Federated Learning in No-Code AI: Revolutionizing Data Security and Efficiency in BFSI

Author(s)	Mr. Ullas Das
Country	India
Abstract	This review examines the impact of using Federated Learning (FL) on No-Code AI tools and how it could change data security in the BFSI industry. Despite keeping the data local on each machine, FL allows different organizations to train AI models together. It gives an overview of what components the model contains, its functions and how accurate it is at predicting things by comparing it to other machine learning models that were used as references. Our work highlights successful cases and uses innovative tools to develop an approach that can boost the prevention, analysis and monitoring of fraud, risks and compliance in BFSI. The challenges included in the paper are heterogeneous data, threats to security and the challenge of scaling, along with future research ideas. These findings matter most to specialists working in AI, along with experts in finance, who need more privacy in their AI solutions.
Keywords	Federated Learning, No-Code AI, BFSI, Data Privacy, Security, Machine Learning, Predictive Performance, Collaborative Learning, Data Heterogeneity, Regulatory Compliance, Fraud Detection, Risk Assessment, Model Aggregation, AI for Financial Services.
Field	Engineering
Published In	Volume 7, Issue 3, May-June 2025
Published On	2025-06-30

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9687-828-838	editor@ijfmr.com

International Journal For Multidisciplinary Research