Machine Learning in Forecasting Motor Insurance Claims
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Maximilien Baudry & Christian Y. Robert, 2019. "A machine learning approach for individual claims reserving in insurance," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 35(5), pages 1127-1155, September.
- Jan Reig Torra & Montserrat Guillen & Ana M. Pérez-Marín & Lorena Rey Gámez & Giselle Aguer, 2023. "Weather Conditions and Telematics Panel Data in Monthly Motor Insurance Claim Frequency Models," Risks, MDPI, vol. 11(3), pages 1-18, March.
- Paruchuri, Harish, 2020. "The Impact of Machine Learning on the Future of Insurance Industry," American Journal of Trade and Policy, Asian Business Consortium, vol. 7(3), pages 85-90.
- Jessica Pesantez-Narvaez & Montserrat Guillen & Manuela Alcañiz, 2019. "Predicting Motor Insurance Claims Using Telematics Data—XGBoost versus Logistic Regression," Risks, MDPI, vol. 7(2), pages 1-16, June.
- Roel Henckaerts & Marie-Pier Côté & Katrien Antonio & Roel Verbelen, 2021. "Boosting Insights in Insurance Tariff Plans with Tree-Based Machine Learning Methods," North American Actuarial Journal, Taylor & Francis Journals, vol. 25(2), pages 255-285, April.
- Knighton, James & Buchanan, Brian & Guzman, Christian & Elliott, Rebecca & White, Eric & Rahm, Brian, 2020. "Predicting flood insurance claims with hydrologic and socioeconomic demographics via machine learning: exploring the roles of topography, minority populations, and political dissimilarity," LSE Research Online Documents on Economics 105761, London School of Economics and Political Science, LSE Library.
- Marjan Qazvini, 2019. "On the Validation of Claims with Excess Zeros in Liability Insurance: A Comparative Study," Risks, MDPI, vol. 7(3), pages 1-17, June.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Seyed Farshid Ghorashi & Maziyar Bahri & Atousa Goodarzi, 2024. "Developing and comparing machine learning approaches for predicting insurance penetration rates based on each country," Letters in Spatial and Resource Sciences, Springer, vol. 17(1), pages 1-29, December.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Emer Owens & Barry Sheehan & Martin Mullins & Martin Cunneen & Juliane Ressel & German Castignani, 2022. "Explainable Artificial Intelligence (XAI) in Insurance," Risks, MDPI, vol. 10(12), pages 1-50, December.
- Zhiyu Quan & Changyue Hu & Panyi Dong & Emiliano A. Valdez, 2024. "Improving Business Insurance Loss Models by Leveraging InsurTech Innovation," Papers 2401.16723, arXiv.org.
- Zuleyka Díaz Martínez & José Fernández Menéndez & Luis Javier García Villalba, 2023. "Tariff Analysis in Automobile Insurance: Is It Time to Switch from Generalized Linear Models to Generalized Additive Models?," Mathematics, MDPI, vol. 11(18), pages 1-16, September.
- Yanez, Juan Sebastian & Pigeon, Mathieu, 2021. "Micro-level parametric duration-frequency-severity modeling for outstanding claim payments," Insurance: Mathematics and Economics, Elsevier, vol. 98(C), pages 106-119.
- Gao, Lisa & Shi, Peng, 2022. "Leveraging high-resolution weather information to predict hail damage claims: A spatial point process for replicated point patterns," Insurance: Mathematics and Economics, Elsevier, vol. 107(C), pages 161-179.
- Christopher Blier-Wong & Hélène Cossette & Luc Lamontagne & Etienne Marceau, 2020. "Machine Learning in P&C Insurance: A Review for Pricing and Reserving," Risks, MDPI, vol. 9(1), pages 1-26, December.
- Yaojun Zhang & Lanpeng Ji & Georgios Aivaliotis & Charles C. Taylor, 2024. "Bayesian CART models for aggregate claim modeling," Papers 2409.01908, arXiv.org.
- Stephan M. Bischofberger, 2020. "In-Sample Hazard Forecasting Based on Survival Models with Operational Time," Risks, MDPI, vol. 8(1), pages 1-17, January.
- Catalina Lozano-Murcia & Francisco P. Romero & Jesus Serrano-Guerrero & Jose A. Olivas, 2023. "A Comparison between Explainable Machine Learning Methods for Classification and Regression Problems in the Actuarial Context," Mathematics, MDPI, vol. 11(14), pages 1-20, July.
- Łukasz Delong & Mario V. Wüthrich, 2020. "Neural Networks for the Joint Development of Individual Payments and Claim Incurred," Risks, MDPI, vol. 8(2), pages 1-34, April.
- Hu, Changyue & Quan, Zhiyu & Chong, Wing Fung, 2022. "Imbalanced learning for insurance using modified loss functions in tree-based models," Insurance: Mathematics and Economics, Elsevier, vol. 106(C), pages 13-32.
- Simon CK Lee, 2020. "Delta Boosting Implementation of Negative Binomial Regression in Actuarial Pricing," Risks, MDPI, vol. 8(1), pages 1-21, February.
- Zhang, Yaojun & Ji, Lanpeng & Aivaliotis, Georgios & Taylor, Charles, 2024. "Bayesian CART models for insurance claims frequency," Insurance: Mathematics and Economics, Elsevier, vol. 114(C), pages 108-131.
- Nan Zhou & José Luis Vilar‐Zanón & Jose Garrido & Antonio José Heras‐Martínez, 2024. "Measuring climate change from an actuarial perspective: A survey of insurance applications," Global Policy, London School of Economics and Political Science, vol. 15(S7), pages 34-46, November.
- Verschuren, Robert Matthijs, 2022. "Frequency-severity experience rating based on latent Markovian risk profiles," Insurance: Mathematics and Economics, Elsevier, vol. 107(C), pages 379-392.
- Vali Asimit & Ioannis Kyriakou & Jens Perch Nielsen, 2020. "Special Issue “Machine Learning in Insurance”," Risks, MDPI, vol. 8(2), pages 1-2, May.
- Christopher Grumiau & Mina Mostoufi & Solon Pavlioglou & Tim Verdonck, 2020. "Address Identification Using Telematics: An Algorithm to Identify Dwell Locations," Risks, MDPI, vol. 8(3), pages 1-12, September.
- Nemanja Milanović & Miloš Milosavljević & Slađana Benković & Dušan Starčević & Željko Spasenić, 2020. "An Acceptance Approach for Novel Technologies in Car Insurance," Sustainability, MDPI, vol. 12(24), pages 1-15, December.
- Eduardo Ramos-P'erez & Pablo J. Alonso-Gonz'alez & Jos'e Javier N'u~nez-Vel'azquez, 2020. "Stochastic reserving with a stacked model based on a hybridized Artificial Neural Network," Papers 2008.07564, arXiv.org.
- Gu, Zheng & Li, Yunxian & Zhang, Minghui & Liu, Yifei, 2023. "Modelling economic losses from earthquakes using regression forests: Application to parametric insurance," Economic Modelling, Elsevier, vol. 125(C).
More about this item
Keywords
insurance; claims; forecasting; machine learning;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jrisks:v:11:y:2023:i:9:p:164-:d:1242230. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.