Basel II/III Retail Credit Risk Modelling— Building Models for Risk Management & Basel II/III

Retail Credit Risk Modelling
All banks and banking groups are expected to adopt the basic approaches for the computation of capital requirements for credit risk, market risk and operational risk.

Under Basel II/III, capital adequacy generally hinges around the proper estimation of Basel II/III risk parameters: PD, LGD, and EAD. These parameters are used on one hand as inputs to credit portfolio models, and on the other hand, to compute risk weighted assets and hence, regulatory capital. At the same time, they are also inputs into a bank’s ICAAP.


Credit risk continues to constitute the greatest challenge to banks, financial services providers and regulators worldwide. The modelling of Retail credit risk therefore becomes a necessary and obligatory process for every bank. Under Basel II/III, banks are encouraged to have own internal Modelling units. Equipping staff with the necessary modelling skills goes a long way in helping firms save money and create value in the staff members driving the Basel II/III project.

Whether required for the Standardized or Advanced Internal Ratings Based approaches or to give your firm a competitive advantage in the management of credit risk, delegates will gain an excellent footing in this specialist field. This course is designed to equip participants with the knowledge and techniques to enable them to build credit risk models within their organizations with minimal or no help.

This is not an academic theory course but a hands-on workshop packed with Practical Exercises to cement YOUR understanding and develop skills in building models towards Basel II/III compliance.. 

After completion of the course, you will have sound knowledge and understanding of the various techniques and approaches for Retail Credit Risk Modelling.

You will LEARN HOW TO:

  • Select suitable techniques to build models for a variety of retail and wholesale portfolios.
  • Apply advanced techniques such as the Logistic regression to model PD.
  • Document credit risk models to Basel II/III standards
  • Calculate various metrics for model validation and monitoring
Ideally, you must bring your own laptop with Microsoft Excel.
The training course is interactive in nature, proactive, pragmatic, action-based, non-theoretical and non-academic. We will do Case Studies based on Industry-specific examples. You will also receive a package of detailed notes (for your reference after the workshop).


The course is designed to benefit and empower the following professionals: Credit Risk Analysts and Loan officers  |  Model Developers   |  Model Validators  |  Internal Auditors  |  Model Risk Auditors  |  Financial regulators  |  Bank Supervisors or Central Bank Personnel  |  Retail & Other Finance personnel  |  Financial and Investment analysts  | Executives and Managers  |  Professionals in financial services industry   |  Interested parties


This Retail Credit Risk Modelling workshop covers the following concepts:

  • Overview of Retail Credit Risk
  • The Basel II Pillars & Basel II Compliance
  • Retail versus Wholesale
  • Credit Risk Modelling
  • Probability of Default (PD)
  • Loss Given Default (LGD)
  • Exposure at Default (EAD)

1. Overview of Basel II Retail Credit Risk Modelling and Models

  • Definition of Credit Risk
  • Wholesale versus Retail Models
  • Overview of the retail portfolio
  • The modelling process – A to Z

2. Pillar 1: Minimum Capital Requirements

  • Credit Risk – Approaches to measure credit risk
  • The Standardized Approach – The Simple and Comprehensive Approaches
  • Credit Risk – The 2 Internal Ratings-Based Approaches (IRB)
  • Expected and Unexpected Loss – BIS
  • IRB – PD, LGD, EAD, M, K
  • Examples of Capital Requirements

3. PD Modelling

  • The Scoring Concept
  • PD Estimation Techniques
  • The Logit Model
  • Case Study: Building a Retail PD and a Retail Credit Rating Model
  • PD Model Validation – The Kolmogorov-Smirnov Test

4. LGD Modelling

  • Modelling Loss Given Default (LGD)
  • Key drivers of LGD
  • Estimating Recovery Rates
  • Modelling LGD using regression
  • A Mortality-based approach to estimate recovery rates – IRB
  • Case Study: Building an LGD model for a particular portfolio
  • LGD Model Validation

5.    EAD Modelling

  • Estimating credit conversion factors (CCF)
  • Risk drivers for CCF
  • Estimating Loan Equivalents (LEQ)
  • Risk drivers for LEQ
  • Case Study: Building an EAD model for a particular portfolio
  • EAD Model Validation

6.    Putting Everything Together

  • Retail Expected Loss calculation
  • PIT and TTC PD, LGD and EAD
  • Capital Requirements Calculations
  • Risk Weighted Assets Calculations
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