Postgraduate Course: Credit Scoring and Data Mining (MATH11040)
Course Outline
| School | School of Mathematics |
College | College of Science and Engineering |
| Course type | Standard |
Availability | Not available to visiting students |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Credits | 10 |
| Home subject area | Mathematics |
Other subject area | Operational Research |
| Course website |
https://info.maths.ed.ac.uk/teaching |
Taught in Gaelic? | No |
| Course description | 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. |
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
| Additional Costs | None |
Course Delivery Information
|
| Delivery period: 2013/14 Block 4 (Sem 2), Not available to visiting students (SS1)
|
Learn enabled: Yes |
Quota: None |
|
Web Timetable |
Web Timetable |
| Course Start Date |
24/02/2014 |
| Breakdown of Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 13,
Seminar/Tutorial Hours 6,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
79 )
|
| Additional Notes |
|
| Breakdown of Assessment Methods (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| No Exam Information |
Summary of Intended Learning Outcomes
- 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 perform standard scorecard monitoring, tracking and validation assessments, understanding the statistical and business implications of the results
- 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('s), managing over-rides, the use of risk based pricing, and compliance with regulations |
Assessment Information
| See 'Breakdown of Assessment Methods' and 'Additional Notes' above. |
Special Arrangements
| None |
Additional Information
| Academic description |
Not entered |
| Syllabus |
Not entered |
| Transferable skills |
Not entered |
| Reading list |
Not entered |
| Study Abroad |
Not entered |
| Study Pattern |
Not entered |
| Keywords | CSDM |
Contacts
| Course organiser | Dr Julian Hall
Tel: (0131 6)50 5075
Email: J.A.J.Hall@ed.ac.uk |
Course secretary | Mrs Frances Reid
Tel: (0131 6)50 4883
Email: f.c.reid@ed.ac.uk |
|
© Copyright 2013 The University of Edinburgh - 13 January 2014 4:40 am
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