(in Polish) Development and management of Credit Risk Models in the banking area

optional courses

As part of the course, students have the opportunity to learn the methods and process of the regulatory and non-regulatory credit risk models development, as well as the methods of their monitoring and validation. The subject matter focuses not only on statistical tools and their applications, but also describes the entire model life cycle in the bank. It is an academic course where knowledge is imparted by practitioners, therefore includes the latest and advanced practices applied in banks. During the course examples would be presented in R and Python.

1. Introduction to risk modeling in the bank.

a. What is a model?

b. What is risk, why should we model it?

2. Introduction to credit risk.

a. What is credit risk?

b. Why measure and model credit risk at all?

c. Does the performance of credit risk models impact a bank’s competitiveness?

d. Types of credit risk models.

3. Regulatory and non-regulatory models in credit risk management.

a. Basel (I,II, III).

b. Differences between AIRB, SA, FIRB.

c. Purpose of calculating capital and ratios (Tier1, Tier 2).

d. IFRS9 related topics.

e. Examples of using non-regulatory models.

4. Model life cycle.

a. Basic elements of model development – initiation, data collection, model development and evaluation, validation and implementation.

b. Does model development end with fitting to data?

c. What technologies can be used for model development and implementation?

d. How to determine which model is the best?

5. Data sources and data preparation.

a. What is default?

b. What is write off?

c. Data merging.

6. Data Quality analysis.

a. Which dimensions should be checked?

b. Types of missing data.

c. Methods for data imputation.

7. PD model development.

a. Target variable.

b. Univariate analysis.

c. Multivariate analysis.

d. Calibration.

8. EAD model development.

9. LGD model development.

10. Model validation.

a. What is it?

b. Purpose and frequency of the model validation.

c. Model validations vs regulatory requirements.

d. Model validation in the context of standards – stability, traceability.

11. Monitoring.

a. Regulatory requirements.

b. Model classification.

c. Model environment.

d. Model performance.

12. Implementation.

a. Characteristic of good implementation.

b. Examples.

1. Loeffler G., Posch P.N. (2011), Credit risk modeling using Excel and VBA, Wiley Finance. 2. Hong Kong Institute of

Bankers (2012), Credit risk management, Wiley, Singapore. 3. Lando D. (2004), Credit risk modeling. Theory and

applications, Princeton University Press, Princeton and Oxford. 4. Vasicek O.A. (2002), The distribution of loan portfolio value, Computer science. 5. BCBS (2005), An Explanatory Note on the Basel II IRB Risk Weight Functions, BIS. 6. Matuszyk A. (2012), Zastosowanie analizy przetrwania w ocenie ryzyka kredytowego klientów indywidualnych, Cedewu, Warszawa. 7. Matuszyk A., Mues C., Thomas LC. (2010), Modelling LGD for unsecured personal loans: Decision tree approach, Journal of the Operational Research Society 61 (3), 393-398.

The students will learn about the importance of credit risk models for the functioning of a commercial bank. They will understand the model development process starting from data preparation, through model estimation, quality assessment, validation, implementation to monitoring. Students will also learn how to conduct a comprehensive credit risk assessment of a portfolio.

KW01, KW02, KW03, KU01, KU02, KU03, KK01, KK02, KK03

All students will be obliged to:

• be present at the classes (according to common University of Warsaw rules)

• pass the test exam