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Napier AI’s podcast series aims to connect trending fintech topics with financial crime compliance, equipping financial institutions with the insights they need to disrupt financial crime.
In our fifth episode, Napier AI’s Data Scientist, Marcus Markland, and Paysafe’s VP Strategic Initiatives, Giacomo Austin, are joining our podcast to discuss the data science behind machine learning (ML) for financial crime compliance, and the pragmatic steps to implementing it in a compliance team.
Staying one step ahead of money launderers has long been a headache for compliance teams. The Payments sector is facing regulatory scrutiny. Even for highly qualified data analysts working with advanced machine learning systems, the challenge of identifying complex illegal transactions that are designed to blend with legal ones, is considerable.
To detect illicit financial flows and patterns in a tsunami of incoming data, with limited human resources, makes countering the increasingly sophisticated methods employed by criminals all the harder.
In this podcast episode, we cover:
- How machine learning speeds up decision-making and operational efficiency in compliance teams
- The difference between supervised and unsupervised machine learning models
- Practical examples of using machine learning models in FCC
- How to correct the danger of machine learning biases in compliance teams
- The future of implementing ML in payments organisations
Want to learn more about the right way to implement AI? Download our eBook ‘5 key AI AML considerations for payments firms’.
