FIN30520 Machine Learning in Finance

Academic Year 2020/2021

This module provides an accessible account of the field of statistical learning, with a specific focus on applications in operational and credit risk related problems in banking. It will show students how to make sense of the vast and complex data sets that have emerged in the field of 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 modelling and prediction techniques will be studied and implemented: linear regression, classification, resampling methods, shrinkage approaches, tree based methods, support vector machines and clustering. The preferred software environment for the implementation of statistical computing and graphics in this module is R. 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.

Student Effort Hours: 
Student Effort Type Hours
Lectures

24

Tutorial

12

Autonomous Student Learning

90

Total

126

Approaches to Teaching and Learning:
Not yet recorded 
Requirements, Exclusions and Recommendations
Learning Recommendations:

One Regression Analysis module.


Module Requisites and Incompatibles
Incompatibles:
STAT30240 - Predictive Analytics I, STAT30250 - Advanced Predictive Analytics, STAT30270 - Statistical Machine Lrng, STAT40750 - Statistical Machine Lrn (OL), STAT40770 - Adv Pred Analytics (online), STAT40790 - Predictive Analytics I (online


 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Class Test: Mid-term exam Week 8 n/a Graded No

60

Group Project: Assignments Varies over the Trimester n/a Graded No

40


Carry forward of passed components
Not yet recorded
 

Not yet recorded

Please see Student Jargon Buster for more information about remediation types and timing. 
Not yet recorded
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112). New York: springer.

Other relevant books and journal articles will be recommended throughout the semester.
Name Role
Mr Shivam Agarwal Tutor
Parvati Neelakantan Tutor