Abstract
Claim prediction is an important process in an automobile insurance industry to prepare the right type of insurance policy for each potential policyholder. The volume of available data to construct the model of the claim prediction is usually large. Nowadays, deep neural networks (DNN) becomes more popular in the machine learning field especially for unstructured data likes image, text, or signal. The DNN model integrates the feature selection into the model in the form of some additional hidden layers. Moreover, DNN is suitable for the large volume of data because of its incremental learning. In this paper, we apply and analyze the accuracy of DNN for the problem of claim prediction which has structured data. First, we show the sensitivity of the hyperparameters on the accuracy of DNN and compare the performance of DNN with standard neural networks. Our simulation shows that the accuracy of DNN is slightly better than the standard neural networks in term of normalized Gini.
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References
Weerasinghe, K.P.M.L.P., Wijegunasekara, M.C.: A comparative study of data mining in the prediction of auto insurance claims. European Int. J. Sci. Technol. 5(1), 47–54 (2016)
Fauzan, M.A., Murfi, H.: The accuracy of XGBoost for insurance claim prediction. Int. J. Adv. Soft Comput. Appl. 10(2) (2018)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)
Montavon, G., Samek, W., Muller, K.R.: Methods for interpreting and understanding deep neural networks. Digit. Signal Process. 73, 1–15 (2018)
Guo, C., Berkhahn, F.: Entity embeddings of categorical variables. arXiv preprint arXiv:1604.06737 (2016)
Panchal, F.S., Panchal, M.: Review on methods of selecting a number of hidden nodes in the artificial neural network. Int. J. Comput. Sci. Mob. Comput. 3(11), 455–464 (2014)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Acknowledgements
This work was supported by Universitas Indonesia under PIT 9 2019 grant. Any opinions, findings, and conclusions or recommendations are the authors’ and do not necessarily reflect those of the sponsor.
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Saputro, A.R., Murfi, H., Nurrohmah, S. (2019). Analysis of Deep Neural Networks for Automobile Insurance Claim Prediction. In: Tan, Y., Shi, Y. (eds) Data Mining and Big Data. DMBD 2019. Communications in Computer and Information Science, vol 1071. Springer, Singapore. https://doi.org/10.1007/978-981-32-9563-6_12
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DOI: https://doi.org/10.1007/978-981-32-9563-6_12
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