Join us as we explore the growing pressure on the insurance industry to quantitatively evaluate, monitor, and disclose the ESG adequacy of commercial insured portfolios. In recent years, institutions, regulatory bodies, and the public have increased their demands for transparency. However, accessing ESG data and metrics, especially for SMEs, remains a significant challenge due to the lack of regulatory pressure and limited available information.
What practical solutions are available to overcome these challenges? And how can they be transformed into opportunities?
Discover how you can access ESG data and improve the profitability of your commercial portfolio by leveraging the proven correlation between ESG factors and loss ratios, while contributing to the insurance industry’s broader sustainability goals.
Learning Objectives
At the end of this session, delegates will be able to:
- Explain how to integrate a sustainability assessment into the commercial underwriting processes without involving the client in a tedious ESG questionnaire
- Describe which ESG data and key indicators are accessible, and how to determine their correlation with the insurance risk based on real case studies
- Outline how to improve loss ratio by underwriting more sustainable and ESG-compliant businesses
About the presenters
Mattia Bongini, Data Science Manager
With over a decade of experience in advanced analytics and machine learning, Mattia Bongini is a seasoned Data Science Manager at CRIF. He leads teams in developing cutting-edge analytics solutions for insurance and banking clients, focusing on sustainability, risk-based pricing, fraud detection, and marketing analytics. Mattia has supported his clients with several impactful projects, including pricing sophistication models for consumer and commercial insurance, and the estimation of sustainability metrics for corporates, like GHG emissions, transition risk and biodiversity impact.
Prior to joining CRIF in 2018, Mattia contributed to statistical initiatives at the European Central Bank and held research positions at Université Paris-Dauphine and the Technical University of Munich, focusing on machine learning for complex systems and optimal control. He holds a Ph.D. in Applied Mathematics from the Technical University of Munich.
