Higher risks for commercial banks correspond to lower probability of access to financing transactions. Climate change risk strongly impacts bank loan supply. In particular, in the tourism industry, it is noteworthy that lenders charge higher interest rates for mortgages that face a greater risk of rising sea levels. As loans are one of the most important businesses for commercial banks, innovative strategies can lead to the design of a composite bank loan supply for building resilience, especially against physical climate risk. In this work, we propose a new tool, which is an insured loan relying on a climate change risk-sharing mechanism, where we develop a bioclimatic composite indicator based on machine learning naïve technique.

Life insurance profitability depends on reliable mortality risk projections and pricing. While the COVID-19 pandemic has caused disruptions around the world, this is a temporary mortality shock likely to dissipate. In this paper, we investigate the long-run impact of COVID-19 on life insurance profitability. Due to the long-run dynamics of the mortality characterised by a decreasing effect of the COVID-19 mortality acceleration, we suggest proactive mortality risk management by implementing prompt premium adjustments, in order to increase the resilience of the business.

Cyberspace is a dynamic ecosystem consisting of interconnected data, devices, and individuals, with multiple network layers comprising identifiable nodes. Location‐based information can significantly improve cyber resilience decision‐making and facilitate the development of innovative cyber risk pricing tools. This article is based on a methodology that uses company geospatial data to accurately estimate the number of expected losses arising from cyberattacks. Our approach aims to build and compare statistical spatial models that allow pricing cyber policies more effectively than traditional non‐spatial methods by incorporating all available data. By accounting for spatial dependence, we can assess the risk of data breaches and contribute to the design of more efficient cyber risk policies for the insurance market.

In this paper, we contribute to the topic of the non-performing loans (NPLs) business profitability on the secondary market by developing machine learning-based due diligence. In particular, a loan became non-performing when the borrower is unlikely to pay, and we use the ability of the ML algorithms to model complex relationships between predictors and outcome variables, we set up an ad hoc dependent random forest regressor algorithm for projecting the recovery rate of a portfolio of the secured NPLs. Indeed the profitability of the transactions under consideration depends on forecast models of the amount of net repayments expected from receivables and related collection times. Finally, the evaluation approach we provide helps to reduce the ”lemon discount” by pricing the risky component of informational asymmetry between better-informed banks and potential investors in particular for higher quality, collateralised NPLs.