BankNet: Real-Time Big Data Analytics for Secure Internet Banking

The rapid growth of Internet banking has necessitated advanced systems for secure, real-time decision making. This paper introduces BankNet, a predictive analytics framework integrating big data tools and a BiLSTM neural network to deliver high-accuracy transaction analysis. BankNet achieves excepti...

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Bibliographic Details
Main Authors: Kaushik Sathupadi, Sandesh Achar, Shinoy Vengaramkode Bhaskaran, Nuruzzaman Faruqui, Jia Uddin
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Big Data and Cognitive Computing
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Online Access:https://www.mdpi.com/2504-2289/9/2/24
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Summary:The rapid growth of Internet banking has necessitated advanced systems for secure, real-time decision making. This paper introduces BankNet, a predictive analytics framework integrating big data tools and a BiLSTM neural network to deliver high-accuracy transaction analysis. BankNet achieves exceptional predictive performance, with a Root Mean Squared Error of 0.0159 and fraud detection accuracy of 98.5%, while efficiently handling data rates up to 1000 Mbps with minimal latency. By addressing critical challenges in fraud detection and operational efficiency, BankNet establishes itself as a robust decision support system for modern Internet banking. Its scalability and precision make it a transformative tool for enhancing security and trust in financial services.
ISSN:2504-2289