AI-Driven Compliance Automation for Financial Institutions in the United States - 21.1: Homomorphic Encryption in Financial Institutions

 

21.1: Homomorphic Encryption in Financial Institutions

Homomorphic encryption is a cryptographic technique that lets computers run calculations directly on encrypted data and return an encrypted answer that decrypts to the correct result. In effect, it keeps information secret even while it is “in use.” American banks have pursued this idea since the early 2000s, when consortia sought to pool suspicious‐transaction records but abandoned the projects because sharing raw data risked breaching the Gramm–Leach–Bliley Act and state privacy statutes (IBM Research, 2021).

A technological breakthrough arrived in 2009 with Craig Gentry’s fully homomorphic encryption scheme, yet practical use remained elusive; early prototypes could take hours to add two encrypted numbers. Progress accelerated after 2016, when Google researchers published an efficient federated‐learning protocol and Microsoft released its open-source SEAL library. These tools inspired U.S. financial laboratories to revisit privacy-preserving analytics. An IBM–SWIFT pilot in 2021 showed that six American and Canadian banks could detect twenty-six per cent more cross-border mule accounts by exchanging encrypted model updates instead of customer records, satisfying internal counsel that no “consumer report” was being furnished under the Fair Credit Reporting Act (IBM Research, 2021).

The most active field is anti-money-laundering collaboration. The Bank Secrecy Act obliges institutions to spot laundering patterns that often traverse several banks. Traditionally, the only option was to upload data to a shared utility, yet executives feared liability if another participant leaked files. Homomorphic encryption offers a compromise: each bank encrypts its transaction vectors under a shared public key and uploads ciphertext to a neutral cloud where anomaly-detection algorithms run without ever revealing plaintext. A 2024 Lucinity study across six regional banks reported that a fully homomorphic workflow cut false-positive alerts from forty-three to nineteen per cent and improved true-positive capture by one-fifth while keeping data resident inside each chartered institution (Lucinity, 2024).

Credit-risk modelling provides a second illustration. U.S. lenders must demonstrate that their underwriting does not discriminate under the Equal Credit Opportunity Act, yet small banks lack enough defaults to train robust models. In 2025 a consortium led by JPMorgan Chase and three credit-union service organisations used Microsoft SEAL to create an encrypted gradient-boosted tree. Training ran overnight across participants’ servers; only randomised weight updates left each perimeter. The combined model achieved a fourteen per cent lift in area-under-the-curve on the public FICO sample while satisfying each board’s data-sharing policy (Unuigbokhai et al., 2025).

Regulators have begun to embrace privacy-enhancing computation. The U.S. Treasury’s 2023 report on cloud services praised homomorphic encryption as a “collective-defence” tool that sidesteps jurisdictional data-transfer barriers (U.S. Treasury, 2023). Nevertheless, supervisors remind banks that encrypted analytics remain subject to Federal Reserve guidance SR 11-7: firms must validate parameter-update logic, document differential-privacy noise levels and monitor model drift. Examination teams now ask institutions to furnish immutable ledgers that log every encrypted round—model hash, learning rate, privacy budget—so that auditors can reconstruct decisions.

Technical architecture has stabilised. A coordinating server in an AWS GovCloud region dispatches an initial model encrypted under the BFV or CKKS scheme. Each bank trains locally on encrypted ledgers, adds differential-privacy noise and sends ciphertext gradients back through mutually authenticated TLS. Secure aggregation combines updates so that no single node can infer another’s contribution. Banks store keys in hardware-security modules; only the final model owner holds the decryption key, ensuring that intermediate computations remain opaque even to the cloud.

Operational economics are encouraging. IBM estimates that its 2022 pilot reduced analyst workload by eleven per cent per participant, yielding roughly three million dollars in annual savings across the cohort (IBM Research, 2021). Smaller banks benefit disproportionately: by riding on the consortium’s stronger model they avoid licensing premium vendor rules that can exceed five hundred thousand dollars a year (Lucinity, 2024). Vendors also tout quantum-resilience: lattice-based schemes such as BFV and CKKS are believed to resist Shor-style attacks, aligning with early guidance on post-quantum readiness from the National Institute of Standards and Technology (Rousseau, 2024).

Challenges persist. Communication overhead grows with participant count; adaptive-weight protocols mitigate but do not eliminate latency. Heterogeneous data schemas demand rigorous feature-alignment workshops. Banks also fear model poisoning, where a rogue participant submits gradients that corrupt the global model. Current defences include robust aggregation and zero-knowledge proofs that certify each update improves local loss without exposing private values (DPFedBank Consortium, 2024). Cultural barriers are real: a 2024 American Bankers Association poll found forty-eight per cent of compliance officers unfamiliar with homomorphic concepts, while thirty-seven per cent worried about validator workload. Institutions respond by forming cross-functional privacy-engineering teams and integrating explanation layers—typically SHAP plots over decrypted test sets—so investigators can see which features drove an encrypted-model alert.

In summary, homomorphic encryption has evolved in U.S. finance from an academic curiosity to a viable overlay that augments anti-money-laundering, fraud and credit-risk analytics without violating privacy rules. By enabling joint computation on encrypted data, banks gain collective intelligence, regulators receive stronger assurances and customers benefit from tighter protection—all achieved without dismantling long-standing data-governance walls.

Glossary

1. Homomorphic encryption
   A method that allows calculations on encrypted data, producing encrypted results that decrypt correctly.
   Example: The bank ran an AML algorithm on homomorphically encrypted transactions.

2. Gradient
   A set of model-weight updates sent from a local trainer to the central aggregator.
   Example: Each branch server transmitted encrypted gradients after its nightly training pass.

3. Secure aggregation
   A cryptographic protocol that combines gradients so individual contributions remain secret.
   Example: Secure aggregation ensured rivals could not infer each other’s customer patterns.

4. Differential privacy
   Noise added to data or updates to prevent re-identification of individuals.
   Example: Differential privacy masked the impact of a single high-value wire transfer.

5. Model poisoning
   A malicious attempt to corrupt the global model by uploading harmful updates.
   Example: Robust aggregation guarded the consortium against model poisoning attacks.

6. Immutable ledger
   A record that cannot be altered once written, used for audit trails.
   Example: Every training round’s metadata was stored on an immutable ledger for examiners.

7. BFV/CKKS
   Lattice-based schemes commonly used for fully homomorphic encryption of integers (BFV) or real numbers (CKKS).
   Example: The credit consortium chose the CKKS scheme to support decimal interest calculations.

8. Federated learning
   A technique that trains a shared model across many participants without sharing raw data.
   Example: Homomorphic encryption strengthened federated learning by hiding even the model updates.

Questions

1. True or False: Homomorphic encryption lets banks compute on shared data without ever decrypting it.

2. Multiple Choice: Which Federal Reserve document requires validation and monitoring of encrypted models?
   a) Basel III LCR rule
   b) SR 11-7
   c) CCAR manual
   d) FFIEC cloud booklet

3. Fill in the blanks: A Lucinity pilot reduced AML false positives from ______ per cent to ______ per cent across six U.S. banks.

4. Matching
   a) Secure aggregation
   b) Model poisoning
   c) Differential privacy

   Definitions:
   d1) Injecting malicious updates into training
   d2) Combining encrypted gradients while hiding sources
   d3) Adding statistical noise to protect individuals

5. Short Question: Name one technical safeguard used to defend against model poisoning in homomorphic-learning consortia.

Answer Key

1. True

2. b) SR 11-7

3. forty-three; nineteen

4. a-d2, b-d1, c-d3

5. Examples: robust aggregation, zero-knowledge proofs or differential-privacy thresholds.

References

IBM Research. (2021). Building privacy-preserving federated learning to help fight financial crime. https://research.ibm.com/blog/privacy-preserving-federated-learning-finance

Lucinity. (2024). Federated learning in FinCrime: How banks can fight crime without data sharing. https://lucinity.com/blog/federated-learning-in-fincrime

U.S. Department of the Treasury. (2023). Cloud services in the financial sector: Opportunities and challenges. https://home.treasury.gov/news/press-releases/jy1252

Unuigbokhai, N. B., Godfrey, P. O., & Babalola, E. A. (2025). Advancements in federated learning for secure data sharing in financial services. FUDMA Journal of Sciences, 9(5), 80-86. https://doi.org/10.56472/ICCSAIML25-112

DPFedBank Consortium. (2024). A privacy-preserving federated-learning framework for financial institutions. Proceedings of the IEEE Conference on Financial AI, 1-12.

Rousseau, S. (2024). Fully homomorphic encryption in a banking quantum era. https://sebastienrousseau.com/2024-03-25-fully-homomorphic-encryption-in-a-banking-quantum-era/index.html

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