The new reality: Smarter strategies with AI
Tax refund season represents one of the most demanding and high-risk periods for financial institutions. As ACH transaction volumes increase significantly, so does the potential for fraud—especially from increasingly advanced mule networks and synthetic identities that are designed to blend in with legitimate refund activity. In such a high-volume environment, traditional detection methods can struggle to keep pace, leaving institutions more vulnerable to financial loss, compliance challenges, and operational pressure.
This session will help financial institutions confidently navigate the complexities of tax refund season. You’ll learn how to measure and model ACH volume surges driven by refund activity, giving you greater visibility into operational pressures and often-overlooked risk gaps. We’ll also explore how self-learning behavioral analytics can improve detection speed and accuracy by identifying mule accounts and synthetic identities before funds are posted, enabling earlier and more effective intervention.
Additionally, the session will cover ways to reduce risk exposure through automated, Nacha-aligned, risk-based holds and returns, supporting compliance with upcoming 2026 refund fraud requirements. Finally, you’ll discover how cloud-native FRAML workflows can help you scale efficiently—handling increased transaction volumes while minimizing false positives and maintaining operational control. These insights will help your organization stay ahead of emerging threats, safeguard customers, and turn the challenges of tax season into a strategic advantage.
Speakers
