Machine learning techniques for enhancing security in financial technology systems

William Clement Aaron 1, Obehi Irekponor 2, Ngozi Tracy Aleke 3, Linda Yeboah 4 and Jennifer E Joseph 5, *

1 Independent Investigator, Cloud AI Consultant, Mitchell Martin New York, USA.
2 Independent Researcher, Giant Eagle Inc., Pittsburgh, USA.
3 College of Computing, Illinois Institute of Technology, Illinois, USA.
4 Independent researcher, Philip Morris International, South Africa.
5 Applied statistics and decision analytics, Western Illinois University, Illinois.
 
Review
International Journal of Science and Research Archive, 2024, 13(01), 2805–2822.
Article DOI: 10.30574/ijsra.2024.13.1.1965
Publication history: 
Received on 01 September 2024; revised on 11 October 2024; accepted on 14 October 2024
 
Abstract: 
The financial technology (fintech) industry has transformed the way financial services are delivered, offering enhanced convenience, accessibility, and efficiency. However, this rapid digitization has also increased the sector’s exposure to a wide array of security risks, including cyberattacks, fraudulent activities, data breaches, and insider threats. As traditional security measures struggle to keep pace with the growing sophistication of these threats, machine learning (ML) techniques have emerged as promising tools for reinforcing security in fintech systems. Machine learning models can analyze vast amounts of data, detect anomalous behavior, predict potential risks, and respond to security threats in real-time. This paper explores the potential of machine learning to enhance security in financial technology by addressing key challenges such as anomaly detection, fraud detection, intrusion detection systems (IDS), and risk management. We provide an overview of common machine learning algorithms—such as decision trees, neural networks, support vector machines (SVM), and clustering methods—that are applied to these security tasks. Additionally, we discuss the evaluation metrics used to measure the accuracy, precision, recall, and overall effectiveness of these models. Through real-world case studies, we highlight successful implementations of machine learning in fintech security, offering insights into best practices and lessons learned. Despite the many benefits, there are also significant challenges and limitations associated with the adoption of machine learning in fintech, including concerns over data privacy, the accuracy and reliability of models, and the computational resources required for large-scale deployment. Looking ahead, this paper identifies emerging trends and potential innovations that could further enhance security in fintech, from advancements in deep learning to the integration of artificial intelligence with blockchain technology. Finally, we propose areas for future research to address unresolved issues and support the continued development of machine learning-driven security solutions in the fintech industry.
 
Keywords: 
Machine Learning in Fintech; Fraud Detection; Anomaly Detection; Cybersecurity AI in Fintech; Data Privacy
 
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