The Problem Banks Already Know They Have
A single mortgage application contains the borrower's full name, Social Security number, date of birth, spouse's name, children's names, employer, annual income, bank account numbers, credit card numbers, credit score, and complete address history. That file passes through brokers, processors, underwriters, title companies, appraisers, and closing attorneys — 15 or more parties, each with a copy of everything.
One breach at any vendor in that chain triggers a class-action lawsuit, regulatory investigation, and reputational damage that no amount of credit-monitoring can offset. The 2024 financial services average breach cost was $5.9 million, with mortgage-origination breaches routinely exceeding $20 million when litigation and remediation are included.
And that is just mortgages. Wire transfers, fraud detection, AML screening, biometric authentication, credit decisioning, and regulatory reporting all carry the same structural flaw: the systems that process sensitive data must see that data in plaintext. Until now.
H33-Banking uses Fully Homomorphic Encryption (FHE) to compute on data that remains encrypted throughout the entire lifecycle. The processing system never sees the plaintext. The result is decryptable only by the authorized party. Zero-knowledge proofs attest that the computation was performed correctly, and post-quantum signatures ensure every handoff is harvest-proof through 2035 and beyond.
1 Encrypted Mortgage & Loan Underwriting
Every mortgage touches 15+ parties. Every party sees raw SSNs, income, credit scores, spouse names, children's names. One breach at any vendor and it is a class action. The CFPB is actively tightening third-party oversight requirements, and Fannie Mae's Desktop Underwriter already processes millions of applications per month — each one in plaintext.
H33-Banking keeps the entire loan file encrypted end-to-end. Underwriting models run on ciphertext via CKKS approximate arithmetic. Credit decisioning happens without the system ever seeing plaintext income or debt-to-income ratios. ZKP attestation proves the decision was computed correctly on the actual encrypted data. Dilithium signatures (ML-DSA, FIPS 204) sign every handoff between parties.
Eliminates the number-one source of mortgage data breaches — third-party vendor exposure. The CFPB's 2025 guidance on third-party risk management makes banks liable for vendor breaches. H33-Banking removes the liability by removing the plaintext.
2 Cross-Bank Encrypted Fraud Network (H33-Share)
Fraud rings win because banks cannot share what they know. Bank A's fraud team wants to ask Bank B: "Have you seen this SSN open three accounts in 30 days?" But the Gramm-Leach-Bliley Act (GLBA) and state privacy laws make sharing customer data between institutions illegal. The result: fraud rings exploit the gap between banks to the tune of $2–4 billion per year across the top-10 institutions alone.
H33-Share solves this with FHE-BFV encrypted scoring. Bank A encrypts the query SSN. Bank B runs an encrypted match against their encrypted watchlist. Neither bank sees the other's data. No plaintext crosses any firewall. ZKP proves the match result is honest. The output is binary — "match" or "no match" — and nothing else leaks. Signal contributions are free. Score queries start at $0.06 and drop to $0.006 at volume.
Consortium fraud detection has been the industry's white whale for 20 years. Nobody has solved the legal and privacy barrier. FHE eliminates it entirely. No GLBA violation because no plaintext is shared. No regulatory exposure because no customer data leaves the bank's encryption boundary.
3 Biometric Vault (Zero-Knowledge Customer Identity)
Banks are racing toward biometric MFA — face, voice, fingerprint. But after the Clearview AI lawsuits and Illinois BIPA ($650M+ in settlements), storing biometric templates in the clear is a ticking legal bomb. Texas, Washington, and the EU have followed with their own biometric privacy laws. Every stored template is a potential class-action trigger.
H33's Biometric Vault performs enrollment and matching entirely on ciphertext. The bank never possesses an unencrypted biometric template. Matching runs at 32 users per FHE batch at 38.5 microseconds per authentication. BIPA-proof, GDPR-proof, and CCPA-proof by architecture, not by policy or contract language.
Every bank wants biometric auth. Every bank's legal team is terrified of biometric data liability. This removes the liability entirely — you cannot leak what you never had in the clear.
4 Quantum-Safe Wire Transfer Signing
SWIFT and Fedwire move over $5 trillion per day. Wire fraud cost banks $1.8 billion in 2024 alone. Nation-states are already harvesting encrypted wire instructions for future quantum decryption — the "harvest now, decrypt later" attack. A single compromised wire instruction could redirect $100 million or more. NIST's post-quantum migration deadline is 2035, but the OCC is already asking about PQ readiness in examination cycles.
H33-3-Key attestation signs every wire transfer with a triple-nested signature chain: Ed25519 for immediate classical verification, Dilithium (ML-DSA) for quantum-safe binding, and FALCON for compact proof-of-origin using an independent lattice family. Temporal binding mathematically proves the wire instruction existed before any potential key compromise event.
Banks that get breached via quantum attack in 2030 because they did not upgrade by 2027 will face existential regulatory and litigation consequences. Wire transfer data harvested today is already at risk. The cost of a single fraudulent wire dwarfs a decade of H33 licensing.
5 Encrypted Regulatory Reporting
Banks submit Call Reports, CCAR stress tests, and BSA/AML filings containing millions of individual customer records to the OCC, FDIC, and Federal Reserve. Regulators need to verify the math — they do not need to see every account. But current reporting sends the raw underlying data, creating massive exposure surfaces. The 2024 FDIC data handling incidents underscored how even regulators themselves are vulnerable to data mishandling.
The bank computes regulatory aggregates on FHE-encrypted customer data. It submits the encrypted report along with a ZKP proving the aggregation was computed correctly on the real underlying data. The regulator can verify mathematical correctness without accessing a single individual customer record. Exposure drops by 99 percent while compliance remains provably complete.
Regulators themselves want this. After years of data-handling scandals, both sides of the reporting relationship benefit from reducing the plaintext surface. The bank reduces exposure; the regulator gets verifiable correctness without custody risk.
6 Encrypted Credit Decisioning & Fair Lending Proof
DOJ and CFPB fair lending investigations cost banks $100 million or more in settlements. The fundamental problem: proving a credit model is not discriminatory requires exposing the model internals during litigation discovery, which banks consider trade secrets. Current fair lending audits are statistical approximations that take months and still leave room for legal challenge.
Run the credit scoring model on CKKS-encrypted applicant data. ZKP attestation proves the model produced the same output regardless of protected-class inputs — race, gender, age — without revealing the model weights or the applicant data. The result is a mathematical fair lending proof, not a statistical estimate. Both the bank and the regulator get definitive answers without exposing anything.
Replaces years of litigation discovery with a cryptographic proof that takes seconds. The bank protects its model IP. The regulator gets mathematical certainty instead of statistical inference. Both sides win. The first bank to adopt this sets the standard every other bank gets measured against.
7 Encrypted AML/Sanctions Screening
Every transaction gets screened against OFAC, FinCEN, and internal watchlists. The screening vendors — Actimize, Oracle FCCM, Fircosoft — see every customer name and every transaction. One vendor breach exposes the entire customer ledger. And BSA/AML screening is non-negotiable: banks that skip it lose their charter. The result is a forced dependency on third-party vendors who hold the keys to the kingdom.
FHE-encrypted name and entity matching against encrypted watchlists. The Boolean evaluator handles fuzzy matching (Levenshtein distance, phonetic similarity) entirely on ciphertext. The screening engine never sees a single customer name in plaintext. Alerts are returned encrypted and decryptable only by the bank's compliance team.
BSA/AML is mandatory. Every bank must screen. But the third-party risk of handing a vendor your entire customer ledger is enormous. H33-Banking solves both requirements simultaneously: full OFAC/FinCEN compliance with zero vendor plaintext exposure.
One Platform, One API
These are not seven disconnected products. They are seven use cases running on a single cryptographic stack, accessible through a single API call. The underlying components are shared across every product:
| What the Bank Gets | H33 Component | Standard |
|---|---|---|
| Data never leaves encryption | BFV-256 / CKKS FHE | Lattice-based |
| Computation proof | STARK ZKP attestation | SHA3-256 |
| Quantum-safe signatures | H33-3-Key (Dilithium + FALCON) | FIPS 204 / 206 |
| Key exchange without exposure | Kyber ML-KEM | FIPS 203 |
| Biometric without liability | FHE biometric matching | 128-dim vectors |
| Threat detection | ML agents (Harvest + SideChannel) | Sub-microsecond |
| Compliance attestation | HATS conformance certificate | Public standard |
| Production throughput | 2.17M auth/sec @ 38.5µs | Benchmarked on Graviton4 |
Regulatory Alignment
H33-Banking is not a research project. It maps directly to the compliance frameworks banks are already required to follow:
| Regulation | Requirement | H33-Banking Coverage |
|---|---|---|
| GLBA (Gramm-Leach-Bliley) | Protect customer NPI | FHE: data never in plaintext |
| BSA/AML | Transaction screening | Encrypted AML screening (Product 7) |
| ECOA / Fair Lending | Non-discriminatory credit | ZKP fair lending proof (Product 6) |
| OCC PQ Guidance | Quantum readiness | H33-3-Key on all operations |
| BIPA / CCPA / GDPR | Biometric data protection | Biometric Vault (Product 3) |
| CFPB Third-Party Risk | Vendor data oversight | No vendor sees plaintext (Product 1) |
| FFIEC / CCAR | Stress test reporting | Encrypted reporting (Product 5) |
| SOX / PCI-DSS | Access controls, audit trail | ZKP attestation + Dilithium chain |
The Economics
Banks understand transaction pricing. H33-Banking charges per operation, not per seat or per server. At volume, a mid-size bank doing 500,000 wire transfers per month at $0.006 per operation spends $3,000 per month on quantum-safe wire signing — less than a single hour of wire fraud investigation. A mortgage lender processing 10,000 loans per month at $0.025 per encrypted underwriting operation spends $250 per month to eliminate their largest class-action exposure surface.
The cost of not adopting is calculable. The average mortgage data breach settlement exceeds $20 million. A single fraudulent wire can move $100 million. A BIPA class action starts at $1,000 per violation — per biometric template — with no cap. Against those numbers, H33-Banking is not a cost. It is the least expensive insurance a bank can buy.
We do not ask banks to trust a whitepaper. We show them a live query. Paste real-format PII into our interactive FHE demo, watch it encrypt in the browser, watch AI process the ciphertext on the server, watch it decrypt on the client. The server never saw the plaintext. That is not a claim. It is a live, verifiable, repeatable demonstration. Twenty minutes to see the entire pipeline run. Request a demo →