Analysing Customer Behaviour Using Simulated Transactional Data

Ryan Butler, Edwin D. Simpson

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

1 Citation (Scopus)


This paper explores a novel technique that can aid firms in ascertaining a customer’s risk profile for the purpose of safeguarding them from unsuitable financial products. This falls under the purview of Know Your Customer (KYC), and a significant amount of regulation binds firms to this standard, including the Financial Conduct Authority (FCA) handbook Section 5.2. We introduce a methodology for computing a customer’s risk score by converting their transactional data into a heatmap image, then extracting complex geometric features that are indicative of impulsive spending. This heatmap analysis provides an interpretable approach to analysing spending patterns. The model developed by this study achieved an F1 score of 94.6% when classifying these features, far outperforming alternative configurations. Our experiments used a transactional dataset produced by Lloyds Banking Group, a major UK retail bank, via agent-based modelling (ABM). This data was computer generated and at no point was real transactional data shared. This study shows that a combination of ABM and artificial intelligence techniques can be used to aid firms in adhering to financial regulation.
Original languageEnglish
Title of host publicationProceedings of the 15th International Conference on Agents and Artificial Intelligence
Publication statusPublished - 2023

Publication series

ISSN (Print)2184-433X


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