DevOps-Enabled Agentic Deep Learning for Insurance Fraud Intelligence

Authors

  • Dhanaraj Sathiri Author

Keywords:

DevOps Driven Data Engineering, Agentic Artificial Intelligence, Insurance Fraud Detection, Cloud Native Automation, Machine Learning Operations, Continuous Integration Continuous Deployment, Data Governance Frameworks, Risk Sensitive Decision Making, Automated Model Deployment, Predictive Fraud Analytics, Scalable AI Systems, Reproducible Data Pipelines, Secure Data Architectures, Regulatory Compliance In Insurance, Ethical AI Governance, Legacy System Modernization, Autonomous Decision Systems, Operational AI Control, Model Lifecycle Management, Enterprise AI Adoption

Abstract

The study investigates DevOps-driven data engineering for agentic AI in insurance fraud detection with a focus on cloud-native automations. Data are centrally secured, accessible, governed, of reproducible quality, properly profiled, and efficiently prepared. Design, development, and deployment encompass extensive safeguards for risk-sensitive decision-making. A superset of continuous integration/continuous deployment tailored to machine learning as well as data governance underlie the approach, demystifying the technology stack and enabling success at operational scale. The investigation is motivated by regulatory, economic, and ethical pressures on the industry, demand for predictive solutions, and pain points of legacy monitoring systems. Automation of machine-learning model development and deployment mitigates issues of scalability, reproducibility, and security while incorporating risk assessment.

Organizations worldwide are increasingly turning to artificial intelligence in a bid to detect fraudulent activity in all its forms. Such systems, however, generally stem from one-off proof-of-concept initiatives, operate in isolation, and lack sufficient controls for operational deployment in support of everyday decision-making. Nevertheless, agentic artificial intelligence—actant solutions capable of executing decisions without direct human intervention—promises a transformative competitive advantage for business leaders. Integrating governance and control ensures that these automated systems operate within an acceptable risk tolerance, align with organizational values, and minimize unintended consequences. Insurance fraud detection solutions typically fall short of these requirements.

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Additional Files

Published

2026-03-27

Versions

Data Availability Statement


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Issue

Vol. 4 No. 01 (2026): January-March 2026

Section

Articles