Editorial on the Special Issue on Insurance: complexity, risks and its connection with social sciences

Over the last decade, insurance market has been characterized by significant changes. New regulations, as Solvency II (European Commission 2009) and IFRS17 (European Commission 2021) paved the way to an increasing focus on risk assessment and on market consistent valuation of assets and liabilities. Also the growth of artificial intelligence (AI), used to perform complex computational tasks, is revolutionising financial services, particularly within insurance practices (see EIOPA 2021). Nowadays, data represent a primary strategic asset and a source of competitive advantage in financial firms and the inherent value of predictive analytics in insurance is showing itself in myriad applications (see, e.g., Hyong and Errol 2015; Maynard et al. 2022). Additionally, an emerging wave of insurtech solutions are trying to transform insurance business through the introduction of Big Data, Machine Learning, and AI capabilities (see, e.g., McFall et al. 2020). In the insurance context, several fields have been characterized by the use of Machine Learning and AI methodologies. Predictive analytics tools can collect data from a variety of sources to better understand and predict the behaviour of policyholders. Companies are indeed collecting data from telematics, distribution channel and customers interactions, smart homes and even social media to better understand and manage their relationships, claims, and underwriting and so on.