Credit Scoring and Data Mining (P00702)

? Credit Points : 10 ? SCQF Level : 11 ? Acronym : MAT-P-CSDM

Large scale databases - data warehouse and data archives; statistical approaches (clustering, discrimination, regression); non statistical approaches, including neural networks and genetic algorithms; commercial software; applications such as clustering, segmenting and scoring. Introduction to credit scoring. Setting up a scoring system. Statistical techniques used in credit scoring. Other approaches to credit scoring. Use of behavioural scoring. Techniques used in behavioural scoring systems. Monitoring and updating scoring systems. Developments in scoring systems.

Entry Requirements

? Pre-requisites : PGs only

Subject Areas

Home subject area

Operational Research, (School of Mathematics, Schedule P)

Delivery Information

? Normal year taken : Postgraduate

? Delivery Period : Block 4 only

? Contact Teaching Time : 2 hour(s) per week for 10 weeks

Summary of Intended Learning Outcomes

Understanding of 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. Understanding of real life application of data mining, including clustering, segmentation and scoring.