Description: An overview of bias and fairness in machine learning models, as they apply to use cases in life insurance.

Description: Feature article discussing how actuaries are affected by the need for maintaining controls and the use of IT resources for meeting these needs.

Description: The National Association of Insurance Commissioners (NAIC) is in the middle of a project to change the real-world economic scenario generator (RW ESG) prescribed for principle-based reserve and capital calculations. Explore a brief summary of the NAIC's RW ESG project and gain a general overview of RW ESGs. Learn how RW ESGs impact principle-based reserve and capital levels, their potential impact on life insurance companies and consider differences between the currently prescribed RW ESG and the proposed RW ESG.

Description: Article Permutations And Computations by Moordhead in The Actuary, February 1979, Volume 13, Number 2

Description: An overview of the formation of the Committee of Professional Associations on Federal Statistics, initiated by the Joint Ad Hoc Committee on Government Statistics, in response to the need for statistical advisory committees to government agencies to promote better data collection and dissemination, contracting, forms clearance, planning, coordination, monitoring, staffing, and strengthening of agency functions.

Description: As the use of predictive models and novel datasets becomes more prevalent, a focus on consumer protection is being highlighted by regulators, ethicists, and industry insiders. Over time, increased rigor should and will be applied in model development and implementation. These issues are not new. Hundreds of individuals have worked on these issues in the laboratory, the board room, and the court room. The Civil Rights Act of 1964, Uniform Guidelines of the US EEOC, the Principles for the Validation and Use of Personnel Selection and other sources are intended to protect the rights of citizens and consumers and provide guidance to professionals in the development and use of decision-making tools. Drawing heavily on examples and parallels from psychology and employment practices as well as other disciplines, concepts such as model bias, fairness, adverse impact, disparate treatment and the like will be presented. Suggestions to ensure model validity, demonstrate model and data relevancy and minimize the potential for adverse impact to protected classes will also be offered. Each of these considerations will be linked to recent dialogue and concerns in the Life Insurance sector.