FIN3020S Intro to Machine Learning

Academic Year 2023/2024

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.

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Curricular information is subject to change

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
Lectures

20
Specified Learning Activities

80
Autonomous Student Learning

150
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 Open Book Exam Component Scale Must Pass Component % of Final Grade
Assignment: Team Assignment Unspecified n/a Graded No

40
Examination: Online Examination Coursework (End of Trimester) No Graded No

60

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
Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
 

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