Towards Socially Responsible (Re)Insurance Underwriting Practices: Readily Available ‘Big Data’ Contributions to Optimize Catastrophe Risk Management
26 Pages Posted: 25 Feb 2016 Last revised: 10 Sep 2017
Date Written: February 24, 2016
Advances in big data methodologies with high degrees of granularity and transparency have made it possible to enhance the discussion on socially responsible (re)insurance underwriting practices. This article offers a definition of the microeconomic concept of socially responsible (re)insurance policy underwriting. This proposition draws on data components from natural peril and financial data modelling to bring it truly alive. In the view of the authors this proposition enhances transparency in risk metrics definitions, and hence improves the overall decision-making and underwriting process for an insurance policy or a reinsurance contract. Where consistently implemented and used this proposition builds and facilitates the basis for a more socially responsible (re)insurance policy underwriting, primarily but not limited to the context of (re)insurance of risks from natural catastrophes.
As such this paper aims to address various audiences: stakeholders at (re)insurers with responsibilities for fair risk pricing, fairness and customer interests in the underwriting process and specific IT interests and process in catastrophe risk modelling; stakeholders with responsibilities for ethical, social, responsible and fair risk transfers including investors, pension funds and NGOs; and stakeholders with responsibilities to further the use of available big-data at insurance companies, customer fairness and future (re)insurance products.
By addressing this audience this paper aims to contribute to discussions on the fair sharing of climate change risks, the evolving reinsurance market and regulatory requirements on fairness in (re)insurance risk transfers as part of evolving financial market regulations.
Keywords: Sustainable (re)insurance premiums, big data capabilities for insurance underwrting
JEL Classification: G22
Suggested Citation: Suggested Citation