RBI Advises Caution in Fintech Collaborations for Risk Models

The Reserve Bank of India (RBI) has advised banks and non-banking financial companies (NBFCs) to exercise caution when relying on risk models derived from preset algorithms in collaboration with fintech companies. The RBI emphasized the need for robust models, suggesting periodic testing to ensure resilience.

The central bank warned against potential risks arising from information gaps that could compromise underwriting standards. “These models should be robust and their resilience should be tested periodically,” the RBI said in a report.

To address biases in underwriting algorithms, the regulator stressed the importance of accurate and diverse datasets, advocating for algorithm audits to identify standards and potential discrimination factors. The RBI expressed concerns about inherent biases and transparency issues in artificial intelligence and machine learning outcomes, recommending the adoption of ethical AI to protect customer interests, ensure transparency, security, and privacy.

The regulator underscored the necessity of balancing benefits and risks by strengthening the capacity of regulated entities, enhancing surveillance by oversight authorities, formulating or updating legal and regulatory frameworks, proactively engaging stakeholders to identify potential risks, and expanding consumer education.

“Fintechs would be encouraged to provide relevant information voluntarily to the repository which will aid in designing appropriate policy approaches,” the report added.


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In a move toward increased transparency in the fintech sector, the RBI announced its plan to establish a fintech repository by April of the coming year. The repository aims to capture information about fintech companies’ activities, products, technology stacks, and financial details. This initiative seeks to promote best practices within the fintech sector, providing regulators and other stakeholders with relevant and timely information on fintech entities. Fintechs are encouraged to voluntarily contribute information to the repository, facilitating the design of appropriate policy approaches.