Cybersecurity threats in banking: Unsupervised fraud detection analysis

Abstract

Customers all over the world now enjoy remarkable levels of accessibility and convenience thanks to the digital transformation of the banking industry. However, technology has also brought up new difficulties, including cybersecurity. The incapacity of conventional rule-based fraud detection strategies to keep up with the rapid evolution of cyber threats has generated interest in flexible and efficient approaches like unsupervised learning. The potential of unsupervised learning to improve fraud detection in the banking sector is examined in this article. The article addresses the disadvantages of traditional methods, the benefits of unsupervised learning, and how cybersecurity measures may be affected. A thorough framework for putting unsupervised fraud detection strategies into practice, including data preprocessing, feature engineering, isolation forest implementation, thresholding, and assessment, is provided in the methodology section. To further improve anomaly detection frameworks, future efforts propose integrating advanced machine learning techniques, dynamic thresholding, enhanced feature engineering, and continuous model monitoring. In summary, this essay offers useful insights on using modern machine learning algorithms to reduce cybersecurity threats and ensure the security of digital transactions within the banking industry.