Quantification of Credit Risk (Croatian perspective)

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Quantification of Credit Risk(Croatian
perspective)
Stjepan Anic, Dejan DonevErste Steiermärkische
Bank d.d.
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ToC
1. Components of Credit risk
2. Quantification - You can manage what you can
measure
3. First things first - Scoring Rating Models
4. Tasks of a modern risk manager
5. Required Competences
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Risk Components
Risk (two components)
Exposure
Uncertainty
Regulatory acknowledged types of risk
Operational Risk
Market Risk
Credit Risk
Uncertainty
Exposure
Recovery risk
Default risk
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You can manage what you can measure
Credit Risk
Uncertainty
Exposure
Recovery risk
Default risk
LGD k,j f ( k, j ) k 1, ... , p j1, ... ,
q ( p ? no. of collateral types q ? no. of types
of facilities)
EaD f ( i , j ) l 1, ... , r ( r ? no. of
clients )
PDi f i (Rating grade) i 1, ... , n ( n
?number of exposure classes )
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Scoring 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)

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Problem with data

• WARNING !
• 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)

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Scoring Model development
t12 m
t
Loan applications / Annual financial statements
Not defaulted
Binomial event
Defaulted
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Methodology

• 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 were trying to solve

Statistics
Data-bases
Data Mining
Machine learning
Visualisation
IT technology
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Tasks 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 ? Datacollection 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.)

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Required 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

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Four major competences
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All four planets in this Risk Orbit have to
function perfectly, otherwise we could be
facing...
consequences of truly cosmic proportions !
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Thank you for your attention!