Enhancing data privacy in financial services: The role of zero-knowledge proofs and federated AI

Alex Lyashok and Prashant Sarode
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Alex Lyashok: 1 Vista Pl, USA
Prashant Sarode: TheoremLabs.io, USA

Journal of AI, Robotics & Workplace Automation, 2023, vol. 2, issue 4, 327-331

Abstract: This paper analyses the challenges of balancing anonymity, utility and security in financial services. It argues that the traditional approach of using clearinghouses to enhance utility has come at the expense of anonymity. However, the advent of privacy-enhancing technologies like zero-knowledge proofs and federated AI has begun to minimise these trade-offs. The paper provides a case study of Merit Protocol, a company that is using these technologies to address the problem of predatory payday loans. Merit Protocol’s platform allows employers to pre-underwrite loans for their employees without sharing sensitive data. This approach empowers employers to support their employees’ financial needs while maintaining privacy and reducing dependency on traditional credit agencies. The paper concludes by discussing the challenges that the financial services industry must address in order to fully realise the potential of privacy-enhancing technologies. These challenges include navigating legacy compliance frameworks and improving the ease of use of these technologies. Readers can expect to gain a deeper understanding of the challenges of balancing anonymity, utility and security in financial services. They will also learn about the potential of privacy-enhancing technologies to address these challenges.

Keywords: data privacy; ZK proof; financial services; data security; federated AI; AI; federated learning (search for similar items in EconPapers)
JEL-codes: G2 M15 (search for similar items in EconPapers)
Date: 2023
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