Age, Cars, and Claims: Decoding the Insurance Landscape

Authors

  • Aditya T Department of Artificial Intelligence and Machine Learning, Jayaprakash Narayan College of Engineering, Chowdarpally, Telangana, India
  • M. Bharathi Department of Artificial Intelligence and Machine Learning, Jayaprakash Narayan College of Engineering, Chowdarpally, Telangana, India

DOI:

https://doi.org/10.48001/jodpba.2024.119-12

Keywords:

Car insurance modeling, Decoding, Mathematical modeling, Predictive analytics, Statistical models

Abstract

Embark on a journey through the realms of car insurance modeling, where the fusion of statistical and mathematical prowess unveils the secrets behind predicting claim frequency, severity, and overall costs. This enchanted exploration not only guides you through the wizardry of Python but also empowers you with the art of crafting insurance products, navigating risk, and orchestrating business strategies. If the arcane world of Car Insurance Modeling beckons you, join this mystical narrative, where algorithms and Python spells converge, weaving a tale of predictive mastery. Illuminate your path and delve into the enchantment of modeling automotive destinies with code as your guide.

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References

Abdelhadi, S., Elbahnasy, K., & Abdelsalam, M. (2020). A proposed model to predict auto insurance claims using machine learning techniques. Journal of Theoretical and Applied Information Technology, 98(22). 3428-3437. https://www.jatit.org/volumes/Vol98No22/8Vol98No22.pdf.

Guelman, L. (2012). Gradient boosting trees for auto insurance loss cost modeling and prediction. Expert Systems with Applications, 39(3), 3659-3667.

https://doi.org/10.1016/j.eswa.2011.09.058.

Poufinas, T., Gogas, P., Papadimitriou, T., & Zaganidis, E. (2023). Machine learning in forecasting motor insurance claims. Risks, 11(9), 164.

https://doi.org/10.3390/risks11090164.

Staudt, Y., & Wagner, J. (2021). Assessing the performance of random forests for modeling claim severity in collision car insurance. Risks, 9(3), 53.

https://doi.org/10.3390/risks9030053.

Vassiljeva, K., Tepljakov, A., Petlenkov, E., & Netsajev, E. (2017, May). Computational intelligence approach for estimation of vehicle insurance risk level. In 2017 International Joint Conference on Neural Networks (IJCNN) (pp. 4073-4078). IEEE.

https://doi.org/10.1109/IJCNN.2017.7966370

Published

2024-02-21

How to Cite

Aditya T, & M. Bharathi. (2024). Age, Cars, and Claims: Decoding the Insurance Landscape . Journal of Data Processing and Business Analytics, 1(1), 9–12. https://doi.org/10.48001/jodpba.2024.119-12

Issue

Section

Articles