Business Problem

• Labor-Intensive Extraction: Underwriters had to manually extract claims history from variously formatted loss run documents, leading to inefficiencies. 
• Data Inconsistencies: Unstructured data made it difficult to standardize extraction, leading to inaccurate risk assessments. 
• Delayed Risk Analysis: Slow processing times hindered the ability to assess risk promptly and negotiate policy premiums effectively. 
• Compliance Challenges: Inaccurate data extraction could result in non-compliance with industry regulations, impacting insurance decision-making.  

About Ledgebrook

Loss run documents contain crucial historical claims data required for risk assessment. Manually extracting this data was time-consuming and prone to errors. goML built an automated loss run extraction service to streamline this process. 

Solution

goML implemented a fully automated loss run data extraction pipeline: 

AI-Based Text Extraction: Leveraging AWS Textract, key claims data was extracted from unstructured PDFs and scanned documents.

Real-Time Search & Insights: Fast querying and retrieval of historical claims data for underwriting teams.

Lambda-Powered Data Processing: AWS Lambda functions processed the extracted data, standardizing it for structured storage.

Automation & Workflow Integration: The solution seamlessly integrated into Ledgebrook’s underwriting workflows, reducing dependency on manual processing. 

Centralized Data Repository: Extracted loss run data was stored in a PostgreSQL RDS database for easy retrieval and analysis.

AI-Powered Segregation & Classification: AWS Bedrock processed extracted text to segregate policies and claims. 

Architecture

• User Interaction & API Triggers 
  POST /store-lossruns → Triggered inside the Document Service. 
  User uploads Loss Run Files, which are stored in AWS S3. 
• Document Processing & Text Extraction 
  AWS Textract extracts: 
  Text details from the documents. 
  Tabular data and form structures as key-value pairs. 
• AI-Powered Segregation & Classification 
  AWS Bedrock processes extracted text: 
  Segregation pipeline built to split text into multiple policies and claims. 
  Generates a Schema for Policies and Claims Segregation. 
• Data Structuring & Storage 
  Bedrock extracts a Data Dictionary for every claim's text chunk. 
  Stores structured Loss Run responses in a database (presumably PostgreSQL or S3). 
• Webhook Integration & Execution 
  Triggers a Webhook Endpoint once processing is completed. 
  Passes the structured Loss Run response to the Document Service for final execution. 
• Data Retrieval & Response Generation 
  GET /loss-runs/{aiDocumentSessionToken} allows users to fetch the Loss Run Response. 
  Query retrieves stored Loss Run data from the database. 
  Returns structured response to the user.