Modeling and Predicting of Motor Insurance Claim Amount using Artificial Neural Network
V SelvaKumar1, Dipak Kumar Satpathi2, P. T. V. Praveen Kumar3, V.V. Haragopal4
1V SelvaKumar*, Research Scholar, Department of Mathematics, Birla Institute of Technology and Science-Pilani, Hyderabad, Telangana, India. Assistant Professor, Bhavan‟s Vivekananda College of Science, Humanities and Commerce, Hyderabad, (Telangana), India.
2Dipak Kumar Satpathi, Associate Professor, Department of Mathematics, Birla Institute of Technology and Science-Pilani, Hyderabad, (Telangana), India.
3P. T. V. Praveen Kumar, Assistant Professor, Department of Mathematics, Birla Institute of Technology and Science-Pilani, Hyderabad, (Telangana), India.
4V.V. Haragopal, Visiting Professor, Department of Mathematics, Birla Institute of Technology and Science-Pilani, Hyderabad, (Telangana), India.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 24, 2020. | Manuscript published on March 30, 2020. | PP: 4751-4757 | Volume-8 Issue-6, March 2020. | Retrieval Number: F9873038620/2020©BEIESP | DOI: 10.35940/ijrte.F9873.038620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In India the insurance industry is in its growth stage. It consists of 58 insurance companies of which 24 in life and 34 are non-life insurance. The Non-life Insurance companies which cater to motor insurance business presently utilize different trend models to forecast paid claim amount. Motor Insurance Claim amount prediction is one of the most difficult tasks to accomplish in financial forecasting due to the complex nature of data points. The main objective of this study is to determine a reliable time series forecasting model to predict own damage (OD) claim amount of motor insurance data in India from 1981 to 2016. In this context, the annual time series claim data was collected and modeled by using the Generalized linear model (GLM), Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) method. The validation of the model has been done by comparison of predicted and actual values for the period of 36 years. Also, different types of possible models were evaluated using Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE) for accuracy. The results showed that ANN outperformed other traditional time series models (GLM & ARIMA) for predicting the future own damage claim amount with a lesser residual error. Further, the outcome of these data analytics studies would help Insurance companies to have an idea about the expected future claim amounts with more accuracy. Thus, predicting the Motor insurance’s own damage claim will help insurance companies to budget their future revenue.
Keywords: Stationary Process, GLM, ARIMA, Neural Network, TRAINLM, RMSE.
Scope of the Article: Artificial Intelligence.