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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2018/2019

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DRPS : Course Catalogue : School of Mathematics : Mathematics

Postgraduate Course: Credit Scoring (MATH11148)

Course Outline
SchoolSchool of Mathematics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryCredit 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 None
Information for Visiting Students
Pre-requisitesNone
High Demand Course? Yes
Course Delivery Information
Academic year 2018/19, Available to all students (SV1) Quota:  126
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
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
Reading List
None
Additional Information
Graduate Attributes and Skills Not entered
KeywordsCSc
Contacts
Course organiserDr Joerg Kalcsics
Tel: (0131 6)50 5953
Email: Joerg.Kalcsics@ed.ac.uk
Course secretaryMiss Gemma Aitchison
Tel: (0131 6)50 9268
Email: Gemma.Aitchison@ed.ac.uk
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