Real-Time Credit Card Fraud Detection Using AdaBoost
DOI:
https://doi.org/10.69996/fmep.2025019Keywords:
Credit Card Fraud Detection, AdaBoost, Hybrid Majority Voting (AHMV), Machine Learning, Financial SecurityAbstract
Strong and smart detection systems are required to combat credit card fraud, which is a major problem for online retailers and banks. In order to enhance classification performance on extremely unbalanced datasets, this work presents an enhanced fraud detection model that integrates the AdaBoost algorithm with an AHMV ensemble technique. The approach integrates multiple base classifiers and applies a voting mechanism that enhances the sensitivity and precision of fraud detection, thereby minimizing false positives. The AHMV model beats conventional individual classifiers in every experimental metric (accuracy, recall, precision, and F1-score) when tested on publicly accessible credit card datasets. Apt for use in real-time fraud monitoring systems, the suggested framework guarantees strong detection capabilities while preserving computing economy. A trustworthy and scalable solution to the ever-changing credit card fraud protection scenario is brought to light by this effort.
References
[1] I. D. Mienye and N. Jere, “Deep learning for credit card fraud detection: A review of algorithms, challenges, and solutions,” IEEE Access, 2024.
[2] P. Chatterjee, D. Das and D. B. Rawat, “Digital twin for credit card fraud detection: Opportunities, challenges, and fraud detection advancements,” Future Generation Computer Systems, vol.158, pp.410-426, 2024.
[3] A. 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, no.1, pp.145-174, 2023.
[4] A. 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, viol.8, no.1, pp.6, 2024.
[5] A. Mniai, M. Tarik and K. Jebari, “A novel framework for credit card fraud detection,” IEEE Access, vol.11, pp.112776-112786, 2023.
[6] I. Y. Hafez, A. Y. Hafez, A. Saleh, A. A. Abd El-Mageed and A. A. Abohany, “A systematic review of AI-enhanced techniques in credit card fraud detection,” Journal of Big Data, vol.12, no.1, pp.6, 2025.
[7] P. Gupta, A. Varshney, M. R. Khan, R. Ahmed, M. Shuaib and S. Alam, “Unbalanced credit card fraud detection data: A machine learning-oriented comparative study of balancing techniques,” Procedia Computer Science, vol.218, pp.2575-2584, 2023.
[8] I. D. Mienye and Y. Sun, “A deep learning ensemble with data resampling for credit card fraud detection,” IEEE Access, vol.11, pp.30628-30638, 2023.
[9] C. Yu, Y. Xu, J. Cao, Y. Zhang, Y. Jin and M. Zhu, “Credit card fraud detection using advanced transformer model,” In 2024 IEEE International Conference on Metaverse Computing, Networking, and Applications (MetaCom), IEEE, pp.343-350, 2024.
[10] M. Habibpour, H. Gharoun, M. Mehdipour, A. Tajally, H. Asgharnezhad, A. Shamsi and S. Nahavandi, “Uncertainty-aware credit card fraud detection using deep learning,” Engineering Applications of Artificial Intelligence, vol.123, no.106248, 2023.
[11] I. Nadher and S. M. Hameed, “Credit card fraud detection challenges and solutions: A review,” Iraqi Journal of Science, pp.2287-2303, 2024.
[12] S. Jiang, R. Dong, J. Wang and M. Xia, “Credit card fraud detection based on unsupervised attentional anomaly detection network,” Systems, vol.11, no.6, pp.305, 2023.
[13] S. Bakhtiari, Z. Nasiri and J. Vahidi, “Credit card fraud detection using ensemble data mining methods,” Multimedia Tools and Applications, vol.82, no.19, pp.29057-29075, 2023.
[14] H. Fanai and H. Abbasimehr, “A novel combined approach based on deep Autoencoder and deep classifiers for credit card fraud detection,” Expert Systems with Applications, vol.217, no.119562, 2023.
[15] R. Van Belle, B. Baesens and J. De Weerdt, “CATCHM: A novel network-based credit card fraud detection method using node representation learning,” Decision Support Systems, vol.164, no.113866, 2023.
[16] K. Vinay Kumar, N. C. Santosh and Narasimha reddy soor, “Data Analysis and Fair Price Prediction Using Machine Learning Algorithms,” Journal of Computer Allied Intelligence, vol.2, no.1, pp.26- 45, 2024.
[17] S. N. Pundkar and M. Zubei, “Credit card fraud detection methods: A review,” In E3S Web of Conferences, EDP Sciences, vol.453, pp.01015, 2023.
[18] E. Strelcenia and S. Prakoonwit, “Improving classification performance in credit card fraud detection by using new data augmentation,” AI, vol.4, no.1, 2023.
[19] A. Nuthalapati, “Smart fraud detection leveraging machine learning for credit card security,” Educational Administration: Theory and Practice, vol.29, no.2, pp.433-443, 2023.
[20] Swara Snehit Patil, “Artificial Intelligence: A Way to Promote Innovation,” Journal of Sensors, IoT & Health Sciences, vol.2, no.1, pp.1-5, 2024.
[21] M. Abdul Salam, K. M. Fouad, D. L. Elbably and S. M. Elsayed, “Federated learning model for credit card fraud detection with data balancing techniques,” Neural Computing and Applications, vol.36, no.11, pp.6231-6256, 2024.
[22] K. G. Dastidar, O. Caelen and M. Granitzer, “Machine learning methods for credit card fraud detection: A survey,” IEEE Access, 2024.
[23] Archita Nayak and Kanti Paul, “Energy, Economic and Environmental Scenario and Race to LowCarbon Economy among BRICS Countries,” International Journal of Advance Research and Innovation, vol.12, no.4, pp.1-10, 2024.
[24] S. S. Sulaiman, I. Nadher and S. M. Hameed, “Credit Card Fraud Detection Using Improved Deep Learning Models,” Computers, Materials & Continua, vol.78, no.1, 2024.
