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Explainable Deep Learning Framework for Financial Fraud Detection and Intelligent Risk Analysis

Dr.R.Sugumar

Professor, Department of Computer Science & Engineering, SIMATS Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, India

Abstract

Financial fraud has become a major challenge for banking institutions, digital payment platforms, insurance companies, and financial organizations due to the rapid growth of online transactions and sophisticated cybercrime techniques. Traditional fraud detection systems often fail to identify complex and evolving fraudulent activities while maintaining transparency in decision-making processes. This study proposes an Explainable Deep Learning Framework for Financial Fraud Detection and Intelligent Risk Analysis that combines advanced deep learning algorithms with explainable artificial intelligence techniques to improve fraud detection accuracy, transparency, and risk assessment. The framework enables financial institutions to identify suspicious transactions, analyze fraud patterns, generate interpretable predictions, and support intelligent decision-making for enhanced financial security and operational efficiency.

Article Information

Journal

International Journal of Emerging Trends in Engineering and Management Research

Volume (Issue)

Vol. 7 No. 5 (2022): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR)

DOI

https://doi.org/10.15662/ijetemr.2022.0705001

Pages

12520-12528

Published

September 5, 2022
Copyright

Open Access

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

How to Cite

Dr.R.Sugumar (%2022). Explainable Deep Learning Framework for Financial Fraud Detection and Intelligent Risk Analysis. International Journal of Emerging Trends in Engineering and Management Research , Vol. 7 No. 5 (2022): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 12520-12528. https://doi.org/10.15662/ijetemr.2022.0705001

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