Postgraduate Course: Credit Scoring (MATH11148)
Course Outline
| School | School of Mathematics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | Credit context - lending money, credit products, credit cycle. Scorecard Development - available data, variable refinement, variable selection, reject inference, segmentation, scorecard measurement. Modelling - statistical approaches (clustering, discrimination, regression); non statistical approaches, including neural networks and genetic algorithms. Scorecard Implementation - setting the cut-off's, monitoring, tracking and validation, over-ride management, scorecard adjustment. Difference across the credit cycle - application scoring, behavioural scoring, etc. Related topics - regulation, risk based pricing, use of Markov Models in credit. |
| Course description |
See summary description.
|
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
Students MUST have passed:
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | Only for students on School of Mathematics PGT programmes, and students on the Advanced Technology for Financial Computing MSc. |
Course Delivery Information
|
| Academic year 2025/26, Not available to visiting students (SS1)
|
Quota: 0 |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 13,
Supervised Practical/Workshop/Studio Hours 6,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
79 )
|
| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Coursework 100% |
| Feedback |
Not entered |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Appreciate the different methodologies involved in developing scorecards, together with their strengths and weaknesses.
- Be familiar with the credit cycle, and with the typical range of consumer credit products on offer.
- Have understood and developed many of the techniques that are used at stages in the scorecard development process such as coarse and fine classing, variable selection, reject inference and fine tuning.
- Be capable of performing standard scorecard monitoring, tracking and validation assessments, understanding the statistical and business implications of the results, and be familiar with a range of issues affecting data quality and what can be done to improve data quality.
- Be able to support a lender in a variety of practical business issues and decisions in credit scoring, such as setting the scorecard cut-off, managing over-rides, the use of risk based pricing, and compliance with regulations.
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Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | CSc |
Contacts
| Course organiser | Dr Skarleth Carrales Escobedo
Tel:
Email: mcarrale@exseed.ed.ac.uk |
Course secretary | Miss Gemma Aitchison
Tel: (0131 6)50 9268
Email: Gemma.Aitchison@ed.ac.uk |
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