Program Overview

Finance and Artificial Intelligence (IFTE0002)

Key Information

The Finance and Artificial Intelligence module is offered by the Faculty of Engineering Sciences, specifically the Civil, Environmental and Geomatic Engineering department. This module has a credit value of 15. However, it is restricted to students enrolled on the MSc Banking and Digital Finance program.

Alternative Credit Options

There are no alternative credit options available for this module.

Description

Artificial intelligence (AI) has become a central component in financial applications, defined as computer systems designed to perform tasks that typically require human intelligence. This module provides principles for understanding how finance is integrated into computer system design to construct algorithms for optimal financial decision-making. Building on standard financial theory, it introduces modern machine-learning techniques and methods in finance, focusing on a data-driven approach where machine learning techniques are implemented using either simulated or real data.

Learning Outcomes

• Have a deep understanding of how AI is applied in finance, particularly how machine learning methods impact financial modeling and the differences between applying machine learning in finance and engineering.
• Learn different machine learning techniques (such as supervised and unsupervised learning) and their applications in financial modeling, including portfolio and credit risk modeling. For instance, estimating the probability of bank failure or defaultable loans.
• Know how to apply and analyze ML algorithms on real financial data with Python.
• Gain practical grounding in machine learning for decision-making and its applications in finance and banking.

Reading List

The module is based on a wide variety of references, but the following may be consulted to start with:

• De Prado, M.L., 2018. Advances in financial machine learning. John Wiley & Sons.
• G廨on, A., 2017. Hands-on machine learning with Scikit-Learn and TensorFlow: concepts, tools, and techniques to build intelligent systems. O'Reilly Media, Inc.

Module Deliveries for 2026/27 Academic Year

Intended Teaching Term: Term 1, Postgraduate (FHEQ Level 7)

Teaching and Assessment

• Mode of study: In person
• Intended teaching location: UCL East
• Methods of assessment:
  • 50% In-class activity
  • 50% Group activity
• Mark scheme: Numeric Marks

Other Information

• Number of students on module in previous year: 57
• Module leader: Dr. Ramin Okhrati

Last Updated

This module description was last updated on 10th March 2026.