Paper Title
A Deep Learning Based Approach with Regularization for Credit Card Fraud Detection on Imbalanced Datasets
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Registration ID: IJNRD_326150
Published ID: IJNRD2606190
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Keywords
Credit Card Fraud Detection, Machine Learning, Feature Selection, Imbalanced Datasets, Probabilistic Classifier, Classification Accuracy
Abstract
The rapid growth of digital transactions and online banking has significantly increased the occurrence of credit card fraud. Conventional rule-based fraud detection systems are often unable to cope with the evolving and sophisticated nature of fraudulent activities. Consequently, Machine Learning (ML) and Deep Learning (DL) techniques have emerged as effective solutions for identifying fraudulent transactions. Millions of credit card transactions occur every day, making manual inspection impractical. Machine learning and deep learning algorithms can automatically process vast amounts of transaction data and identify suspicious activities in real time. Fraudsters continuously develop new techniques to bypass traditional security mechanisms. Unlike static rule-based systems, ML and DL models can learn from historical transaction patterns and adapt to emerging fraud strategies, thereby improving detection capability. This work proposes a supervised machine learning algorithm to be trained to detect credit card frauds based on the Bayes Net with penalty based regularization. It is shown that the proposed approach attains higher classification accuracy compared to existing work.
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How To Cite (APA)
Raj Kumar Ahirwar & Ms. Pooja Hardiya (June-2026). A Deep Learning Based Approach with Regularization for Credit Card Fraud Detection on Imbalanced Datasets . INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(6), b781-b787. https://ijnrd.org/papers/IJNRD2606190.pdf
Issue
Volume 11 Issue 6, June-2026
Pages : b781-b787
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Paper Reg. ID: IJNRD_326150
Published Paper Id: IJNRD2606190
Research Area: Other area not in list
Author Type: Indian Author
Country: -, -, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2606190.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2606190
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