Revolutionizing Fraud Detection: Machine Learning Algorithms In Financial Transactions

Abstract

Fraud detection in financial transactions is a critical challenge that demands advanced and reliable methodologies. This study explores the integration of both supervised and unsupervised machine learning techniques to develop a robust fraud detection model. By employing a hybrid approach, combining Decision Trees with Support Vector Machines (DT+SVM) and Neural Networks with Random Forests (NN+RF), we aim to enhance the model's predictive capabilities and adaptability to evolving fraud patterns. Our methodology begins with comprehensive data collection, aggregating transaction data from financial institutions and industry reports. The preprocessing stage involves cleaning the data to remove duplicates, correcting inconsistencies, and normalizing the dataset to standardize independent variables. Feature selection is conducted using statistical methods and machine learning techniques, such as correlation analysis and recursive feature elimination (RFE), to identify the most relevant predictors of fraud. In the model development phase, we apply various machine learning algorithms. Neural Networks (NN), Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM), and Gradient Boosting Machines (GBM) are individually trained and evaluated. The hybrid models—NN+RF and DT+SVM—are then constructed by combining the outputs of these individual models to leverage their strengths. The models are trained and validated using a labeled dataset, with performance evaluated through cross-validation techniques and metrics including accuracy, precision, recall, and F1 score. The results demonstrate that the hybrid models significantly outperform the individual methods. Specifically, the NN+RF model achieves an accuracy of 97.3%, with a precision of 94.2%, recall of 95.8%, and an F1 score of 95.0%. The DT+SVM model excels further, with an accuracy of 97.5%, precision of 94.7%, recall of 95.9%, and an F1 score of 95.3%. The integration of neural networks' pattern recognition capabilities with the ensemble strength of random forests, and the interpretability of decision trees with the precision of SVMs, provides a powerful framework for fraud detection systems.