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The use of AI in AML transaction monitoring

How much do you know about AI in AML?

The need for future-proof compliance software has been increasing. Both regulators and financial institutions agree that existing processes and platforms lack flexibility and are no longer efficient in detecting and combating the new, more sophisticated illicit activities.

"An institution has an automated and self-learning transaction monitoring system commensurate with its risk profile" - Post-event transaction monitoring process for payment service providers, Dutch National Bank (DNB).

Automation enhanced by machine learning has become a strong recommendation from European regulatory guidance. Nevertheless, before implementing artificial intelligence to your processes it is important to understand how it works.

Introduction

Financial institutions are on the frontline in the battle to combat financial crimes, such as fraud, money laundering and terrorism financing. These criminal activities impose massive costs on the global economy both financially and socially.

To combat these crimes, regulatory authorities have adopted increasingly comprehensive and demanding regulations that put the onus on financial institutions to identify, process, and alert the authorities of suspicious transactions.

However, around the globe, banks are struggling to make meaningful inroads into the problem despite significantly increasing financial investment and expanding compliance teams year after year.

Clearly, the approach to anti-money laundering (AML) detection and prevention needs to change – and technology, like artificial intelligence (AI) and machine learning (ML) may provide part of the solution...