Advanced Methodologies for Enhancing Credit Card Fraud Detection Utilizing Machine Learning, Blockchain Technologies, and Cryptographic Principles

Authors

  • Jitender Tanwar, Dipak Vijaykumar Bhosale, Vijay More, Vijit Srivastava, Tareek Pattewar, Kumar P., Pallavi Deshpande

Keywords:

Credit Card Fraud Detection, Machine Learning, Blockchain Technology, Supervised Learning, Unsupervised Learning, Ensemble Methods, Smart Contracts, Decentralized Ledger, Cryptographic Principles, Fraudulent Transactions, Data Security, Financial Technology, Transaction Transparency System Integration, Mathematical Optimization

Abstract

Credit card fraud remains a significant challenge within the financial sector, impacting both institutions and consumers. Traditional fraud detection methods, although useful, often struggle to keep pace with the evolving tactics of fraudsters. This paper introduces a novel approach to enhancing credit card fraud detection by integrating advanced machine learning algorithms with blockchain technology. The machine learning models deployed in this study, encompassing supervised, unsupervised, and ensemble techniques, are engineered to identify fraudulent transactions through pattern recognition and anomaly detection. Simultaneously, blockchain technology is used to secure transaction data, utilizing its decentralized and immutable ledger properties to prevent tampering and ensure transparency.

The methodology section elaborates on data collection and preprocessing, the specific machine learning models applied, and the implementation of blockchain components such as smart contracts and decentralized ledgers. A comprehensive mathematical foundation supports the integration of these technologies, detailing the cryptographic principles and optimization algorithms involved. The experimental setup and results validate the proposed system's ability to detect fraudulent transactions with high accuracy and security. A practical case study demonstrates the system's application in real-world scenarios, underscoring its practical benefits and potential challenges.

This research advances the field by presenting a secure, robust, and scalable solution for credit card fraud detection. The findings highlight the critical role of integrating machine learning and blockchain technologies to combat complex financial fraud. Future research directions are also identified, emphasizing the need for ongoing innovation and ethical considerations in fraud detection systems.

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References

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Published

10.07.2024

How to Cite

Jitender Tanwar. (2024). Advanced Methodologies for Enhancing Credit Card Fraud Detection Utilizing Machine Learning, Blockchain Technologies, and Cryptographic Principles. International Journal of Intelligent Systems and Applications in Engineering, 12(23s), 150–162. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6606

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Section

Research Article