**About the Special Issue Editors**

**Jens Perch Nielsen** is an actuary from Copenhagen, but also a statistician from UC-Berkeley. He worked as an appointed actuary in his youth and led various product development departments, before specializing in research and development. In 1999, he became the research director of RSA, with responsibilities in life, as well as non-life, insurance. From 2006 to 2012, he worked as an entrepreneur, and he is still the co-owner and board member of Copenhagen-based ScienceFirst, London-based Operational Science and Cyprus-based Emergent. He is co-author of more than 100 scientific papers in peer-reviewed journals of actuarial science, economics, econometrics and statistics, and a book on Quantitative Operational Risk Models. He is an Associate Editor for several journals.

**Vali Asimit** joined Cass Business School in January 2011, as a Lecturer in Actuarial Science. Previously, he was a Lecturer in Actuarial Science at the University of Manchester, for two years. Prof. Asimit studied Economics at the Academy of Economic Studies, Bucharest, Romania. He has an MSc in Statistics from the University of Western Ontario, Canada, where he also pursued his doctoral research on Dependence Modelling with Applications in Finance and Insurance. As part of his academic work, he has published and acted as a referee for international, statistical and actuarial journals. Prof. Asimit received the 2010 Fortis Award for the best Insurance: Mathematics and Economics (IME) journal paper, which was presented at the 14th International Congress of IME.

**Ioannis Kyriakou** obtained his PhD in Finance from City, following his MSc in Risk and Stochastics from LSE, and his BSc in Actuarial Science from City. He completed his Diploma in Actuarial Techniques at the Institute and Faculty of Actuaries, UK. He works in the area of quantitative methods, on both the development of numerical techniques and applications in the fields of operations research and management science, finance, actuarial science and sector studies, including derivatives, risk management, shipping, commodities, pension product design and communication, stock returns forecasting, and machine learning. He is the Director of the world-renowned Cass MSc in Actuarial Science and MSc in Actuarial Management. Previously, he worked for Lloyd's Treasury and Investment Management.

### *Editorial* **Special 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)20-7040-0990

Received: 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 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.

#### **References**


#### *Risks* **2020**, *8*, 54


c 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
