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Ris
erv
ato
& c
on
fid
en
zia
le
Istanbul, 18 December 2014
Rating system validation, LGD model and Risk appetite
| 2 |
AGENDA
Rating system validation
LGD models development
Early warning for risk appetite
| 3 |
Regulatory input
CREDIT RISK MODELS
Origination
Credit Strategy
Loan PricingEarly Waring
Risk Appetite Framework
The international regulatory capital standards recommended by the Basel Committee (known as Basel standards) in response to the recent financial crisis have been transposed into Turkish law through secondary legislation enacted by the Banking Regulation and Supervision Agency (BRSA)
| 4 |
Calculation of capital minimum requirements and risk parameters
PD, LGD and EAD are the fundamental risk parameters for evaluating the credit risk level and consequently defining the minimal capital requirements
PD
Credit risk Parameters
EADLGD
Reflects the Probability of Default of the counterparty
Reflects the expected % of
loss on a facility after default
Reflects the expected amount of exposure at the time of the default
Internal Rating Based Foundations
Internal rating
Supervisory values (*)
Internal Rating Based Advanced
Standard
Internal rating
Internal LGD Internal EAD
Supervisory values
External rating
Supervisory values (*)
Supervisory values
| 5 |
Validation Process
Regulatory Back-ground and Framework
Accuracy ConsistencyInternal Validation
ProcessRisk estimation
system
Assessment on internal rating system performance (backtesting) but for IRB banks.
Judgmental analysis on methodological approach applied in the rating system in comparison to the best practices.
Application and use of rating in all business areas (use test) and in the firm-wide decisioning governance.
Constant monitoring on the quality of all the informative tools performing in the rating system.
The Basel Committee defines widely the concept of validation..
“…500 : Banks (adopting IRB approach) must have a robust system in place to validate the accuracy and consistency of rating systems, processes, and the estimation of all relevant risk components. A bank must demonstrate to its supervisor that the internal validation process enables it to assess the performance of internal rating and risk estimation systems consistently and meaningfully.”
| 6 |
Validation Process
Validation Process in the Basel Scheme
Internal Validation
Supervisory Examination
Validation of rating system
Validation of rating process
Internal Use by credit officers
Reporting and problem handling
Data qualityRisk Components
BenchmarkingBacktesting
PD LGD EAD
Model Design
IT SYSTEM
The scheme below shows key components of Validation Process in Basel Scheme
| 7 |
Validation Process
Validation Process in the Basel Scheme
The scheme below shows major actors and “theoretical” steps of the validation process
Governance /Reporting
Information Systems
Model design
Use test
Data
Risk components
VALIDATION OF THE RATING SYSTEMS
Supervisor examination
Internal audit/compliance
Validation/Authorization
dossier
VALIDATION OF THE RATING PROCESS
Individual bank Supervisor
Regulatory capital
RWA= x8%
EAD 1,06xxPD LGD ;f (
x
(
Credit approval
Credit risk management perspective
Regulatory perspective
M
(
;
Credit monitoring
……
Authorization to the use of IRB
parameters
Default risk -PD
Loss given default
LGD
IRB parameters
Exposure at default
EAD
| 8 |
PDDEVELOPMENT
Basel Standars
IMPLEMENTATION
LGD EAD
Internal credit models
QualitativeVALIDATION
The internal credit models system should be validated in order to be compliant to Basel Standards; the validation process takes into account two different areas: quantitative and qualitative
Validation of internal credit risk models
Quantitative
| 9 |
The model’s design is validated on the basis of the rating model’s documentation. In this context, the scope, transparency and completeness of documentation are already essential validation criteria
• Delineation criteria for the rating segment• Description of the rating method/model
type/model architecture used• Reason for selecting a specific model type• Completeness of the (best practice) criteria
used in the model• Data set used in statistical rating
development• Quality assurance for the data set• Model development procedure• Quality assurance/validation during model
development• Documentation of all model functions• Calibration of model output to default
probabilities
Data qualityModel design Internal Use test
In statistical models, data quality stands out as a goodness-of-fit criterion even during model development. Moreover, a comprehensive data set is an essential prerequisite for quantitative validation
• Completeness of data in order to ensure that the rating determined is comprehensible
• Volume of available data, especially data histories
• Representativity of the samples used for model development and validation
• Data sources• Measures taken to ensure quality and
cleanse raw data.
Validating the internal use of the rating models (use test) refers to the actual integration of rating procedures and results into the banks in-house risk management and reporting systems. With regard to internal use, the essentialaspects of the requirements imposed on banks using the IRB approach under Basel II
• Design of the banks internal processes which interface with the rating procedure
• as well as their inclusion in organizational guidelines
• Use of the rating in risk management (in credit decision-making, risk-based
• pricing, rating-based competence systems, rating-based limit systems, etc.)
• Conformity of the rating procedures with the banks credit risk strategy
• Functional separation of responsibility for ratings from the front office
• Employee qualifications• User acceptance of the procedure• The users ability to exercise freedom of
interpretation in the rating procedure
Validation – Qualitative Area
The qualitative test is meant to certify the relevance and quality of the data used and the correct application of the quantitative methods. A rating process should only be carried out if the internal credit models system receives a positive assessment during the qualitative test
| 10 |
The term discriminatory power refers to
the fundamental ability of a rating
model to differentiate between bad
(credit default occurs) and good (credit
default not occurs) cases
stabilityperformance calibration
The assignment of default
probabilities to a rating models
output is referred to as calibration.
The quality of calibration depends
on the degree to which the default
probabilities predicted by the
rating model match the default
rates actually realized
• Frequency Distribution of Good and Bad Cases
• Transaction matrix
• Binomial Test• Chi-Square Test• PD actual vs PD fitted
• Accuracy Ratio• ROC Curve• Errors the 1° and 2° type• Cumulative frequency bad /good cases• Denisty function for bad/good cases• Kolmogorov-Smirnov Test• CAP Curve
• Changes in the discriminatory power of a
rating model given forecasting horizons of
varying length and changes in
discriminatory power as loans become
older
• Changes in the general conditions
underlying the use of the model and their
effects on individual model parameters
and on the results the model generates
benchmark
back-testing
Validation – Quantitative Area
Quantitative validation is required for all credit models in use and it should primarily be performed with the data gained through use of the model in the bank. A quantitative test could provide the information concerning the performance of the credit models
| 11 |
AGENDA
Rating system validation
LGD models development
Early warning for risk appetite
| 12 |
Approaches to LGD estimation
There are broadly 2 ways of measuring LGD
Focus of the presentation
MARKETLGD
WORKOUTLGD
It makes use of Market Data
The Loss Value of the market assets after the default event, is the base for the LGD Estimation
It needs a liquid asset market before and after default
CorporateSMERetail
It makes use of Internal Data
It needs as first step the analytic calculation of the LGD observed on historical default events, on the basis of expenses, charges and recoveries, observed in a period of time
It needs the collection of the cash flow characterizing the contracts after the default event, and the adoption of appropriate actualization hypothesis.
-Sub-Task- -Description-
Large CorporateFinancial Instit.Sovereign
-Counterparties-
| 13 |
The loss is measured according to an “economic logic” (not accounting) considering the effect of time in the loan recovery
The perimeter is represented by the default positions which have completed their process of debt recovery
LGD is usually defined as the ratio of losses to exposure at default, and it considers:
the set of estimated recovery cash flows to be received by the lender resulting from the workout and/or collections process, properly discounted
Workout expenses (collections, legal, etc)
Downturn: LGD must reflect an appraisal of the expected loss on a transaction in case of default and in a downturn scenario (periods where PD are above the average).
Approaches to LGD estimation
What do we know and we expect about Loss Given default?
A
Recovery flows
Exposure at default (EAD)
B
LGD=
EAD – discounted recovery flows + discounted expenses
EAD
A – B + C
A
=
Methodological foundations
Expenses
C
Closing of the collection actions
Definition
Defa
ult
| 14 |
Approaches to LGD estimation
Workout estimation can approached differently in terms of Model Design
Performing DEFAULTPerforming
LOSSLGDFull
model
MODEL FULLY COMPLIANT AND ALIGNED WITH THE RECENT EVOLUTIONS OF THE REGULATOR
WRITE-OFF 1BLGD
1BLGD
crcure
Default B2LOSS danger
Performing
LOSS
cr1
p
p1
2
11 11
B
BB EAD
EADcrppLGDLGD
2BEAD 1BEAD
PerformingPerforming
Cure rate (Partial) Model
| 15 |
High level project outiline – LGD workout estimation
Preliminary assessment
Credit portfolio analysis
Credit Management Process analysis
■ Analysis of the active loan portfolio with a breakdown by:
I. Product
II.Collateral
III.…
■ Interviews with process owners
I.Debt collection
■ Data extractions from identified archives
■ Construction of Development Data mart
■ Estimation of LGD parameters
Gap analysis
Identifying the relevant dimensions of analysis
Identification of Key processes
characteristics Any limitations
or constraints to the estimation process
Pro
ject
ph
ases
Ou
tpu
tA
cti
vit
y
To properly address LGD estimation, CRIF proposes a project framework composed of 6 worksteps
Modelingframework
■ Defining the methodological framework for calculating LGD in compliance with the Basel requirements
Technical meetings on LGD modeling methodologyin line with the process and data characteristics
Data extractions and risk parameters estimation
Improvement expectations
■ Identification of areas for improvement of the LGD estimates
■ Prioritization and planning of future activities
Analysis which aims to identify main areas of improvement and gap towards local regulatory requirements framework and international best practice
Data request Presentation of
the results (estimates, performance,.)
Proposed action plan
| 16 |
Project outiline –LGD workout estimation
Preliminary assessment
Credit portfolio analysis
Credit Management Process analysis
■ Analysis of the active loan portfolio with a breakdown by:
I. Product
II.Collateral
III.…
■ Interviews with process owners
I. Debt collection
■ Data extractions from identified archives
■ Construction of Development Data mart
■ Estimation of LGD parameters
Gap analysisIdentifying the relevant dimensions of analysis
Identification of Key processes
characteristics Any limitations or
constraints to the estimation process
Pro
ject
ph
ases
Ou
tpu
tA
cti
vit
y
High level description of Preliminary Assessment phase
Modelingframework
■ Defining the methodological framework for calculating LGD in compliance with the BII requirements
Technical meetings on LGD modeling methodologyin line with the process and data characteristics
Data extractions and risk parameters estimation
Improvement expectations
■ Identification of areas for improvement of the LGD estimates
■ Prioritization and planning of future activities
Default definition Discount rate Analysis of
the expenses
Identifying main areas of improvement and gap towards local regulatory requirements framework and international best practice
Data request Presentation of
the results (estimates, performance,.)
Proposed action plan
Downturn LGD
Areas of investigation
s
| 17 |
It allows the use of relevant factors: statistically meaningful model variables
It easily allows the usage of continuous explanatory variables
It provides a measure of the marginal contribution of each variable as well as their interaction
High level project outiline -workout estimation
Preliminary assessment
Credit portfolio analysis
Credit Management Process analysis
■ Analysis of the active loan portfolio with a breakdown by:
I. Product
II.Collateral
III.…
■ Interviews with process owners
I. Debt collection
■ Data extractions from identified archives
■ Construction of Development Data mart
■ Estimation of LGD parameters
Gap analysis Identifying the relevant dimensions of analysis
Identification of Key processes
characteristics Constraints to the
estimation process
Pro
ject
ph
ases
Ou
tpu
tA
cti
vit
y
High level description of Modeling framework phase
Modelingframework
■ Defining the methodological framework for calculating LGD in compliance with the BII requirements
Technical meetings on LGD modeling methodology
Data extractions and risk parameters estimation
Improvement expectations
■ Identification of areas for improvement of the LGD estimates
■ Prioritization and planning of future activities
Traditional/empirical approach
Identifying main areas of improvement and gap towards local regulatory requirements framework and international best practice
Data request Presentation of
the results (estimates, performance,.)
Proposed action plan
Approach to LGD estimation Econometric model
It is based on empirical evidences (descriptive statistics)
It does not give evidence of the statistical significance of adopted risk drivers
It is strongly affected by sample size
| 18 |
High level project outiline -workout estimation
Preliminary assessment
Credit portfolio analysis
Credit Management Process analysis
■ Analysis of the active loan portfolio with a breakdown by:
I. Product
II.Collateral
III.…
■ Interviews with process owners
I. Debt collection
■ Data extractions from identified archives
■ Construction of Development Data mart
■ Estimation of LGD parameters
Gap analysis Identifying the relevant dimensions of analysis
Identification of Key processes
characteristics Any limitations or
constraints to the estimation process
Pro
ject
ph
ases
Ou
tpu
tA
cti
vit
y
High level description of Data extractions and Risk Parameters estimation phase
Modelingframework
■ Defining the methodological framework for calculating LGD in compliance with the BII requirements
Technical meetings on LGD modeling methodologyin line with the process and data characteristics
Data extractions and risk parameters estimation
Improvement expectations
■ Identification of areas for improvement of the LGD estimates
■ Prioritization and planning of future activities
DM Design DM Validation
LGD Estimation
StructuralSpecification
Data Requirements definitions
Data mart building
Data QualityCriteria definitions
CorrectiveActions
Data Aggregation Criteria definition
LGD Workout
VintageCorrection
Final LGD Model
Identifying main areas of improvement and gap towards local regulatory requirements framework and international best practice
Data request Presentation of
the results (estimates, performance,.)
Proposed action plan
Key milestones
| 19 |
High level project outiline -workout estimation
Preliminary assessment
Credit portfolio analysis
Credit Management Process analysis
■ Analysis of the active loan portfolio with a breakdown by:
I. Product
II.Collateral
III.…
■ Interviews with process owners
I. Debt collection
■ Data extractions from identified archives
■ Construction of Development Data mart
■ Estimation of LGD parameters
Gap analysis Identifying the relevant dimensions of analysis
Identification of Key processes
characteristics Any limitations or
constraints to the estimation process
Pro
ject
ph
ases
Ou
tpu
tA
cti
vit
y
High level description of Data extractions and Risk Parameters estimation phase
Modelingframework
■ Defining the methodological framework for calculating LGD in compliance with the BII requirements
Technical meetings on LGD modeling methodologyin line with the process and data characteristics
Data extractions and risk parameters estimation
Improvement expectations
■ Identification of areas for improvement of the LGD estimates
■ Prioritization and planning of future activities
Segments Type of facilityWith/without
collateral/guarantees
Identifying main areas of improvement and gap towards local regulatory requirements framework and international best practice
Data request Presentation of
the results (estimates, performance,.)
Proposed action plan
Segmentation criteria
The LGD model will be differentiated according to any segmentation criteria in order to capture the peculiarities and specific business practices in debt collection management
•Retail•SME•Corporate
•Unsecured•Personal •Mortgages•…
•Committed•Uncommitted• …
| 20 |
AGENDA
Rating system validation
LGD models development
Early warning for risk appetite
| 21 |
Overview
The recent financial crisis has shown that an effective RAF is an useful and relevant way to obtain a good Governance of a financial institution
For this reason national and international regulators are placing greater attention to this topic in order to ensure that there is consistency between the risks actually undertaken and those perceived by decision-making bodies of the Institute
Overview
The RAF has the objective to support the corporate bodies for the decision making in order to increase the awarness about the risks linked to the business model
The financial institutions should develop an effective RAF that is institution-specific and that reflects its business model and organisation, as well as to enable financial institutions to adapt to the changing economic and regulatory environment in order to manage new types of risk
Objective
Capital Risk
Strategy
Shareholders
Rating agencies
Customers
Regulator
Top management
The Risk Appetite Framework (RAF) defines the amount and the type of risks that the financial institution wants to assume according to its ability and to the strategic objectives and business that it has agreed
| 22 |
Key Definitions
The maximum level of risk that the financial institution can assume given its current level of resources before breaching constraints determined by regulatory requirements or other restrictions imposed by the shareholders
Which is the level of risk that the bank can undertake?
dimension 1
dimension 3dimension 4
dimension 2dimension 5
Risk Capacity
Risk Profile
It is the risk actually undertaken, measured in a certain moment
Which is the level of risk that the bank is undertaking at the moment?
Risk Appetite
The aggregate level and types of risk a financial institution is willing to assume within its capacity to achieve its strategic objectives and business plan
Which is the level of risk that the bank wants to undertake?
The RAF is an useful tool that is able to synthesize the risk profile of an Institution. It expresses the capacity (Risk Capacity), the appetite (Risk Appetite) and the actual amount of risk (Risk Profile) undertook both at organization level and both for each individual business unit / type of risk (size)
| 23 |
Risk Appetite Framework
Process to define the Risk Appetite(1)
(1) The process should be linked to the size and the proprety of the financial institution
Methodology definition
Approval process
Risk LimitsReporting
and monitoring
Early warning
1
2
34
5Actors involved• Board• Commitees• Risk
Management• Finance• Business Unit
1. Methodology definition of RA: process to definine the position of the target risk appetite in terms of quality and quantity, based on both internal and external aspects
2. Approval process of RA: determination of the phases and the key players to approve the placement of the risk appetite defined
3. Conversion of RA in Risk Limits: application of the position of the RA in the ordinary management of the institute by establishing limits and specific processes
4. Reporting and monitoring: development and definition, at the overall level, of a system of analysis and communication of trend risks that is specific to business unit and type of risk
5. Early warning: development of a system of risk thresholds and definition of the means to monitor these thresholds, in addition to the definition of the underlying management process
The development of an effective Risk Appetite is an interactive and circular process in order to achieve a continuous improvement of the methodologies, governance processes and operational tools. The Early Warning system represents the main tool useful for monitoring the correct application of the RAF Framework
FOCUS OF THE NEXT SLIDES
| 24 |
Framework
Early Warning Systems have to be indented within the ongoing monitoring of the performing (non delinquent) credits
Anticipating, as much as possible, situations of potential deterioration providing to the managers a solution to prevent the default of the counterparty or limit the damage. (Control of the First Level)
Objectives1
Perimeter2Performing credits at high risk, or credit lines not yet expired/exceed but showing a higher degree of risk (i.e: fault signals) or because of a review by the relationship manager on other performing loans.
Tools3Warning signals and internal behavioral indicators or external data integrated with internal rating (where available) based on statistics and implemented as a decisional tree.
IT4Credit Monitoring solutions and sophisticated systems able to produce a monitoring report connection with a “laboratory” environment; new outsourcing services (EWAAS)
| 25 |
Objectives
• As within any decision-making system, the Early Warning system requires a definition of a target
event to be developed and subsequently measured.
• Since the objective is to anticipate the transition to the default, the target event is designed to
intercept a 'preliminary‘ stage which is made to match with the "deterioration" of the positions,
combining quantitative elements (increased days overrun) with “managerial” elements (worsening
of the Customer position).
• From the operational point of view the target of the "deterioration" of the positions can be
analyzed through the exploration of "roll rate", as shown in the following example:
• Within this framework, Early Warning models’ target type deviates significantly from the definition
of “default” both in terms of events and time horizon considered.
PerformingUnder
monitoring Credit control Past due 90 Past due 180 Ordinary breach Restuctured Credit Severe breach Inconclusive
Performing 96%
Under monitoring 10% 80%
Credit control 75%
t0 statusWorst status over last 6 months
5%
4%
10%
20%
| 26 |
Perimeter
The scope refers to a segmentation of the “Banking book”, well represented (in reverse order of severity)
Handling class DescriptionA. Trial, legal handling Credit collection through legal actions
B. Deteriorated position Substandard loans, past due, restructured
C. Breaches Formally performing, but showing significant breaches (i.e. expired or exceeded credit)
D. Performing high risk Credit lines not yet expired/exceed but showing a higher degree of risk (i.e: fault signals as early warning; classification under observation for other reasons; speculative grade rating; etc.).
E. Performing high risk Credit lines not yet expired/exceed for which has been given rise to a review of the relationship (increase of overdraft due to a prolonged tension in the use, increased maturity, etc.)
F. Performing All other performing credits
Early Warning Sysyetms usually take into account cases D, E and F
| 27 |
• «Performance» definition
• Historical indicators data analysis
• Implementation of a decision tree
• Supplement the decision tree with breaches and Business evaluation
• Grouping breaches in order to prioritize the correction actions to be carried out («Traffic light»)
• Workload measurement and evaluation
• Functional document aimed at supporting the actual implementation
• It’s a shared process with credit expert, sales network and external suppliers
• Drafting a Long List with different priority levels (High, Medium, Low) for each (Corporate, SME and Retail)
• Availability analysis of the internal/external data sources
• Technical analysis support
Acti
vit
ies
Delivera
ble
s
BacktestingModel developmentLong list definition «Traffic light» design Implementation
1 2 3 4 5
• Choosineg an «out of time» sample
• Data Analysis
• Performance measurement and evaluation
Approach – Designing a decision tree
| 28 |
The long list of indicators is formed based on ”groups” which differ in information source used
Sources
GROUP Sources
STAGESInternal "stage" (Handling status - custom classification)
PUBLIC INFORMATIONAll public information, based on their availability on the market
RISK INDICATORS External sources info (i.e. Credit Bureau) INTERNAL INFO Whatever available within the BankBALANCE SHEETS If/where publicly availableCOMPOSED INDEXES Any available index / indicator already computed
| 29 |
Indicators
• Exceeding the granted limit (from Credit Bureau)
When the Customer is in the situation of exceeding a limit granted by a competitor Bank
• Trend of usage for overdrafts
When the Customer tends to use the overdraft often close to its limit
• Persistent breaches
Exceeding a certain ‘%’ of the overdraft limit for more than a ‘X’number of days:
- [a] number of days
- [b] ‘%’ of excess of the overdraft limit
1. “Light” breach – if [a] < 90 days AND [b] <= 3%
2. “Medium” breach – if [a] > 90 days OR [b] > 3%
3. “Heavy” breach – if [a] > 90 days AND [b] > 3%
E X A M P L E
| 30 |
Actions execution and continuous monitoring
Analysis and definition proposal of the handling class, action plan and provisions
1
E X A M P L E
• All operations in the credit monitoring are based on an iterative process, starting from the reports of
early warning or recognition of specific anomalies. They are divided into the following (theoretical)
phases:
Early Warning report / Breaches detection
2
3
Decision about of the handling class, action plan and provisions4
The IT solutions - Design
| 31 |
Integration with other internal
procedures (Core Banking, CRM, …)
Link to other procedures
(Core Banking, CRM, …)
Regular
Monitor
Initial past due iniziale
Past due 30
Past due 90 / 180
Anomalies
Restructured
Breaches
Charge off
Actions
Classification
Ordinary handling
Special Credits
Special Credits
Branch, Regular Credits
Branch, Non-regular Credits
Non-regular Credits
Branch
Branch
Non-regular Credits
Early Warning Credit Monitoring System
Perf
orm
ing
Defa
ult
Segment definition(Retail, SME, Corporate)
Decision processMonitoring
external toolsOther tools
Analysis and definition
E X A M P L E
Action plan Provision
The IT solution – a functional example
| 32 |
Performance measurement
The presence of a laboratory environment, where basic information, indicators, suggested actions, timing and methods of application and quantitative results are historically tracked in order to monitor and correct the Early Warning System, is a pre-condition for the evolution of the system itself.
EW Data Mart
Actions execution and continuous monitoring
Analysis and definition proposal of the handling class, action plan and provisions
1
Early Warning report / Breaches detection
2
3
Decision about of the handling class, action plan and provisions
4
Info out of the process but potentially useful
Provisions/corrections
Effectiveness BenchmarkingPrediction capacity
Simulation New system
The IT solutions – Laboratory
| 33 |
Risk Management Practice
Crif Decision Solutions
Via M. Fantin 1-340131 Bologna
Tel.: + 39 051 4176111Fax.: + 39 051 4176010