Module: Financial Machine Learning & Automation

Latest Module Specifications

Current Academic Year 2025 - 2026

Module Title	Financial Machine Learning & Automation
Module Code	FBA1027 (ITS: EF5178)
Faculty	DCU Business School	School	DCU Business School
NFQ level	9	Credit Rating	7.5

Description

This module develops knowledge of the techniques of machine learning to learn from financial data and build new types of financial models. Through the application of artificial neural networks, support vector machines, random forests, and gradient boosting, we will bring new understanding to financial models. The machine learning techniques are applied to build, for example, asset pricing models, credit acceptance / rejection models, and fraud detection models. There will also be coverage of the practical issues of setting up secure data infrastructures for implementing these techniques in the firm. A second focus will be on how to use machine learning, as well as other tools, to implement automation to financial services provision in the firm. The micro is delivered through the Python language.

Learning Outcomes

1. Demonstrate understanding of the key machine learning techniques of benefit to financial modeling
2. Apply machine learning techniques appropriately in a variety of practical financial context
3. Understand the data needs, and appropriate and secure data handling skills, for working with financial models
4. Implement automation to financial decision making based on financial machine learning and understanding of financial technology

*Type*	*Hours*	*Description*
Workload	Full time hours per semester
Lecture	12	Formal classes (lecture / workshop), one hour per week delivered live online, with a recorded option available.
Lecture	12	Formal classes (lecture), one hour per week delivered in pre-recorded online learning
Assignment Completion	76	Individual assignment and group project.
Independent Study	87.5	Class related reading and activities, guided reading and further learning.
Total Workload: 187.5

Section Breakdown
CRN	10690	Part of Term	Semester 1
Coursework	0%	Examination Weight	0%
Grade Scale	40PASS	Pass Both Elements	Y
Resit Category	RC1	Best Mark	N
Module Co-ordinator	Michael Dowling	Module Teacher

Section Breakdown
CRN	11942	Part of Term	Semester 1
Coursework	0%	Examination Weight	0%
Grade Scale	40PASS	Pass Both Elements	Y
Resit Category	RC1	Best Mark	N
Module Co-ordinator	Michael Dowling	Module Teacher

Type	Description	% of total	Assessment Date
Assessment Breakdown
Report(s)	Individual assessment: The student will demonstrate learning through worksheet-based application of financial machine learning concepts. [Submitted around Week 9 of the module]	50%	Week 27
Group project	Group work: Students will develop a financial project that incorporates machine learning to develop a financial model, and automation to allow some element of automated model decision making. There will be an individual reflective report attached to the group project (20%), as well as a group presentation (30%). [Submitted in end-of-teaching week]	50%	Sem 2 End

Reassessment Requirement Type
Resit arrangements are explained by the following categories; RC1: A resit is available for both^* components of the module. RC2: No resit is available for a 100% coursework module. RC3: No resit is available for the coursework component where there is a coursework and summative examination element. ^* ‘Both’ is used in the context of the module having a coursework/summative examination split; where the module is 100% coursework, there will also be a resit of the assessment

Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities

Regression-based machine learning
Exploration of various techniques associated with regression. Including setting up data for testing, pre-processing. To cover techniques such as linear regression, gradient boosting, random forests, MLP. Techniques to be applied through sklearn in Python with finance case studies.

Grouping-based machine learning
Exploration of various techniques associated with clustering and grouping. Starting with logistic regression, decision trees, SVM, deep learning clustering and grouping.

Other machine learning
Examining techniques such as outlier detection and its benefit for fraud detection and credit default risk in finance. Also examining (very briefly) the use of text-based machine learning models. Data security and data protection.

Automation for FinTech
Techniques and tools for implementing automation in financial technology. Examined from a robotic process automation perspective as well as a user experience perspective. Advice from expert practitioners on practical implementation.

Indicative Reading List

Books:

Aurélien Géron: 2019, Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow, O'Reilly, 819, 1492032646
Chris Albon: 2018, Python Machine Learning Cookbook, O'Reilly Media, 366, 9781491989388

Articles:
None

Other Resources

0: DataCamp (various courses), www.datacamp.com,