Case Study · Banking & Financial Services
Real-Time Fraud and Risk Detection Across Millions of Daily Transactions
Detection Accuracy
97.3%
Decision Latency
<2ms
False Positives
−61%
Overview
Financial institutions using BankGuard achieve detection accuracy above 97% at full transaction volume with sub-2ms latency. Models are trained on each institution's own fraud history — not generic data — producing systems that reflect actual risk patterns.
The Challenge
Rules-based systems are easy to circumvent and generate high false positive rates. Generic ML models perform well on benchmarks but miss institution-specific patterns. The gap between claimed and actual performance is largest in production — where it matters most.
The BankGuard Approach
BankGuard trains on the institution's own historical transaction data, capturing the specific behavioural signatures of fraud at that institution. Deployment is into the live transaction pipeline with configurable thresholds and full regulatory audit trails on every decision.
Results
Institutions report 97%+ detection accuracy, with false positives 50-65% lower than previous systems. Sub-2ms latency means no impact on payment processing. Every flagged transaction generates an explainable audit trail satisfying regulatory model governance requirements.