AI Enablement for Insurance Claims Processing

AI Enablement for Insurance Claims Processing

Business Problem:

Insurance claims processing taking 15 days average. 400 staff manually reviewing documents. Claims fraud rate high due to manual detection limitations.

Solution:

Star Systems built an Agentic AI claims processing system using IBM watsonx, integrated with existing core insurance platform. Document OCR + LLM-based validation + fraud scoring agent.

Measurable Outcome:

  • Claims processing time reduced from 15 days to 4.5 days.
  • Fraud detection accuracy improved by 43%.
  • 180 staff reallocated to higher-value customer service roles.

Overview

Insurance company was under increasing pressure to process claims quickly while ensuring accuracy and minimizing any fraud. The manual review process involved many employees reviewing and verifying documents. Though the system worked on a small scale, as the number of claims kept increasing, this system started getting inefficient.

Customers had to wait for a long period of time, and managing service quality posed a problem for the organization. Besides, fraud detection had become difficult due to manual review. To solve the above problems, the company decided to adopt an intelligent claims processing solution based on artificial intelligence (AI).

Business Challenges

The average processing period for the company was about 15 days, and that wasn’t sufficient in an industry like insurance. Multiple manual processes were necessary to verify claims, such as document verification, validation of the policyholder’s eligibility, and approvals from different departments. Not only did this increase the time taken, but there was also an increased risk of human error.

It employed close to 400 people just for verifying claims, which was costly due to low operational efficiency. Although there were many people working on this process, efficiency was lacking since it involved a lot of repetitive work.

  • Close to 400 employees engaged in manual reviews
  • Operational expenses are high but efficiency is minimal
  • Repetitive nature of tasks affects employee productivity

Fraud detection was another critical issue. Manual review systems struggled to identify complex fraud patterns, especially as fraudulent tactics became more advanced. This led to higher fraud rates and financial losses, highlighting the need for a more intelligent and data-driven approach.

Impact on Business

AI-based claim processing system resulted in tangible improvements in terms of processes performed. The automation of documents’ management, validation, and fraud detection helped achieve faster results and greater accuracy through Agentic AI Development. Not only did this process enhance customer satisfaction, but it also enabled the company to allocate human resources to perform functions that are directly related to business benefits.

  • DTime for claim processing shortened from 15 days to 4.5 days.
  • Fraud detection became more accurate by 43%.
  • 180 personnel relocated to other customer-oriented jobs.
  • Increased efficiency with lower operational costs.

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