Programme in preparation

Risk Management: Credit Risk / Counterparty Risk

in collaboration with the House of Training Quality Circle in Risk Management with ALRiM


The objective of the seminar is to provide the participants with a strong knowledge of key aspects of credit risk assessment with a focus on client creditworthiness, rating and credit models, implementation of a default probability framework for banks.

The participants will get acquainted with the main theoretical foundations of assessment and measurement and with the practical techniques used in dealing with the daily issues facing risk management departments.

By following this seminar, the participants will be able to integrate the learned concepts into real practice in their respective work as the course aims at mixing theoretical and practical aspects of risk management in a systematic way.

Target Group:

Banking professionals with at least 3 years’ experience working in a banking environment, ideally with an exposure to credit processes. Some minimal mathematical proficiency is required to attend day 3.

Location & duration:

in HoT-ATTF partner countries:3 - 4 days (depending if sequential translation or not)


Detailed programme Explode

Day 1

1. Introducing Credit risk

  • Type of Financial Contracts (Loans, Bonds, OTC Derivatives and ForEx)
  • Current vs. Potential Exposure
  • Credit Event (Default)
  • Financial Loss
  • Overview of quantitative tools

2. Assessing Credit Risk

  • Rating Systems
  • Default Probability (PD)
  • Loss Given Default (LGD)
  • Exposure at Default (EAD)

Day 2

3. Measuring Credit Risk (Expected & Unexpected Losses)

  • Economic Capital Measures: Value-at-Risk and Expected Shortfall Metrics
  • Portfolio Approaches: The Role of Correlations in Credit Risk

4. Stress Testing and Scenario Analysis

5. Back Testing Value-at-Risk and Model Calibration

6. Basel Frameworks

Day 3

7. Scoring using expert judgement

8. Revising a PD using new information

9. Considering multiple determinants of default at once

  • Naïve Bayes
  • Logistic regression
  • Algorithmic approaches

10. Incorporating cycles

11. Model validation

12. Model governance


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