While some industries resisted the implementation of strict data protection regulations, seeing them as hampers to progress, others saw them as a driver for a market change. These market changes stem from scientific research: many Privacy Enhancing Technologies (PETs) have been a topic of research for a long time, but it was only recently that they became a product with practical implementations.
Dutch banks have a task and responsibility in combating financial crime. Supervision of money laundering has been further tightened in recent years and all kinds of large fines have been handed out to Dutch banks for failing to comply with legislation.
Criminal transaction flows can be obfuscated by spreading transactions over multiple banks. Collaboration between banks is key to tackling this; however, data sharing between banks is often undesirable for privacy reasons or is restricted by legislation. Research institute TNO and Dutch banks ABN AMRO and Rabobank are researching the feasibility of using MPC to detect money laundering.
Sharing and analysing data is a fundamental part of detecting financial crime. The more relevant information we can analyse, the more effectively our crime analysts can work. While our clients’ privacy is paramount, an innovative approach could reconcile these two key goals. The Dutch scientific research organisation TNO is testing this together with Rabobank and ABN AMRO. A new system using ‘fake data’ is showing promising results.
Collaboration between financial institutions helps to improve detection of fraud. However, exchange of relevant data between these institutions is often not possible due to privacy constraints and data confidentiality. An important example of relevant data for fraud detection is given by a transaction graph, where the nodes represent bank accounts and the links consist of the transactions between these accounts.