Description: Statistics show that the risk of fatality associated with motorcycle crashes far exceeds that of automobiles hereby Connecticut introduced mandatory motorcycle safety training. This paper develops a unifying framework to quantify the effectiveness of such mandatory programs and to translate this in terms of a possible insurance rate reduction.

Description: Research projects in pension - Pension Risk Management: Derivatives, Fiduciary Duty and Process.

Description: This case study is intended to educate insurance professionals on historical insurer impairments and insolvencies and possible future prevention indicators.

Description: The goal of this paper is to study how a full-range tail dependence copula would be useful in assessing tail risks in a regression setting.

Description: This research report that provides a literature survey of methodologies applying machine learning to insurance claim modeling.

Description: The Survey of Emerging Risk has been successfully conducted in North America for 16 years, consistently providing reports that track risk managers' opinions on emerging topics transcending the current business cycle, with strategic plans extending over decades. Since 2022, the survey has been specifically introduced to the Asia-Pacific markets with local language support, aiming to gauge actuarial and risk management professionals' opinions on emerging risks in the Asian market.

Description: In this paper, the author uses simple risk portfolios to discuss the abilities and shortcomings of the current methodologies for Value-at Risk [VaR], and suggests methodologies which can be used to facilitate the aggregation of VaR measures.

Description: This is the key findings research report for the fifteenth Emerging Risk Survey

Description: Emerging Risks Survey: Guide for Use

Description: This paper explores areas where fuzzy logic models may be applied to improve risk assessment and risk decision-making. It discusses the methodology, framework and process of using fuzzy logic systems for risk management. With the help of practical examples, it is hoped that it will encourage wise application of fuzzy logic models to risk modeling.