Description: This article updates the reader on the Doctoral Stipend process.

Description: Actuarial concepts of credibility can be extended to encompass additional information provided by new machine learning estimates. New statistical ideas also indicate how to generalize to blend arbitrarily many estimators, while not double-counting common information. For example, historical claim costs and risk scores both leverage diagnosis code information. We introduce two new estimators, one based on partial correlation and the other based on the machine learning-derived Shapley statistic, to address these limitations.

Description: The COVID-19 pandemic has disrupted the global economy at a level never imagined. The economic fallout has been catastrophic. Record high unemployment, staggering revenue loss, bankruptcy and an uncertain future have businesses—and consumers—searching for a beacon of hope. In the quest for a lifeline, actuarial teams are quickly being thrust into the spotlight

Description: Article discussing the benefits of the Chartered Enterprise Risk Analyst, CERA, credential.

Description: Introduces the concept of total risk analysis, a methodology used to answer questions like "what are the chances we will lose more than $1 million"

Description: The article introduces two previously published articles.

Description: With coinsurance of an existing block of long-duration insurance contracts, there may be existing reinsurance that remains in force. The new coinsurance is typically net of such “inuring reinsurance.” In this article, we look at approaches that the new reinsurer might apply to account for the effects of inuring reinsurance.

Description: In this article, we introduce a novel machine learning-based model that analyzes and predicts policyholder behaviors at the individual policy level. Policyholder embedding representations are learned using a Recurrent Neural Network (RNN) that takes into account the sequence of historical transactions associated with a policy. Additionally, a Neural Network with a multi-head architecture is trained to predict the policyholder's next action based on the policyholder embedding and other policy attributes. The resulting model offers several advantages over traditional experience studies and assumption-setting methods, including the ability to set policy-level assumptions that improve reserve accuracy, predict multiple behaviors simultaneously while capturing the interrelationships between them, and generate policyholder embedding representations that are useful for segmentation.