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Home > Client Success > Auto Insurer Reduces Demand Letter Processing Time with IDP Solution

The Client

Established in the year 1989, the client provides low-cost, high-quality property and casualty insurance products, with a specialization in automobile insurance. It is one of the largest privately held P&C insurance firms in the United States.

The Problem

A demand letter is a crucial document in the auto insurance claims process that formally requests compensation from an insurance company for damages and injuries sustained in an auto accident. In order to make the claims process faster, the client needed to streamline the data extraction process from demand letters in different templates which can be used in an external system while minimizing errors.

  • Document-intensive business processes:
    The client received approximately 400 demand letters from 200 attorneys every day, each utilizing different templates and data covering 20 fields.
  • Requirement of meticulous data extraction:
    There was a need to meticulously streamline the data extraction process and transfer the extracted data to the Guidewire system for further processing to improve the accuracy and efficiency of their property and casualty insurance claims processes.
  • Higher Operational Cost:
    The client was manually extracting the required fields from demand letters, which was time-intensive and inefficient. It was imperative to automate the data extraction process to reduce the associated operational costs while utilizing resources for more value-generating tasks.

challenges

Overwhelming volume of documents to be processed

Lack of a streamlined data extraction process

Longer turn-around-time for manual demand letter processing

Utilization of manual, error-prone approach towards data extraction

The Solution

Damco’s insurance technology experts carefully assessed the client’s business requirements and decided to implement Damco’s AI-powered Insurance Document Processing (IDP) Solution to streamline data extraction process from the received demand letters. Being a highly flexible and scalable solution, it matched the client’s dynamic business requirements like a glove while handling the demanding workload with ease.

Analyzing the business requirement and developing the AI solution roadmap

  • Damco started the project with an in-depth analysis of the existing technology landscape, business processes, and client’s requirements to decide on the most-apt digital solution while establishing a detailed implementation roadmap.

Deployment of Intelligent Document Processing (IDP) solution

  • The team deployed custom insurance Document Processing (IDP) solution, which boasts an accuracy of over 95% in data extraction.
  • The implemented solution involves robust business logic to reduce human error, ensuring greater accuracy in the data extraction and processing.
  • The solution comes with the added ‘Edit’ feature to correct any inaccurately extracted data.

Business logic-based data validation

  • Post data extraction, the records are stored within the API configured claims center where data validation is performed on the basis of business logic for claim number, claimant name, and other important details to maximize the pre-process accuracy.

Transfer of data to Guidewire

  • Once details are successfully validated in the system, they are transferred to Guidewire for further processing.

The Result

The implementation of Intelligent Document Processing (IDP) solution as the answer to the client’s pressing needs streamlined and optimized automated data extraction and processing while augmenting efficiency and precision.

  • A significant reduction of 60% in the time taken to process demand letters from attorneys.
  • An accuracy rate of 95% in data extraction, leading to improved efficiency.
  • A reduction of 80% in document processing errors after the automation of end-to-end processes.
  • Reduction in operational costs, underpinned by AI-powered automated insurance data extraction and processing.
Auto Insurer Optimized Data Extraction Process

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