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Optimizing Insurance Claims and Reporting with GenAI

Client Overview

Our client is a prominent insurance company that provides a range of policies, including life, health, auto, and property insurance. They manage thousands of claims and customer interactions every day. Despite having an advanced system for tracking claims, the client faced challenges in generating insightful, accurate, and timely reports from their vast data.

Business Impact

  • The client can now generate complex reports in real time without waiting for IT teams to write queries or process data.
  • The fraud detection reports help the client’s risk management team identify potential fraud more quickly, reducing losses.
  • Claims departments are now able to track the status of claims efficiently, helping to improve customer services.

Statisics

50%
Average Cost Reduction
60%
Reduction on Processing Time
180%
Remarkable ROI

Challenge

The client required an automated solution to generate various reports, such as:

  • Pending claim amounts
  • Claims by status (e.g., processed, rejected, in-progress)
  • Customer claims history
  • Claims by policy type (health, auto, life)
  • Fraud detection reports Generating these reports manually or through traditional systems was time-consuming, error-prone, and could take several hours or even days to complete.

Solution

To streamline their reporting process, we implemented a GenAI solution powered by the Llama 3.2 model integrated into the client’s existing claims management system. This solution processes natural language requests from users, converts those into SQL queries, executes them against the client’s claims database, and returns structured, well-formatted JSON-based reports.

Key Features of the Solution

  • Natural Language Query Processing:   The system interprets user inputs like:
    • What is the total amount of pending claims?
    • How many claims are still in-progress for auto policies?
    • Show me claims by status for health insurance policies
    The Llama 3.2 model processes these requests and accurately converts them into SQL queries.
  • SQL Query Generation:   The natural language inputs are converted into optimized SQL queries, allowing the system to pull data from the database in real-time without the need for manual query writing or data entry.
  • Real-Time Data Integration:   The solution connects directly with the client’s claims management system and executes the SQL queries in real time, providing the most up-to-date reports.
  • Automated Report Generation:   Results from the queries are structured into easy-to-read JSON reports, which can be further visualized or shared with stakeholders for analysis.
  • Customizable and Scalable:   The solution can be easily customized to support additional reports or new types of claims. As the client’s needs evolve, the system can grow to incorporate more complex data queries.

Sample Reports Generated:

  • Pending Claims Report: 
    • A detailed list of all pending claims with amounts, claimants, and policies involved.
    • Example query: “Show me all claims that are pending approval in the past month.”
  • Claims by Status: 
    • A summary of claims categorized by their current status (e.g., processed, rejected, under review).
    • Example query: “How many claims are in-progress for health insurance policies as of today?”
  • Claims by Policy Type: 
    • Breakdown of claims based on policy type (life, health, auto, property).
    • Example query: “Generate a report of claims for auto insurance policies that have been rejected in the past 6 months.”
  • Fraud Detection: 
    • Identification of patterns or anomalies that may indicate fraudulent claims.
    • Example query: “List all claims filed in the past month with unusually high amounts and rejected status.”
  • Customer Claims History: 
    • An overview of each customer’s claim history, including all claims filed and their current status.
    • Example query: “Show me all claims filed by customer X in the past year.”

Benefits

  • Automation:  The solution automates the entire process of query generation and report creation, significantly reducing manual effort and human error.
  • Real-Time Insights:   Users can get instant access to the latest data, improving decision-making and operational efficiency.
  • Enhanced Accuracy:  The automated system ensures consistent, error-free reports, eliminating discrepancies in data.
  • Cost-Effective:   By reducing reliance on manual data extraction and report writing, the solution helps the client save on labor costs and time spent on routine tasks.
  • Scalibility:   As the insurance company expands or diversifies its offerings, the solution can easily accommodate additional queries, reports, or more complex data requirements.

Conclusion

With the implementation of the GenAI-powered solution, the insurance company has transformed how it handles claims data and reporting. By leveraging natural language processing and AI-driven automation, the client can now generate insightful, accurate, and timely reports that enable better decision-making, enhance operational efficiency, and reduce the risk of fraud.