2021/22 Undergraduate Module Catalogue
LUBS2226 Applied Credit Analytics
10 creditsClass Size: 32
Module manager: Nicholas Wilson
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2021/22
|LUBS2224||Credit and Financial Analytics|
This module is not approved as a discovery module
Module summaryThe 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.
ObjectivesThe 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.
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.
- 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
- 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.
|Delivery type||Number||Length hours||Student hours|
|Private study hours||75.00|
|Total Contact hours||25.00|
|Total hours (100hr per 10 credits)||100.00|
Private studyThis could include a variety of activities, such as reading, watching videos, question practice and exam preparation.
Opportunities for Formative FeedbackYour teaching methods could include a variety of delivery models, such as face-to-face teaching, live webinars, discussion boards and other interactive activities. There will be opportunities for formative feedback throughout the module.
Methods of assessment
|Exam type||Exam duration||% of formal assessment|
|Online Time-Limited assessment||48 hr||100.00|
|Total percentage (Assessment Exams)||100.00|
Students will have to complete an online assessment at the end of the module. This will take place during the examinations period and will be time bound. The assessment will not take 48 hours to complete, but students will have a 48 hour time period in which to complete it. The resit for this module will be 100% by examination.
Reading listThere is no reading list for this module
Last updated: 30/06/2021 15:19:59
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