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The EU’s AML/CFT framework presents the banking industry with a dilemma: how can banks fulfil their KYC obligations, while also protecting the confidentiality of client data? According to INNOPAY’s Vince Jansen, three emerging types of ‘trust tech’ could provide the answer for transaction monitoring. “Artificial intelligence offers the potential for banks to achieve higher standards of compliance at lower costs, and trust tech could enable them to deploy AI at scale,” he says.

The EU’s anti-money laundering (AML) and countering the financing of terrorism (CFT) supervisory and regulatory framework presents a conflict of interests for the banking industry. On the one hand, to fulfil their responsibility as the first line of defence and avoid the risk of fines, banks are legally obliged to monitor clients (Know Your Customer), spot anomalies in transactions data and share information about potentially suspicious behaviour with governments and supervisory bodies. On the other hand, they have a legal responsibility to protect the confidentiality of client data. Today, this leaves the banking industry struggling with a €90bn cost problem in the EU alone.

The potential power of AI

So how can this conflict of interests be resolved? The topic of artificial intelligence (AI) seems to be on everyone’s lips nowadays, and there are a growing number of real-life use cases in which the adoption of AI is solving problems and giving all kinds of businesses a competitive advantage. Indeed, used at scale, AI offers the potential for banks to achieve higher standards of compliance at lower costs. After all, a correctly trained AI-based prediction model would be much more powerful in detecting otherwise indiscernible links or patterns in the complex web of transaction data – and ultimately require less human intervention – than the banks’ current data analysis methods. 

The opportunities and risks of data pooling 

When it comes to successful AI use cases, however, the crucial keyword is ‘training’. To achieve optimally accurate predictions and self-learning, an AI prediction model must be trained on huge volumes of data from multiple, dependable sources. In the ideal situation in the AML/CFT context, the data from all banks should be ‘pooled’ into a combined dataset. 

This idea – the use or adoption of AI in a collaborative environment – isn’t new. In fact, it is even suggested by the EU. But as mentioned above, this concept of ‘data pooling’ makes it difficult for banks to guarantee that client data will be sufficiently protected and securely shared, without risks of security breaches. As a result, the use of AI at scale in the banking industry appears to have reached an impasse. 

Emerging trust tech could overcome the impasse

At INNOPAY, we believe that the following three emerging types of ‘trust tech’ (sometimes also referred to as Privacy Enhancing Technologies or PETs) have the potential to play a role in the context of data pooling for transaction monitoring purposes: