Privacy Technologies for Financial Intelligence

TL;DR

This paper surveys how privacy and confidential computing technologies can enhance collaborative financial crime detection while maintaining data privacy across organizations.

Contribution

It provides a comprehensive overview of privacy technologies applicable to financial intelligence and identifies opportunities for their integration in crime detection.

Findings

Privacy technologies enable secure cross-organization data analysis.

Enhanced collaboration improves detection of complex financial crimes.

Privacy-preserving methods maintain data confidentiality while enabling analysis.

Abstract

Financial crimes like money laundering and terrorism financing can have significant impacts on society, including loss of trust in the integrity of the financial system, misuse and mismanagement of public funds, increase in societal problems like drug trafficking and illicit gambling, and loss of innocent lives due to terrorism activities. Effective detection of complex financial crimes remains a formidable challenge for regulators and financial institutions because the critical data needed to establish patterns and criminality are often dispersed across multiple organisations and cannot be linked due to privacy constraints around large-scale data matching. Recent advances in privacy and confidential computing technologies, which enable private and secure data analysis across organisations, offer a promising opportunity for regulators and the financial industry to come together to enhance their collaborative risk detection while maintaining privacy standards. This paper, through a survey of the financial intelligence ecosystem, seeks to identify opportunities for the utilisation of privacy technologies to improve the state-of-the-art in financial-crime detection.