Postgraduate Course: Credit Risk Management (CMSE11122)
Course Outline
| School | Business School |
College | College of Humanities and Social Science |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Available to all students |
| SCQF Credits | 15 |
ECTS Credits | 7.5 |
| Summary | This course introduces students to theory and practice in credit risk management. Students will consider the application of credit scoring and the methods for credit scoring using scorecards. In particular, students will learn certain techniques required by a lender for effective loan management and for compliance with capital requirement regulations.
This course build on students' knowledge gained in the core courses during Semester 1 of the programme, therefore complementing the other courses and minimising overlap of materials.
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| Course description |
This course builds upon core courses of the programmes. The course introduces basic concepts and techniques of risk assessment and risk management in consumer credit. The course demonstrates major forms of risk modelling which retail financial lenders experience. Credit risk is considerably topical given the difficulties throughout the world economies that were precipitated by excessive lending to high risk borrowers.
The teaching objectives are to teach the students the theoretical background and practical implementation of risk management in retail credit risk. The course will teach the application of credit scoring and the methods for credit scoring using scorecards.
This course introduces students to the theory and practice in credit risk management. Students will consider the application of credit scoring and the methods for credit scoring using scorecards. In particular, students will learn certain techniques required by a lender for effective loan management and for compliance with capital requirement regulations.
This course relies on the knowledge students gain in the core courses during Semester 1 of their programme, therefore complementing the other courses and minimising the overlap of material.
Syllabus
Stages in Scorecard Construction
Data Preparation and Different Types of Models.
Measuring Performance
Linear Regression and Discriminant Analysis
Logistic Regression
Model Implementation
Practical Issues
Monitoring and Tracking
Student Learning Experience
The course will be taught by lectures, including 5 sessions based in computer laboratories. Each lecture will be of two hours in length with a break between the first and second hour.
Beyond the timetabled teaching, students are expected to undertake independent study activities such as reading and completing numerical and computer exercises.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | For Business School PG students on MSc in Marketing & Business Analysis, MSc in Banking & Risk Management, MSc in Accounting & Finance, MSc in Finance & Investment, MSc in Financial Management. |
Information for Visiting Students
| Pre-requisites | Business School Postgraduate Students Only |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2015/16, Available to all students (SV1)
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Quota: 80 |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
150
(
Lecture Hours 20,
Supervised Practical/Workshop/Studio Hours 15,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 3,
Directed Learning and Independent Learning Hours
110 )
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| Assessment (Further Info) |
Written Exam
100 %,
Coursework
0 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Assessment of this course is through an exam (weighted 100%). |
| Feedback |
Feedback on formative assessed work will be provided within 15 working days of submission, or in time to be of use in subsequent assessments within the course, whichever is sooner. Summative marks will be returned on a published timetable, which has been made clear to students at the start of the academic year.
Students may ask questions in lectures to assess their knowledge.
Feedback on exercises will be provided on an individual basis depending on each individual question or difficulty. Generic solutions to computer-based tasks and example problems will be provided. Feedback will also comprise generic feedback on the exam. |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
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| Main Exam Diet S2 (April/May) | Credit Risk Management | 2:00 | |
Learning Outcomes
On completion of this course, the student will be able to:
- Estimate, implement and evaluate credit risk assessment methods for individual loans to corporate and retail borrowers
- Understand and critically discuss methods of monitoring and tracking model performance;
- Understand and critically discuss methods of measuring and assessing the credit risk of portfolios of loans
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Reading List
| Thomas, L., Edelman, D. and Crook, J. (2002) Credit Scoring and its Applications. SIAM: Philadelphia. |
Additional Information
| Graduate Attributes and Skills |
Cognitive Skills:
After completing this course, students should gain:
- the ability to quantitatively interpret and understand the methodology and outputs of classification algorithms;
- the ability to quantitatively evaluate the predictive performance of classification algorithms;
- the ability to select appropriate quantitative methods to model repayment performance by individuals and companies.
Subject Specific Skills:
After completing this course, students will gain:
- a basic ability to model the probability of default for a sample of loans;
- the ability to assess the performance of credit scoring models;
- the ability to understand, speak and write the language of credit risk analysis.
By the end of the course students will be expected to:
- be able to communicate technically complex issues coherently and precisely;
- be able to advance reasoned and factually supported arguments in written work;
- have acquired lifelong learning skills and personal development so as to be able to work with self-direction;
- have skills in time management and prioritisation. |
| Keywords | Mark-CRM |
Contacts
| Course organiser | Dr Galina Andreeva
Tel: (0131 6)51 3293
Email: |
Course secretary | Miss Ashley Harper
Tel: (0131 6)51 5671
Email: |
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