AI Fraud Detection for Fintech

Third party fraud vendors are trained on generic patterns across their book of merchants: great baseline, but they can't see your customer graph. Our systems run on your data: your device fingerprints, your merchant history, your specific fraud patterns. Most fintechs run both: vendor for baseline, our graph for defensibility and lift.

Yes. Common pattern: your existing vendor makes the auth decision; our graph runs in parallel and flags rings, ATO patterns, and synthetic identities the vendor missed for offline review. Over time, high confidence graph decisions get promoted into the live auth path.

Every decline includes an audit trail: which features triggered which threshold, which graph paths were involved, which model version made the call. For adverse action eligible declines we emit ECOA compliant reason codes that trace to specific graph features.

Common benchmarks: 30 to 50% reduction in fraud losses on the categories the graph specifically targets (rings, ATO, synthetic identity), and 10 to 20% reduction in false positive declines because the graph provides context traditional models miss. Payback is typically 6 to 12 months at fintech scale.

8 to 12 weeks. Weeks 1 to 3: data audit, feature engineering, historical backtest. Weeks 4 to 8: production graph build + real time scoring path. Weeks 9 to 12: shadow mode alongside your existing system, then cutover with your fraud ops team.