Credit Risk Models

4 days 7-10 Nov 2016, London United Kingdom £4,295.00 + VAT* Download brochure Add to basket

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This 4-day course reviews credit models as implemented in major financial institutions while pointing to significant improvements made in light of the financial crisis.

The course covers four building blocks of credit modelling:

  • Internal credit scoring (rating) models
  • Pricing models for credit derivatives and structured credit products
  • Counterparty risk assessment models based on potential future exposure at default
  • Capital allocation models for credit risk, also known as credit portfolio models
  • For each building block, the course dedicates one full training day.

Each block starts with a concept review of the models applied and an assessment on how they fared during the crisis of 2007-2009. Key potential areas of model improvements as well as back / stress testing mechanisms are being discussed in an interactive way.

Course participants will be challenged via live case studies to apply their experiences for improving models. The trainer will moderate the discussions while soliciting creative solutions from the participants and will draw conclusions ? which will be compared against industry best practices.

Credited by GARP - Global Association of Risk Professionals (GARP)

Who should attend


  • Financial Risk Managers
  • Credit Managers
  • Financial Engineers
  • Financial Industry Regulators
  • Auditors
  • Credit Portfolio Managers
  • Asset Managers


We work with a series of expert instructors, please select the course location of interest to review the credentials of who will be delivering the programme.

Dr. Richard Flavell

The course director is a consultant in the financial services industry. Until recently, he was Director of Financial Engineering at Lombard Risk Systems, one of the leading providers of derivative trading systems around the world. In this role he led a team responsible for the mathematical development of Lombard’s derivative trading and risk management systems. At the same time, he also undertook extensive client/product training and consultancy projects.

Prior to his role at Lombard Risk, he was Head of Financial Engineering at ANZ Merchant Bank in London, and was Reader in Finance at The Management School, Imperial College, which is part of the University of London. He has worked with many banks and financial institutions around the world, advising them on their derivative and risk management activities. He has an international reputation for his expertise in swaps, other derivatives and risk management.

He has also published widely in both academic and professional literature, his most recent book on Swaps and other Derivatives was published in December 2009, and he is currently writing a book on bank risk management. His approach to training is structured and practical. He has extensive experience and success in teaching both recent entrants to the derivatives markets and risk management, as well as highly experienced technical experts and market participants.



Hotel in Central London

All  courses are held at four or five star venues in Central London, Zone 1. We strive to provide you with a training environment of the highest quality, to ensure that the whole learning experience exceeds your expectations.

Your training venue will be confirmed by one of our course administrators approximately 3-4 weeks before the course start date.

Related Courses


We can bring this course to your company's office.

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We’ll be here to support you every step of the way. From the initial consultancy through to evaluating the success of the full learning experience. We'll ensure you get the maximum return on your training investment.

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Day 1

Exposure Estimation

•     Starting point: how did traditional credit risk assessment work – and what’s wrong
•     Overview of modern credit risk assessment
•     The development, implementation, maintenance and validation of  Exposure-At-Default models
•     How do banks enter into credit risk? Discussing the various on and off-BS products that expose banks to credit risk
•     Modelling a simple derivative product such as a FX forward
•     Modelling a more complex derivative product such as an Interest Rate swap
•     Aside: the Libor-based model for modelling interest rates
•     Modelling a portfolio of derivative products using simulation
•     Expected Positive Exposure measures for derivatives as requirement under regulation
•     The concept of a netting set
•     Model Risk and Model Validation


Day 2

Probability of Default (PD) Estimation

The Development, Implementation, Maintenance and Validation of Probability of Default Models and Techniques

•     Cohort modelling
•     Including extensions to handle low default portfolios such as:
•     Confidence interval modelling
•     Deutsche Bundesbank/BoE confidence interval models
•     Federal Reserve model for migration duration
•     OCC and other Bayesian approaches
•     Saaty’s AHP models for including subjective estimates

Market Based Methodologies

•     Credit default swaps
•     Non-governmental debt spreads
•     Equity markets (based on Moody’s KMV “distance to default” model)

Day 3

Probability of Default (PD) Estimation (continued)

The Development, Implementation, Maintenance and Validation of Credit Scorecarding Models

•     Some basic accounting and economic principles
•     Maximum likelihood techniques
•     Linear discriminant models
•     Use of subjective data
•     Maximum Expected Utility models
•     Non-linear models
•     Logistic (and probit) regression models
•     Handling multi-collinearity
•     Retail models vs corporate models
•     Categorisation models
•     Adjusting the models for the economic cycle
•     Macro-economic models
•     Regime models

Loss-Given-Default (LGD) Estimation

The Development, Implementation, Maintenance and Validation of Loss-Given-Default Models

•     Distressed cash-flow valuation
•     Selection of the appropriate discount rate
•     Factor models
•     Use of subjective estimation
•     Stochastic LGDs
•     Joint modelling of PDs and LGDs 

Day 4

Credit Portfolio Modelling

Estimation of Credit Portfolio Loss Distributions
•     Basic principles and techniques underpinning Monte Carlo simulation methods
•     Alternative methods for estimating inter-asset correlations, including 1-factor (Vasicek) and multi-factor approaches
•     The development, implementation, maintenance and validation of  default risk Monte Carlo models for constant EADs
•     The development, implementation, maintenance and validation of  default/migration risk models for constant EADs
•     The development, implementation, maintenance and validation of  default/migration risk models for probabilistically estimated EADs
•     The development, implementation, maintenance and validation of  default/migration time-dependent risk models
•     The development, implementation, maintenance and validation of  large-scale approximation models as applied to the retail and SME sectors
•     x VA modelling: CVA, DVA, etc

End of Course

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