Description: This article examines employee stock option ESO cost accounting, the characteristics of ESO schemes, the requirements and recommendation of FAS 123R and SAB 107. It also explains why the lattice model is probably the best approach to measuring the cost of stock option grants.

Description: This is the fourth in a 4-part series where Anders Larson and Shea Parkes discuss predictive analytics with high cardinality features. In the prior episodes we focused on approaches to handling individual high cardinality features, but these methods did not explicitly address feature interactions. Factorization Machines can responsibly estimate all pairwise interactions, even when multiple high cardinality features are included. With a healthy selection of high cardinality features, a well tuned Factorization Machine can produce results that are more accurate than any other learning algorithm.

Description: Lin Fangcheng argues that it is easy to digitalize by setting up new departments, but the real battle is to digitalize the incumbent departments that are predominantly spreadsheet-users. In order to reap the full potential of digitalization, we must abandon the spreadsheet and go digital. He discusses the reasons why the spreadsheet, hindered by its inherent design, has become a bottleneck for higher efficiency, and how to adopt low-code digital technology in a “together” mode. He further demonstrates that digitalization lays a solid foundation for artificial intelligence.

Description: Electronic Health Records (EHR) have increased in importance due to the in-person medical visit restrictions caused by the Covid-19 pandemic. Access to EHRs for insureds - apart from EHR vendors and providers - is now also prescribed by federal law. Combined, electronic medical data access has accelerated faster than expected. These trends are examined for potential impact and opportunities for actuaries in data aggregation.

Description: The article provides a general modeling technique to enhance base actuarial assumptions using multiple linear regression and AI techniques. The modeling framework provides a novel way to combine risk factors impacting a base actuarial assumption from various independent data sources to create an aggregate risk adjustment factor that is logical and consistent.

Description: We explore the use of mito, a Python package that allows users to use excel-like point-and-click interface with large datasets in Python.

Description: An introduction to the methods and parameters for Bayesian Hyperparameter Optimization in a Life Insurance context.

Description: Feature article that presents and in-depth discussion on Information Entropy and how it relates to actuaries.

Description: Describes the Predictive Analytics Hack-a-Thon held in 2022 and its outcomes.

Description: Article discussion information to included in an engagement letter and suggestions on how to negotiate them with clients.