Using Machine Learning for Risk Management within an Insurance Company
Solvency capital requirement (SCR) is the amount of funds that insurance and reinsurance companies operating within the EU are required to hold. The SCR is set at a level that ensures that insurers and reinsurers can meet their obligations to policyholders and beneficiaries over the following 12 months with a 99.5 percent probability. To calculate required capital, large number of possible future scenarios must be simulated which requires large amount of computing power and time. In this talk we highlight how Machine Learning can be used to make these calculation estimates at a fraction of time and resources providing significant value to multiple stakeholders within risk management, investments and product development teams.
Rakesh Rana works as Lead Data Scientist at Nordea Life & Pensions, Sweden. His work focuses on applying AI to solve business problems and create customer value. Rakesh received his M.S. degree in Finance and PhD in Computer Science from Chalmers/University of Gothenburg, Sweden. His work and research interests revolve around using data science and machine learning algorithms mainly within the financial domain.