Automating Policy Verification and Discrepancy Detection with Dociphi
About the Client
The client’s internal team is responsible for ensuring that promised insurance coverage is accurately reflected in issued policies. Traditionally, reviewers manually compared 10 to 30 data points per policy across multiple documents, including initial proposals, carrier quotes, and final issued policies. While thorough, this process required significant effort and time.
To enhance efficiency and accuracy, the client partnered with Quantiphi to implement Dociphi, an AI-powered Intelligent Document Management Platform. The solution leveraged advanced LLM technologies to automate policy verification and discrepancy detection.
Problem Statement
Reviewing insurance policies manually involved extracting, comparing, and validating data across multiple documents for each policy. The process was resource-intensive, time-consuming, and relied heavily on manual diligence, limiting the team’s capacity to quickly confirm compliance and identify discrepancies.
The client sought a solution that could automate extraction of data points from multiple policy documents, accurately identify discrepancies between initial proposals, carrier quotes, and issued policies and reduce manual effort while maintaining precision and reliability.
Challenges
The client’s policy verification process faced the following challenges:
- Manual Effort: Entirely manual comparison of multiple documents slowed down processing.
- Time-Intensive: Reviewers spent excessive time searching through 2 to 5 documents per policy.
- Discrepancy Detection: Identifying inconsistencies accurately across proposals, quotes, and issued policies was difficult and error-prone.
The Solution
Quantiphi implemented Dociphi to automate policy verification and discrepancy detection. Leveraging advanced LLM technologies, Dociphi streamlined the extraction, classification, annotation, and indexing of policy-related data across multiple documents.
Key Capabilities of the Solution:
- Automated Extraction & Comparison: Extracted data points from client proposals, carrier quotes, and issued policies.
- Discrepancy Detection: Accurately identified differences between initial and final documents.
- High Accuracy: Achieved 84.57% accuracy in entity identification and comparison.
- Enhanced Traceability: Linked extracted data to source documents for auditability and review.
Results and Impact Created
With Dociphi, the client achieved significant improvements in policy verification:
- 31.05% reduction in time required to review each policy.
- Projected annual cost savings of $783,336.
- Improved accuracy and reliability in identifying discrepancies across policy documents.
- Enhanced operational efficiency, enabling reviewers to focus on higher-value validation tasks.
By automating the extraction and comparison of policy data, Dociphi enabled the client to accelerate reviews, reduce manual effort, and ensure precise compliance verification.