The new Definition of Default and credit risk models in ...
Transcript of The new Definition of Default and credit risk models in ...
The new Definition of Default and credit risk models in the Covid-19 context.IRB and IFRS9 Validation.
ABI SUPERVISION, RISKS & PROFITABILITY, 22nd September 2020
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The emergence of Covid-19Implications for credit risk management at banks…
NPL Strategy
Scenarios/Outlook
Approval
Early Warning+
Collection
PD, LGD and CCF
Classification
Loan Management
systems
Classification engines
Stress Testing+
Capital Plan
«Satellite» Models
Provisioning
!
► Policy review from Regulatory requirements► Dedicated process for SICR/UTP testing► Review of distressed restructuring process with
conjoint regard to the new DoD
Classification of moratoria
► Structuring, build and reconciliation of data environment for moratoria
► Implementation of process and model adaptations
► Adaptation of Regulatory and Accounting Disclosures for Industry focus and otherchannels of Covid-19 impact
Adaptation of credit risk management systems!
► Review of macroeconomic impacts► NPL flow volatility over a multi-year horizon► Impact of Moratoria on UTP flows and migrations
NPL Strategy review!
► Renewed IFRS9 Transitional Arrangements► Impacts on buffers and (Total) Capital Ratios► Alignment of Capital and Recovery Plan► Improvement of stress testing framework
Steering of Regulatory support!
!
► Re-definition of credit policies with Industry focus► Insertion of focus on moratoria in support of loan
approval and underwriting
Loan Policy and Procedures
Adaptation to Covid-19
Financial/Regulatory Reporting
► Staging and accounting of Moratoria► Policy review for IFRS9 scenarios and models► Management of Overlays
Adequacy of Provisioning framework!
► Consistency with Macro scenarios and (tempraray) re-weighting of Long-Run estimates
► Integration with internal models
Industry/Single-name Outlook!
► Calibration on current scenarios► Further industry focus on PD models
► possible view to EBA/ECB 2021 Stress Tests► LGD differentiation per Industry► Possible F-L adjustments for exposures under
moratoria
Covid-19 enhancements for IFRS9 and stress testing models!
► Integration of customer-level analysis with aggregate Forward-Looking view
► IFRS9► possible PIT adjustments from moratoria► possbile impacts of drawdowns on actual CCFs
► AIRB: upcoming impacts as per EBA GL on IRB modelling/CRR Art. 500
Covid-19 enhancements for PD and LGD models !
!
► Integration of moratoria and related monitoring► Integration of F-Looking view into loan
watchlisting
Strengthening of Early Warning
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2016 2017 2018 2019 2020
Guidelines on the application of the definition
of default
RTS on materiality threshold of past due credit
obligation
Guidelines on PD/LGD estimation
RTS/GLs on Downturn period and Downturn LGD
CRR Art. 500
RTS on the assessment methodology for the IRB
Approach
ECB IRB Harmonisation
ECB Guidance to banks on non-performing loans
IRB Harmonisation1
New DoD
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2-Step «Model Change»Application Package
IRB model target recalibration+
Selected EGIM adaptations
Supervisory Approval
AIRB Model recalibration
March 2018 Addendum
2021
TRIMI + TRIMIXGroundwork
Final Draft
2-Step Go-LiveSupervisory Approval
TRIM Guide Internal Models Guide. General Topics.
2022
The new Definition of Default…in progress on the revised EBA IRB Roadmap…
Consolidated EGIM.
1-Step «Model Change»Application Package
Assessment► Gap Analysis/Action Plan► Impact Analysis
«Best Effort» Recalibration► Historical reconstruction of new DoD► Model recalibration
Parallel Run
New DoD Go-Live
Supervisory Approval
IRB RWA Go-Live*
New DoD InternalValidation
AIRB As-Is
IRB model finalisation
Finalised IRB First Validation
* Non-mandatory deadline for selected Low-Default portfolios, targeted for final IRB harmonisation at 2023-end.
New UTP Triggers
Pillar II Calendar ProvisioningPillar I NPE CoverageExpectations for NPE Coverage
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Basel vs. IFRS9…beyond IRB models…
Stress TestingLGD models
Basel
IFRS9
► Long-Run PD
► Likely Range of Variability
► Forward-Looking Lifetime PD
Basel
IFRS9
► IRB Ratings
► Distressed Restructurings
► 90DPD counters
►D(PD) Thresholds for SICR detection
► Equivalent Minimum rating downgrade schemes
► Forbearance
► 30DPD counters
Basel
IFRS9
Basel
IFRS9
► “Satellite” models for PD, LGD and ELBE/Stage 3 LGD
► Prospective Stage Transition calculation
► Prospective LGD calculation
► Prospective ECL calculation
Common data and risk modelling layer for IRB, ICAAP/SREP and IFRS9 approaches
Coverage and rationalisation of overarching model management process
Tightening IRB Standards
►Standards for New DoD in the models RDS
►Art.500/IW adjustments for LGD
►MoCs
►Calibration of ELBE and LGD “In Default”
Validation and Assurance
► Increased scope for Internal Validation in all risks to Capital
►ECB Validation Reporting
► IFRS9 validation pre-requisite to Assurance
►Model Risk Management
NPL Management
►Backtesting of “individual assessment” LLPs
►Cascading of NPL Plan on Accounting framework
►CRR/SREP Calendar Provisioning
… to Business Impact
► Planning/budgeting
► NPL Strategy
► Loan Underwriting
► Early Warning
► Long-Run/Downturn LGD/LGDD
► LGD of “Massive Disposals”
► PIT ELBE
► Forward-Looking Lifetime LGD
► Sale Scenarios
► IFRS Discounting
► IFRS Costs
Staging AllocationPD models
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The Validation Continuum…to Internal Control.
IRB Systems
Base Scenario
Stress Scenario
Validation Framework
Stress Testing
Accounting framework
Capital Adequacy framework
IFRS9
Reperformingand Sensitivity
Macroeconomic scenarios
Forecasting data and Systems
Validation Continuum
IFRS9
CRR/EBA/EGIMModel Design
IRB Model Backtesting
Credit risk “actuals”
“Satellite” model Backtesting
Lifetime metrics backtesting
IFRS9/GPPCModel Design
Default and modelling requirements are cascaded through Regulatory and Accounting applications into the related ValidationContinuum, providing an overarching framework for controls of model design, backtesting and IT implementation testing.
Classification
AIRB Models
Credit Processes
Stress/Downturn calibration
Use Test
RWA data and Systems
Prospective Impairment
“Satellite” models backtesting
Fwd-Looking calibration
SICR backtesting
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Credit risk modelling and validationAn ongoing convergence path in the market
Model recalibrationfrom „New
DoD“ Application
Re-calibration of LGD modelsbased on
historical new DoD and Incomplete Workouts
Initial AIRB Application
Redevelopment of PD/LGD/EAD modelsunder
New EBA requirements for Default and IRB Harmonisation
Internal Validation for AIRB Model Change Applicationfrom
New Definition of Default and IRB Harmonisation
Internal Validation of IFRS9 model updatesstemming from
Covid-19 adjustments and AIRB Model Change
AIRB Validation for„New DoD“ Application
IFRS9 ModelValidation
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IFRS9 Impairment ValidationGovernance, processes and models through Covid-19
IFRS9 Impairment Validation
• Emergence of several new diverse market practices
• Validation cycles vs. Fast Closings
• Sale Scenarios vs. IRB CRR treatment of Massive Disposals
• Covid-19 impacts:
• Increasing relevance of Management Overlays
• Macroeconomic scenarios: cliff effect in levels and volatility
• ECL and Staging:
• new focus on Industries
• SICR sensitivity to FLI
• (retention of) approaches for mitigating pro-cyclicality
Current Industry Challenges
ii. Design of models and scenarios
iii. Data and IT implementation
iv. Reperforming and Backtesting
Governance and Supervision
Process and Control Framework
1. IFRS9 Parameters2. Staging Allocation
Process3. Lifetime ECL
Calculation
i. Documentation
The impact of the Covid-19 emergence on the Impairment framework has far-reaching implications, beyond uncertainty reflectedin forward-looking estimates, as volatility induced and initial mitigation applied are increasingly needing to be managed in a «newnormal» setting.
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IFRS9 Impairment ValidationValidation and model challenge for Covid-19 impacts
Lifetime PD
Lifetime LGD
IFRS9 modelling approaches
Static treatment of Moratoria
Cure assumptions
Management Overlays on F-L PD
F-L framework for Loss Given Liquidation
Validation and Model Challenge
▪ Projection of segment-level moratoria portfolio shares
▪ Interval estimation of expected default flow for Industry-levelmoratoria rates
▪ Extrapolation testing of PD «satellite» models at segment/industrylevel against modelling of segmented default rate time series
▪ Testing significance of Industry-level differentiation of cure rates
▪ Testing for correlation between Corporate PDs and CRE price Indices
MacroeconomicScenarios
Interpolation of baseline scenario forecasts
Re-weighting of multiple macroeconomic scenarios
Overweighting of «Long Run» FLI
▪ Re-allocation of calendar-year forecasts by Accounting forecasting period
▪ Comparison test between «Favourable» scenario and «Consensus» pre-Covid scenarios
▪ Comparison Test between «Long Run» parameters and parametersconditional on macroeconomic «Long Run» trends
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Diagnostics
Model Challenge
The Way ForwardMachine Learning in credit risk model validation
The EY Solution for ML-aided validation covers a full spectrum of model challenge activities, enabling process automation with AImodules for challenging the development of a tailored suite of model type and parameters consistent with model Tiering.
Reference Data Set
Validation Assumptions
Model under assessment
Challenge Approach
AutomatisedClosed-Form approaches
AutomatisedMachine-Learning
approaches
Challenger Model
Performance comparison
Inp
ut
Mo
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l C
ha
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Automation and Machine Learning in credit risk model validation
Variable clustering performed with Supervised Learningtechniques, enabling maximization of heterogeneity of(e.g.) PD/LGD across grades, constrained to satisfyvalidation assumptions.
Enhanced challenger variable selection with SupervisedLearning techniques, enabling an efficient multi-variableapproach to catch non linear relationship at validationand development stages.
Challenger model estimates based on a wide range ofSupervised Learning techniques, driven by alternativeaccuracy/interpretability trade-offs consistent withModel Tier, including Classification and Regression Trees(CART), Bagging and Random Forest.
Wide set of generalised performance KPIs in line withSupervisory guidance and bank’s own validationpractices, including performance metrics andassumptions testing.
VariableSelection
Clustering
Ou
tpu
t
ECB Validation Reporting
Model Tier
Assumptions Testing
Bank Backtesting
KPIs
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Machine Learning in credit risk model validationM-L Challenge for LGD Validation at a primary IRB Bank
-75% FTE compared to standard model validation
process
+293 bps of Goodness-Of-Fit Index compared to challenged
model
The use of the EY Solution has enabled a significant reduction in effort and a significant gain in performance KPIs used for dialogue with the Supervisor, highlighting feasible improvements in developed models under validation
+284 bps of discriminatory power compared to challenged
model
Process Automation within the model validation module
Constrained model validation
+Process Automation
+Machine Learning
Highly streamlined LGD validation process through use of Process Automation and
Machine Learning techniques
Challenger estimates from on a wide range of Supervised Learning techniques, driven by
model/parameter-specific accuracy/interpretability trade-offs
Selected highly interpretable Machine Learning techniques highlight room for further improvement for the challenged model
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-80% FTE compared to standard model validation
process
20+ automatically filled reporting templates
covering
30+ modelsfor all AIRB
segments/parameters
100% coverage of Supervisory requirements
Process Automation within model backtesting
Consistency with Supervisory Instructions
+Process Automation
Highly automated Internal Validation and ECB Validation Reporting through Process
Automation
Automatically filled reporting templates covering Supervisory instructions on an end-to-end basis
Room for further efficiency gain in backtesting standardization over the full spectrum of credit
risk models
Machine Learning in credit risk model validationValidation Reporting Backtesting at a primary IRB Bank
The use of the EY Solution for ECB Validation Reporting has enabled a significant reduction in effort and critically reduced therequired elapsed for anticipated testing of alternative model/parameter specifications
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