Credit Card Fraud Detection Utilizing Advanced ML and Blockchain Technologies
Keywords:
transactions, integrated, immutable, accuracy, decentralizedAbstract
Credit card fraud poses a significant challenge to financial institutions and consumers worldwide. Traditional fraud detection methods often fall short in addressing the evolving sophistication of fraudulent activities. This research proposes an innovative approach by harnessing advanced machine learning (ML) techniques and blockchain technology to enhance fraud detection capabilities.The study utilizes a comprehensive dataset comprising diverse transactional features, encompassing variables such as transaction amount, location, and time. Various ML models, including anomaly detection, supervised learning (Random Forest and Gradient Boosting with ensemble techniques), and deep learning (custom Recurrent Neural Networks along with a mix of xgboost), are employed to analyze this dataset. Preliminary experimentation yields promising accuracy scores, with anomaly detection achieving approximately 99.9% accuracy, 99.8% recall, 99.9% sensitivity and an f1 score of 99.9% in detecting fraudulent transactions.
Furthermore, blockchain technology is integrated to ensure the integrity and transparency of transaction records. By leveraging blockchain's decentralized and immutable ledger, the system enhances security and trust in financial transactions.
The findings of this research underscore the potential of combining advanced ML algorithms with blockchain technology to develop a robust credit card fraud detection system. Such an integrated approach not only strengthens fraud prevention measures but also fosters greater confidence among stakeholders in digital financial transactions.
Downloads
References
P. Chatterjee, D. Das and D. Rawat, "Securing financial transactions: Exploring the role of federated learning and blockchain in credit card fraud detection," Authorea Preprints, 2023.
T. Baabdullah, A. Alzahrani, D. B. Rawat and C. Liu, "Efficiency of Federated Learning and Blockchain in Preserving Privacy and Enhancing the Performance of Credit Card Fraud Detection (CCFD) Systems," Future Internet, vol. 16, p. 196, 2024.
K. Patel, "Credit card analytics: a review of fraud detection and risk assessment techniques," International Journal of Computer Trends and Technology, vol. 71, p. 69–79, 2023.
H. T. Tien, K. Tran-Trung and V. T. Hoang, "Blockchain-data mining fusion for financial anomaly detection: A brief review," Procedia Computer Science, vol. 235, p. 478–483, 2024.
Y. Ren, Y. Ren, H. Tian, W. Song and Y. Yang, "Improving transaction safety via anti-fraud protection based on blockchain," Connection Science, vol. 35, p. 2163983, 2023.
D. Mienye and N. Jere, "Deep Learning for Credit Card Fraud Detection: A Review of Algorithms, Challenges, and Solutions," IEEE Access, 2024.
Z. Salekshahrezaee, J. L. Leevy and T. M. Khoshgoftaar, "The effect of feature extraction and data sampling on credit card fraud detection," Journal of Big Data, vol. 10, p. 6, 2023.
Mniai, M. Tarik and K. Jebari, "A novel framework for credit card fraud detection," IEEE Access, 2023.
H. Du, L. Lv, A. Guo and H. Wang, "AutoEncoder and LightGBM for credit card fraud detection problems," Symmetry, vol. 15, p. 870, 2023.
H. Du, L. Lv, H. Wang and A. Guo, "A novel method for detecting credit card fraud problems," Plos one, vol. 19, p. e0294537, 2024.
O. Kilickaya, "Credit Card Fraud Detection: Comparison of Different Machine Learning Techniques," International Journal of Latest Engineering and Management Research, vol. 9, p. 15–27, 2024.
D. Mienye and Y. Sun, "A deep learning ensemble with data resampling for credit card fraud detection," IEEE Access, vol. 11, p. 30628–30638, 2023.
T. R. Noviandy, G. M. Idroes, A. Maulana, I. Hardi, E. S. Ringga and R. Idroes, "Credit card fraud detection for contemporary financial management using xgboost-driven machine learning and data augmentation techniques," Indatu Journal of Management and Accounting, vol. 1, p. 29–35, 2023.
R. Khalid, N. Owoh, O. Uthmani, M. Ashawa, J. Osamor and J. Adejoh, "Enhancing credit card fraud detection: an ensemble machine learning approach," Big Data and Cognitive Computing, vol. 8, p. 6, 2024.
Cherif, A. Badhib, H. Ammar, S. Alshehri, M. Kalkatawi and A. Imine, "Credit card fraud detection in the era of disruptive technologies: A systematic review," Journal of King Saud University-Computer and Information Sciences, vol. 35, p. 145–174, 2023.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
