The insurer suffers from reputation of slow processing of claims. The issue is with manual steps that take time to process manually. They have identified one of the problems with the claims process – initial segregation of claims into specific categories and assigning them to the right team through a technology driven solution. They also want to explore how text analytics based processing of claims data can also help them with targeted investigations and better claims management.
Faster processing of claims
The client wanted to use text analytics based solution to identify the claim type and assign it to the right team automatically at the time of claim registration.
Greater than 90% accuracy in categorising claims
72% reduction in human error in assigning claims to the right team
Reduction of 80% time in assigning claims
The text analytics based automation of claims registration has reduced the dependence on human resources and improved operational efficiencies.
Unstructured data challenges
Insurance as a business line utilizes the services of a number of data experts – underwriters, actuaries etc. These experts have a good grasp of advanced analytics based solution design. However, as an industry, Insurance companies hardly make use of machine learning and artificial intelligence based solutions using the unstructured data sources that the insurers generate on a daily basis. There is a general inertia to keep doing what has been working.
The key challenge is the realisation of the value of unstructured data and the solutions they can enable. Claims processing is one such solution that requires extracting the information from unstructured text data to reduce the time taken to assign claims after registering them. This machine learning driven solution approach also has a potential to generate benefits in flagging claims and faster settlements.
A technology leap for the Insurer
The insurance client generates a lot of unstructured text data in the claims registration process. They wanted to utilize these data to bring operational efficiencies by machine learning based solution for claims segregation and assignment to the right team.
The added benefits of this solution are:
a. Reducing the claims settlement time – improving customer satisfaction and operational efficiency
b. Flagging of dubious claims and targeted investigations
c. Reduction in human errors in claims registration process, improving the resolution time
The machine learning based solution has brought multiple benefits to the insurer in the claims process.