Case Study: Healthcare
Underwriting Analytics increases accuracy of cost predictions
Insurance companies often fail to accurately predict pricing for group customers.
Identify input data points viz. Claims Data, Enrollment data, Prescription Data and Member Data our endeavour is to improve pricing and customer service for our insurance client’s group insurance customers. Once this objective is clear we interacted with Acturials, Underwriting and Products team to discover and understand the nature of costs and their current pricing methodology which will help us develop a predictive model for costs in a cohort basis on various input data parameters. This results in a pricing methodology incorporating the cost prediction model into the current underwriting process.
82% accuracy in cost predictions at a personalized level tremendously improves pricing accuracy and customer service by proactively identifying potentially high claimants high risk customers. The insurance company then has the opportunity to provide care for its customers by helping them prevent health adversaries.