Advanced Methodologies for Enhancing Credit Card Fraud Detection Utilizing Machine Learning, Blockchain Technologies, and Cryptographic Principles
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 OptimizationAbstract
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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Aljehane, N.O., & Alenzi, H.Z. (2020). Fraud detection in credit cards using logistic regression. International Journal of Advanced Computer Science and Applications (IJACSA), 11(12). DOI: 10.14569/IJACSA.2020.011127.
Babich, V., & Hilary, G. (2020). Distributed ledgers and operations: What operations management researchers should know about blockchain technology. Manufacturing & Service Operations Management, 22(2), 223–240. DOI: 10.1287/msom.2019.0823.
Husejinović, A. (2020). Credit card fraud detection using C4.5 decision tree classifiers and Naive Bayesian. Periodicals of Engineering and Natural Sciences, 8(1), 1–5. ISSN 2303-4521. DOI: 10.21533/pen.v8i1.1196.
Kumar, N.M., & Mallick, P.K. (2018). Blockchain technology for security issues and challenges in IoT. Procedia Computer Science, 132, 1815–1823. DOI: 10.1016/j.procs.2018.05.142.
Li, W., Andreina, S., Bohli, J.-M., & Karame, G. (2017). Securing proof-of-stake blockchain protocols. In Data Privacy Management, Cryptocurrencies and Blockchain Technology (pp. 297–315). Springer. DOI: 10.1007/978-3-319-70278-0_19.
Liu, G., Xuan, S., Li, Z., Zheng, L., Jiang, C., & Wang, S. (2020). Random forest for credit card fraud detection. Journal of Finance and Data Science, 6, 64–71. DOI: 10.1016/j.jfds.2019.11.001.
Liu, X., Chen, R., Chen, Y.-W., & Yuan, S.-M. (2018). Off-chain data fetching architecture for Ethereum smart contract. In 2018 International Conference on Cloud Computing, Big Data and Blockchain (ICCBB) (pp. 1–4). IEEE. DOI: 10.1109/ICCBB.2018.8756097.
Lo, S.K., Xu, X., Staples, M., & Yao, L. (2020). Reliability analysis for blockchain oracles. Computers & Electrical Engineering, 83, 106582. DOI: 10.1016/j.compeleceng.2020.106582.
Meenakshi, Singh, S., & Itoo, F. (2021). Comparison and analysis of KNN, Naïve Bayes and Logistic Regression machine learning algorithms for credit card fraud detection. International Journal of Information Technology, 13, 1503–1511. DOI: 10.1007/s41870-020-00532-0.
Nirmal Raj, T., & Sudha, C. (2017). Credit card fraud detection on the internet using K nearest neighbour algorithm. IPASJ International Journal of Computer Science (IIJCS), 5(11), 1-6. DOI: 10.1007/s41870-017-0021-4.
Sankhwar, S., Gupta, D., Ramya, K., Rani, S.S., Shankar, K., & Lakshmanaprabu, S. (2020). Improved grey wolf optimization-based feature subset selection with fuzzy neural classifier for financial crisis prediction. Soft Computing, 24(1), 101–110. DOI: 10.1007/s00500-019-03942-6.
Tariq, U., Ibrahim, A., Ahmad, T., Bouteraa, Y., & Elmogy, A. (2019). Blockchain in internet-of-things: a necessity framework for security, reliability, transparency, immutability and liability. IET Communications, 13(19), 3187–3192. DOI: 10.1049/iet-com.2018.6096.
Thang, C., Toan, P.Q., Cooper, E.W., & Kamei, K. (2006). Application of soft computing to tax fraud detection in small businesses. In 2006 First International Conference on Communications and Electronics (pp. 402–407). IEEE. DOI: 10.1109/CCE.2006.1568915.
Wright, A., & De Filippi, P. (2015). Decentralized blockchain technology and the rise of Lex Cryptographia. SSRN Electronic Journal. DOI: 10.2139/ssrn.2580664.
Zainneddine, H., Haque, R., Taher, Y., Hacid, M.-S., & Makkileurbanne, S. (2019). An experimental study with imbalanced classification approaches for credit card fraud detection. IEEE Access, 7, 93010–93022. DOI: 10.1109/ACCESS.2019.2927380.
Li, W., Wu, W., & Li, X. (2024). Enhancing Credit Card Fraud Detection: An Ensemble Machine Learning Approach. Big Data and Cognitive Computing, 8(6), 4–27. DOI: 10.3390/bdcc8060006.
Li, H., Ren, J., & Liu, Q. (2024). Efficiency of Federated Learning and Blockchain in Preserving Privacy and Enhancing the Performance of Credit Card Fraud Detection Systems. Future Internet, 16(6), 196. DOI: 10.3390/fi16060196.
Sankhwar, S., Ramya, K., Gupta, D., Rani, S.S., & Shankar, K. (2024). Credit Card Fraud Detection Using Blockchain and Simulated Annealing k-Means Algorithm. SpringerLink. DOI: 10.1007/s00500-019-04216-5.
Liu, G., Wang, J., & Chen, L. (2023). Deployment of Deep Learning in Blockchain Technology for Credit Card Fraud Prevention. SpringerLink. DOI: 10.1007/s10462-022-10117-3.
Rahman, S., Li, X., & Zhang, Y. (2023). Credit Card Fraud Detection Using Machine Learning and Predictive Models: A Comparative Study. SpringerLink. DOI: 10.1007/s10115-023-01784-4.
Wu, P., Jiang, X., & Lin, F. (2022). Machine Learning Approaches to Credit Card Fraud Detection in Financial Technology. ScienceDirect. DOI: 10.1016/j.fin.2022.101020.
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