Module overview
The module will start by defining the concept of data analytics and demonstrating the processes in three steps: data pre-processing, data mining and post-processing. Next, we will zoom into the data mining step and distinguish three types of data mining: descriptive/diagnostic data analytics (e.g. clustering, association rules), predictive data analytics (e.g. regression and classification), and prescriptive data analytics. The module will then illustrate how machine learning models can be successfully used to develop different application areas with a focus on retail credit risk. The theoretical concepts will be illustrated using real-life application cases and the relevant software.
Aims and Objectives
Learning Outcomes
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- Understand the process of data analytics, particularly in the retail lending sector.
- Interpret the output of statistical techniques used for the main data analytics applications.
- Demonstrate a critical understanding of different types of data analytics methods and the problems they can solve.
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- Assess the relevance of statistical package outputs to the decisions being addressed.
- Work with software to develop data analytics solutions, such as predictive scorecards, clustering models, and different types of regressions.
- Identify the statistical models appropriate for analysing the various decisions that confront a data analyst in different industries.
Transferable and Generic Skills
Having successfully completed this module you will be able to:
- Critically analyse practical difficulties that arise when implementing retail credit risk models; understand the cross-fertilisation potential to other business contexts (e.g. fraud detection, marketing, CRM, etc.).
- Demonstrate an ability to use world-class software and to interpret its output in the relevant techniques.
- Manage time and tasks effectively in the context of individual study.
Syllabus
- Introduction to data analytics and retail credit risk modelling
- Introduction to credit risk and the related regulations, such us the Basel II and III regulations and Internal Rating-Based (IRB) models
- Descriptive/diagnostic analytics (e.g. basic descriptive statistics and visualizations)
- Data pre-process (e.g. sampling, missing value and outlier handling, etc.)
- Classification approaches (e.g. logistic regression, decision trees, etc.) for scorecard development
- Performance measurements for scorecards (e.g. ROC curves, Lift, Gini, etc.)
Learning and Teaching
Teaching and learning methods
The module is delivered through pre-course reading and lectures. The various concepts will be illustrated using real-life credit scoring data and software. In addition there will be some in-lecture exercises.
Type | Hours |
---|---|
Teaching | 24 |
Independent Study | 126 |
Total study time | 150 |
Resources & Reading list
Textbooks
Thomas, L.C., Edelman, D.B. and Crook, J.N. (2018). Credit Scoring and Its Applications. Philadelphia, PA: SIAM.
Hastie, T., Tibshirani, R., and Friedman, J (2013). The Elements of Statistical Learning. NJ, USA.: Springer.
Assessment
Formative
This is how we’ll give you feedback as you are learning. It is not a formal test or exam.
Feedback
- Assessment Type: Formative
- Feedback: Formative group feedback on students’ performance in in-lecture exercises will be provided verbally immediately after the exercises. If possible, students will use technological tools to answer questions in the exercises.
- Final Assessment: No
- Group Work: No
Summative
This is how we’ll formally assess what you have learned in this module.
Method | Percentage contribution |
---|---|
Individual report | 100% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
Method | Percentage contribution |
---|---|
Individual report | 100% |
Repeat
An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.
Method | Percentage contribution |
---|---|
Individual report | 100% |
Repeat Information
Repeat type: Internal & External