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AI-Powered Document Classification for Reinsurance Efficiency

  • Location : USA
  • Industry : Insurance

Project Summary

Our client, a leading US-based reinsurance company, struggled with manually sorting large volumes of claim documents, which was time-consuming and error-prone. They partnered with us to develop an AI-based document classification system to streamline classification, improve accuracy, and reduce manual effort.

  • Business Function: Reinsurance
Insurance-Client-Challenges

Challenges

  • The client received numerous unstructured & complex documents daily, making sorting into categories time-consuming.
  • The manual classification process was slow and prone to errors, leading to costly delays and misclassifications.
  • As the company grew, the rising document volume became harder to manage without a scalable solution.
  • In the reinsurance industry, classification errors could have major financial impacts, making accuracy crucial.

Solutions

To address these challenges, we developed an AI-powered document classification solution that integrated seamlessly with the client’s existing systems.

  • We built the solution using a robust tech stack, including .Net Core Web APIs for the backend, React for the front end, and Azure Language Service for AI-powered natural language processing (NLP). Azure Language Studio was instrumental in developing and fine-tuning the AI models.
  • We implemented an AI model that automatically classifies incoming documents into categories such as property, casualty, and medical claims, leveraging NLP to accurately understand and sort the content.
  • We developed a React-based web interface that allows employees to monitor classifications, review documents, and make adjustments.
  • We implemented a system that manages growing document volumes and integrates with the client's database for accurate storage and retrieval.
  • We optimized the AI model using Azure Language Studio, training it on the client's document types for high accuracy in real-world scenarios.
Genre-Insurance-Solution

Results

  • We achieved 98.72% accuracy in document classification with NLP, surpassing client expectations and reducing errors compared to manual processing.
  • The system significantly cut classification time, freeing employees to focus on higher-value tasks.
  • The solution was scalable and capable of handling the growing volume of documents as the company expanded its operations.
  • By automating the classification process, the client realized substantial cost savings in terms of labor and operational efficiency.

Tech Stack

  • React
  • Dot Net Core Web APIs
  • Azure Language Service(AI), Azure Language Studio

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Technology

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  • Python
  • MEAN Stack
  • MERN Stack

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