Abstract :

This Study, Titled A Case Study On Financial Fraud Detection With Big Data Analytics At State Bank Of India,evaluates The Operational Efficacy And Financial Feasibility Of Integrating Big Data Analytics Into The Fraud Detection Frameworks Of Indias Largest Public Sector Bank. With The Exponential Rise In Digital Banking Transactions Through UPI, Internet Banking, And Mobile Applications, Financial Institutions Face Unprecedented Risks Of Sophisticated Fraud, Including Identity Theft, Account Takeover, Phishing, And Loan Defaults. This Research Investigates The Implementation Of A Big Data Analytics Platform At State Bank Of India (SBI), Focusing On The Integration Of Machine Learning Algorithms (such As Random Forest, XGBoost, And Deep Neural Networks) For Real-time Transaction Monitoring And Anomaly Detection. The Study Conducts A Cost-benefit Analysis Of The Technological Investment, Evaluating Capital Expenditure, Operational Costs, And The Direct Financial Benefit Of Fraud Loss Prevention From 2021 To 2025. Standard Financial Appraisal Techniques—Net Present Value (NPV), Internal Rate Of Return (IRR), Payback Period (PBP), And Benefit-Cost Ratio (BCR)—are Applied To Determine The Profitability And Long-term Feasibility Of The Investment. The Data Analysis Indicates That The Implementation Of Big Data Analytics Has Enabled SBI To Reduce Annual Fraud Losses By Over 80% While Significantly Increasing Transaction Monitoring Capacity And Accuracy. The Findings Demonstrate That Strategically Deployed Big Data Frameworks Not Only Mitigate Systemic Risk But Also Achieve High Financial Profitability And Operational Efficiency. The Study Concludes That Big Data Analytics Is An Indispensable Tool For Securing Modern Banking Infrastructure, Offering Practical And Technical Insights For Banking Executives, Policymakers, And Security Architects Seeking To Build Resilient, Fraud-resistant Financial Systems In An Economically Sustainable Manner.

Published:

05-9-2026

Issue:

Vol. 26 No. 9 (2026)

Page Nos:

74-83

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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