How to Build AI-Powered Fraud Detection Products

What happens when AI is used by both fraudsters and the teams trying to stop them? In this webinar, Amazon Senior Technical Product Lead Manav Kapoor speaks on how product managers can build AI-powered fraud detection products. He explores why detection alone is no longer enough, how deepfakes and synthetic identity are reshaping the threat landscape, and what it takes to design enforcement systems that scale. From real-world case studies to a practical fraud PM framework, this session breaks down the hard tradeoffs product leaders must navigate to protect customers without damaging experience.

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Show Notes:

1. Fraud is the only product category where success is measured by invisibility—if nothing happens, the product is working.
2. Fraudsters use the same AI technologies as defenders, making it a constantly evolving adversarial arms race.
3. Detection maturity is high (92–98% accuracy in leading implementations), but enforcement maturity still lags.
4. Global fraud losses are accelerating rapidly, with AI-driven fraud significantly increasing the scale and sophistication of attacks.
5. Deepfakes and synthetic identities are reshaping fraud, enabling high-value attacks without malware or credential breaches.
6. A single enforcement gap—like missing human review for high-value actions—can result in massive financial losses.
7. Fraud is industrialized: coordinated networks of bad actors operate like structured businesses.
8. Fraud products create ecosystem-level impact, affecting reputation, partners, regulators, and long-term trust—not just transaction losses.
9. In co-branded credit cards, fraud complexity increases due to multi-stakeholder risk profiles and brand reputation exposure.
10. One fraud model cannot serve multiple partners equally; multi-tenant risk models are often necessary.
11. Consortium intelligence (shared fraud signals across institutions) significantly improves detection performance beyond isolated data models.
12. False positives are extremely expensive—costing more in lost revenue and customer trust than the fraud itself in many cases.
13. Fraud products are only as strong as their worst false positive experience.
14. Expanding existing AI models to more surfaces often delivers more impact than building entirely new models.
15. The biggest opportunity in the next 12–24 months lies in automating enforcement, not improving detection accuracy.
16. LLMs are better suited for enforcement tasks—such as evidence synthesis, policy mapping, audit trail creation, and customer communication—than for core detection.
17. Models decay over time; continuous retraining, feature engineering, and health monitoring are mandatory.
18. Traditional A/B testing fails in fraud environments due to ethical risk exposure, adversarial adaptation, and asymmetric fraud impact.
19. Safe deployment requires shadow scoring, backtesting, staged rollouts, expert review, and continuous monitoring.
20. The hardest tradeoff for fraud PMs is balancing precision with customer experience—over-optimization for fraud reduction can damage revenue and trust.