CASE STUDY

Claims analytics reduces claims approval effort by 45% for health insurance provider in US

Sep 22, 2016

Business Challenge: 

Improving claims approval process. A leading health insurance provider in US wanted us to conduct data analysis for insurance claims and help automate the approval process.

Situation: 

Cumbersome manual claims approval process

The health insurance firm had an existing manual claims approval process. The client, a health insurance firm, was facing issues and challenges of huge workload, not enough resources and resulting backlog, and wanted to understand if rules can be set to automate a part of the process.

Solution/Approach:

 Big data analytics, claims categorization and claims approval model development

We conducted big data based analysis and quantified patterns in historical approval decisions. Also, we categorized the claims based on risk level. Based on these rules, we built a model to approve future claims on the condition that they would be approved based on existing rules and practices.

Impact: 

45% reduction in claims management effort

Based on our solutions, the health insurance gained better visibility on the claims approval patterns and automated process for claims approval. This helped the client develop a categorized approach for claims based on risk and financial burden with reduction in effort by 45%.

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