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SAS Italy Milan Training
Center / 24 - 26 September 2008 |
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Bart Baesens
School of Management - University of Southampton
Department of Applied Economic Sciences / K.U.Leuven |
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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. |
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| 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
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| Lecturer: Prof. 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. |
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Course
Outline
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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 exposure 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
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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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.
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Course Infos
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THE COURSE IS HELD IN
ENGLISH
Course venue: SAS Italy Milan Training Center
- Via C.Darwin 20/22, 20143 Milan. Details...
Course price: EUR 1.800,00 + VAT
(20%) per
participant; price include printed course materials and coffee-break.
10% discount
off is available for the second (and further) participant from
the same organization.
Payment Terms: The course fee must be paid
in full prior to the course start date (24 September). Cancellation
of Courses by SAS SAS reserves the right to cancel the course
at any time without liability. In these circumstances
delegates will be refund of course fees paid.
Deadline of the application:5 September.
Cancelation is possible at the latest: 12 September. In case
of a later
cancelation, 100% of the course fee will
be
invoiced.
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Bookings
To
book this course please call us on +39
02 831 341
Course booking is confirmed once the following booking
form has been completed and received by SAS Italy
office via fax (+39 02 8313 4425).
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