FHE for Financial Services:
Secure Data Processing Without Exposure

Financial services handle some of the most sensitive data in existence. FHE enables powerful data processing while keeping financial information encrypted—addressing regulatory requirements and customer privacy concerns simultaneously.

Financial Data Sensitivity

Financial data requires exceptional protection:

• Account balances and transactions reveal spending patterns
• Credit histories affect life opportunities
• Investment portfolios are competitively sensitive
• Regulatory penalties for breaches are severe

FHE allows processing this data without exposure.

Use Cases in Finance

Credit Scoring

Calculate credit scores on encrypted financial histories:

Fraud Detection

Analyze transaction patterns without seeing transactions:

• Run fraud detection models on encrypted transaction streams
• Identify anomalies without accessing account details
• Reduce insider threat risk

Risk Analysis

Multi-party financial analysis with privacy:

• Banks calculate aggregate risk metrics collaboratively
• Each institution's data remains encrypted
• Regulators can verify without seeing individual records

Regulatory Compliance

Prove compliance without data exposure:

• Encrypted audit trails
• Privacy-preserving regulatory reporting
• Confidential compliance verification

Implementation Architecture

// FHE financial processing flow
// Client encrypts sensitive financial data
const encryptedTransactions = await fhe.encrypt(
  transactions,
  clientKey
);

// Send to processing service
const encryptedRiskScore = await riskService.calculate(
  encryptedTransactions
);

// Only client can decrypt result
const riskScore = await fhe.decrypt(
  encryptedRiskScore,
  clientKey
);

Regulatory Alignment

FHE helps meet financial regulations:

• GDPR: Data minimization—processors never see plaintext
• PCI DSS: Cardholder data remains encrypted throughout processing
• SOX: Confidential processing of financial records
• Banking secrecy laws: Cross-border processing without data exposure

Performance Considerations

Financial workloads vary in FHE performance:

• Simple calculations (balances, totals): Milliseconds
• Credit scoring models: Seconds
• Complex risk simulations: Minutes

For real-time needs like transaction authentication, H33 achieves sub-millisecond FHE performance.

Integration Path

Start with targeted FHE adoption:

• Begin with non-critical analytics workloads
• Prove out performance and security properties
• Gradually expand to more sensitive applications
• Consider hybrid approaches with other privacy technologies

FHE is transforming how financial services handle sensitive data. Early adopters gain competitive advantage in privacy-conscious markets while reducing regulatory risk.