Project Aurora: using data to combat money laundering across firms and borders

The BIS Innovation Hub's Nordic Centre is launching Project Aurora to explore how the latest data technologies can be used to combat money laundering across financial institutions and borders.

Fighting money laundering, terrorism financing, fraud and tax evasion helps to bolster trust in payments systems and financial stability, a matter of common interest to the public and private sectors. Today's payments systems are complex ecosystems that involve several different categories of private and public actors (including commercial banks, payment services providers, fintech companies, central banks and regulators). As a result, complexity and fragmentation are increasing, as is the granularity, velocity and availability of data. 

As one of the hardest activities to detect in the world of financial crime, money laundering represents a very complex data challenge. The estimated amount of money laundered globally is between 2 and 5% of global GDP. Criminals operate in networks and across borders. The professional money laundering networks adjust quickly and flexibly to shifting factors such as regulation. Meanwhile, most financial institutions rely on siloed data and isolated systems to monitor transactions and are often prohibited by regulation from sharing information with other financial institutions. 

Yet the current approach to fighting money laundering puts most of the burden on financial institutions, which are liable if they let through transactions that turn out to be illegal. Consequently, banks tend to overreport (defensive reporting), leading to a high number of false positives (when legitimate customers are flagged as potentially suspicious), which can be costly and time-consuming, while leading to an unnecessary exposure of sensitive customer information. 

Furthermore, financial institutions decided not to work with certain clients if the volume of business doesn't justify the increased compliance costs and perceived risks (a trend also known as "de-risking"). This has led to several countries losing access to the global financial system, in practice acting as a financial exclusion force. The unintended consequence, if entire national systems become marginalised in this way, is that money laundering, tax evasion and terrorist financing may actually become more pervasive.

According to the Financial Action Task Force, almost all large money laundering schemes are cross-border in nature, spanning various business sectors. Spotting different money laundering patterns is complex, requiring different data points and data sources as well as the ability to connect them across different systems in order to better identify suspicious flows and patterns. As this requires a data-driven approach, current data technologies are capable of playing a pivotal role in better fighting money laundering.

Project Aurora will investigate the use of advanced technologies, such as privacy-enhancing technologies, machine learning methods, network analysis, and the use of additional data sources and machine-readable typologies (to represent money laundering patterns in a machine-readable format) in a proof of concept that aims to show how information could be shared in a private, secure and compliant way to detect suspicious transactions across financial institutions and borders.