When and why should AI be used in AML practices? What are its benefits and risks?
What considerations should payments organisations take to comply with new AI regulation?
How are payments firms elevating their risk based assessments to keep up with financial crime compliance and compete more effectively?
- Gary Wright - Head of Research, Finextra
- Dr. Janet Bastiman - Chief Data Scientist, Napier
- Nitendra Rajput - Senior Vice President and Head of AI Garage, Mastercard
- Skyler Nesheim - CTO, Dwolla
The global payments sector is coming under increased regulatory scrutiny for its approach to Anti-Money Laundering (AML), where previously this spotlight was aimed more specifically at banks. Issuers, processors, and payment service providers need to invest to upgrade financial crime compliance technologies and processes. However, there are ways to manage and even offset this cost for payments organisations. Enter AI, which is fast becoming table-stakes in a modern AML approach. Leveraging AI for efficiency in AML operations is obvious, but must be done with an extreme focus on ‘explainability’ to be compliant.
While leveraging AI insights for product development and pricing differentials, and commercialising those insights back to customers, compliance with new data and AI regulations is critical.
The greater detail lies in really understanding how these models work, with explainability weaved in, so that analysts understand how specific decisions were made, and the discrepancy between a real anomaly versus evidence of money laundering.
Where and how payments firms approach AI and data regulation in the future will become major factors in the battle against financial crime and money laundering, as well as competitive differentiators in an increasingly crowded marketplace.
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Three recommendations to streamline and monetise financial crime compliance
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