Hemang
Upadhyay
Senior Product Manager at LG Electronics USA
"From Signals to Decisions: Designing AI-Driven Fraud Detection and Real-Time Risk Forecasting Systems"
"From Signals to Decisions: Designing AI-Driven Fraud Detection and Real-Time Risk Forecasting Systems"
Wed May 13 - 12:10 PM EDT/New York (See in local time)
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05/13/2026 12:10 PM
05/13/2026 12:30 PM
America/New_York
#WTGC2026
"From Signals to Decisions: Designing AI-Driven Fraud Detection and Real-Time Risk Forecasting Systems"
#WTGC2026
"From Signals to Decisions: Designing AI-Driven Fraud Detection and Real-Time Risk Forecasting Systems"
https://www.womentech.net/ringcentral
https://www.womentech.net/ringcentral
"From Signals to Decisions: Designing AI-Driven Fraud Detection and Real-Time Risk Forecasting Systems"
"From Signals to Decisions: Designing AI-Driven Fraud Detection and Real-Time Risk Forecasting Systems"
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Session: From Signals to Decisions: Designing AI-Driven Fraud Detection and Real-Time Risk Forecasting Systems
Real-time financial transactions demand decisions in milliseconds, yet most fraud systems still rely on static rules, delayed batch models, or opaque scoring mechanisms that struggle to adapt to evolving threats.This session presents a practical, production-grade AI framework for fraud detection and risk forecasting built for real-time transaction flows. We will break down how machine learning, streaming architectures, and behavioral signals can be orchestrated to move from reactive fraud prevention to predictive, risk-aware decisioning.
Attendees will learn how to design systems that combine event-driven pipelines, feature stores, anomaly detection models, and feedback loops to continuously recalibrate risk at transaction time—without sacrificing latency, explainability, or regulatory compliance. The talk emphasizes architecture patterns, model strategies, and real-world tradeoffs, rather than theory or vendor-specific tools.
This session is ideal for technologists and product leaders looking to build or modernize fraud systems that operate reliably at scale in high-velocity financial environments.
Key Takeaways:
How to architect real-time fraud pipelines using streaming data and low-latency inference
Techniques for risk forecasting vs. binary fraud detection (probabilistic decisioning)
How to combine rules, ML models, and behavioral signals into a unified risk engine
Strategies for model explainability, monitoring, and continuous learning in regulated systems
Common failure modes in production fraud systems—and how to avoid them
Key Takeaways
- How to combine rules, ML models, and behavioral signals into a unified risk engine
- How to architect real-time fraud pipelines using streaming data and low-latency inference
- Techniques for risk forecasting vs. binary fraud detection (probabilistic decisioning)
- Strategies for model explainability, monitoring, and continuous learning in regulated systems
- Common failure modes in production fraud systems—and how to avoid them
Bio
Strategic Product leader with 15+ years delivering innovation and $240M+ in scalable solutions for top US E-commerce companies. Specialized in impactful product management and the integration of advanced automation and AI features including Sprinkler self-service chatbots, Coveo AI-powered search results, and AI-driven product recommendations for LG Electronics. Driven by results, I empower cross-functional teams to launch cutting-edge digital products for US markets, championing agile process improvement, deep user understanding, and high customer retention (20%+ gains). Skilled in translating complex strategies into actionable roadmaps and measurable business outcomes. Core Skills: Product Strategy • AI/ML-Driven Products • Generative AI Tools • Chatbots • Automation at Scale • Data-Driven Roadmaps • Agile Product Leadership • Customer Experience • Go-to-Market Strategy • Stakeholder Management • Product Analytics • UAT & Wireframing • KPIs/OKRs • User Stories • B2C/B2B transformation