The client company is one of the leaders in the health insurance industry and wants to cement its place by offering accurate insurance costs to its customers based on their lifestyles. Their technology-driven solution aims to gain user trust and make optimum use of the gathered user data.

  • Industry

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Project Summary

Our client is a Fortune 500 healthcare insurance company that required a model to calculate the insurance charges based on the lifestyle habits of its customers. We created a state-of-the-art model to optimize the price of health insurance by taking into account the information of the users. The model was made using accelerated machine learning for hypothesis testing and visualization to help the company make informed business decisions. It also overcomes the challenges this industry faces regarding the accuracy of the price and analyzing complex data sets. The model had a positive impact on the business by saving around 41% of evidence costs and reducing manual labor by 70%

  • Collecting important customer information that is crucial for making business decisions
  • Generating value from that customer data to facilitate the decision-making process.
  • Our client wanted a model that can process the information and provide the charges that should be levied to the customer based on their lifestyle habits.
  • Price optimization procedure is a complex notion and is a disputable issue.
  • High accuracy of the model was mandated to avoid any kind of disputable outcome.
  • Numerous combinations of the data were supposed to be created to train the model under different cases.
  • We proposed using Hypothesis Testing and Visualization to leverage customer's health information like smoking habits, body-mass index, age, and gender.
  • This information would then help in creating a model that will analyze it using the above mentioned techniques.
  • The model would aid in checking statistical evidence to make valuable decisions of insurance business like charges for health insurance.
  • The new AI-driven accelerated machine learning model and workflow is estimated to have saved ~41% of evidence costs on claim
  • It also resulted in less than 4% decline leakage into the approved insurance profiles.
  • Manual work of data combination and evaluating the profiles was reduced to 70%

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