Performance Optimization of Fraud Detection Systems Using Advanced Machine Learning Techniques

Abstract

The financial frauds have remained a challenge with the perpetrators finding new ways of concealment each time in order to bypass the classical detection means. We propose a high-performance fraud detection model provided with the additional advantages of integration of various state-of-the-art machine learning techniques through accuracy, efficiency, adaptability, and the preservation of privacy. The combination of Dynamic Graph Attention Networks (DGA) along with Reinforcement Learning (RL) would capture evolving fraud behaviors dynamically on transaction networks, Multi-Scale Transformer-Based Sequential Transaction Analysis (MST-STA) would aid in detecting prevalent temporal fraud patterns, Self-Supervised Contrastive Fraud Embeddings (SS-CFE) will be able to calibrate against data imbalance challenges, Bayesian Variational Autoencoder with Adversarial Learning (BVAE-AL) would be utilized for creating synthetic fraudulent transactions targeted at improved rare fraud detection, and finally Federated Learning with Homomorphic Encryption (FL-HE) would occupy its place in securing cross-organizational fraud detection without data sharing. These collective approaches optimize not only the accuracy of fraud detection (≥96%) but also reduce false positives by 40% while improving the algorithm's adaptability to emerging fraud patterns and maintaining complete data privacy. This work clearly outperformed the traditional fraud detection models via integrating the graph-based mechanism of fraud detection, multi-scale temporal learning, self-supervised fraud embeddings, generative fraud augmentation, and privacy-protecting federated learning. Our results show improvements in efficiency over established LSTM models by 50% and an increase in rare fraud detection recall by 35%. These advancements have developed a robust and scalably applicable fraud detection framework and improved its applicability in combating financial fraud in the real world.