Cloud-Native AI and Deep Learning Models for Real-Time Fraud Detection and Cybersecurity in Financial Institutions

Abstract

Financial institutions face escalating fraud and cyber-threat volumes as transaction velocity, digital channels, and adversary sophistication grow. Real-time detection is essential to minimize monetary loss and reputational damage, but it requires systems that combine low latency, high throughput, adaptive learning, privacy preservation, and operational resilience. This paper investigates cloud-native architectures and deep learning (DL) models tailored for real-time fraud detection and cybersecurity in banking, payments, and capital-market environments. We synthesize best practices from stream processing, model serving, feature stores, and MLOps to propose an end-to-end reference architecture: event ingest (Kafka/pub-sub) → streaming feature extraction (Flink / ksqlDB) → ensemble hybrid DL + graph models (LSTM/Transformer + GNN) with online learning, served via Kubernetes/Kubeflow for autoscaling, A/B canarying, monitoring, and explainability (SHAP/local explanations). The methodology covers data labeling strategies, concept-drift detection and mitigation, privacy techniques (differential privacy, federated learning), and regulatory alignment. In experiments (simulated and production-like replay), hybrid graph-aware DL models improve detection recall for organized/relational fraud patterns while keeping false positives within operational tolerance when combined with risk-based scoring and human analyst-in-the-loop triage. We discuss tradeoffs — latency, interpretability, model decay, and privacy — and provide recommendations for practitioners and researchers to deploy robust, auditable, cloud-native fraud detection systems.