Description: This article includes a description of robotic process automation as it relates to actuarial departments. Robotic process automation is becoming more prevalent in actuarial departments as different regulatory changes cause the processes to be more complex. As robotic process automation takes over more jobs, actuaries will have to enhance and evolve their skillsets to better complement the new tools. This article provides an introduction for actuaries to inform them what they should expect, and how they can adapt to these changes.

Description: The minimum distance estimator proposed allows for the selection of the best fitting family member without prior distributional assumptions. It is shown that this estimator is consistent and can be made either robust or asymptotically efficient. Its asymptotic distribution is derived and and illustrated with automobile insurance data. From the Actuarial Research Clearing House 1990 Vol. 1

Description: Explores the applications and techniques of model governance, focusing on benefits such as model literacy, efficiency, and confidence.

Description: Report on the EuSpRIG Annual Meeting last July in Paris, which focused on modeling.

Description: Feature article discussing what American actuaries can learn from their European counterparts about actuarial modeling.

Description: This paper focuses on graduation of mortality data and considers models that are functions of age, and duration since selection. From the Actuarial Research Clearing House 1990 Vol. 1.

Description: Commentary on practical observations resulting from constructing and running Economic Capital models. Possible standardization of assumptions and result interpretation are morphing even though use of E.C. models has now been around for years.

Description: Summary of the SOA research project on PBR Simplified Methods. Describes the multi-risk scenario generator, central estimate assumptions, assumption objectivity measures, and regulatory demonstrations per VM-20.

Description: Spark offers a way to get some dramatic speed and scalability advantages over the Map-reduce methodology whenever you are doing iterative data processing and particularly when you want interactive data analysis capability. Find out why “lazy execution” can be a desirable characteristic when dealing with big data.

Description: Producing a crystal-clear balance roll-forward on time, obtaining a fresh model output the first time or providing model results for your business partners with a tight turnaround all depend on one thing: how fast the model engine runs. Authors explored various reasons behind significant computational power consumption, and discussed different ways to speed the computational process such as in-force population compression, model use case consolidation, code simplification and streamlining, and infrastructure automation, etc.