Machine Learning Based Predictive Models for Secure Financial Transactions and Cyber Threat Detection

Abstract

The rapid growth of digital financial systems has significantly increased the risk of cyber threats, including fraud, identity theft, and unauthorized transactions. Traditional rule-based security mechanisms are often inadequate in detecting sophisticated and evolving attack patterns. This study explores the application of machine learning–based predictive models to enhance the security of financial transactions and improve cyber threat detection. By leveraging historical transaction data and behavioral analytics, machine learning algorithms such as decision trees, support vector machines, neural networks, and ensemble methods can identify anomalies and predict fraudulent activities in real time. The proposed approach focuses on developing adaptive models capable of learning from dynamic datasets to detect both known and unknown threats. Additionally, feature engineering, data preprocessing, and model optimization techniques are discussed to improve prediction accuracy and reduce false positives. The research highlights the importance of integrating machine learning systems into financial infrastructures to ensure robust cybersecurity frameworks. Experimental results demonstrate that predictive models significantly outperform traditional methods in terms of accuracy, efficiency, and scalability. The study concludes that machine learning offers a proactive and intelligent solution for safeguarding financial systems against emerging cyber threats.