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FIN3020S

Academic Year 2024/2025

Intro to Machine Learning (FIN3020S)

Subject:
Finance
College:
Business
School:
Business
Level:
3 (Degree)
Credits:
10
Module Coordinator:
Dr Vassilios Papavassiliou
Trimester:
Autumn and Summer (separate)
Mode of Delivery:
Blended
Internship Module:
Yes
How will I be graded?
Letter grades

Curricular information is subject to change.

This module provides an accessible account of the field of machine learning, with a specific focus on applications in operational and credit risk related problems in banking. The focus of the module is on principally important areas of application of statistical learning in the field: anti money laundering, credit card delinquency, financial reporting fraud and the protection of vulnerable clients. The most important machine learning modelling and prediction techniques will be studied and implemented. Of critical importance, irrespective of the prediction technique deployed, is the evaluation of the performance of the model. Model performance evaluation is, thus, a key conceptual and technical focus of the module. The preferred software environment for the implementation of statistical computing and graphics in this module is RapidMiner. With the explosion of “Big Data” problems in the finance of banking, the methodologies and applications introduced in this module are in high demand in industry.

About this Module

Learning Outcomes:


Have a comprehensive appreciation of the key issues involved in predictive analytics in banking.
⦁ Understand fundamental ideas which underpin the methodologies introduced.
⦁ Demonstrate a knowledge of the institutional and regulatory contexts of the illustrated application areas in banking.
⦁ Be able to explain in detail and model in practice classification related problems in banking.
⦁ Have an appreciation of the role of economic policy and regulation in the predictive analytics in banking field.

Indicative Module Content:

Have a comprehensive appreciation of the key issues involved in predictive analytics in banking.
⦁ Understand fundamental ideas which underpin the methodologies introduced.
⦁ Demonstrate a knowledge of the institutional and regulatory contexts of the illustrated application areas in banking.
⦁ Be able to explain in detail and model in practice classification related problems in banking.
⦁ Have an appreciation of the role of economic policy and regulation in the predictive analytics in banking field.

Student Effort Hours:
Student Effort Type Hours
Specified Learning Activities

80
Autonomous Student Learning

150
Lectures

20
Total

250

Approaches to Teaching and Learning:
Discussion, cases studies, practical explorations, etc

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Assignment(Including Essay): Continuous Assessment Week 7 Standard conversion grade scale 40% No
40
 No
Exam (Online): Examination Week 15 Standard conversion grade scale 40% No
60
 No

Carry forward of passed components
Yes
 

Remediation Type Remediation Timing
Repeat Within Two Trimesters
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Name Role
Yusra Anas Tutor
Dr Christina Burke Tutor
Ms Michele Connolly Doran Tutor
Professor Cal Muckley Tutor
Chee Shong Tan Tutor
Charlene Tan Puay Koon Tutor
Samantha Teng Tutor
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