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Third-Party Risk Management “Essential” As More Banks Partner with FinTechs

The OCC’s acting deputy comptroller for the Office of Financial Technology discussed agency supervision of banks’ use of financial technologies

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  • Written by  Banking Exchange staff
Third-Party Risk Management “Essential” As More Banks Partner with FinTechs

Third-party risk management is “essential” to ensure the safety of consumers and maintaining bank safety and soundness as more banks partner with third-party fintech companies.

Donna Murphy, the Office of the Comptroller of the Currency’s (OCC) acting deputy comptroller for the Office of Financial Technology, gave a statement on the agency’s supervision of banks’ expanding relationships with FinTechs.

She spoke before the House Subcommittee on Digital Assets, Financial Technology and Inclusion, Committee on Financial Services of the US House of Representatives.

Research by software provider Finastra in April 2023 found 75% of global banks intend to connect to an average of three FinTechs or service providers within the next 18 months to achieve digitization and STP goals.

Earlier this year, the OCC, FDIC, and Federal Reserve published ‘Interagency Guidance on Third-Party Relationships: Risk Management’, which reminds banks of their responsibility to operate in a safe and sound manner, in compliance with applicable laws and regulations regardless of whether their activities are performed in-house or outsourced.

Murphy said: “Banks’ relationships with third parties, including fintech companies, continue to expand... the use of third parties has significant potential benefits, but strong third-party risk management is essential to avoid harm to consumers or weakening of bank safety and soundness.”

Murphy also spoke about the growing use of AI and machine learning in the banking sector.

She said: “With regard to all uses of AI, the OCC remains focused on the potential risks of adverse outcomes if bank use of AI is not properly managed and controlled.

“Potential adverse outcomes can be caused by poorly designed underlying mathematical models, faulty data, changes in model assumptions over time, inadequate model validation or testing, or limited human oversight.”

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