Interpretable Agentic Intelligence for Fraud Detection and Automated Decision-Making in Digital Insurance
DOI:
https://doi.org/10.64137/31079377/IJMSD-V2I3P102Keywords:
Agentic Fraud Detection, Insurance AI Workflows, Transparent AI Systems, Explainable Fraud Analytics, Claims Validation Processes, Human-Centric AI Governance, Audit Trail Management, Role-Based Access Control, Fraud Detection Architecture, Decision Transparency FrameworksAbstract
Fraud remains a significant challenge in digital insurance. AI-driven detection models enhance efficiency but risk being black boxes, resulting in unvalidated alerts. Agentic AI addresses these weaknesses. Agentic AI applies human-centered agenda-setting and autonomy concepts to agents and workflows, enabling AI to carry out agents' decisions. Agentic AI supports transparent workflows, aiding validation of fraud detection and providing transparent explanations for decisions. This understanding can be applied to resolve agentic roles in fraud detection and the handling of claims flagged as fraudulent. Four questions explore these aspects: What is being done? When? Why? By whom? An overview of the architecture is presented. Insurance claimants, agents, underwriters, auditors, and regulators are the key stakeholders. Responsibilities, access controls, validation mechanisms, and audit trail requirements are defined, ensuring that humans retain ultimate decision-making freedom and accountability throughout the process.
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