Description: This discussion looks at general topics associated with electronics such as storage methods. From Transactions of Society of Actuaries 1960, Vol. 12, No. 34.

Description: Machine learning algorithms fit models based on patterns identified in data and can be very complex. In this report, we describe and illustrate a range of methods for interpreting machine learning models from the growing field of Interpretable Machine Learning (IML).

Description: Actuarial modeling has gone through several distinct phases largely defined by the underlying technology platform. Today, cloud computing is again redefining the technology foundations of actuarial modeling and related processes.

Description: This primer paper seeks to introduce and update the actuarial profession to the topic of insurance policies and genetics and study the impacts on insurance and potential responses.

Description: This literature review provides insights on emerging technologies that interact with the actuarial profession within the space of microinsurance for the purpose of establishing a frame of reference for actuaries to use in their work. Furthermore, it intends to spark readers’ interest in, and create a vision for, how microinsurance and its associated technologies may impact the actuarial industry in the future.

Description: A brief overview of recent AIT research papers in summarized format.

Description: This is a discussion about computer models and simulation tools for life insurance companies. From Transactions of Society of Actuaries 1969, Vol. 21, No. 59AB

Description: Research project that reviewed and inventoried the range of quality and efficiency measures that demonstrate the performance of hospitals and physicians. Besides outlining key areas of consideration for quality and efficiency measurement, this report also describes future opportunities for actuaries and other health professionals interested in this evolving area. The inventory part of this report is in a separate document.

Description: This research report addresses two purposes: [1] to incorporate phase transitions into a copula framework, and [2] to calculate the probability of defaulting in the context of a portfolio of correlated mortgages when a contagion effect exists. Understanding and measuring the effects that contagion has on the probability of defaulting, for both single assets and portfolios of assets, are crucial to collateralized debt obligation [CDO] markets.

Description: In this report, we will try to provide an in-depth review of current Machine Learning and Deep Learning models and will explain how those models works and their possible applications in the actuarial field. The recent studies and successes from the industry will also be discussed. We intend to bring more actuaries onboard about those technologies and start to think about how to bring them into our daily work.