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Special Issue “Machine Learning in Insurance”

Faculty of Actuarial Science and Insurance, Cass Business School, City, University of London, 106 Bunhill Row, London EC1Y 8TZ, UK
Author to whom correspondence should be addressed.
Risks 2020, 8(2), 54;
Submission received: 2 May 2020 / Accepted: 5 May 2020 / Published: 25 May 2020
(This article belongs to the Special Issue Machine Learning in Insurance)
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 developments or 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 the time scale enables a very complex correlation structure to be modelled via one-dimensional models only. 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 be incorporated in the reserving process without violating standard mathematical statistics. The paper does 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 the spirit of machine learning, both use model selection and validation to choose the best-predicting model out of a complex array of possibilities. The paper by Bermúdez et al. (2020) also considers claim count models based on new actuarial techniques.
The remaining papers in this collection pertain also to finance. Assa et al. (2019) study deposit insurance pricing, whereas Bärtl and Krummaker (2020) the accurate prediction of export credit insurance claims. With a focus on deriving solvency capital requirements, Krah et al. (2020) analyze adaptive machine learning approaches to proxy modelling of life insurance companies. The paper by Sarabia et al. (2020) revisits the ideas of the so-called semiparametric methods which are very useful when applying machine learning in insurance. For the modelling of prior knowledge, the authors introduce classes of distributions for financial data. They then illustrate the proposed procedures with data on stock returns. Finally, Mammen et al. (2019) apply machine learning to forecast the conditional variance of long-term stock returns measured in excess of different benchmarks, considering the short and 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 their contributions. It is our hope that the research articles that were assembled for this Special Issue will cast light on the field and prove a fruitful reading for our audience.


  1. Assa, Hirbod, Mostafa Pouralizadeh, and Abdolrahim Badamchizadeh. 2019. Sound deposit insurance pricing using a machine learning approach. Risks 7: 45. [Google Scholar] [CrossRef] [Green Version]
  2. Bärtl, Mathias, and Simone Krummaker. 2020. Prediction of claims in export credit finance: A comparison of four machine learning techniques. Risks 8: 22. [Google Scholar] [CrossRef] [Green Version]
  3. Bermúdez, Lluís, Dimitris Karlis, and Isabel Morillo. 2020. Modelling unobserved heterogeneity in claim counts using finite mixture models. Risks 8: 10. [Google Scholar] [CrossRef] [Green Version]
  4. Bischofberger, Stephan M. 2020. In-sample hazard forecasting based on survival models with operational time. Risks 8: 3. [Google Scholar] [CrossRef] [Green Version]
  5. Elpidorou, Valandis, Carolin Margraf, María Dolores Martínez-Miranda, and Bent Nielsen. 2019. A likelihood approach to Bornhuetter–Ferguson analysis. Risks 7: 119. [Google Scholar] [CrossRef] [Green Version]
  6. Krah, Anne-Sophie, Zoran Nikolić, and Ralf Korn. 2020. Machine learning in least-squares Monte Carlo proxy modeling of life insurance companies. Risks 8: 21. [Google Scholar] [CrossRef] [Green Version]
  7. Mammen, Enno, Jens Perch Nielsen, Michael Scholz, and Stefan Sperlich. 2019. Conditional variance forecasts for long-term stock returns. Risks 7: 113. [Google Scholar] [CrossRef] [Green Version]
  8. Pesantez-Narvaez, Jessica, Montserrat Guillen, and Manuela Alcañiz. 2019. Predicting motor insurance claims using telematics data—XGBoost versus logistic regression. Risks 7: 70. [Google Scholar] [CrossRef] [Green Version]
  9. Qazvini, Marjan. 2019. On the validation of claims with excess zeros in liability insurance: A comparative study. Risks 7: 71. [Google Scholar] [CrossRef] [Green Version]
  10. Sarabia, José María, Faustino Prieto, Vanesa Jordá, and Stefan Sperlich. 2020. A note on combining machine learning with statistical modeling for financial data analysis. Risks 8: 32. [Google Scholar] [CrossRef] [Green Version]

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MDPI and ACS Style

Asimit, V.; Kyriakou, I.; Nielsen, J.P. Special Issue “Machine Learning in Insurance”. Risks 2020, 8, 54.

AMA Style

Asimit V, Kyriakou I, Nielsen JP. Special Issue “Machine Learning in Insurance”. Risks. 2020; 8(2):54.

Chicago/Turabian Style

Asimit, Vali, Ioannis Kyriakou, and Jens Perch Nielsen. 2020. "Special Issue “Machine Learning in Insurance”" Risks 8, no. 2: 54.

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