Stop payment fraud, not business with AI-powered detection

PaymentsProtection.ai uses dynamic AI- and ML-powered rule engines to continuously monitor every transaction in near real-time, classifying each as genuine or fraudulent.

It builds a precise picture of normal behavior and spots deviations that signal fraud by evaluating all activity—not just flagged events—and learning from patterns across 215 million annual transactions. 

PaymentsProtection.ai analyzes a wide range of signals, including velocity checks, device fingerprinting, anomaly detection, blacklists, and configurable rules.

It tracks key risk indicators like ACH return rates, credit card chargeback and decline rates, unusual transaction volumes, and behavioral patterns—covering threats like account takeovers, card testing, and bust-out fraud.  

Industry-tuned AI models and sophisticated rule sets are specifically designed to reduce false positives—the platform targets a 1:3 ratio of confirmed fraud to false positive alerts.

By using adaptive, continuously learning intelligence rather than rigid rules, it approves legitimate transactions while blocking bad ones, targeting a 50–70% reduction in fraud losses without slowing the customer experience.

Absolutely. Clients work directly with risk experts to define custom thresholds that automatically decline high-risk transactions, flag suspicious activity, or escalate cases for review.

Rules and configurations can be refined on a periodic basis without costly service engagement contracts, and the platform is built to reflect each client's unique industry, payment mix, and risk tolerance.

Deployment is designed to be fast and low-lift—existing payment processing clients can be up and running in weeks with minimal coding required.

The platform complements existing tools by consolidating cross-channel monitoring across ACH, credit and debit cards, and digital wallets into one view, closing visibility gaps that fragmented point solutions typically leave behind.

Escalated alerts can be routed to either the client's own risk team or managed by fraud analysts on their behalf, based on agreed SLAs.