Learning in Insurance”, which represents a compilation of ten high-quality articles discussing avant-garde developments or introducing new theoretical or practical advances in this field

Asimit, V.

Kyriakou, I.

Nielsen, J. P.

English

It is our pleasure to prologue the special issue on &ldquo;Machine Learning in Insurance&rdquo;, which represents a compilation of ten high-quality articles discussing avant-garde developments or introducing new theoretical or practical advances in this field [...

Vali Asimit

Ioannis Kyriakou

Jens Perch Nielsen

Multidisciplinary Digital Publishing Institute

Special Issue “Machine Learning in Insurance”

City Research Online

              City, University of London Institutional RepositoryCitation: Asimit, V. ORCID: 0000-0002-7706-0066, Kyriakou, I. ORCID: 0000-0001-9592-596X and Nielsen, J. P. ORCID: 0000-0002-2798-0817 (2020). Special Issue “Machine Learning in Insurance”. Risks, 8(2), 54.. doi: 10.3390/risks8020054 This is the published version of the paper. This version of the publication may differ from the final published version. Permanent repository link:  https://openaccess.city.ac.uk/id/eprint/24235/Link to published version: http://dx.doi.org/10.3390/risks8020054Copyright and reuse: City Research Online aims to make research outputs of City, University of London available to a wider audience. Copyright and Moral Rights remain with the author(s) and/or copyright holders. URLs from City Research Online may be freely distributed and linked to.City Research Online:            http://openaccess.city.ac.uk/            publications@city.ac.ukCity Research OnlinerisksEditorialSpecial Issue “Machine Learning in Insurance”Vali Asimit , Ioannis Kyriakou and Jens Perch Nielsen *Faculty of Actuarial Science and Insurance, Cass Business School, City, University of London, 106 Bunhill Row,London EC1Y 8TZ, UK; alexandru.asimit.1@city.ac.uk (V.A.); ioannis.kyriakou@city.ac.uk (I.K.)* Correspondence: Jens.Nielsen.1@city.ac.uk; Tel.: +44-(0)20-7040-0990Received: 2 May 2020; Accepted: 5 May 2020; Published: 25 May 2020It is our pleasure to prologue the special issue on “Machine Learning in Insurance”,which represents a compilation of ten high-quality articles discussing avant-garde developmentsor introducing new theoretical or practical advances in this field.Two articles deal with reserving in non-life insurance. In the first one, Bischofberger (2020)provides an innovative approach to understanding operational time in this context: reverting thetime scale enables a very complex correlation structure to be modelled via one-dimensional modelsonly. Validation is performed appropriately based on state-of-the-art machine learning principles.The second paper on reserving by Elpidorou et al. (2019) shows that prior knowledge can beincorporated in the reserving process without violating standard mathematical statistics. The paperdoes provide a likelihood principle to incorporate prior knowledge.There are two articles on telematics in insurance by Qazvini (2019) and Pesantez-Narvaez et al.(2019), where the authors present complicated mathematical statistical methodologies. Within thespirit of machine learning, both use model selection and validation to choose the best-predicting modelout of a complex array of possibilities. The paper by Bermúdez et al. (2020) also considers claim countmodels based on new actuarial techniques.The remaining papers in this collection pertain also to finance. Assa et al. (2019) study depositinsurance pricing, whereas Bärtl and Krummaker (2020) the accurate prediction of export creditinsurance claims. With a focus on deriving solvency capital requirements, Krah et al. (2020) analyzeadaptive machine learning approaches to proxy modelling of life insurance companies. The paper bySarabia et al. (2020) revisits the ideas of the so-called semiparametric methods which are very usefulwhen applying machine learning in insurance. For the modelling of prior knowledge, the authorsintroduce classes of distributions for financial data. They then illustrate the proposed procedures withdata on stock returns. Finally, Mammen et al. (2019) apply machine learning to forecast the conditionalvariance of long-term stock returns measured in excess of different benchmarks, considering the shortand long-term interest rate, the earnings-by-price ratio, and the inflation rate.We are indebted to all the reviewers who collaborated and thankful to all the authors for theircontributions. It is our hope that the research articles that were assembled for this Special Issue willcast light on the field and prove a fruitful reading for our audience.ReferencesAssa, Hirbod, Mostafa Pouralizadeh, and Abdolrahim Badamchizadeh. 2019. Sound deposit insurance pricingusing a machine learning approach. Risks 7: 45. [CrossRef]Bärtl, Mathias, and Simone Krummaker. 2020. Prediction of claims in export credit finance: A comparison of fourmachine learning techniques. Risks 8: 22. [CrossRef]Bermúdez, Lluís, Dimitris Karlis, and Isabel Morillo. 2020. Modelling unobserved heterogeneity in claim countsusing finite mixture models. Risks 8: 10. [CrossRef]Bischofberger, Stephan M. 2020. In-sample hazard forecasting based on survival models with operational time.Risks 8: 3. [CrossRef]Risks 2020, 8, 54; doi:10.3390/risks8020054 www.mdpi.com/journal/risksRisks 2020, 8, 54 2 of 2Elpidorou, Valandis, Carolin Margraf, María Dolores Martínez-Miranda, and Bent Nielsen. 2019. A likelihoodapproach to Bornhuetter–Ferguson analysis. Risks 7: 119. [CrossRef]Krah, Anne-Sophie, Zoran Nikolic´, and Ralf Korn. 2020. Machine learning in least-squares Monte Carlo proxymodeling of life insurance companies. Risks 8: 21. [CrossRef]Mammen, Enno, Jens Perch Nielsen, Michael Scholz, and Stefan Sperlich. 2019. Conditional variance forecasts forlong-term stock returns. Risks 7: 113. [CrossRef]Pesantez-Narvaez, Jessica, Montserrat Guillen, and Manuela Alcañiz. 2019. Predicting motor insurance claimsusing telematics data—XGBoost versus logistic regression. Risks 7: 70. [CrossRef]Qazvini, Marjan. 2019. On the validation of claims with excess zeros in liability insurance: A comparative study.Risks 7: 71. [CrossRef]Sarabia, José María, Faustino Prieto, Vanesa Jordá, and Stefan Sperlich. 2020. A note on combining machinelearning with statistical modeling for financial data analysis. Risks 8: 32. [CrossRef]c© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open accessarticle distributed under the terms and conditions of the Creative Commons Attribution(CC BY) license (http://creativecommons.org/licenses/by/4.0/).

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