Description: The SOA Research Institute is seeking researchers to study the application of geospatial imagery, high-resolution imagery, and artificial intelligence in loss estimate analytics and underwriting within the insurance industry.

Description: Presenters will address optimal new business written premium mix at the firm and state levels under varying competitive conditions. They will explore the use of mean-variance optimization to determine a company's optimal new business written premium mix for a given geographical area, predicting statewide product mixes using varying rate regulation scenarios, involving profit caps and measuring/testing a market's competitiveness for a given line of business. The scope of the ideas to be presented is international, applying to property, liability, individual and group life; individual and group health; disability and accidental insurance. Examples will be presented for general insurance in the United States

Description: A detailed look at charges to the FSA pathway in the GI track that is planned for 2023.

Description: Feature articles discussing the risks created by climate change.

Description: The aim of this project has three aspects: (1) providing a comprehensive literature review on telematics car driving data analytics; (2) developing various visual tools and a data cleaning procedure for telematics data; and (3) extracting risk factors from individual trips and improving classic classification ratemaking models with those risk factors.

Description: March 2017 issue of Reinsurance News

Description: This report predicts farm-level crop yield distributions in the absence of farm-level yield data.

Description: Reserving and ratemaking calculations are vital to how insurance companies perform financially. Current-day practices for predicting future loss ratio or pure premiums for reserving and ratemaking rates are not based on low prediction errors—mean squared error (MSE) criterion. This report provides mathematically optimal weights that lead to minimum MSE prediction errors, and the formulas are validated using retro-testing with real-world datasets. In this report, two main theorems are discussed, each addressing prediction of future loss ratio or pure premium and improving upon current practices for how rates and reserves are calculated.

Description: This report focuses on analyzing and managing climate change and carbon risk in the equity investment portfolios of insurance company and pension fund assets.