Deep Learning for Real-Time Credit Card Fraud Detection

Abstract

Deep Learning for Real-Time Fraud Detection in High-Volume Payment Systems Fraud detection has evolved from static rule-based systems to real-time AI operating within milliseconds. In modern payment ecosystems, transactions must be approved in under 100ms while fraud patterns shift continuously and fraudulent activity represents less than 1% of total volume. Balancing speed, accuracy, and customer experience makes this a uniquely complex AI challenge. This talk explores how deep learning enables scalable, adaptive fraud detection at scale. Unlike rule-based models relying on manual thresholds, deep learning automatically captures non-linear relationships across high-dimensional signals: transaction attributes, behavioral velocity, device intelligence, and contextual embeddings derived from prior activity. We examine some of the architectures in consideration: feedforward networks for transaction scoring, LSTMs for sequential behavior modeling, and autoencoders for unsupervised anomaly detection, alongside hybrid designs that pair neural models with rule-based safeguards for deterministic edge cases. We also address the class imbalance problem head-on: with sub-1% fraud rates, optimizing accuracy is misleading; the real challenge lies in precision-recall tradeoffs that reflect genuine business costs. System design principles receive equal emphasis: stateless inference, feature stores that pre-compute behavioral embeddings to meet latency constraints, horizontal scalability, and continuous feedback loops enabling rapid model adaptation. Finally, we discuss explainable AI via SHAP, bridging model interpretability and regulatory transparency and where that gap remains open.

Bio

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Balakumaran Sugumar is a senior technology leader and AI researcher specializing in real-time fraud detection and secure payment systems. With over 16 years of experience in banking and financial technology, he designs scalable, cloud-native platforms that integrate deep learning, distributed systems, and explainable AI. He has authored multiple IEEE conference papers and actively contributes to advancing trustworthy AI in high-risk financial applications through research, speaking, and industry leadership.