Faster and Efficient Underwriting with Intelligent Data Extraction
Overview.
An A-rated global provider of property, casualty, professional liability, and specialty insurance and reinsurance, overwhelmed by manual data processing for quote requests, partnered with Coforge. Coforge's AI-powered solution, SLICE (Self Learning Intelligent Content Extractor), automates data extraction from various formats (emails, attachments) improving underwriter productivity by 10-20%.
This insurer received 300-400 requests for quotes in a day in unstructured, semi-structured, and structured formats over emails and attachments, and faced inefficiencies in processing quote requests:
Manual Data Extraction: Underwriters spent significant time manually reviewing requests in various formats (emails, Word, PDF, Excel). Also, The manual process was error-prone requiring rework.
Unstructured Data: The unstructured nature of requests (emails, attachments) hampered efficient processing.
Slow Workflow: Manual data entry slowed down the quote submission cycle, impacting business growth. Loss in productivity eventually impacts the total business written.
Solution
Coforge deployed its proprietary solution accelerator, SLICE, offering advanced technological capabilities, to automate data extraction, and data ingestion and streamline the underwriting process:
Intelligent Content Extraction: SLICE utilizes computer vision, machine learning, and natural language processing to extract data from 50+ email sources that include SOVs and slip details.
Self-Learning Model: The model learns from historical data and underwrites’ feedback to continuously improve accuracy. The AI engine would also learn to make recommendations based on historical data apart from the ruleset output.
Flexible Architecture: Underwriters can override system recommendations where necessary, which can be used for self-learning for the system for future decisions.
Modular Design: The solution was deployed for the Political risks line of business to start with but was modular and scalable enough that it can be easily adapted to other lines of business.
The Impact
10-20% Increase in Underwriters’ Productivity by Automated data extraction
Faster Quote Processing Near real-time data, extraction streamlined the quote submission cycle
Improved Accuracy Through automated verification of insurance documents, reduced errors.
Enhanced User Experience The web-based solution offered seamless access for underwriters
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