MAT012 Credit Risk Scoring
Spring Semester
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10 credits
Outline Description of Module:
The course aim is to present a comprehensive review of the objectives, methods and practical implementations of credit and behavioural scoring in particular and data mining in general. It involves understanding how large data sets can be used to model customer behaviour and how such data is gathered, stored and interrogated and its use to cluster, segment and score individuals. The aim is to look at the largest application in more detail. Credit scoring is the process of deciding, whether or not to grant or extend a loan. Sophisticated mathematical and statistical models have been developed to assist in such decision problems.
On completion of the module a student should be able to:
Knowledge and Understanding
By the end of this unit, you will be able to
Understand the of basics of data mining
Have knowledge of real application of data mining, including clustering, segmentation and scoring.
Understand statistical and alternative methods of constructing scoring rules.
Understanding how to process data prior to model building.
Ability to assess and monitor a scorecard.
Awareness of current and new applications of credit scoring techniques.
Subject Specific Intellectual (Cognitive) Skills
Work with software to develop credit scoring solutions;
develop a scorecard using very advanced data mining techniques
Transferable (Key/General) Skills
Understand the practical difficulties that arise when implementing scorecards;
Understand the cross-fertilisation potential to other business contexts (e.g. fraud Detection, CRM,marketing).
How the module will be delivered:
The unit is delivered through lectures including group exercises discussions and case studies, reading, personal reflection, and computer laboratory classes.
Skills that will be practised and developed:
Transferable (Key/General) Skills
Understand the practical difficulties that arise when implementing scorecards;
Understand the cross-fertilisation potential to other business contexts (e.g. fraud Detection, CRM,marketing).
How the module will be assessed:
Assessed coursework (100%). This will consist of two parts. The first will be discussion of a number of issues that arise in credit scoring and data mining.
The second will be to build scorecards using SAS / R / Python / Excel for real data. This will include all the aspects of scorecard building- data preparation, variable selection, and coarse classification model building techniques. It will also involve testing the scorecards on holdout samples and calculating the predictions measures for the different scorecards.
Syllabus content:
Introduction to Data mining and Credit scoring
What is data mining? Databases, data warehousing and data management. Objectives of data mining; Origins of credit and credit lending to consumers; judgmental approaches; introduction of credit scoring; philosophical approach to credit scoring. Overview of use of scoring systems; how credit scoring fits into lenders risk assessment process; what data is needed; role of credit scoring consultancies; testing the scorecard; relation with information system; application form; role of credit bureau; overrides and manual interventions; need for monitoring ; relationship with portfolio of bank products.
Statistical Methods for Scorecard Development
Statistical methods in credit scoring and classification methods in data mining; discriminant functions; logistic regression approach; classification trees; non-parametric approaches including nearest neighbours;
Other Credit Scoring Techniques
Mathematical programming and goal programming approaches; neural networks; genetic algorithms and other combinatorial optimisation approaches; expert systems; support vector machines Lab class on using techniques to build scorecard
Practical Issues of Scorecard Performance
Selecting sample; definitions of good and bad; choice of variables; credit bureau data; discarding variables; weights of evidence; coarse classifying continuous variables; chi square measures and information statistics;reject inference; adjusting cut-off scores; over-rules and their effect on the scorecards.
Measuring Scorecard Performance
Hold-out samples, and jack-knifing; bootstrapping; Measuring discrimination- change-over sets; Measuring scorecards –Gini coefficient, ROC curves; Kolmogorov-Smirnov statistic.
Behavioural Scoring and Profit Scoring
Markov Chain models of repayment and usage behaviour; definition of states of Markov chain in repayment behaviour; Mover-stayer and other multi-class models. Profit scoring; variable pricing and adverse selection;, generic scorecards; including economic information in scorecards; Lab class on coarse classifying and variable choice
Survival Analysis Approaches and Customer Lifetime Values
When not if events occur; survival analysis; proportional hazards models, use in profit scoring; application to customer lifetime value
BaselAccord and other Applications of Scoring Methodology
Credit risk modelling; and impact on credit scoring .Debt recovery; credit extension; fraud prevention; provisioning for bad debt; transaction authorization; pre-approval; mortgage scoring; small business scoring; credit reference guarantees. Direct marketing; prisoner release; housing allocation; university admissions. proteomics
General Data Mining Objectives and Algorithms
Task, structure, score function, optimization methods, data management techniques. Clustering, regression, classification and data. Basket analysis, share of wallet. Non credit scoring examples of data mining in business
Essential Reading and Resource List:
Please see Background Reading List for an indicative list.
Background Reading and Resource List:
L.C. Credit Models; Profit, Pricing and Portfolios, OUP, Oxford ( 2009)
L.C.Thomas, J.N.Crook, D.B.Edelman, Credit Scoring and its Applications, SIAM Press, Philadelphia, (2002)
H.McNab, A Wynn, Principles and Practice of Consumer Credit Risk Management, CIB Publishing, Canterbury (2000)
L.C.Thomas, J.N.Crook, D.B.Edelman, Readings in Credit Scoring , OUP, Oxford (2004)
E. Mays. Credit Scoring for Risk managers, South Western, Mason, (2004)
D.M.Hand, H.Mannila, P.Smyth, Principles of Data mining, MIT Press ( 2001)
N. Siddiqi Credit Risk Scorecards, Wiley/SAS (2006)
D.M.Hand, H.Mannila, P.Smyth, Principles of Data mining, MIT Press ( 2001)
N. Siddiqi Credit Risk Scorecards, Wiley/SAS (2006)
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