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Financial Fraud Detection using Machine Learning and Deep Learning Models
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 185 - Number 49 |
| Year of Publication: 2023 |
| Authors: Arjun Gopichander Ravichander, Aera K. Leboulluec, Peter L. Leboulluec |
10.5120/ijca2023923324
|
Arjun Gopichander Ravichander, Aera K. Leboulluec, Peter L. Leboulluec . Financial Fraud Detection using Machine Learning and Deep Learning Models. International Journal of Computer Applications. 185, 49 ( Dec 2023), 32-37. DOI=10.5120/ijca2023923324
Abstract
Digital payments of all kinds are increasing all over the world. For instance, in 2018, payments totaling $578 billion were processed by PayPal. It is of utmost importance for financial institutions like banks and credit card companies to find fraudulent transactions in real time to withhold any suspicious transaction as majority of traditional approaches are manual, which is not only inefficient, expensive, and imprecise but also impractical. By analyzing a large amount of financial data, machine-learning-based methods can intelligently detect fraudulent transactions. Most banks and other financial institutions have dedicated teams of dozens of analysts working on automated systems to identify potentially fraudulent transactions through their products. In this research, publicly available data was used on different payment transactions, and solved the issue of fraud detection using different machine learning techniques. Machine learning and Deep Learning techniques was implemented for fraud detection and demonstrate that fraudulent and non-fraudulent transactions can be distinguished through exploratory analysis.
References
- Maurya and A. Kumar, “Credit Card Fraud Detection System using machine learning technique,” 2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom), 2022.
- Mehbodniya, I. Alam, S. Pande, R. Neware, K. P. Rane, M. Shabaz, and M. V. Madhavan, “Financial fraud detection in healthcare using machine learning and Deep Learning Techniques,” Security and Communication Networks, vol. 2021, pp. 1–8, 2021.
- Sardeshmukh, S. Reddy, B. P. Gautham, and A. Joshi, “Bayesian networks for inverse inference in manufacturing Bayesian networks,” 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), 2017.
- H. Shu, “Bayesian inference in Census-House Dataset,” 2021 International Conference on Signal Processing and Machine Learning (CONF-SPML), 2021.
- Krasic and S. Celar, “Telecom fraud detection with machine learning on imbalanced dataset,” 2022 International Conference on Software, Telecommunications and Computer Networks (SoftCOM), 2022.
- J. B, J. A. R, and D. P. Ganesh, “Credit card fraud detection with unbalanced real and synthetic dataset using Machine Learning Models,” 2022 International Conference on Electronic Systems and Intelligent Computing (ICESIC), 2022.
- M. S., “Survey paper on fraud detection in Medicare using machine learning,” International Journal of Psychosocial Rehabilitation, vol. 24, no. 5, pp. 4170–4174, 2020.
- P. Singh, V. Chauhan, S. Singh, P. Agarwal, and S. Agrawal, “Model for credit card fraud detection using machine learning algorithm,” 2021 International Conference on Technological Advancements and Innovations (ICTAI), 2021.
- S. Dandotia and S. K. Tiwari, “Detection of credit card fraud transactions using machine learning algorithms techniques with data driven approaches: A comparative study,” International Journal of Innovative Research and Growth, vol. 10, no. 11, 2021.
- S. khan, S. Kumar, and M. H. Kumar, “Credit card fraud detection using machine learning,” International Journal of Scientific and Research Publications (IJSRP), vol. 11, no. 6, pp. 60–67, 2021.
- Z. Zhao and T. Bai, “Financial fraud detection and prediction in listed companies using smote and machine learning algorithms,” Entropy, vol. 24, no. 8, p. 1157, 2022.
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