Machine Learning Algorithms for Fraud Detection in Financial Transactions

Abstract

Fraud detection in financial transactions is a critical challenge in modern financial systems. With the increasing volume and complexity of financial transactions, traditional rule-based systems are becoming less effective in identifying fraudulent activities. Machine learning (ML) algorithms have emerged as a powerful tool for detecting fraud by analyzing large datasets and identifying patterns indicative of fraudulent behavior. This paper explores the application of various ML algorithms, such as decision trees, support vector machines (SVM), neural networks, and ensemble methods, in the context of fraud detection in financial transactions. The study compares the performance of these algorithms based on accuracy, precision, recall, and F1-score, highlighting the strengths and limitations of each approach. Additionally, the paper discusses feature engineering techniques, the importance of imbalanced datasets, and the challenges associated with real-time fraud detection. The results demonstrate that ML algorithms, particularly ensemble methods and deep learning techniques, show significant promise in improving the accuracy and efficiency of fraud detection systems. This research provides valuable insights for financial institutions seeking to implement AI-driven solutions for combating fraud.