Postgraduate Course: Credit Risk Management (CMSE11122)
||College||College of Arts, Humanities and Social Sciences
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
||Availability||Not available to visiting students
|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 builds on students' knowledge gained in the core courses during Semester 1 of the programme, therefore complementing the other courses and minimising overlap of materials.
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.
Entry Requirements (not applicable to Visiting Students)
||Other requirements|| Students enrolling on this course should have a knowledge of SAS software, and have taken a course in Statistics.
Course Delivery Information
|Academic year 2022/23, Available to all students (SV1)
|Learning and Teaching activities (Further Info)
Lecture Hours 12,
Seminar/Tutorial Hours 6,
Programme Level Learning and Teaching Hours 3,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
||50% coursework (group) - assesses all course Learning Outcomes
50% coursework (individual) - assesses all course Learning Outcomes
||Formative feedback: Students should ask questions in classes to assess their knowledge.
Summative: feedback will be provided on the assessment.
|No Exam Information
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.
Thomas, L., Edelman, D. and Crook, J. (2017) Credit Scoring and its Applications. SIAM: Philadelphia.
Anderson, R. (2007) The Credit Scoring Toolkit. Oxford University Press: Oxford.
Baesens, B. (2014) Analytics in a big data world : the essential guide to data science and its application. Hoboken, New Jersey : Wiley
Crook, J., Edelman, D. and Thomas, L. (2007) "Recent Developments in Consumer Risk Assessment" European Journal of Operational Research. Vol. 183, No. 3, pp.1447-1465.
Mays, E. (2001) Handbook of Credit Scoring. Glenlake: London.
Siddiqi, N. (2006) Credit risk scorecards : developing and implementing intelligent credit scoring. Wiley: New Jersey.
Thomas, L. (2009) Consumer Credit Models. Oxford University Press: Oxford.
Thomas, L., Edelman, D. and Crook, J. (2004) Readings in Credit Scoring. Oxford University Press: Oxford.
Van Gestel, T. and Baesens, B. (2009) Credit Risk Management. Oxford University Press: Oxford.
|Graduate Attributes and 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.
|Course organiser||Dr Galina Andreeva
Tel: (0131 6)51 3293
|Course secretary||Ms Heather Ferguson
Tel: (0131 6)50 8074