Credit Scoring and Data Mining

Learning Outcomes

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

• work with software to develop credit scoring solutions; develop a scorecard using data mining techniques.

Transferable and Generic Skills

Having successfully completed this module you will be able to:

• critically analyse practical difficulties that arise when implementing scorecards; understand the cross- fertilisation potential to other business contexts (e.g. fraud detection, CRM).

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

• the potential of KDD and data mining for developing scorecards.

Introduction:

• Knowledge Discovery in Data
• The KDD process model
• Descriptive versus predictive data mining
• Credit scoring: problem statement, origins and objectives
• The Basel II and III regulations
• Risk management
• Consumer credit scoring, Behavioural Scoring, Collection Scoring, Bankruptcy Prediction
• Risk Based Pricing (customization of credit products)
• Customised scorecards versus generic scorecards
• Developing scorecards

Data pre-processing:

• Selecting the sample
• Segmentation
• Example variables needed for application and behavioural scoring
• Oversampling versus Undersampling
• Credit scoring characteristics
• Application form characteristics
• Credit bureau characteristics
• Reject inference
• Definitions of good and bad
• Binary versus three-way classification (good, bad, and indeterminate)
• Outlier detection
• Missing values
• Nominal variables versus Ordinal variables

Data mining:

• Basic concepts of classification
• Classification techniques (logistic regression, decision trees, neural networks)
• Overfitting versus generalisation
• Input selection (Filters, Wrappers …)
• Setting the cut-off
• Measuring scorecard performance (ROC curves, Lift, Gini …)

Post processing:

• Reporting
• Strategy curve
• Profit scoring
• Recalibrating scorecards
• Tracking scorecards

Teaching and learning methods

The module is delivered through pre-course reading and lectures. The various concepts will be illustrated using real-life credit scoring data and software. In addition there will be some in-lecture exercises.

Resources & Reading list

Textbooks

Hastie, T., Tibshirani, R., and Friedman, J (2013). The Elements of Statistical Learning. NJ USA: Springer.

Baesens, B., Rosch, D., Scheule, H (2016). Credit Risk Analytics: Measurement Techniques, Applications, and Examples in SAS. NJ, USA: Wiley.

Thomas, L.C., Edelman, D.B. and Crook, J.N. (2002). Credit Scoring and Its Applications. Philadelphia, PA: SIAM.

Thomas, L.C. (2009). Consumer Credit Models: Pricing, Profit, and Portfolios. New York: Oxford University Press.

Baesens, B. ( (2014). Analytics in a Big Data World: The Essential Guide to Data Science and its Applications. Hoboken, NJ: Wiley.

Anderson, R. (2007). The Credit Scoring Toolkit. New York: Oxford University Press.

Van Gestel, T. and Baesens, B. (2009). Credit Risk Management. Basic concepts: financial risk components, rating analysis, models, economic and regulatory capital. New York: Oxford University Press.

Formative

Formative assessment description

Summative

Summative assessment description

Referral

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Repeat

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Repeat Information

Repeat type: Internal & External