Description: Python has become the fastest-growing major programming language in the world. It has surged in popularity over the last decade, following the development of its industry-leading capabilities in data science, machine learning and artificial intelligence, and it is now one of the most globally popular and widely used languages. In this article, I discuss some of the reasons for its popularity and explain why many actuaries and insurers have started using Python.

Description: Technology has streamlined countless processes in our daily lives, and insurance is no stranger to this transformation. Technology can make insurance simpler and more personal for consumers, and can enable actuaries to better analyze unstructured data, make good decisions, create new products, and find channels to enhance profitability, provided they can tame the flood of information pouring in from across the digital ecosystem.

Description: Read the December 2018 issue of Small Talk published by Smaller Insurance Company Section. Practical information on trends that impact sections and activities.

Description: This paper examines a differential equation model, whose solutions have yield curve shapes. The paper also examines the relevance of the model with historical monthly U. S. Treasury nominal rates. From the Actuarial Research Clearing House 1992 Vol. 1 .

Description: This article sheds light on the various ways machine learning can uncover patterns within data to add unique value and insights to businesses.

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: Summarized description of four books on data visualization and their common themes

Description: The guest editorial details some valuable lessons during the author’s career as an employee benefits (mostly retirement) consultant, turned P&C predictive modeling, reporting and data warehousing lead, turned an analytic strategist and independent consultant.

Description: Principal component analysis is an unsupervised linear transformation mainly used for dimension reduction. Determine key numerical variables with maximum variances in a dataset, identify the correlation and removal of redundant variables are the key aspects of this exploratory data analysis.