You can now read the latest publication of Annals of Actuarial Science (AAS), a special issue themed around managing the risk of mortality shocks.
It includes a guest editorial that considers how actu-arial perspectives can help in a pandemic, as well as papers addressing the effect of COVID-19 on areas such as portfolio management, modelling future mortality scenarios, life insurance in Italy, and capital requirement for demographic risk.
The issue is freely available to IFoA members via the Actuarial Knowledge Hub at actuar-ies.org.uk/user/login/athens
Insurance analytics – call for papers
AAS is inviting submissions for a forthcoming special issue: ‘Insurance analytics: prediction, ex-plainability and fairness’.
The growing use of advanced analytics in insurance has generated numerous opportunities, including more accurate predictive modelling powered by machine learning and artificial intelligence methods, use of novel and unstructured datasets, and automation of key operations. New applications and adaptations of predictive modelling techniques are making advances in insurance, and we are seeing rapid progress in machine learning methods outside the insurance sector.
However, we face challenges around the transparency of complex algorithmic models and the economic and societal impacts of their use in decision making. Regulators may require models to be explainable so that the basis for decision making can be analysed, and significant attention is also being paid to ensuring that models do not discriminate unfairly.
This special issue will aim to capture leading academic thinking and industry applications in ad-vanced insurance analytics, from long-established areas such as pricing and reserving to more nov-el ones such as claims fraud. The editors are looking for papers that show advances in predictive accuracy, explainable modelling and fairness, and reflect on recent progress in these areas.
Papers can be submitted until 30 September 2023. The issue will appear in 2024, but all papers ac-cepted for publication will be published online when produced.
Full details, including suggested areas of interest, can be found at bit.ly/AAS_insurance_analytics
