AI-driven fraud detection: Models, architectures, governance, and future directions

Abstract

With the exponential growth of digital transactions, organizations across banking, fintech, e-commerce, and telecommunications face increasingly sophisticated fraud attempts. Traditional fraud detection systems, primarily rule-based and manually configured, struggle to keep pace with evolving fraud patterns and exhibit high false-positive rates. Artificial Intelligence (AI), particularly machine learning (ML) and deep learning (DL), has emerged as a transformative solution by enabling pattern recognition, anomaly detection, behavioral analytics, and real-time decisioning at scale. This paper provides a structured overview of AI-driven fraud detection models, their technical components, data pipelines, deployment architectures, and evaluation frameworks. It compares traditional rule-based approaches with supervised, unsupervised, and hybrid AI methods, and discusses practical challenges such as class imbalance, concept drift, data quality, and latency constraints in real-time payment environments. The paper also highlights explainability challenges, regulatory implications under frameworks such as GDPR and PSD2, and future innovations including federated learning, graph neural networks, and generative AI for adversarial testing and synthetic data generation. Experimental discussion and case-style examples from card-not-present, account takeover, and telecom subscription fraud scenarios illustrate how AI can significantly improve fraud detection accuracy and operational efficiency while emphasizing that careful governance, model monitoring, and responsible AI practices are essential for trustworthy deployment.