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  1. LTIMindtree is now LTM | It’s time to Outcreate
  2. Insights
  3. Enhancing the customer experience by modernizing a leading North American bank's contact center

Streamlining Product Filing with Generative AI for a Global Insurance Company

Aug 01, 2024

  • About Client
  • Business Challenges
  • Solution Highlights
  • Business Benefits
  • About Client
  • Business Challenges
  • Solution Highlights
  • Business Benefits

About Client

A leading American specialty insurance company offering a comprehensive range of innovative insurance products in property and sports. Their robust portfolio and strategic market presence make them a trusted partner in safeguarding the future.

Business Challenges

  • Complex and time-consuming processes - The manual process of extracting relevant details from product filings and rate files was lengthy and laborintensive, often taking several months. This process led to inefficiencies, was prone to human error and lacked scalability.
  • Data extraction complexity - Creating product definition templates from multiple complex sources made extracting relevant information challenging. The traditional approach was both time-consuming and error-prone
  • Scalability and accuracy issues -The manual process struggled to scale with increasing document volumes and complexities, impacting the reliability of extracted information used in business processes.

Solution Highlights

LTM developed a Companion Bot (Requirement Bot) for analysts using Amazon Bedrock. This bot analyzed multiple documents to generate complete risk, eligibility, coverage, pricing details, forms, and rules required for framing product offerings, significantly reducing the time for product filing across different coverages and attributes.

  • Customizations for coverages - Established efficient prompt engineering techniques for optimal performance across different coverages, processing accurate and reliable policy and coverage attributes through custom-built validators
  • Feedback loop and model optimization - Gathered feedback from policyholders to improve policy design and customer experience, optimizing document processing, AI models, and data storage. 
  • Integration and automation - Integrated AI-driven workflows for intelligent document processing, reducing manual intervention and errors. The solution design was forwardlooking, enabling adaptability to various input file combinations for future scalability. 

Business Benefits

  • Increased operational efficiency: Achieved a significant reduction in the time required to generate product templates from product filings, streamlining the creation of coverage, forms, and rules matrices. As a result, efficiency improved by 50% in overall document processing and analysis workflows.
  • Enhanced data accuracy: Reduced the risk on coverage and policy narratives by 20% through consistent and accurate summaries, minimizing the risk of misinterpreting critical information. This led to more reliable data for business decision-making and reduced manual errors, resulting in more precise input for other business processes
  • Scalable and robust process: The solution accommodated increasing volumes and complexities of input files without compromising performance. The bot's adaptability to diverse document types ensured long-term applicability and robustness of the solution.
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Case Study
calendar June 3, 2024
Modernize Your Workloads on Snowflake with PolarSled
David Althoff
Chief Business Officer, Insurance

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