- Gurjeet Singh, Chief executive and co-founder, Ayasdi, and member of the technical advisory boards for HSBC and the Commodity Futures Trading Commission
- Patrick Dutton, Regional head of intelligence, analytics and systems delivery, financial crime threat mitigation, HSBC North America Holdings
- Scott Nathan, Managing director, financial intelligence and anti-money laundering utility lead, Accenture Financial Services
- Jim Arndts, Senior vice-president, director of strategy, transformation and governance enterprise financial crimes compliance, US Bank
Financial crime – particularly money laundering – is a critical element in the overall risk profile of a bank. Fines associated with failure to have effective institutional controls in place can extend from hundreds of millions to billions of dollars. Furthermore, the complexity of the challenge has increased significantly in recent years as the sophistication of money launderers has grown.
Faced with these significant challenges, many banks have responded by massively scaling their anti-money laundering (AML) functions. Sophisticated financial institutions, however, are turning to machine intelligence to develop innovative approaches that eliminate risk, improve operational efficiency and retain flexibility to evolve with threats. More importantly, many of these advancements work within the existing AML infrastructure by finding the optimal lever points to inject intelligence.
In this webinar, a panel discusseses current trends and practical challenges – as well as how innovators are working to overcome them. Key topics under discussion include:
- How banks deal with the challenge of dynamic segmentation to counter an ever-changing threat
- How institutions can expand the features they are able to incorporate into their models while still satisfying regulators
- How to identify lever points in an AML software stack that will deliver the largest return on investment
- Emerging approaches to handling unlabeled data.