ScamShield Case Study

My Contribution

Product Strategist & Team Lead

• Defined the 0-to-1 product vision Identified the opportunity for a consumer-facing scam detection tool and established the strategic direction, balancing AI capabilities with user trust and transparency.
• Led AI governance and risk mitigation Established guardrails for responsible AI use in fraud detection: explainability requirements, false positive thresholds, and bias monitoring protocols.
• Drove UX strategy for trust and clarity Ensured every alert explained why something was flagged, avoiding black-box AI patterns that erode consumer trust. Prioritized clarity over comprehensiveness.

The Problem

Scams are evolving faster than consumers can adapt

AI-generated scams are growing in volume and sophistication. Phishing emails now mimic legitimate institutions with near-perfect accuracy. Deepfake voice calls impersonate family members. Consumers are increasingly unable to distinguish real from fraudulent, and existing tools are reactive at best, notifying victims after the damage is done.

Vulnerable populations, including older adults and non-native speakers, are disproportionately targeted. The gap between scam sophistication and consumer readiness is widening, and no consumer-facing tool addresses detection in real time with clear, actionable guidance.