In March I wrote about the anti-money laundering (AML) project of the Bank for International Settlements (BIS). The project on their site is described as follows:

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.

On 31 May BIS announced it has concluded the project in the article ‘BIS concludes Project Aurora, a proof of concept based on the use of data, technology and collaboration to combat money laundering across institutions and borders‘. In the article they mention the final report of the project (pdf). According to the report financial surveillance is the solution for the current problems in AML/CFT.

The current system of privatisation of crime fighting is inadequate

From the article:

The project was initiated with the aim of addressing the most pressing limitations in the battle against money laundering. The payments systems landscape involves a complex interplay of private and public entities, including commercial banks, payment services providers, fintech companies, central banks and regulatory authorities. This complexity often results in fragmentation, which criminals exploit.(…)

Project Aurora started by leveraging a comprehensive synthetic data set that represents real-world domestic and international payments data. To ensure the protection of sensitive information, advanced privacy-enhancing technologies were employed, drawing on machine learning and other advanced analytical tools while the data remain encrypted. Subsequently, algorithms were trained on this synthetic data set to detect various patterns, known as “typologies,” associated with money laundering activities across institutions and countries. (…)

The project findings highlighted the effectiveness of employing advanced analytics and technologies that adopt a behavioural-based analysis approach, which focuses on understanding the relationships between different individuals and businesses and identifying anomalies from normal behaviour. The results demonstrated that such methods are more effective in detecting money laundering networks than are the current rules-based approach, which is limited by its siloed nature.

In the executive summary of the report it is noted that the current system of AML/CFT is extremely expensive and that it does not work:

However, this approach is ineffective as many payment transactions are complex and involve interconnected networks that span multiple financial institutions and borders. Criminals operate in networks and exploit this complexity.

It shows that with the current privatisation of crime fighting, money is being squandered on a massive scale.

The BIS-solution: ‘collaborative analytics and learning (CAL)’ (financial surveillance)

The authors of the report note that due to the inadequateness of the current system the informal world government, the Financial Action Task Force (FATF), is looking for other methods (footnotes removed):

In response to some of these challenges, the Financial Action Task Force (FATF) has identified that data-sharing and collaborative analytics are critical for effective antimoney laundering and countering the financing of terrorism (CFT) efforts. 10 In its Stocktake on data pooling, collaborative analytics and data protection,  the FATF outlined several technologies and approaches that could be used to improve AML/CFT efforts, including different approaches to data-sharing, privacy-enhancing technologies (PET), advanced analytics, data standardisation and data protection. Digital transformation to enhance AML/CFT efforts is a strategic priority of the FATF.

The solution to the problem in the summary is referred to as ‘collaborative analytics and learning (CAL)‘, a friendly way of indicating financial surveillance. BIS believes that the human rights problems of such surveillance systems can  be overcome:

The protection of individual and fundamental rights to privacy can be a concern when considering the use of data and technologies to fight financial crime. Data privacy and protection, and countering financial crime are important public interests that are not opposed to each other. They should be supported by the right technological tools and by a balanced legal framework.

According to the summary of the report a successful proof of concept (PoC) of a financial surveillance system was built.

Final remarks

The BIS-project shows that the ‘bancair sleepnet‘, monitoring of transactions by the financial institutions as a collective, that the Dutch government proposed in October 2022, is part of an international trend.

It is an unhealthy situation that these new surveillance concepts are developed without starting a public discussion on the matter.

Addition 21 June 2023
Article on the report: AML efficiency from networks and AI by Isabelle Castro Margaroli, 20 June 2023.

Addition 23 June 2023
Google is entering the AML/CFT market: Google gooit AI in de strijd tegen witwaspraktijken. Not surprising.

Addition 18 August 2025
BIS published Project Hertha: Identifying financial crime patterns in real-time retail payment systems: “Protecting payment systems from financial crime, while upholding user privacy, is an important challenge in delivering the future of payments. Building on the findings from Project Aurora, BIS Innovation Hub’s Project Hertha explored how transaction analytics could help identify financial crime patterns in real-time retail payment systems, while using the minimum set of data points. (…) Project Hertha found that payment system analytics could be a valuable supplementary tool to help banks and payment service providers (PSPs) spot suspicious activity.“