Explainable Machine Learning Framework for Secure Real-Time Financial Fraud Detection Across Cloud-Native Banking Systems
Abstract
Financial fraud has become increasingly sophisticated as banking services migrate toward cloud-native architectures, real-time payment platforms, microservices, and digital customer channels. Conventional rule-based fraud detection systems often struggle to identify rapidly evolving attack patterns while generating excessive false positives and providing limited explanations for their decisions. This research proposes an Explainable Machine Learning Framework for secure real-time financial fraud detection across cloud-native banking systems. The framework integrates machine learning-based behavioral analysis with explainable artificial intelligence, real-time stream processing, cloud-native security controls, and adaptive risk scoring. Transactional, behavioral, device, network, and contextual features are processed through a scalable machine learning pipeline to classify suspicious activities and generate dynamic fraud-risk scores. Explainability mechanisms provide interpretable reasons behind fraud predictions, enabling analysts, compliance teams, and banking customers to understand high-risk decisions. The proposed architecture incorporates secure APIs, containerized services, encryption, identity management, continuous monitoring, and Zero Trust principles to protect sensitive financial information. Model evaluation considers detection accuracy, precision, recall, F1-score, false-positive rate, latency, scalability, and explanation quality. The framework aims to improve fraud detection effectiveness while maintaining transparency, regulatory accountability, operational resilience, and customer trust in modern cloud-native banking environments.
Article Information
Journal |
International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
|---|---|
Volume (Issue) |
Vol. 7 No. 6 (2024): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
DOI |
|
Pages |
15372-15381 |
Published |
November 18, 2024 |
| Copyright | |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Mattias Andersson (2024). Explainable Machine Learning Framework for Secure Real-Time Financial Fraud Detection Across Cloud-Native Banking Systems. International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , Vol. 7 No. 6 (2024): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , pp. 15372-15381. https://doi.org/10.15662/IJAESIT.2024.0706011 |
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