1. Quantification of Credit Risk (Croatian perspective) Stjepan Anić, Dejan Donev Erste & Steiermärkische Bank d.d.

2. ToC 1. Components of Credit risk 2. Quantification - You can manage what you can measure 3. First thing’s first - Scoring & Rating Models 4. Tasks of a modern risk manager 5. Required Competences

3. Risk Components Risk (two components) ExposureUncertainty Market Risk Credit Risk Operational Risk Regulatory acknowledged types of risk Uncertainty Default risk Recovery risk Exposure

4. You can manage what you can measure Credit Risk Uncertainty Default risk Recovery risk Exposure PD i = 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 )

5. 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)

6. 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)

7. Scoring Model development Defaulted Loan applications / Annual financial statements Not defaulted Binomial event t t+12 m

8. 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 we’re trying to solve Data Mining Data-bases Statistics Visualisation IT technology Machine learning

9. 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  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.)

10. 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

11. Four major competences Analyticalexpertise Technicalexpertise Methodologicalexpertise Businessexpertise

12. All four planets in this Risk Orbit have to function perfectly, otherwise we could be facing... consequences of truly cosmic proportions !

13. Thank you for your attention!