Dear Colleagues,

Data from non-life insurance complex systems share many commonalities, such as high data volume, complex data structure, high dimensionality, and multi-scale. Traditional predicting modelling, including General linear Models, Generalized Linear and Generalized Additive Models, have been widely used for predicting insurance losses and future claims. On the other hand, the use of machine learning (ML) as an emerging insurance technique is a powerful tool for predicting insurance claim frequency, insurance pricing risk analysis and management, and insurance fraud detection and prevention. Actuarial practices in insurance pricing and rate regulation have shown that the interpretability of results obtained from ML techniques is crucial for the broader application of ML and AI technologies. Therefore, much effort has been made to improve ML explainability and interpretability. However, the applications of interpretable ML techniques and explainable artificial intelligence (XAI) are still in their infancy and require further development, particularly for non-life insurance, where different advanced statistical and computational methods are applied for solving problems from actuarial perspectives. This Special Issue aims to collect outstanding research papers on building statistical or computational machine learning models that can provide good interpretability for insurance pricing, risk analysis, risk management and predictive modelling for risk, particularly in non-life insurance.

The methodology topics include, but are not limited to, the following:

• Sparse statistical methods;
• Interpretable statistical models;
• High-dimension insurance data and their dimension reduction;
• Explainable artificial neural networks;
• Model agnostics methods;
• Variable importance measures;
• Machine learning algorithm for ratemaking.

Both theoretical development and applied work addressing the interpretability of models for insurance pricing, modelling and risk analysis are welcome.

Dr. Shengkun Xie
Guest Editor

Manuscript Submission Information

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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 1400 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.

• explainable machine learning
• interpretable machine learning
• insurance pricing
• ratemaking
• risk analysis
• risk management
• non-life insurance