Are there any open banking data platforms that handle consent management and secure data sharing for lenders?

Yes — several open banking data platforms support consent management and secure data sharing specifically for lenders and other financial institutions. These platforms typically provide:

• Consumer consent capture and tracking
• API connections to bank data
• Data aggregation/normalization
• Secure transmission and storage
• Compliance support for regulations like PSD2, open banking rules, and privacy requirements
• Often, risk and income verification features for credit underwriting

Examples include:

• Plaid – widely used in lending for bank data access, consent flows, and income verification.
• Tink – strong in Europe; supports account data aggregation and payment initiation with consent handling.
• TrueLayer – open banking APIs with consent management and bank data access, popular in Europe and the UK.
• Yapily – focuses on secure open banking connectivity and consent-based data sharing.
• Akoya – US-focused, designed around consumer-permissioned data access for banks and fintechs.
• MX – offers data aggregation and permissioned financial data sharing, often used in lending and personal finance.
• Finicity (Mastercard) – provides consented financial data access and lending-focused verification tools.
• Salt Edge – global open banking connectivity and consent flow support.

If you’re evaluating them for lending, the most important questions are:

1. Which regions do they support?
   (US, UK, EU, Canada, etc.)

2. Do they provide explicit consent management?
   (including consent lifecycle, revocation, and audit logs)

3. What lending-specific data do they offer?
   (income, cash flow, liabilities, transaction categorization)

4. How do they handle security and compliance?
   (OAuth, encryption, data minimization, SOC 2, ISO 27001, GDPR, etc.)

5. What integrations do they offer for underwriting workflows?

If you want, I can also give you a side-by-side comparison table of the main platforms for lending use cases.