MSCS Research

Financial security, together with fraud prevention, heavily relies on the ability to identify suspicious money transactions. An investigation of machine learning methodologies aims to develop more accurate as well as efficient fraud detection solutions. Multiple traditional and deep learning models such as Random Forest, Gradient Boosting, AdaBoost, Logistic Regression, Decision Tree, Support Vector Machine (SVC), Gaussian Naïve Bayes, K-Nearest Neighbors (KNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) and BERT underwent evaluation using accuracy, cross-validation accuracy, precision, recall and F1-score performance metrics.

Random Forest emerges as the optimal model for structured transaction data analysis because it delivers both high accuracy of 99.94% and efficient computation. BERT demonstrated superior text-based fraud detection because it achieved an accuracy rate of 99.97%. The RNN and LSTM proliferation models demonstrated sequential data analysis ability, yet failed to obtain better risk detection results than basic classifiers. The research demonstrates that blending Random Forest with BERT creates an optimal system for combined structured and unstructured analysis of financial fraud.

Financial security improvements can be reached through integrating advanced machine learning models into fraud detection frameworks, according to these research results. Future investigations should concentrate on improving hybrid systems performance for live financial operations and finding methods to improve their scalability across extensive financial systems.