Quantification of Credit Risk (Croatian perspective) Stjepan Anić, Dejan Donev Erste &...
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Transcript of Quantification of Credit Risk (Croatian perspective) Stjepan Anić, Dejan Donev Erste &...
Quantification of Credit Risk
(Croatian perspective) Quantification of Credit Risk
(Croatian perspective)
Stjepan Anić, Dejan DonevErste & Steiermärkische Bank d.d.
Stjepan Anić, Dejan DonevErste & Steiermärkische Bank d.d.
ToCToC
1. Components of Credit risk1. Components of Credit risk
2. Quantification - You can manage what you can measure2. Quantification - You can manage what you can measure
3. First thing’s first - Scoring & Rating Models3. First thing’s first - Scoring & Rating Models
4. Tasks of a modern risk manager4. Tasks of a modern risk manager
5. Required Competences5. Required Competences
Risk ComponentsRisk Components
Risk
(two components)
ExposureUncertainty
Market RiskCredit Risk Operational Risk
Regulatory acknowledged types of risk
Uncertainty
Default risk Recovery risk
Exposure
You can manage what you can measureYou can manage what you can measure
Credit Risk
Uncertainty
Default risk Recovery risk
Exposure
PDi = f i (Rating grade)
i = 1, ... , n
( n number of exposure classes )
LGD k,j= f ( k, j )
k = 1, ... , p; j=1, ... , q
( p no. of collateral types
q no. of types of facilities)
EaD = f ( i , j )
l = 1, ... , r
( r no. of clients )
Scoring & Rating ModelsScoring & Rating Models
• Credit quality of a client is analyzed, modeled and ranked
• Credit Scoring Transformation of input variables describing banks’ client in numbers, sum of which (credit score) gives numeric estimate of his credit quality– Privates socio-demographic data– Corporates financial ratios
• Credit Rating grouping of score bins (plus some other things)
• Predictive aspect of score/rating forecast default tendency of a client within the one year horizon (PD scoring/rating)
CR spectrum
INput variables
Rating migration
Problem with dataProblem with dataWARNING ! Experience shows that many problems emerge from unsatisfactory quality and
availability of data Models are as good and accurate as are the data on which they are developed Time needed for preparation of raw data for the purposes of modeling is usually
dramatically underestimated (during the phase of project planning)
Scoring Model developmentScoring Model development
Defaulted
Loan applications
/Annual
financial statements
Not defaulted
Binomial
event
t t+12 m
MethodologyMethodology
Data mining Techniques used to find patterns and relations within the data Proper usage of DM techniques for model building requires knowledge
about business problem we’re trying to solve
Data Mining
Data-bases Statistics
VisualisationIT technology
Machine learning
Tasks of a modern risk managerTasks of a modern risk manager IDENTIFICATION OF RISK RELEVANT INFORMATION
creating a list of necessary RM measures and procedures for all types of products and clients
PROPER RECORDING OF IDENTIFIED INFORMATION Centralized Risk DWH Data–collection in hands of people which understand the data and their usage
CALCULATION OF RISK PARAMETERS transformation of recorded info into prediction of possible losses (construction of a probability of loss distribution)
INTERPRETATION AND USAGE OF RESULTS RM must insure that resulting risk parameters (PD, EL, CapReq, etc.) are used throughout the bank in a consistent manner (loan decisioning, portfolio mngmt, planning, provisioning, pricing, etc.)
Loss Dist
RiskCapital
Required CompetencesRequired Competences TECHNICAL EXPERTISE IT competences (knowing how to retrieve
data from data-bases, SQL, basic programming skills - VBA)
METHODOLOGICAL EXPERTISE skills in quantitative analytical modeling (mathematical and statistical modeling, econometrics) and skills in predictive data-mining (SAS, MATLAB, SPSS, etc.)
BUSINESS EXPERTISE knowing the business (banking & finance, risk management, CRM, etc.)
ANALYTICAL (AND ABSTRACT THINKING) MINDSET can transform business problems into abstract terms and solve them like mathematical problems in algorithmic form
MODERN RM ENVIRONMENT = CROSS-FUNCTIONAL TEAMS
Four major competencesFour major competences
AnalyticalAnalyticalexpertiseexpertise Technical Technical
expertiseexpertise
MethodologicalMethodologicalexpertiseexpertiseBusinessBusiness
expertiseexpertise
All All fourfour planets in this Risk planets in this Risk Orbit have to function Orbit have to function perfectly, otherwise perfectly, otherwise we we could be facingcould be facing......consequences consequences of truly cosmic of truly cosmic proportions !proportions !
Thank you for your attention!Thank you for your attention!Thank you for your attention!Thank you for your attention!