The course aim is to present a comprehensive review of the objectives, methods and practical implementations of consumer 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. Credit scoring is the process of deciding, whether or not to grant or extend a credit product by a financial lender. Sophisticated mathematical and statistical models have been developed to assist in such decision-making activities.

• Work with statistical software to develop credit scoring solutions;
• Develop a scorecard using advanced data mining techniques;
• Critique how financial firms such as mortgage lenders make business decisions based on credit scoring techniques;
• Summarise improvements in regulating credit risk by financial regulatory bodies;
• Detail practical difficulties that arise when implementing scorecards;
• Identify cross-fertilisation potential to other business contexts (e.g. fraud detection, CRM, marketing).

You will be guided through learning activities appropriate to your module, which may include:

• Weekly face to face classes (e.g. labs, lectures, exercise classes)
• Electronic resources to support the learning (e.g. videos, exercise sheets, lecture notes, quizzes)

Students are also expected to undertake at least 150 hours of self-guided study throughout the duration of the module, including preparation of formative assessments.

Subject Specific Intellectual (Cognitive) Skills:

• Prepare data prior to model building;
• Apply supervised learning algorithms to constructing scoring rules;
• Assess scorecard performance;
• Appraise new applications of credit scoring techniques.

Transferable (Key/General) Skills:

• Express complex technical details clearly in written form;
• Detail research to support written arguments;
• Demonstrate effective time-management.

1. Introduction to Data mining and Credit scoring
2. Statistical Methods for Scorecard Development
3. Practical Issues of Scorecard Performance
4. Measuring Scorecard Performance
5. Behavioural Scoring and Profit Scoring
6. Survival Analysis Approaches
7. Basel Accord and other Applications of Scoring Methodology