Problem Statement:
The manual audit process for approved insurance claims is highly time-consuming, error-prone, and inefficient, leading to potential fraud, compliance risks, and financial discrepancies. The lack of automation in critical verification and fraud detection tasks results in delays, inconsistencies, and operational inefficiencies.
Key Issues in the Manual Process:
Time-Consuming & Labor-Intensive
- Claim selection, document retrieval, and verification require significant manual effort.
- Cross-checking claim details against policy terms and fraud patterns is slow.
High Risk of Human Errors
- Manual data entry and document verification increase the likelihood of missed fraud indicators.
- Policy compliance checks may be inconsistently applied, leading to incorrect approvals.
Fraud Detection is Limited & Reactive
- Fraud patterns are difficult to detect manually, as they require analyzing large datasets.
- Suspicious activities (duplicate claims, exaggerated amounts) often go unnoticed.
- Investigations occur after fraud has already happened, rather than proactively preventing it.
Poor Scalability & Bottlenecks
- As claim volumes grow, the audit team struggles to keep up, leading to backlogs.
- High workload increases burnout risk for auditors, further impacting efficiency.
The As-Is Flow diagram
Need for Agentic Automation
The above challenges highlight the need for an Agentic Automation solution that can:
Speed up claim audits with Agentic AI-driven document verification.
Reduce human errors by enforcing standard compliance checks.
Enhance fraud detection with Agentic AI-based pattern recognition.
Improve classification with Agentic AI-assisted decision-making.
Ensure scalability to handle large claim volumes efficiently.
