Machine learning driven fraud detection pipelines for digital banking products

Abstract

Digital banking products have transformed financial service delivery by enabling real-time transactions, Omni channel access, and data-driven personalization. However, this transformation has also intensified fraud risk, as attackers exploit scale, automation, and system complexity to conduct increasingly sophisticated attacks. Traditional rule-based fraud detection systems struggle to keep pace with evolving fraud patterns, resulting in high false-positive rates, delayed response, and customer friction. Machine learning (ml) has emerged as a critical enabler of adaptive, scalable, and accurate fraud detection in digital banking environments. This paper examines machine learning–driven fraud detection pipelines designed for modern digital banking products. It analyzes end-to-end pipeline architectures encompassing data ingestion, feature engineering, model trAIning, real-time inference, and continuous learning. Through architectural synthesis, threat pattern analysis, and expert-informed evaluation, the study proposes a machine learning fraud detection pipeline framework aligned with performance, security, and regulatory requirements of digital banking. The findings demonstrate that well-designed ml-driven pipelines significantly improve fraud detection accuracy, reduce false positives, and enable real-time intervention without degrading customer experience. The paper positions fraud detection pipelines not merely as analytical systems, but as mission-critical product capabilities that safeguard trust, financial integrity, and regulatory compliance in digital banking ecosystems.