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Document Intelligence
Stop fraud before it costs you
Capabilities
Score every transaction against 200+ fraud signals in under 50 milliseconds. Detect anomalous patterns, velocity attacks, and synthetic identities without adding friction to legitimate transactions.
Identify altered, forged, and fabricated documents including modified bank statements, photoshopped IDs, and tampered certificates. Detect pixel manipulation, font inconsistencies, and metadata anomalies.
Map connections between accounts, devices, and identities to uncover fraud rings and collusion patterns. Identify shell accounts, mule networks, and organized fraud operations through graph analysis.
Models retrain continuously on new fraud patterns, reducing the detection lag that rule-based systems suffer. Novel fraud techniques are identified through unsupervised anomaly detection before they are formally categorized.
Use Cases
According to the Association of Certified Fraud Examiners, organizations lose 5% of revenue to fraud annually, with a median loss of $117,000 per case. The AI Fraud Detection platform analyzes every transaction across 200+ signals including amount patterns, geographic anomalies, device fingerprints, and behavioral biometrics in under 50 milliseconds. A 2024 LexisNexis True Cost of Fraud study found that AI-powered fraud detection reduces false positive rates by 70% compared to rule-based systems while catching 95% of fraudulent transactions. The platform identifies account takeover attempts by detecting login anomalies — unusual devices, impossible travel speeds, and behavioral pattern deviations. Synthetic identity fraud, where criminals combine real and fabricated identity elements, is detected through cross-database verification and identity graph analysis. Real-time decisioning blocks fraudulent transactions at the point of authorization while legitimate customers experience no additional friction. Investigation dashboards provide analysts with complete case files including transaction timelines, network connections, and risk scores for efficient case resolution.
The National Health Care Anti-Fraud Association estimates that healthcare fraud costs the industry $68 billion annually, representing 3-10% of total healthcare expenditure. The platform analyzes insurance claims for billing anomalies, phantom patients, upcoding patterns, and duplicate submissions across provider networks. A 2025 Coalition Against Insurance Fraud study found that AI fraud detection identifies 40% more fraudulent claims than traditional audit methods while processing claims 5x faster. Document verification detects altered medical records, fabricated prescriptions, and modified diagnostic reports through pixel-level image analysis and cross-reference with pharmacy and laboratory databases. Provider network analysis identifies suspicious referral patterns, unusual billing relationships, and geographic impossibilities — such as a patient receiving treatment at two facilities 500 kilometers apart on the same day. Pre-payment fraud detection blocks suspect claims before payment disbursement, recovering an average of $23 for every $1 invested in AI fraud prevention according to healthcare payer industry benchmarks.
According to Juniper Research, e-commerce merchants lost $48 billion to online payment fraud globally in 2023, with card-not-present fraud accounting for 73% of losses. The AI platform screens every online transaction for fraud indicators including mismatched billing and shipping addresses, high-velocity card testing, device reputation signals, and behavioral anomalies during the checkout process. A 2024 Visa Digital Commerce study found that AI fraud screening reduces chargebacks by 60% while approving 8% more legitimate transactions that rule-based systems would have incorrectly declined. The system detects account takeover attacks where stolen credentials are used to make purchases, identifying login anomalies and purchase pattern deviations that indicate compromised accounts. Promotional abuse detection identifies customers exploiting referral programs, coupon stacking, and return fraud through cross-account behavior analysis. Friendly fraud — where legitimate customers dispute valid charges — is flagged through compelling evidence collection that includes device fingerprints, delivery confirmation, and behavioral session replay data.
Frequently Asked Questions
Rule-based systems rely on predefined conditions (e.g., flag transactions over a certain amount from specific countries) that criminals learn and circumvent. AI fraud detection analyzes 200+ signals simultaneously, detects novel patterns that rules cannot anticipate, and adapts to new fraud techniques continuously. Rule-based systems generate 90%+ false positives; AI reduces false positives by 70% while catching more actual fraud.
Yes. The platform analyzes document images for pixel manipulation, font inconsistencies, EXIF metadata tampering, and compression artifacts that indicate editing. It compares submitted documents against known-good templates and cross-references extracted data with external databases. Bank statements, identity documents, income proofs, and certificates are all covered. Detection accuracy exceeds 96% for common forgery techniques.
Transaction scoring completes in under 50 milliseconds, adding negligible latency to payment processing flows. The system is designed for inline deployment — it sits in the transaction authorization path and returns approve/decline/review decisions before the payment gateway processes the transaction. This speed supports real-time fraud prevention rather than post-transaction detection.
Yes. Reducing false positives is a primary design goal. The AI considers the full customer context — purchase history, behavioral patterns, device reputation, and session behavior — rather than isolated transaction attributes. This contextual analysis reduces false positives by 70% compared to rule-based systems, meaning fewer legitimate customers are declined or subjected to unnecessary verification steps.
The platform serves banking and financial services (transaction monitoring, KYC verification), insurance (claims fraud, identity verification), e-commerce (payment fraud, account takeover), healthcare (billing fraud, prescription fraud), government (benefits fraud, identity fraud), and telecommunications (subscription fraud, SIM swap detection). Each industry deployment includes pre-configured models trained on industry-specific fraud patterns.
Tell us what you're trying to solve. We'll show you exactly how AI Fraud Detection fits your operations.