Summary
An AI fraud-detection system for banks that replaces SMS one-time codes with real-time voice verification and ML-based transaction risk scoring.
Problem you're solving
SMS-based two-factor authentication is trivially bypassed by SIM swaps, phishing, and now AI voice-cloned scam calls, yet it's still the default fraud check at most banks.
Target user
Banks and fintechs that need stronger transaction verification, plus their end users โ especially those without a smartphone or biometric hardware to fall back on.
Competitors
Legacy SMS 2FA is the incumbent being replaced; app-based biometric auth from banks and providers like Plaid excludes users without a smartphone.
Insight
The fraud problem isn't just detecting risky transactions โ it's verifying identity in a way AI voice cloning can't trivially defeat, and that verification shouldn't require a smartphone.
Solution
A serverless pipeline scores transactions in real time with an ML model, then confirms high-risk transactions via an AI-driven phone call using voice verification, with an optional facial-recognition layer for app users and a dashboard for fraud analysts.
- Next.js
- Tailwind CSS
- OpenAI
- RetellAI
- Supabase
Distribution
Built in a single hackathon weekend; next steps identified were partnering with fintechs for API integration and deploying a live prototype.
Adapting to user feedback
The core design tension โ balancing millisecond-speed verification calls against the risk of AI-cloned voices defeating the system โ was flagged as the top priority for the next iteration, ahead of voice liveness detection.