A Novel Framework for Risk Prediction in the Health Insurance Sector using GIS and Machine Learning

Evaluation of risk is a key component to categorize the customers of the life insurance businesses. The underwriting technique is carried out by the industries to charge the policies appropriately. Due to the availability of data hugely, the automation of underwriting process can be done using data analytics technology. Due to this, the underwriting process becomes faster and therefore quickly processes a large number of applications. This study is carried to enhance risk assessment of the applicants of life insurance industries using predictive analytics. In this research, the Geographical Information Systems (GIS) system is used to collect the data such as Air pollution, Industrial area, Covid-19 and Malaria of various geographic areas of our country, since these factors attribute to the risk of an applicant of life insurance business. Thereafter, the research is carried out using this dataset along with another dataset containing more than 50,000 entries of normal attributes of applicants of a life insurance company. Artificial Neural Network (ANN), Decision Tree (DT), and Random forest (RF) algorithms are applied on both the datasets to predict the risks of the applicants. The results showed that random forest outperformed among all the algorithms, providing the more accurate result.

@article{Baruah2023,
  title     = {A Novel Framework for Risk Prediction in the Health Insurance Sector using GIS and Machine Learning},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {12},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Prasanta Baruah and Pankaj Pratap Singh and Sanjiv kumar Ojah},
  doi       = {10.14569/IJACSA.2023.0141249},
  url       = {https://doi.org/10.14569/IJACSA.2023.0141249}
}