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Risks
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Risks: Feature Papers 2022
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Risks: Feature Papers 2022

A special issue of Risks (ISSN 2227-9091).

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 17099

Special Issue Editor

Prof. Dr. Mogens Steffensen

E-Mail Website
Guest Editor
Department of Mathematical Sciences, University of Copenhagen, Universitetsparken 5, Copenhagen Ø, DK-2100 Copenhagen, Denmark
Interests: life insurance mathematics; asset-liability management; optimal asset allocation; personal finance and insurance; stochastic control theory
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As Editor-in-Chief of the journal Risks, I am pleased to announce the Special Issue “Risks: Feature Papers 2022” is now online. Risks is an international, peer-reviewed scholarly open access journal of research and studies on insurance and financial risk management. In this Special Issue, “Feature Papers”, we aim to publish outstanding contributions in the main fields covered by the journal, which will make a great contribution to the community. The entire issue will be published in book format after it is closed.

We welcome high-quality papers on topics within the scope of the journal. Submitted papers will first be evaluated by the editors. Please note that all the papers will be subjected to thorough and rigorous peer review.

Prof. Dr. Mogens Steffensen
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Risks is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (5 papers)

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Editorial

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2 pages, 263 KiB  
Editorial
Continuing Risks
by Corina Constantinescu, Montserrat Guillen and Mogens Steffensen
Risks 2023, 11(1), 10; https://doi.org/10.3390/risks11010010 - 27 Dec 2022
Viewed by 1473
Abstract
Risks will soon celebrate its tenth anniversary [...] Full article
(This article belongs to the Special Issue Risks: Feature Papers 2022)

Research

Jump to: Editorial

20 pages, 1208 KiB  
Article
Factors Driving Duration to Cross-Selling in Non-Life Insurance: New Empirical Evidence from Switzerland
by Yves Staudt and Joël Wagner
Risks 2022, 10(10), 187; https://doi.org/10.3390/risks10100187 - 27 Sep 2022
Cited by 2 | Viewed by 2813
Abstract
Customer relationship management and marketing analytics have become critical for non-life insurers operating in highly competitive markets. As it is easier to develop an existing customer than to acquire a new one, cross-selling and retention are key activities. In this research, we focus [...] Read more.
Customer relationship management and marketing analytics have become critical for non-life insurers operating in highly competitive markets. As it is easier to develop an existing customer than to acquire a new one, cross-selling and retention are key activities. In this research, we focus on both car and household-liability insurance products and consider the time a customer owning only a single product takes before buying the other product at the same insurer. Based on longitudinal consumer data from a Swiss insurance company covering the period from 2011 to 2015, we aim to study the factors driving the duration to cross-selling. Given the different dynamics observed in both products, we separately study the car and household-liability insurance customer cohorts. Considering the framework of survival analysis, we provide descriptive statistics and Kaplan–Meier estimates along major customer characteristics, contract history and distribution channel usage. For the econometric analysis of the duration, we compare the results from Cox and accelerated failure time models. We are able to characterize the times related to the buying behavior for both products through several covariates. Our results indicate that the policyholder age, the place of residence, the contract premium, the number of contracts held, and the initial access channel used for contracting influence the duration to cross-selling. In particular, our results underline the importance of the tied agent channel and the differences along the geographic region and the urbanicity of the place of residence. By quantifying the effects of the above factors, we extend the understanding of customer behavior and provide a basis for developing models to time marketing actions in insurance companies. Full article
(This article belongs to the Special Issue Risks: Feature Papers 2022)
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22 pages, 739 KiB  
Article
Machine Learning Models and Data-Balancing Techniques for Credit Scoring: What Is the Best Combination?
by Ahmed Almustfa Hussin Adam Khatir and Marco Bee
Risks 2022, 10(9), 169; https://doi.org/10.3390/risks10090169 - 24 Aug 2022
Cited by 17 | Viewed by 6814
Abstract
Forecasting the creditworthiness of customers is a central issue of banking activity. This task requires the analysis of large datasets with many variables, for which machine learning algorithms and feature selection techniques are a crucial tool. Moreover, the percentages of “good” and “bad” [...] Read more.
Forecasting the creditworthiness of customers is a central issue of banking activity. This task requires the analysis of large datasets with many variables, for which machine learning algorithms and feature selection techniques are a crucial tool. Moreover, the percentages of “good” and “bad” customers are typically imbalanced such that over- and undersampling techniques should be employed. In the literature, most investigations tackle these three issues individually. Since there is little evidence about their joint performance, in this paper, we try to fill this gap. We use five machine learning classifiers, and each of them is combined with different feature selection techniques and various data-balancing approaches. According to the empirical analysis of a retail credit bank dataset, we find that the best combination is given by random forests, random forest recursive feature elimination and random oversampling. Full article
(This article belongs to the Special Issue Risks: Feature Papers 2022)
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24 pages, 4936 KiB  
Article
A New Fourier Approach under the Lee-Carter Model for Incorporating Time-Varying Age Patterns of Structural Changes
by Sixian Tang, Jackie Li and Leonie Tickle
Risks 2022, 10(8), 147; https://doi.org/10.3390/risks10080147 - 25 Jul 2022
Viewed by 2108
Abstract
The prediction of future mortality improvements is of substantial importance for areas such as population projection, government welfare policies, pension planning and annuity pricing. The Lee-Carter model is one of the widely applied mortality models proposed to capture and predict the trend in [...] Read more.
The prediction of future mortality improvements is of substantial importance for areas such as population projection, government welfare policies, pension planning and annuity pricing. The Lee-Carter model is one of the widely applied mortality models proposed to capture and predict the trend in mortality reductions. However, some studies have identified the presence of structural changes in historical mortality data, which makes the forecasting performance of mortality models sensitive to the calibration period. Although some attention has been paid to investigating the time or period effects of structural shifts, the potential time-varying age patterns are often overlooked. This paper proposes a new approach that applies a Fourier series with time-varying parameters to the age sensitivity factor in the Lee-Carter model to study the evolution of age effects. Since modelling the age effects is separated from modelling the period effects, the proposed model can incorporate these two sources of structural changes into mortality predictions. Our backtesting results suggest that structural shifts are present not only in the Lee-Carter mortality index over time, but also in the sensitivity to those time variations at different ages. Full article
(This article belongs to the Special Issue Risks: Feature Papers 2022)
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27 pages, 1590 KiB  
Article
The Seven-League Scheme: Deep Learning for Large Time Step Monte Carlo Simulations of Stochastic Differential Equations
by Shuaiqiang Liu, Lech A. Grzelak and Cornelis W. Oosterlee
Risks 2022, 10(3), 47; https://doi.org/10.3390/risks10030047 - 23 Feb 2022
Cited by 6 | Viewed by 3121
Abstract
We propose an accurate data-driven numerical scheme to solve stochastic differential equations (SDEs), by taking large time steps. The SDE discretization is built up by means of the polynomial chaos expansion method, on the basis of accurately determined stochastic collocation (SC) points. By [...] Read more.
We propose an accurate data-driven numerical scheme to solve stochastic differential equations (SDEs), by taking large time steps. The SDE discretization is built up by means of the polynomial chaos expansion method, on the basis of accurately determined stochastic collocation (SC) points. By employing an artificial neural network to learn these SC points, we can perform Monte Carlo simulations with large time steps. Basic error analysis indicates that this data-driven scheme results in accurate SDE solutions in the sense of strong convergence, provided the learning methodology is robust and accurate. With a method variant called the compression–decompression collocation and interpolation technique, we can drastically reduce the number of neural network functions that have to be learned, so that computational speed is enhanced. As a proof of concept, 1D numerical experiments confirm a high-quality strong convergence error when using large time steps, and the novel scheme outperforms some classical numerical SDE discretizations. Some applications, here in financial option valuation, are also presented. Full article
(This article belongs to the Special Issue Risks: Feature Papers 2022)
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