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Finterai, exit report: Machine learning without data sharing

About money laundering

Finterai is a start-up enterprise, established in Oslo in 2021, which aims to supply financial technology (fintech) to banks and regulatory authorities. The company’s services address the global challenges posed by money laundering and the financing of terrorism.

What problem does Finterai seek to resolve?

At its root, money laundering is a way of securing the proceeds of crime. The purpose of money laundering is therefore to make these proceeds appear to have been acquired in a lawful manner by disguising/concealing the funds’ illegal origins before they are integrated into the legal economy.

Money laundering in the Financial Supervisory Authority of Norway's sandbox

Combating money laundering has also been the topic of a project in the Financial Supervisory Authority of Norway’s sandbox. In their project, the Financial Supervisory Authority of Norway (FSAN) and Quesnay assessed opportunities and constraints afforded under the Norwegian Anti-Money Laundering Act for a technical solution for the sharing of information between reporting entities, which could make endeavours to combat money laundering and the financing of terrorism more effective.

Read the project’s final report (finanstilsynet.no).

The financing of terror is the receiving, sending and collection of funds with the intention or knowledge that the money will be used to finance an act of terrorism or will be used by a terrorist group or a person acting on behalf of a terrorist/terrorist group. Terrorism may be financed both by the proceeds of crime and/or by legally acquired funds.

The UN estimates that laundered money accounts for two to five per cent of the global economy – approx. twice the size of Norway’s sovereign wealth fund, the Government Pension Fund Global (GPFG).

Unfortunately, international research also indicates that the public authorities succeed in reclaiming as little as 0.1 per cent of the illegal financial gain. This therefore constitutes a substantial loss for the victims of crimes for profit and for society at large. The EU estimates that it loses up to EUR 1 trillion in money laundering-related tax evasion every year.

Read more about money laundering on the UN’s website

Read the research article “Anti-money laundering: The world's least effective policy experiment? Together, we can fix it”

Read more about the EU’s estimate of how much is lost in money laundering-related tax evasion every year

According to the Norwegian Anti-Money Laundering Act, financial institutions must strive to prevent money laundering and the financing of terrorism. In practice, this means, for example, that they have a responsibility to ensure they are not misused to conceal the origins of ill-gotten gains. As a result of this responsibility, financial institutions must understand their customers’ transactions and assess the risk of money laundering.

A lot of wasted effort

Finterai believes that the main problem in the fight against money laundering and the financing of terrorism is the overwhelming amount of futile investigative work. Banks are compelled to use electronic surveillance systems, but Finterai considers these systems to be extremely imprecise. This leads to many “false positive” transactions being investigated. A large number of false positives means that transactions are presumed to be suspicious when they are not actually criminal.

Under the Anti-Money Laundering Act, banks are obliged to investigate all suspicious transactions. A large number of false positive transactions therefore generates a great deal of investigative work for the banks. In light of this, Finterai is therefore offering a machine learning tool to improve the banks’ electronic surveillance systems.

The challenge with using machine learning for this purpose is that money laundering and the financing of terrorism account for a tiny fraction of the total number of financial transactions in most banks. This means that the strength of the “criminality signal” in the data is weak. Most machine learning models must therefore have vast quantities of data for them to work well – more than each bank has on its own.

Impact of a stronger “criminality signal”

If the banks were able to share data among themselves, the “criminality signal” would be strong enough for machine learning to work well. It would improve the banks’ electronic surveillance systems and increase the likelihood of the investigative effort being targeted more effectively:

  1. The total investigative load would be reduced.
  2. It would be possible to take practical steps to counteract real money laundering attempts at an early stage.

The problem with sharing data is that transactions contain personal data. But could this problem be resolved by means of federated learning?

What services does Finterai offer?

Finterai develops machine learning technology based on federated learning, a service which helps banks work together to combat financial crime. The model is trained to identify suspicious transactions partly on the basis of transaction history. Finterai's concept is for the banks to learn from each other’s datasets. However, this is made difficult by legal restrictions on the type of data the banks can share with one another.

In order for the banks to benefit from each other’s datasets without also sharing personal data, Finterai makes use of federated learning. Finterai’s ambition is to make it easy for a bank to develop robust investigative systems based on machine learning, in conjunction with other banks.