Fraud detection, behavioral analysis, and compliance checks on fully encrypted financial data. No decryption, no plaintext windows, no latency tradeoffs. Your AI models run at wire speed on ciphertext your infrastructure never sees in the clear.
AI fraud detection systems process account numbers, transaction histories, and behavioral patterns. Traditional systems decrypt to analyze — creating an exposure window at every stage of the pipeline.
Every fraud model that ingests raw account data creates a window where that data exists unencrypted in memory. One compromised node, one rogue process, one core dump — and account numbers, SSNs, and transaction histories are exposed.
Fraud scoring pipelines generate intermediate results, debug logs, and cache entries containing financial PII. These artifacts persist in infrastructure you don't fully control — cloud provider caches, CDN edge nodes, observability platforms.
Any engineer with access to the fraud detection pipeline has access to raw financial data. Database admins, DevOps, ML engineers, vendor contractors — each is a potential exfiltration vector. Traditional DLP cannot prevent authorized access abuse.
GLBA, PCI DSS, SOX 404, and state-level privacy laws impose strict data handling requirements. A single compliance failure can result in regulatory action, consent orders, and fines that dwarf the cost of the security program.
Global banks must comply with data residency requirements across dozens of jurisdictions. Moving financial data across borders for centralized fraud analysis creates compliance exposure that FHE eliminates by keeping data encrypted in transit and at rest.
Nation-state adversaries are capturing encrypted financial data today to decrypt when quantum computers mature. RSA and ECC-protected transaction records have a shelf life. Post-quantum encryption from H33 ensures financial data remains protected against future threats.
Encryption adds latency. Banks need sub-millisecond decisions for real-time payments, trading, and fraud scoring. Every millisecond matters. Traditional DLP scans add 50–200ms of overhead — overhead that H33 eliminates entirely.
H33 runs fraud detection, behavioral analysis, and compliance checks on fully encrypted data. 38.5µs per operation. No decryption, no plaintext window, no exposure. The fraud model sees ciphertext and produces a match score — without ever accessing the underlying financial data.
Purpose-built security products for financial services. Each product runs on H33's FHE pipeline — encrypted end-to-end, attested with post-quantum signatures, verified with zero-knowledge proofs.
Behavioral AI fraud detection that never sees your customers' data. Real-time transaction scoring, synthetic identity detection, and account takeover prevention — all on encrypted ciphertext. Sub-500ms P99 latency.
Explore FraudShield →Cross-bank encrypted fraud intelligence network. Share fraud signals without sharing customer data. FHE-encrypted contributions are aggregated homomorphically. GLBA, CCPA, and GDPR compliant by architecture.
Explore H33-Share →Post-quantum key management for financial infrastructure. NIST FIPS 203 (ML-KEM) and FIPS 204 (ML-DSA) compliant. HSM integration, automated key rotation, and quantum-safe key exchange for banking APIs.
Explore VaultKey →Secure API gateway for banking and payment APIs. Post-quantum TLS termination, encrypted request routing, and real-time threat detection. Every API call is attested with Dilithium signatures.
Explore H33-Gateway →SOX 404 continuous control monitoring, automated audit evidence collection, and real-time regulatory change detection. AI-powered compliance that processes financial controls on encrypted data.
Explore AI Compliance →Invisible bot protection for online banking portals. SHA-256 proof-of-work replaces CAPTCHA — no cookies, no fingerprinting, no personal data collection. Free for 2,500 challenges/month.
Explore BotShield →H33-Share enables banks to share fraud signals without exposing customer data. FHE-encrypted signals are accumulated homomorphically. Each bank contributes encrypted fraud indicators; the aggregation produces intelligence without any bank seeing another's raw data.
Fraud signals — entity feature vectors, velocity indicators, confirmed fraud markers — are encrypted using BFV homomorphic encryption inside each bank's HSM. Private keys never leave the institution.
H33 receives encrypted contributions from all consortium members. Homomorphic addition accumulates fraud indicators on ciphertext. No bank's raw data is visible to H33 or any other member at any point.
Consortium queries return encrypted risk scores computed across all member contributions. Only the querying bank can decrypt its own results. ZK-STARK proofs verify computation integrity without revealing inputs.
Each confirmed fraud alert makes the network smarter for every member. Banks that contribute more fraud intelligence receive higher query quotas and better pricing — incentivizing participation and improving detection accuracy across the consortium.
Built for the latency requirements of real-time payments, card-present authorization, and high-frequency compliance checks. No GPU required. Runs on commodity ARM hardware.
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Schedule a 30-minute technical deep dive with our financial services team. We'll walk through the FHE pipeline, demonstrate encrypted fraud scoring, and answer your compliance team's questions.