Resistant AI

Fraud fighters face a mounting challenge as synthetic identities, AI-generated documents, and sophisticated transaction patterns evolve faster than traditional detection systems can adapt

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Fraud fighters face a mounting challenge as synthetic identities, AI-generated documents, and sophisticated transaction patterns evolve faster than traditional detection systems can adapt. Resistant AI addresses this through a unified system that screens documents and transactions simultaneously, catching fraud that single-layer systems miss.

The document fraud detection component processes any document type from any country, identifying fake credentials, tampered files, and AI-generated forgeries in under 20 seconds per document. This matters when processing loan applications, tenant screenings, or insurance claims where forged paychecks, manipulated bank statements, or synthetic identity documents regularly slip through manual reviews. The system doesn't just flag suspicious documents. It provides fully explainable AI models that show analysts exactly why a document got flagged, making the difference between actionable intelligence and vague risk scores.

Transaction monitoring works differently than traditional rules-based systems. Rather than replacing existing infrastructure, Resistant AI enhances current transaction monitoring setups with 80-plus off-the-shelf models targeting specific fraud types. These models detect authorized push payment fraud where customers get tricked into sending money to scammers, Buy Now Pay Later fraud exploiting installment payment schemes, mule accounts used to launder stolen funds, and synthetic identities built from real and fabricated information. Detection happens in under 100 milliseconds, fast enough for real-time payment screening without creating customer friction.

The defense-in-depth approach combines document verification, transaction patterns, behavioral signals, and identity checks into a single analysis. A fraudster might present a legitimate-looking document but exhibit transaction behaviors inconsistent with the stated income, or create a synthetic identity that passes document checks but fails behavioral analysis. This layered detection catches three times more fraud than single-method systems according to performance metrics.

Productivity gains reshape analyst workflows substantially. Manual review volume drops by over 90 percent as the system handles obvious legitimate cases and clear fraud automatically. Analysts work five times faster and cover three times more risk scenarios compared to legacy tooling. The 8,000-plus fraud and compliance analysts using this system work across lending institutions, banks, payment processors, insurance companies, marketplaces, tenant screening services, employment verification firms, and investment platforms.

Integration happens through AWS and connects with existing transaction monitoring systems without requiring full replacement. Organizations keep their current tech stack while layering in advanced detection capabilities. Real-time operation means decisions happen during transactions, not hours later during batch processing when fraudulent payments already cleared.

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