7.7% of revenue lost to fraud. $534 billion globally. Your rule-based system misses what it has never seen before. AI scores in milliseconds, catches novel patterns, and cuts false positives by 80%. Production, not proof of concept.
Measured results from deployed systems, not lab benchmarks
90% fraud identification accuracy. 65-90% better than rule-based systems. 91% of U.S. banks already use AI detection. The gap between AI and rules grows every quarter.
50-80% fewer false alerts. Less customer friction. Lower review costs. Your analysts investigate real fraud, not noise.
4.8:1 average return. 400-580% within 8-24 months. The math is straightforward: every dollar of fraud prevented drops to the bottom line.
Scores, detects, blocks, and learns. At transaction speed.
Hundreds of signals evaluated in milliseconds - device, location, behavior, amount, velocity. Risk decision returned before the transaction completes.
Per-account behavioral profiles. Spending anomalies, login deviations, unusual sequences - detected. Account takeovers caught. Static rules miss this.
$35 billion in synthetic identity fraud. Fabricated identities that pass traditional checks. AI cross-references identity elements and catches them.
ML models plus configurable business rules. Thresholds by product, channel, risk appetite. Rules update as fraud evolves. No manual tuning cycles.
Accounts, devices, addresses, payment methods - relationships mapped. Fraud rings and coordinated attacks identified through connection graph analysis.
Human review needed? Analyst gets full context - score breakdown, similar cases, recommended actions. Investigation time cut. Attention focused where it matters.
Deployed across payments, insurance, lending, and commerce
Card-present and CNP scored in real time. CNP is 50% of ecommerce fraud. Device fingerprints, behavioral biometrics, transaction context - fraud blocked without blocking customers.
Claims analyzed against historical patterns and provider networks. Staged accidents, inflated damages, repeat offenders - flagged. Investigations prioritized by probability and potential loss.
Login patterns monitored. Session behavior tracked. Credential stuffing, SIM swaps, social engineering indicators - detected before funds move.
Structuring, layering, suspicious patterns monitored. AML false positives reduced dramatically. Compliance teams freed from alert fatigue. Regulatory coverage maintained.
42% of issuers saved $5M+ in fraud over two years with AI. Forward Deployed Engineers get detection running fast.
Average across surveyed financial institutions
Fraud does not wait for your POC to finish. Neither should your detection.