Privacy based triage of suspicious activity reports using offline large language models

Legg, Phil; Ryder, Nicholas; Bourton, Samantha; Johnson, Diana; Walker, Reuben

Abstract

Suspicious Activity Reports (SAR) form a vital part of incident response and case management for the investigation of known or suspected money laundering. However, those submitting SARs, and those tasked with analysing SARs, often find the task overwhelming due to the complexity of reporting, the incompleteness of information available, and the ability to classify reports effectively for further processing. We explore the use of Natural Language Processing to facilitate this process. Specifically, we utilise the recent advances of Large Language Models to understand and classify SARs against the glossary code terms set out by the UK National Crime Agency. We also explore the privacy concerns of handling confidential and sensitive data with recent AI advancements and propose the use of offline open-source models, coupled with bespoke fine-tuning, to improve task-specific performance using a model that can be deployed locally without requiring data to be shared with external third parties. Our results show that this approach can yield effective classification accuracy on our test cases, offering a solution to develop bespoke smaller, offline models that maintain privacy and confidentiality, over online models that would compromise data privacy.

• Online Publication Date: Dec 27, 2024
• Publication Date: Dec 27, 2024
• Deposit Date: Mar 20, 2024
• Publicly Available Date: Dec 28, 2025
• Publisher: Taylor & Francis
• Book Title: Advancements in Cyber Crime Investigations and Modern Data Analytics
• Chapter Number: 2
• ISBN: 9781032742700
• DOI: https://doi.org/10.1201/9781003471103
• Public URL: https://uwe-repository.worktribe.com/output/11834462
• Publisher URL: https://www.routledge.com/Advancements-in-Cyber-Crime-Investigations-and-Modern-Data-Analytics/Shandilya-Sujay-Gupta/p/book/9781032742700
• Contract Date: Mar 18, 2024