Machine learning algorithms to prevent fraudulent transactions in real-time

U Somayajulu, A Surender and N Mriganka

Individual Researcher, Pune, Maharashtra, India.
 
Review
International Journal of Science and Research Archive, 2024, 13(01), 2027–2033.
Article DOI: 10.30574/ijsra.2024.13.1.1847
Publication history: 
Received on 19 August 2024; revised on 02 October 2024; accepted on 04 October 2024
 
Abstract: 
In the rapidly evolving landscape of digital finance, the proliferation of online transactions has been accompanied by a significant increase in fraudulent activities. This paper explores the application of machine learning algorithms to detect and prevent fraudulent transactions in real-time, thereby enhancing the security and reliability of financial systems. We investigate various machine learning techniques, including supervised learning models such as Random Forest, Logistic Regression, and Neural Networks, as well as unsupervised methods like anomaly detection. By leveraging these algorithms, we aim to identify patterns and anomalies indicative of fraudulent behavior. Our approach integrates robust preprocessing techniques and real-time data analysis to ensure high accuracy and efficiency. The results demonstrate that machine learning models can significantly reduce the incidence of fraud, providing financial institutions with a powerful tool to safeguard their customers’ assets. This study underscores the potential of machine learning to transform fraud detection, offering a scalable and adaptive solution to one of the most pressing challenges in the financial sector
 
Keywords: 
Artificial intelligence; Cyber threats; Financial services; Data security; Risk assessment
 
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