Join product leaders from Alloy to uncover how financial institutions can use machine learning models to connect point-in-time checks from onboarding and beyond into unified, continuous customer risk profiles to stay ahead of fraud risk.
Recorded on July 16, 2026
Fraud prevention shouldn't slow down your best customers. But when detection is built around rules instead of intelligence, that's exactly what happens. Good users get flagged, teams get overwhelmed, and growth stalls at the point it should be accelerating.
This session is designed for innovative banks and credit unions who want faster, more intelligent fraud detection across the customer lifecycle without interrupting good customer experiences. In this webinar we cover:
Paige Johnston
Senior Product Manager, Alloy
Paige is a Senior Product Manager at Alloy. In her role, Paige focuses on making it easier for clients to connect and leverage their data within Alloy’s platform through initiatives like API development, batch data ingestion, and digital banking partnership optimization. With a strong emphasis on providing a high-quality developer experience, Paige helps ensure Alloy’s platform is both powerful and seamless to implement. Paige brings over seven years of fintech product experience to Alloy, with prior positions at MANTL, CommonBond and Bread, where she helped spearhead platform integrations to deliver the company’s white-label buy now, pay later product to new channels.
Fang Fang Nan
Senior Solutions Engineer, Alloy
Fang Fang Nan is a Senior Solutions Engineer at Alloy. In her role, Fang Fang partners with mid-sized and enterprise banks and credit unions to architect and implement complex integrations that connect their systems to Alloy’s holistic identity solution, supporting identity transformation initiatives and helping teams optimize performance while minimizing fraud risk. She previously led the Solutions Engineering team at a retail fraud AI/ML company and also brings nearly a decade of consulting experience specializing in ERP and end-to-end transaction implementations.
