Machine Learning Approaches for Financial Fraud Detection in Digital Transactions
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Abstract
With the rapid growth of digital payment systems, financial fraud has become a major concern. This paper presents a comparative study of machine learning algorithms for detecting fraudulent transactions in real time. Techniques such as Logistic Regression, Decision Trees, and Gradient Boosting are evaluated using large-scale transaction datasets. The proposed model achieves high precision and recall, minimizing false positives. The study emphasizes the importance of adaptive learning systems in combating evolving fraud patterns and ensuring secure financial ecosystems.
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References
Anand, M., Velu, A., & Whig, P. (2022). Prediction of loan behaviour with machine learning models for secure banking. Journal of Computer Science and Engineering (JCSE), 3(1), 1–13.
Jiwani, N., Gupta, K., & Whig, P. (2021). Novel healthcare framework for cardiac arrest with the application of AI using ANN. In Proceedings of the 5th International Conference on Information Systems and Computer Engineering.
Nadikattu, R. R., Mohammad, S. M., & Whig, P. (2020). Novel economical social distancing smart device for COVID-19. International Journal of Electrical Engineering and Technology (IJEET).
Pulivarthy, P., & Whig, P. (2025). Bias and fairness: Addressing discrimination in AI systems. In Ethical dimensions of AI development (pp. 103–126).
Jupalle, H., Kouser, S., Bhatia, A. B., Alam, N., Nadikattu, R. R., & Whig, P. (2022). Automation of human behaviors and its prediction using machine learning. Microsystem Technologies, 28(8), 1879–1887.
Whig, P., Kasula, B. Y., Yathiraju, N., Jain, A., & Sharma, S. (n.d.). Bone cancer classification and detection using machine learning technique. In Diagnosing musculoskeletal conditions using artificial intelligence and machine learning.