AI-Driven Frameworks for Anomaly Detection: Comparative Analysis of Machine Learning and Deep Learning Models in Financial Fraud and Cybersecurity

ABSTRACT

                                  The proliferation of digital transactions and interconnected networks has exposed organizations to increasingly complex cyber and financial threats, rendering traditional rule-based detection models inadequate. This study develops and evaluates a hybrid Artificial Intelligence (AI) framework for anomaly detection, integrating supervised and unsupervised learning paradigms to enhance fraud detection and cybersecurity resilience. Comparative analyses demonstrate that ensemble supervised models, such as Random Forest and LightGBM, achieve high accuracy (up to 95.8%) and AUC scores (0.97) in structured fraud data, while deep unsupervised models—Autoencoders and Generative Adversarial Networks—effectively identify novel cyber anomalies. Real-time processing through streaming analytics and Long Short-Term Memory (LSTM) architectures further reduces detection latency below 200ms. The findings highlight that AI integration yields a measurable operational advantage, with institutions reporting over $2.22 million in annual cost savings and a 50% reduction in analyst workload. Ethical imperatives, including Explainable AI (XAI), fairness, and cognitive cybersecurity, are underscored as critical for compliance and trust in autonomous detection systems.

Keywords: Artificial Intelligence (AI), Anomaly Detection, Financial Fraud Detection, Cybersecurity, Deep Learning (DL), Explainable AI (XAI)