2024/25 Undergraduate Module Catalogue

LUBS2226 Applied Credit Analytics

10 creditsClass Size: 30

Module manager: Nicholas Wilson
Email: N.Wilson@lubs.leeds.ac.uk

Taught: Semester 2 (Jan to Jun) View Timetable

Year running 2024/25

Co-requisites

LUBS2227

Financial Econometrics

This module is not approved as a discovery module

Module summary

The module aims to provide skills in database management, and applications of multivariate regression methods in credit scoring and credit risk assessment. Techniques for modelling consumer risk (retail lending), consumer fraud, debt recovery processes and corporate risk (bank and trade finance) are widely used and applied in practice within a regulatory environment (e.g. Basel II banking regulations, Financial Conduct Authority). The module will equip you with the quantitative skills, knowledge of the application contexts, credit information infrastructure (Reference and Rating Agencies), current credit management practices in business and the regulatory environment. The skills acquired are highly relevant to the FinTech sector and are transferable to other areas of business i.e. consumer marketing, and big data analytics.

Objectives

The module aims to provide skills in database management, and applications of multivariate regression methods in credit scoring and credit risk assessment. Techniques for modelling consumer risk (retail lending), consumer fraud, debt recovery processes and corporate risk (bank and trade finance) are widely used and applied in practice within a regulatory environment (e.g. Basel II banking regulations, Financial Conduct Authority). The course will equip students with the quantitative skills and knowledge of the application contexts, credit information infrastructure (Reference and Rating Agencies), current credit management practices in business and the regulatory environment.

Learning outcomes
Upon completing of this module, students will be able to:

1. Identify and use modern credit and financial analytical techniques as applied in research in finance and in practice in the financial services and credit-sector within the Basel II/III regulatory framework;
2. Critically evaluate analytical tools used in credit risk modelling and develop the quantitative skills required to assess and manage default risk and the associated metrics used by banks and other lenders;
3. Apply analytical techniques for modelling, analysis and prediction purposes, to aid understanding of their use and value in the evolving sector.

Skills outcomes
Transferable skills:
- Written communication skills
- Critical thinking skills
- Advanced numeracy and database skills
- Computer based statistical analysis

Subject specific skills:
On completion of this module students will be able to:
- Demonstrate advanced problem solving, analytical and quantitative skills by applying current theory and appropriate analytical tools to complex problems in credit risk management
- Detailed knowledge of credit information and ratings systems
- Use and application of database and statistical software

Syllabus

Indicative content:
- Database construction and management
- Statistical Foundations, Descriptive Statistics, Distributions and Hypotheses
- Data reduction techniques: characteristic analysis, dealing with missing data and extremes
- Analysis of Limited Dependent Variables. Probability. Linear probability and Logistic Regression. Models, Tests and Interpretation. Applications in Finance: Credit Risk and Probability of Default. Other Limited Dependent Variables.
- Elementary time series estimation.
- Introduction to credit scoring techniques in retail and corporate lending. Estimation and management of credit risk components: PD (probability of default), LGD (loss given default), and EAD (exposure at default). Assessing and validating credit risk models.

Teaching methods

Delivery type	Number	Length hours	Student hours
Lecture	10	2.00	20.00
Seminar	5	1.50	7.50
Private study hours			72.50
Total Contact hours			27.50
Total hours (100hr per 10 credits)			100.00

Private study

This could include a variety of activities, such as reading, watching videos, question practice and exam preparation.

Opportunities for Formative Feedback

Students participate in bi-weekly seminars and are provided with specimen exam questions to complete in own study time - feedback is provided on attempted answers. Consultation hours provide students with opportunities to discuss their understanding and progress.

Methods of assessment

Exams

Exam type	Exam duration	% of formal assessment
Standard exam (closed essays, MCQs etc)	2 hr 00 mins	100.00
Total percentage (Assessment Exams)		100.00

The resit for this module will be 100% by 2 hour examination.

Reading list

There is no reading list for this module

Last updated: 29/04/2024 16:16:20

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