ItemBook Chapter

Regulatory challenges and compliance in federated learning (FL) for financial applications

Title
Regulatory challenges and compliance in federated learning (FL) for financial applications
Creator
Logeswaran, K.; Savitha, S.; Suresh, P.; Prasanna Kumar, K.R.; Ponselva Kumar, A.P.; Jayasurya, A.S.
Description
The financial sector is increasingly turning toward artificial intelligence (AI) for applications such as fraud detection, credit scoring, and risk management. But that makes it contrary to the regulatory environment. New data protection laws such as the General Data Protection Regulation (GDPR) in Europe and the Digital Personal Data Protection Act (DPDPA) in India impose stringent conditions on data residency, minimization, and sovereignty. This chapter argues that traditional centralized AI systems which require sensitive data to be collected for processing at one site simply do not sit well with these legal requirements, thereby creating massive compliance risks for financial institutions. By way of an extensive architectural study and practical application, this chapter demonstrates that the very basic functions of a traditional AI system tend to contravene prohibitions on cross-border transfers of data. Instead, we propose Federated Learning (FL) as a compliance-by-design solution that solves this sticking point. In other words, by inverting the discredited approachand bringing the algorithm to the data rather than the other way aroundFL ensures that practitioners in different institutions and jurisdictions collaborate on model training without sharing raw data. Only aggregated and anonymized updates on the model are sentinherently complying with certain data residency and data minimization principles. Besides advocating for FL as a core compliant innovation pathway, this chapter also touches on a number of regulatory uncertainties and other potential issues arising from this technology, such as liability, model security, and a need for industry-wide standards. To this end, the chapter clearly states that the adoption of privacy-preserving technologies such as FL has become integral. 2026 selection and editorial matter, Swati Sah, Rejwan Bin Sulaieman, and Aditya Dayal Tyagi; individual chapters, the contributors.
Source
Federated Learning in Finance: Unlocking Privacy-Preserving and Cyber Resilience using AI;pp.1-18
Date
01-01-2026
Publisher
CRC Press
Coverage
Logeswaran K., Department of AI and Data Science Engineering, School of Engineering and Technology, CHRIST University, Bangalore, India; Savitha S., Department of Computer Science and Engineering, K.S.R. College of Engineering, Tamil Nadu, India; Suresh P., Department of Database System, School of CSE, Vellore Institute of Technology, Vellore, India; Prasanna Kumar K.R., Department of Computer Science and Design, Kongu Engineering College, Tamil Nadu, India; Ponselva Kumar A.P., Department of Information Technology, Kongu Engineering College, Tamil Nadu, India; Jayasurya A.S., Department of Electrical and electronics, University Teknoloi Petronas, Petronas, Malaysia
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 978-104086966-6; 978-104111510-6;
Format
online
Language
English
Type
Book chapter

Citation

Logeswaran, K.; Savitha, S.; Suresh, P.; Prasanna Kumar, K.R.; Ponselva Kumar, A.P.; Jayasurya, A.S., “Regulatory challenges and compliance in federated learning (FL) for financial applications,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/24458.

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