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 | Available to all 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 |
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.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | None |
Course Delivery Information
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| Academic year 2015/16, Available to all students (SV1)
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Quota: None |
| Course Start |
Block 4 (Sem 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 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Coursework 100% |
| Feedback |
Not entered |
| No Exam Information |
Learning Outcomes
1. Appreciate the different methodologies involved in developing scorecards, together with their strengths and weaknesses
2. Be familiar with the credit cycle, and with the typical range of consumer credit products on offer
3. 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
4. Be capable of performing perform standard scorecard monitoring, tracking, and validation assessments, understanding the statistical and business implications of the results
5. Be familiar with a range of issues affecting data quality and what can be done to improve data quality
6. 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
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Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | CSc |
Contacts
| Course organiser | Dr Julian Hall
Tel: (0131 6)50 5075
Email: |
Course secretary | Mrs Frances Reid
Tel: (0131 6)50 4883
Email: |
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