A Predictive Analytics Approach for Detecting Fraudulent Financial Transactions Using Supervised Learning

Abstract

The rapid growth of digital banking and online financial transactions has significantly increased the risk of fraudulent activities, posing serious threats to both customers and financial institutions in terms of monetary loss and reputational damage. As banking systems become more complex and interconnected, identifying fraudulent behavior at an early stage has become essential for maintaining security and trust. This study presents an intelligent fraud detection framework based on Machine Learning (ML) techniques to effectively identify suspicious transactions and minimize financial risks. The proposed system utilizes Artificial Intelligence (AI) to automate and accelerate processes such as transaction verification and anomaly detection, thereby reducing manual effort and improving response time. Various supervised learning algorithms are trained on a publicly available dataset to analyze patterns and uncover hidden relationships associated with fraudulent activities. A major challenge addressed in this work is class imbalance, where fraudulent cases are significantly fewer than legitimate ones; to overcome this, data resampling techniques are applied to ensure balanced learning and improved model performance. Additionally, data preprocessing and feature engineering are performed to enhance the quality and relevance of input data. The experimental evaluation demonstrates that the proposed MLbased framework achieves reliable and accurate fraud detection. This approach contributes to strengthening fraud prevention systems, enabling faster decision-making, and ensuring secure and efficient banking operations.