress, having access to a wide range of funding
sources significantly improves the Group’s ability to tap different funding markets. The diversification across products is
complemented by Risk indicators which have been set to monitor tenor concentration and counterparty concentration.
The stability of Deutsche Bank Group’s funding position can be negatively impacted by various forms of industry risks
which often manifests medium to long term structural trends with a potentially significant long-term impact on the
economy and banks’ balance sheets. Deutsche Bank performs ad-hoc analyses on such emerging risks to assess the
impact of such trends on its funding position to ensure that mitigating measures are taken on a timely basis when
deemed necessary. In addition, Treasury evaluates current market access information in its significant funding markets
on a monthly basis with results compiled and presented to the Group Asset and Liability Committee.
Deutsche Bank’s tool for monitoring and managing the Group’s long-term funding profile for more than ten years is the
Funding Matrix. To produce the Funding Matrix, all assets and liabilities are mapped into time buckets corresponding to
their baseline contractual or modelled maturities. This allows the Group to identify expected excesses and shortfalls in
term liabilities over assets in each time bucket, facilitating the management of potential liquidity exposures over time.
The liquidity profile is based on contractual cash flow information. If the contractual maturity profile of a product does
not adequately reflect the liquidity profile or in case of non-maturing products, the maturity is replaced by baseline
modelling assumptions.
Capital Markets Issuance
The main objective of debt issuance is to raise long term funding in the most cost optimal manner. Debt issuance,
encompassing senior unsecured bonds, covered bonds, and capital securities, is a key source of term funding for the
Group and is managed directly by Treasury. At least once a year, following endorsement by the Asset and Liability
Committee, Treasury submits an annual long-term funding plan to the Group Risk Committee for recommendation and
then to the Management Board for approval. This plan is driven by global and local funding and liquidity requirements
based on expected business development. The Group’s capital markets issuance portfolio is dynamically managed
through annual issuance plans to avoid excessive maturity concentrations.
Deutsche Bank holds a license to issue mortgage Pfandbriefe and maintains a program to issue structured covered
bonds. In 2025, the Pfandbrief platform was enriched to support callable Pfandbriefe which further broadens the
bandwidth offered to investors. The Spanish covered bond program (Cedulas) is currently winding down, although there
are plans to restart the program in 2026. Since 2020, the Group has maintained its Green Bond framework which offers
green note issuances to both, institutional and retail investors. Furthermore, multiple green structured notes, green
deposits and repurchase agreements (repos) have been executed. In 2024, the sustainability framework was enriched to
also support social assets. Deutsche Bank also expanded its platform to issue Panda bonds in China. Since 2023, bonds
with a total notional value of CNY 8 billion were issued into the Chinese market.
Liquidity risk monitoring and management
The Finance teams Liquidity and Treasury Reporting & Analysis (LTRA) and Liquidity Data Measurement and Reporting
(LDMR) together own the overall accountability for the accurate and timely production of both external regulatory
liquidity reporting (Pillar 1) as well as internal management reporting (Pillar 2) for the liquidity risk of the Group. In
addition, LTRA is responsible for the development of management information systems and the related analysis to
support the liquidity risk framework and its governance for Enterprise and Treasury Risk Management.
Liquidity stress testing and scenario analysis Liquidity stress testing and scenario analysis
Global internal liquidity stress testing and scenario analysis is used for measuring liquidity risk and evaluating the Group’s
short-term liquidity position within the liquidity framework. This complements the daily operational cash management
process. The long-term liquidity strategy based on baseline contractual or modelled maturities is represented by a long-
term metric known as the Funding Matrix (additional information can be found in the section “Funding risk management
and funding diversification” in this report).
The global liquidity stress testing exposure is managed by Treasury in compliance with the respective risk appetite.
Treasury is responsible for the design of the overall stress test methodology, the choice of liquidity risk drivers and the
determination of appropriate assumptions (parameters) to translate input data into stress testing output. Enterprise and
Treasury Risk Management is responsible for the definition of the stress scenarios. Laid out by the Model Risk
Management Policy and Procedure, Enterprise and Treasury Risk Management and Model Risk Management perform the
independent validation of liquidity risk models. The Finance teams Liquidity and Treasury Reporting & Analysis (LTRA)
and Liquidity Data Measurement and Reporting (LDMR) are responsible for implementing these methodologies and
performing the stress test calculation in conjunction with Treasury, Liquidity Risk Management, Group Strategic Analytics
and Information Technology.
Stress testing and scenario analysis are used to describe and evaluate the impact of sudden and severe stress events on
the Group’s liquidity position. Deutsche Bank has selected four scenarios to calculate the Group’s stressed Net Liquidity
Position. These scenarios are designed to capture potential outcomes which may be experienced by the Group. The most
severe scenario assesses the potential consequences of a combined market-wide and severe idiosyncratic stress event,
including multi-notch downgrades of the Bank’s credit ratings. Under each of the scenarios, the impact of a liquidity
stress event over different time horizons and across multiple liquidity risk drivers, covering all business lines and product
areas and with that all portfolios and balance sheet, is considered. The output from this scenario analysis also feeds the
Group Wide Stress Test run by Enterprise Risk Management, which analyzes liquidity risk in conjunction with the other
defined risk types and evaluates their impact and interplay to both Capital and Liquidity positions.
In addition, potential funding requirements from contingent liquidity risks which can arise under stress, including
drawdowns on lending facilities, increased collateral requirements under derivative agreements, and outflows from
deposits with a contractual rating linked trigger are included in the analysis. Subsequently, countermeasures, which are
the actions the Group would take to counterbalance the outflows incurred during a stress event, are also taken into
consideration. These countermeasures include the usage of the Group’s liquidity reserves and generating liquidity from
other unencumbered, marketable assets without causing any material impact on the Group’s business model.
Stress testing is conducted at a group level and for defined entities relevant for liquidity risk management. The stress
analysis covers a range of time periods out to 1 year depending on the scenario. The most acute stress uses a time period
of three months which is considered to be the critical time period during a liquidity crisis requiring that liquidity is
actively assessed and steered on a Group level. In addition to the consolidated currency stress test, further stress tests
are performed for material currencies, namely euro and U.S. dollar. At the global level as well as for the U.S. entities,
liquidity stress tests also cover a twelve-months period for which a risk appetite limit has been set. Ad hoc analysis may
be conducted to reflect the impact of potential downside events that could affect the Group, such as climate/ESG-
related events. Relevant stress assumptions are applied to reflect liquidity flows from risk drivers and on-balance sheet
and off-balance sheet products. The suite of stress testing scenarios and assumptions are reviewed on a regular basis
and are updated when enhancements are made to stress testing methodologies.
High Quality Liquid Assets High Quality Liquid Assets
High-quality Liquid Assets (HQLA) is a Pillar 1 calculation which feeds into LCR and is a key limit per the risk appetite.
HQLA comprise available cash and cash equivalents and unencumbered high quality liquid securities (including
government and government guaranteed bonds), representing the most readily available and most important
countermeasure in a stress event.
Contingency Funding Planning Contingency Funding Planning
The Group Contingency Funding Plan outlines how Deutsche Bank would respond to an actual or anticipated liquidity
stress event. It specifies the provisions, procedures and action plans for responding to potential disruptions to the Bank’s
ability to fund itself. It covers actions that can be taken to raise cash and/or recover the Bank’s liquidity metrics in breach.
The Contingency Funding Plan outlines governance arrangements for its activation and presents the framework of
liquidity indicators enabling the bank to identify deteriorating market circumstances in a timely manner and that
determine quickly what actions need to be taken, including communication and coordination during a liquidity stress
event. Deutsche Bank has established the Financial Resource Management Council, which is responsible for oversight of
capital and liquidity across contingency, recovery, and resolution scenarios in a defined crisis situation.
Portfolio concentration risk Portfolio concentration risk
Risk concentrations refer to clusters of the same or similar risk drivers within specific risk types (intra-risk concentrations
in credit, market, operational and strategic risks) as well as across different risk types (inter-risk concentrations). They
occur within and across counterparties, businesses, regions/countries, industries and products. The management and
monitoring of risk concentrations is achieved through a quantitative and qualitative approach, as follows:
–Intra-risk concentrations are assessed, monitored and mitigated by the individual risk functions (enterprise, credit,
market, operational and liquidity risk management). This is supported by risk appetite including limit setting on
different levels and/or management according to each risk type
–Inter-risk concentrations are managed through quantitative top-down stress-testing and qualitative bottom-up
reviews, identifying and assessing risk themes independent of any risk type and providing a holistic view across the
bank. The diversification effects between credit, market, operational and strategic risk are measured through an
economic capital model that quantifies the diversification benefit caused by non-perfect correlations between these
risk types. The calculation of the risk type diversification benefit is intended to ensure that the standalone economic
capital figures for the individual risk types are aggregated in an economically meaningful way
The most senior governance body for the oversight of risk concentrations is the Group Risk Committee (GRC).
Environmental, social and governance risk Environmental, social and governance risk
The impacts of rising global average temperatures, the transition to a net zero economy and the enhanced focus on
climate change from society, regulators and the banking sector have led to the emergence of new and increasing sources
of financial and non-financial risks. These include the physical risks arising from extreme weather events growing in
frequency and severity, as well as transition risks as carbon intensive sectors are expected to face higher taxation,
reduced demand and restricted access to financing. These risks can impact Deutsche Bank across a broad range of
financial and non-financial risk types. Financial institutions are facing increased scrutiny on climate and broader ESG-
related issues from governments, regulators, shareholders and other bodies, leading to reputational risks if the Group is
not seen to support the transition to a lower carbon economy, to protect biodiversity and human rights, among other
themes.
Deutsche Bank’s risk strategy recognizes ESG as a theme that represents a broad range of areas of concern related to
environmental, social, or governance factors that cuts across multiple scenarios and risks. It must be ensured that all
non-financial ESG-related risks are identified and adequately assessed to include potential impacts driven by ESG
factors; the bank must ensure that controls are effective and any potential deficiencies are promptly escalated and
addressed. Deutsche Bank is regularly reviewing and enhancing its ESG-related risk management frameworks in
alignment with regulatory guidance and to ensure that ESG risks are actively managed and greenwashing risk is
mitigated. Limitations in terms of data, methodologies and industry standards for measuring and assessing climate and
other environmental risks continue to lead to a high degree of uncertainty in the bank’s climate-related disclosures. Anti-
ESG measures that were already established in some jurisdictions and have been reinforced and taken further may result
in the loss of existing business and the inability to conduct new business within those jurisdictions, while complying may
lead to reputational risks.
The management of risks stemming from environmental factors relies first and foremost with Deutsche Bank’s net zero-
aligned decarbonization targets for eight sectors: Oil and Gas (upstream), Power Generation, Automotive (light duty
vehicles), Steel, Coal mining, Cement, Shipping and Commercial Aviation. The pathways to achieve these targets are
incorporated into the bank’s risk management framework. Environmental risks are assessed through an annual climate
and environmental materiality assessment and internal stress test, across businesses, portfolios and risk types (Credit,
Market, Liquidity, Reputational and Operational). They are monitored through dedicated reports discussed in senior risk
committees and managed through risk appetite thresholds, policies requirements and exclusions (Environmental and
Social policy framework), and portfolio Early Warning Indicators (EWIs). Climate and environmental risks are incorporated
into the credit approval process for corporate clients via enhanced due diligence requirements. For corporate clients in
carbon-intensive sectors, as well as those in sectors vulnerable to climate-physical and nature (or “other environmental”)
risks, new loan requests above selected tenor and rating-based thresholds to corporate clients in carbon-intensive
sectors require a dedicated risk assessment from the Front Office and review by Credit Risk Management. Overall, these
risks are embedded within the bank’s business model and financial planning through the carbon budgets attributed to
the bank’s businesses derived from its decarbonization targets and through the inclusion of environmental risks within
the Internal Capital Adequacy Assessment Process (ICAAP).
The Group Sustainability Committee acts as the main governance and decision-making body for sustainability-related
matters across Deutsche Bank. This includes the assessment of material impacts as well as risks and opportunities for the
Bank. The committee also sets the net zero targets for the bank.
The Management Board has delegated sustainability-related decisions to this committee, which is chaired by the Chief
Executive Officer and the Chief Sustainability Officer (Vice Chair). It receives monthly updates on financed emissions and
net zero alignment.
The Group Risk Committee, chaired by the Chief Risk Officer and established by the Management Board has the mandate
to oversee several risk and capital-related matters. This includes the responsibility for developing the bank’s Climate Risk
Framework. The Committee approves the Bank’s climate and environmental risk appetite, including appetite for
deviation from net zero decarbonization pathways. A number of committees are responsible for the development and
management of specific elements of climate and environmental risk.
The Net Zero Forum receives, in addition to the quarterly reports, monthly flash reports on key metrics (i.e., measuring
alignment with decarbonization targets and the consumption of divisional carbon budgets).