Artificial Intelligence in Finance

Module overview

Artificial intelligence (AI) is transforming how financial institutions analyse data, manage risk, and make investment decisions. This module introduces students to the practical applications of AI and machine learning (ML) in modern banking and finance. It focuses on developing a working understanding of key methods, such as predictive modelling, natural language processing, portfolio management, and risk modelling, while emphasising interpretation, ethical use, operational considerations, and model governance. You will learn how to apply AI tools to real financial datasets to solve a range of financial decision problems, gaining experience in both the analytical design and evaluation of AI-based models. This includes an understanding of model risk, robustness, and explainability in real-world financial settings. The module aims to strike a balance between conceptual understanding and hands-on experience. To this end, we plan to employ accessible programming exercises using appropriate statistical and computational software tools commonly applied in financial analysis to illustrate how AI can extract value from complex financial data. By the end of the module, you will be able to design, evaluate, and communicate AI-based financial models with an appreciation of both your analytical power, practical limitations, and operational implications. The emphasis throughout is on practical relevance and employability – equipping you with the analytical, technical and governance-aware skills increasingly sought by asset managers, banks, investors, fintech firms, and regulators.

Aims and Objectives

Learning Outcomes

Transferable and Generic Skills

Having successfully completed this module you will be able to:

Demonstrate problem-solving skills through applied projects involving real financial datasets.
Communicate analytical results clearly to both technical and non-technical audiences.
Apply knowledge learned in class to real-world problems.

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

Design and validate AI models for forecasting, risk assessment and portfolio decisions using appropriate ML techniques.
Apply suitable AI tools to analyse and interpret financial data using appropriate statistical and computational software tools.
Develop and communicate AI-based financial analyses clearly and in a way that others can reproduce, stating data sources, modelling choices, evaluation approach and key limitations.

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

Evaluate the performance, accuracy, and limitations of different AI models in financial contexts.
Recognise the ethical, legal, and operational risks that arise when AI systems are used in financial services.
Explain the main AI and ML methods used in finance and how they differ from traditional econometric models.

Learning and Teaching

Teaching and learning methods

Teaching and learning on this module combine interactive lectures, guided computer labs/workshops, and project-based learning to ensure students gain both conceptual understanding and practical competence. Each weekly two-hour session will integrate a structured lecture introducing the theoretical and financial context of a topic, followed by a hands-on laboratory activity using appropriate statistical and computational software tools. You will work individually and in small groups to apply AI and machine learning techniques to financial datasets, developing your technical skills and gaining teamwork experience. Lectures will focus on financial interpretation, critical thinking, ethical considerations, and problem solving. Workshops will provide step-by-step coding exercises, demonstrations, and discussions linking theory to practice. Independent study is an essential component of the module; you are expected to complete preparatory readings, review lecture materials, and extend their practical skills through self-directed experimentation with data and models. You are required to complete a project that will integrate methods learned throughout the module and promote reflection on model performance, interpretability, and governance. Overall, the holistic approach aims at boosting employability and confidence in applying AI tools responsibly within the financial sector.

Study time
Type	Hours
Teaching	34
Independent Study	116
Total study time	150

Assessment

Formative

This is how we’ll give you feedback as you are learning. It is not a formal test or exam.

Project Plan and Methodology

Assessment Type: Formative
Feedback: One set of written group feedback will be provided via Blackboard. Students will use this feedback to inform the development of their individual summative coursework. General cohort-level feedback will also be provided in writing via Blackboard Announcements and/or discussed during lectures.
Final Assessment:
Group Work: Yes

Summative

This is how we’ll formally assess what you have learned in this module.

Breakdown
Method	Percentage contribution
Individual project	100%

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Breakdown
Method	Percentage contribution
Individual project	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.

Breakdown
Method	Percentage contribution
Individual project	100%

Repeat Information

Repeat type: Internal & External