Real Time Fraud Detection and Risk Exception Handling in Financial Enterprise Platforms using AI and Cloud Native DevOps
DOI:
https://doi.org/10.15680/ddq05x35Keywords:
Real-Time Fraud Detection, Risk Exception Handling, AI, Cloud Native DevOps, Financial Enterprise Platforms, Anomaly Detection, Automated Remediation, Scalable ArchitectureAbstract
The exponential growth of digital financial services has increased the exposure of enterprise platforms to fraud and operational risks. Traditional rule-based monitoring systems often fail to respond to evolving threats in real time, necessitating intelligent solutions capable of adaptive detection and rapid mitigation. This study investigates the application of artificial intelligence (AI) integrated with cloud-native DevOps practices for real-time fraud detection and risk exception handling in financial enterprise platforms. Leveraging machine learning and deep learning algorithms, the proposed framework identifies anomalous transactions, suspicious API calls, and unusual user behavior with high accuracy and low latency. Integration with cloud-native DevOps pipelines enables automated deployment, monitoring, and scaling of AI models, ensuring continuous availability and resilience under variable workloads. The research outlines a methodology for feature extraction, model training, and evaluation, as well as orchestration of risk handling workflows. Results demonstrate that AI-powered platforms can proactively detect potential fraud, generate risk alerts, and execute automated remediation actions, reducing financial loss and operational downtime. This study emphasizes the synergy between AI and DevOps practices, presenting a scalable, adaptive, and compliant framework for securing financial enterprise platforms in dynamic cloud-native environments.
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