• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2020, Volume: 13, Issue: 21, Pages: 2071-2079

Original Article

Modeling and prediction of third-party claim using a Machine learning approach

Received Date:30 April 2020, Accepted Date:10 June 2020, Published Date:20 June 2020


Background/Objectives: The main objective of this research paper is to build an appropriate mathematical model that helps in forecasting overall claim amount based on the chosen characteristics of the data. Methods/Statistical analysis: In the field of actuarial research, forecasting the third-party claim amount for Motor vehicles is a challenging task, and only limited empirical research studies are done in predicting the claim. In this context, the annual time series historical claim data were collected for a period of 34 years to examine the predictive performance of the linear regression model, exponential smoothing model, autoregressive integrated moving average (ARIMA), artificial neural network (ANN), and hybrid ARIMA-ANN models to predict third party claim amount of motor insurance data in India. Findings: The data are analyzed, and the empirical evidence from the study shows that the ANN model improved the accuracy prediction when compared to Linear Regression, Exponential smoothing model, ARIMA and a hybrid model with respect to the performance criteria such as root mean squared error (RMSE) and mean absolute percentage error (MAPE). Therefore, the ANN model is more potent in forecasting TP claim amounts by considering the adequacy, suitability, and accuracy of the data modeling. Novelty/Applications: This data analytics approach would help motor insurance companies in India to have an idea about the expected future claim amounts. Also, this modeling approach will help the Motor Insurance companies of India to provide a better customer-centric forecasting model, which ensures better claims settlement and management.

Keywords: Claim amount; linear model; stationarity; ARIMA; neural network; TRAINLM 


  1. Cummins JD, Griepentrog GL. Forecasting automobile insurance paid claim costs using econometric and ARIMA models. International Journal of Forecasting. 1985;1(3):203–215. Available from: https://dx.doi.org/10.1016/0169-2070(85)90003-2
  2. Mesike GC, A, Ibiwoye A. Predictive Actuarial Modeling of Health Insurance Claims Costs. International Journal of Mathematics and Computation. 2012;14:974–5718.
  3. Andreeski C, Milosevic B, Njegomir V. Analysis of the life insurance market in the Republic of Macedonia. Ekonomski anali. 2012;57(194):107–122. Available from: https://dx.doi.org/10.2298/eka1294107a
  4. Nyoni T. Modeling and Forecasting Inflation in Burundi using ARIMA Models. Munich Personal Repec Archive MPRA. 2019. Available from: https://mpra.ub.uni-muenchen.de/92444/
  5. MRA, AIAE. Forecasting Egyptian GDP using ARIMA Models. Reports on Economics and Finance. 2019;5:35–47. Available from: https://doi.org/10.12988/ref.2019.81023
  6. Choden, Unhapipat S. ARIMA model to forecast international tourist visit in Bumthang, Bhutan. Journal of Physics: Conference Series. 2018;1039:012023. Available from: https://dx.doi.org/10.1088/1742-6596/1039/1/012023
  7. SLIMANI N, SLIMANI I, SBITI N, AMGHAR M. Traffic forecasting in Morocco using artificial neural networks. Procedia Computer Science. 2019;151:471–476. Available from: https://dx.doi.org/10.1016/j.procs.2019.04.064
  8. Zhang GP. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing. 2003;50:159–175.
  9. Khandelwal I, Adhikari R, Verma G. Time Series Forecasting Using Hybrid ARIMA and ANN Models Based on DWT Decomposition. Procedia Computer Science. 2015;48:173–179. Available from: https://dx.doi.org/10.1016/j.procs.2015.04.167
  10. Omar H, Hong VH, Liu DR. A Hybrid Neural Network Model for Sales Forecasting based on ARIMA and Search Popularity of Article Titles. Computational Intelligence and Neuroscience. 2012;1. Available from: https://doi.org/10.1155/2016/9656453
  11. Mallikarjuna M, Rao RP. Evaluation of forecasting methods from selected stock market returns. Financial Innovation. 2019;5(1). Available from: https://dx.doi.org/10.1186/s40854-019-0157-x
  12. Han SS, Azad TD, Suarez PA, Ratliff JK. A machine learning approach for predictive models of adverse events following spine surgery. The Spine Journal. 2019;19(11):1772–1781. Available from: https://dx.doi.org/10.1016/j.spinee.2019.06.018
  13. Yang C, Delcher C, Shenkman E, Ranka S. Machine learning approaches for predicting high cost high need patient expenditures in health care. BioMedical Engineering OnLine. 2018;17(S1):131. Available from: https://dx.doi.org/10.1186/s12938-018-0568-3
  14. Takeshima T, Keino S, Aoki R, Matsui T, Iwasaki K. Development of Medical Cost Prediction Model Based on Statistical Machine Learning Using Health Insurance Claims Data. Value in Health. 2018;21:S97. Available from: https://dx.doi.org/10.1016/j.jval.2018.07.738
  15. Nti IK, Adekoya AF, Weyori BA. A comprehensive evaluation of ensemble learning for stock-market prediction. Journal of Big Data. 2020;7(1). Available from: https://dx.doi.org/10.1186/s40537-020-00299-5
  16. Assa H, Pouralizadeh M, Badamchizadeh A. Sound Deposit Insurance Pricing Using a Machine Learning Approach. Risks. 2019;7(2):45. Available from: https://dx.doi.org/10.3390/risks7020045
  17. Pesantez-Narvaez J, Guillen M, Alcañiz M. Predicting Motor Insurance Claims Using Telematics Data—XGBoost versus Logistic Regression. Risks. 2019;7(2):70. doi: 10.3390/risks7020070
  18. Box EG, Jenkins GM, Reinsel GC, Ljung GM. Time Series Analysis: Forecasting and Control. Holden-Day. 1976.


© 2020 Selvakumar, Satpathi, Praveen Kumar, Haragopal. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Published By Indian Society for Education and Environment (iSee)


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