Reimagining Digital Banking Security: AI Based Analytics, Cloud Native Deployment, and Real Time Compliance Enforcement

Abstract

The financial services sector is undergoing rapid transformation driven by digitalization, heightened regulatory scrutiny, and increasingly sophisticated threats. Traditional security and compliance frameworks are proving inadequate in the face of real‑time transaction volumes, complex data flows, and evolving risk vectors. This study proposes a model for reimagining digital banking security, integrating three components: (1) AI‑based analytics for advanced threat detection and anomaly monitoring; (2) cloud‑native deployment architectures enabling scalable, resilient, and modular systems; and (3) real‑time compliance enforcement, allowing continuous regulatory adherence rather than periodic audits. The proposed framework is designed to ingest streaming transactional and behavioral data, apply machine learning (ML), deep learning, graph models, and anomaly detection techniques to spot fraud, insider threats, and suspicious patterns. Cloud‑native principles (microservices, containers, orchestration, CI/CD, serverless where applicable) are leveraged to ensure low latency, availability, resilience, and rapid updates. Real‑time compliance is enforced through rule engines, metadata tracking, audit trails, and explainable AI to satisfy regulatory obligations and support auditability.

 We conduct an empirical evaluation using simulated data streams reflective of banking transaction volumes, plus labeled historical data for fraud and compliance violations. Key metrics include detection accuracy (precision, recall, F1), latency (time from event to alert), system scalability (number of simultaneous transactions processed), and compliance false positives/negatives. Results show that the AI‑based analytics component achieves F1‐scores upwards of ≈ 0.95 in fraud detection, with latency on the order of milliseconds per transaction under load. Cloud‑native deployment allows linear scaling with little degradation in performance; real‑time compliance enforcement substantially reduces delayed violations and audit findings. However, challenges exist: data privacy, model interpretability, regulatory acceptance, and operational cost of cloud infrastructure and continuous monitoring are significant.

 This integrative model promises stronger security, operational agility, and better regulatory posture for digital banks. Future work will address safeguards (privacy, bias), regulatory harmonization, and live pilot deployment in real banking environments.