Credit Scoring and Data Mining

Learning Outcomes

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).

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.

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

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

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

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.

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.

Formative

This is how we’ll give you feedback as you are learning. It is not a formal test or exam.

Summative

This is how we’ll formally assess what you have learned in this module.

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.

Repeat Information

Repeat type: Internal & External