Parul
Naib
Post Doc Researcher at University of Central Florida
"Hunting the Invisible: Learnings from building MuleHunter.ai to Detect Financial Crime at Scale"
"Hunting the Invisible: Learnings from building MuleHunter.ai to Detect Financial Crime at Scale"
Fri May 15 - 10:30 AM EDT/New York (See in local time)
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05/15/2026 10:30 AM
05/15/2026 10:50 AM
America/New_York
#WTGC2026
"Hunting the Invisible: Learnings from building MuleHunter.ai to Detect Financial Crime at Scale"
#WTGC2026
"Hunting the Invisible: Learnings from building MuleHunter.ai to Detect Financial Crime at Scale"
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"Hunting the Invisible: Learnings from building MuleHunter.ai to Detect Financial Crime at Scale"
"Hunting the Invisible: Learnings from building MuleHunter.ai to Detect Financial Crime at Scale"
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Session: Hunting the Invisible: Learnings from building MuleHunter.ai to Detect Financial Crime at Scale
Financial institutions are facing a rapidly evolving threat landscape where money mule networks have become a critical enabler of fraud at scale. These networks are highly adaptive, distributed, and increasingly difficult to detect using traditional rule-based systems. In this talk, we look at experiences from the development of MuleHunter.ai- an AI-driven platform built to uncover hidden mule activity by shifting the paradigm from transaction monitoring to behavioral and network intelligence.We will walk through how combining anomaly detection and supervised models enables the identification of coordinated fraud rings rather than isolated suspicious events. The session will highlight key challenges, including fragmented data ecosystems, limited labeled datasets, real-time decision constraints, and the need for explainability in regulated environments.
Beyond the technical architecture, the talk will focus on operational realities- how to integrate AI into fraud workflows, balance customer experience with risk mitigation, and continuously adapt to adversarial behavior. Attendees will gain practical insights into building scalable, trustworthy AI systems for financial crime detection, along with lessons learned from deploying MuleHunter.ai in a production banking environment. The session concludes with a forward-looking perspective on the role of AI in combating financial crime, including the potential of graph intelligence, synthetic data, and collaborative ecosystems across institutions.
Key Takeaways
- The most effective financial crime detection systems combine AI’s ability to uncover hidden patterns at scale with human expertise for judgment and context, creating a human-augmented AI approach that is adaptive, explainable, and operationally trusted.
Bio
Dr. Parul Naib is the ex-Head of AI and Data Science at the Reserve Bank Innovation Hub (RBIH), where she led the development of AI-driven solutions to combat financial fraud, including the flagship MuleHunter.ai platform. With over 20 years of global experience, she has built and led data science and AI teams across leading organizations such as American Express, the World Bank, UnitedHealth Group, and Providence Healthcare.Her work spans fraud detection, risk management, and digital health, and she has contributed to large-scale national initiatives such as Ayushman Bharat and AI-led financial crime prevention systems.
Dr. Naib holds a PhD in Public Health from IIHMR University, a Master’s degree in Economics from the Delhi School of Economics, and a professional certification in Risk Management from Stanford University. Her work focuses on building scalable, ethical AI systems that drive impact in regulated and high-stakes environments.
