The University of Southampton
Courses

MANG6054 Credit Scoring and Data Mining

Module Overview

The module will start by defining the concept of Knowledge Discovery in Data (KDD) as consisting of three steps: data pre-processing, data mining and post-processing. Next, we will zoom into the data mining step and distinguish two types of data mining: descriptive data mining (e.g. clustering, association and sequence rules) and predictive data mining (e.g. regression and classification). The module will then illustrate how KDD can be successfully used to develop credit scoring applications, where the aim is to distinguish good customers from bad customers (defaulters) given their characteristics. The importance of developing good credit scoring models will be highlighted in the context of the Basel II and III guidelines. The theoretical concepts will be illustrated using real-life credit scoring cases and the SAS Enterprise Miner software.

Aims and Objectives

Module Aims

The aim of this module is to provide you with knowledge of Credit Scoring and Data Mining techniques and approaches to develop your knowledge of industry practices in this sector.

Learning Outcomes

Knowledge and Understanding

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

  • Understand the potential of KDD and data mining for developing scorecards
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)

Syllabus

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

Learning and Teaching

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.

TypeHours
Teaching12
Independent Study63
Total study time75

Resources & Reading list

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

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

Thomas, L.C. (2009). Consumer Credit Models: Pricing, Profit, and Portfolios. 

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

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

Anderson, R. (2007). The Credit Scoring Toolkit. 

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

Assessment

Formative

Set exercises - non-exam

Summative

MethodPercentage contribution
Coursework  (2000 words) 100%

Repeat

MethodPercentage contribution
Coursework  (2000 words) 100%

Referral

MethodPercentage contribution
Coursework  (2000 words) 100%

Repeat Information

Repeat type: Internal & External

Costs

Costs associated with this module

Students are responsible for meeting the cost of essential textbooks, and of producing such essays, assignments, laboratory reports and dissertations as are required to fulfil the academic requirements for each programme of study.

In addition to this, students registered for this module typically also have to pay for:

Textbooks

Recommended texts for this module may be available in limited supply in the University Library and students may wish to purchase the core/recommended text as appropriate.

Please also ensure you read the section on additional costs in the University’s Fees, Charges and Expenses Regulations in the University Calendar available at www.calendar.soton.ac.uk.

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