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Health Insurance Claim Prediction Using Artificial Neural Networks

Author

Listed:
  • Sam Goundar

    (The University of the South Pacific, Suva, Fiji)

  • Suneet Prakash

    (The University of the South Pacific, Suva, Fiji)

  • Pranil Sadal

    (The University of the South Pacific, Suva, Fiji)

  • Akashdeep Bhardwaj

    (University of Petroleum and Energy Studies, India)

Abstract

A number of numerical practices exist that actuaries use to predict annual medical claim 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 study 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 was finished, the focus was to decrease the mean absolute percentage error by adjusting the parameters, such as epoch, learning rate, and neurons 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 for BSP Life. Recurrent neural network outperformed the feed forward neural network in terms of accuracy and computation power required to carry out the forecasting.

Suggested Citation

  • Sam Goundar & Suneet Prakash & Pranil Sadal & Akashdeep Bhardwaj, 2020. "Health Insurance Claim Prediction Using Artificial Neural Networks," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 9(3), pages 40-57, July.
    • Handle: RePEc:igg:jsda00:v:9:y:2020:i:3:p:40-57
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    Cited by:

    1. Aruna Malik & Rajeev Kumar, 2022. "Robust RDH Technique Using Sorting and IPVO-Based Pairwise PEE for Secure Communication," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 11(2), pages 1-17, August.
    2. Abha Jain & Ankita Bansal, 2022. "Models for Efficient Utilization of Resources for Upgrading Android Mobile Technology," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 11(2), pages 1-22, August.
    3. Deepti Aggarwal & Sonu Mittal & Vikram Bali, 2021. "Significance of Non-Academic Parameters for Predicting Student Performance Using Ensemble Learning Techniques," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 10(3), pages 38-49, July.

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