Description: Review of a research project, co-sponsored by the SOA, which examined 83 organizations and over 150 programs that measure the quality and efficiency of hospitals and physicians.

Description: Using Healthcare Claims Data to Create Actuarial Models.

Description: As the insurance industry continues to evolve due to a pandemic and rapidly changing economic and regulatory conditions, the role of data scientists and actuaries has become increasingly important. In an interview with the Atidot data science and actuarial team, we discuss what it is like to work at an InsurTech, how data scientists and actuaries can collaborate, and look at some concrete technical examples of where the innovation is having an impact. This article explores what collaboration can look like and how the role of data science and actuary may evolve in the InsurTech industry in the future.

Description: The barrier between humans and machines continues to blur, but curiously, this may have been prediction thousands of years ago in ancient myths. This article looks to how some of the early legends are becoming manifest in modern technology, particularly in the area of elder care. From automation, to humanoid assistants, to mind clones will the age of technology bring a new utopia?

Description: You really can have it all: readily available and relevant information derived from consistent and understood data. Getting at the Truth requires a good datawarehouse design and an appropriate business data model. Add a powerful and flexible Web–based analytical interface to get at the data and success in the pursuit of Truth will happen.

Description: Explores the open vs. closed systems debate with regards to actuarial modeling and exposes the over-simplification. Valuable for anyone who uses models.

Description: Read the April 2017 issue of CompAct published by the Technology Section. Newsletters provide practical information for the working actuary, articles on trends that impact sections and news about section activities.

Description: Natural Language Process (NLP) techniques can be used to find insights in large unstructured data sets and Word Embeddings are pervasive on a wide spectrum of NLP applications. These word representations improve downstream tasks such as Sentiment Analysis, Recommender System, and Topic Modeling. Learn how NLP transforms the Insurance Industry; and how word embeddings and pre-trained models improve the effectiveness and performance of NLP applications. NLP is increasing in importance for insurance companies. Additional application examples are available in session 36.

Description: This article discussed the NEAT family of neural network algorithms. NEAT automatically determines the number of hidden neurons, as well as their structure. Additionally HyperNEAT can scale to very large problem domains.