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Use of Artificial Neural Network for Forecasting Health Insurance Entitlements

Use of Artificial Neural Network for Forecasting Health Insurance Entitlements

Sam Goundar, Akashdeep Bhardwaj, Suneet Sonal Prakash, Pranil Sadal
Copyright: © 2022 |Volume: 15 |Issue: 1 |Pages: 18
ISSN: 1938-7857|EISSN: 1938-7865|EISBN13: 9781683180340|DOI: 10.4018/JITR.299372
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MLA

Goundar, Sam, et al. "Use of Artificial Neural Network for Forecasting Health Insurance Entitlements." JITR vol.15, no.1 2022: pp.1-18. http://doi.org/10.4018/JITR.299372

APA

Goundar, S., Bhardwaj, A., Prakash, S. S., & Sadal, P. (2022). Use of Artificial Neural Network for Forecasting Health Insurance Entitlements. Journal of Information Technology Research (JITR), 15(1), 1-18. http://doi.org/10.4018/JITR.299372

Chicago

Goundar, Sam, et al. "Use of Artificial Neural Network for Forecasting Health Insurance Entitlements," Journal of Information Technology Research (JITR) 15, no.1: 1-18. http://doi.org/10.4018/JITR.299372

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Abstract

A number of numerical practices exist that actuaries use to predict annual medical claims expense in an insurance company. This amount needs to be included in the yearly financial budgets. Inappropriate estimating generally has negative effects on the overall performance of the business. This paper presents the development of Artificial Neural Network model that is appropriate for predicting the anticipated annual medical claims. Once the implementation of the neural network models were finished, the focus was to decrease the Mean Absolute Percentage Error by adjusting the parameters such as epoch, learning rate and neuron in different layers. Both Feed Forward and Recurrent Neural Networks were implemented to forecast the yearly claims amount. In conclusion, the Artificial Neural Network Model that was implemented proved to be an effective tool for forecasting the anticipated annual medical claims. Recurrent neural network outperformed Feed Forward neural network in terms of accuracy and computation power required to carry out the forecasting.