Join this advanced course in order to find out how leading financial institutions worldwide are solving the challenge of building effective and high-performing credit scoring systems in the context of the recently put forward Basel II requirements.
Learn how you will be able to influence profitability on the long run by:
- Improving quality of your portfolio by selecting good customers and improving accuracy of measuring credit risk
- Improving risk management processes
- Setting foundations for risk based pricing
- mproving estimation of capital requirements
- Improving credit rating of your institution
It is the purpose of this seminar to elaborate on all steps ranging from data preprocessing to model implementation, and illustrate how they can be efficiently automated.
Even though credit scoring and Basel II initiatives are already in place at your institution you will benefit from this seminar since it is bringing to you latest developments, best practices and future directions in risk management/Basel II.
The seminar aims at providing a sound mix of both theoretical, technical insights as well as practical implementation details, illustrated by several real-life cases. It will be highly interactively organised.
Quotes from Slovenian and Croatian attendees from previous courses:
'Bart Baesens, lectures can be described as well-chosen mix of practical experience and academic knowledge in the field of credit scoring'
Janez Barle, Ph.D., Senior Adviser Risk Management, Nova ljubljanska banka
‘A seminar offers deep insight into fine fabric of Basel II with high level of understandability and establishes direct links between theoretical principles and practical implementation issues. Lecturer's professionalism and competency are at a very high level giving the seminar additional value.’
Matija Birov, Basel II Project Manager, Privredna banka Zagreb
'Bart Baesens makes complex things become very clear, simple and understandable what is the greatest value of the seminar. A true guideline for the best practice implementation of credit risk models, applicable in many other areas as well. The best seminar I've attended so far.'
Tomislav Grebenar, Leading Risk Management Specialist, Zagrebačka banka
‘As an introduction to the logic, methods and mathematics of the contemporary credit risk models and their implication in the Basel II context, the seminar succeeds admirably. Baesens manages to make even some advanced statistical concepts more readily accessible to practitioners.’
Marin Bušić, Risk Specialist, Zagrebačka banka
‘The content of the seminar was well structured beginning from very basic ideas about Basel II and ending up with very complex and new methods and models for developing different input variables for capital requirements calculation. Every issue was explained in details, well presented and repeated more times if necessary until it was clear to every participant. Presenter was very skilled, had good theoretical knowledge and experience in models development at different banks.’
Risk Specialist, Central bank
Lecturer
Prof. dr. Bart Baesens is assistant professor (Lecturer) at the School of Management from the University of Southampton. His research focuses on the use of data mining and machine learning techniques for credit scoring and customer relationship management (CRM) applications. His findings have been published in various journals and presented at international conferences.
Aims and Scope
In this advanced course, we start with providing an overview of all issues and difficulties that arise when modeling loss given default (LGD) and exposure at default (EAD). We also elaborate on how to do validation, backtesting and stress testing. We then discuss some recent techniques that have been developed for PD, LGD and EAD modeling in the context of the Basel II regulation. More specifically, we will discuss neural networks, support vector machines and Bayesian probabilistic network classifiers. We also discuss how survival analysis may be used to do profit scoring and risk based pricing. The course aims at providing a sound mix of both theoretical, technical insights as well as practical implementation details, illustrated by several real-life cases. It will be highly interactively organised. The target audience consists of people who are involved into building scoring systems (e.g. for Basel II) and/or are responsible for monitoring their behaviour and performance.
Prerequisites
The course assumes that the participants have the following background knowledge:
- Basic implications of the Basel II Capital Accord
- Difference between Application Scoring/Behavioural Scoring/Profit Scoring
- Preprocessing for credit scoring (weights of evidence, outliers, missing values, coarse classification)
- Know how to develop scorecards using logistic regression
- Setting cut-offs; dealing with reject inference
- Measuring scorecard performance
Course Outline
A Review of Basel II
- New developments in the Basel II Capital Accord
- A brief review of PD modeling
- Portfolio models for credit risk
- The Basel II capital requirement formula’s
Modelling LGD and EAD
- Modelling Loss Given Default (LGD)
- Defining LGD
- Measuring collateral
- Workout approach
- Market Approach
- Collection scoring
- Time weighted versus default weighted versus exposured weighted LGD
- Choosing the discount factor and the workout period
- Economic downturn LGD
- Modelling LGD using segmentation
- Shaping the Beta distribution for LGD
- Risk Drivers for LGD
- Modelling LGD using regression
- Modelling Exposure at Default (EAD)
- Estimating credit conversion factors (CCFs)
- Cohort/Fixed time horizon/Momentum approach for CCF
- Risk drivers for CCF
- CAP Curves for LGD and CCF
- Calibrating PD/LGD/CCF
- Correlations between PD, LGD and EAD
- Calculating expected loss (EL)
- Measuring PD, LGD and EAD at the portfolio level
Validating and stress testing PD, LGD and EAD models
- Validating PD, LGD and EAD models
- Quality Control
- Quantitative versus Qualitative validation
- Use testing
- Through-The-Cycle (TTC) versus Point-In-Time (PIT) validation
- Backtesting for PD, LGD and EAD
- Traffic Light Indicator Approach
- Backtesting action plans
- Stress testing for PD, LGD and EAD
- Static versus Dynamic stress stesting
- Correlated Trend Analysis
- Monitoring PD, LGD and EAD models
- Segmenting PD, LGD and EAD models
- Benchmarking
- Internal versus External benchmarking
- Kendall’s tau and Gamma for benchmarking
- Scorecard management
- Low Default Portfolios (LDPs): implementation and validation
- Value-at-risk (VaR) models
- The Merton/Vasicek model for calculating the regulatory capital
New techniques to develop PD, LGD and EAD models for Basel II
- A brief review of traditional techniques for scorecard development
- Neural networks
- The neuron model
- Multilayer perceptrons (MLPs)
- Training an MLP
- Support Vector Machines
- The SVM classification model
- Building scorecards using SVMs
- Real life case study: Using logistic regression and support vector machines to develop a country rating system.
Survival analysis for profit scoring
- Survival analysis for developing customer lifetime models
- The censoring problem
- Survival curves versus hazard curves
- Kaplan Meier analysis
- Proportional hazards regression
- Using survival analysis for LGD modeling and profit scoring
- Risk Based Pricing
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