A Secure Framework for Anti-Money-Laundering using Machine Learning and Secret Sharing

Abstract

Nowadays, the scale of Money Laundering is difficult to estimate in the UK and elsewhere. Proceeds of crimes might be transferred using the available business infrastructure offered by banks, and this is a considerable problem. This paper outlines a novel scheme that allows banks to share information leading to Money Laundering (ML) detection all the while preserving confidentiality and integrity. The main contribution is the overall architecture that aims to improve ML detection by getting other banks to collaborate. In order to get other banks to co-operate, a primary directive of preserving privacy is enforced throughout the framework. The proposed scheme has two particular aspects, one of which is the application of encrypted data used in machine learning for ML detection. Another feature is using secret sharing as a collaborative element in this context. These aspects are found in the three phases of the framework: Signalling to the Auditor, ML Detection and finally Suspicious Activity Report (SAR) Feedback.