Description: Each month in the media we learn of another set of jobs being automated into oblivion or replaced by disruptive technologies. We see taxi drivers replaced by Uber and Lyft now, and soon by self-driving cars. An Oxford University study of 702 occupations showed insurance underwriters in 698th place, with a 99% probability of computerization. They were safer for obsolescence by automation than only four occupations such as hand sewers and telemarketers.

Description: Actuaries provide special subject matter expertise to question (or validate) the results of data analytics models. Other quants often cannot do this.

Description: This is a gentle introduction to fuzzy logic. It explains the concept and use of linguistic variables, reference ranges, fuzzification, and defuzzification without the usual advanced mathematics that sometimes obscure these ideas.

Description: Continuation of a tutorial on Fuzzy (vs. Boolean, or 'crisp') logic and how you can use it to create actuarial models that better fit the real world. The article provides basic theory in non-technical terms and shows applications using the R programming language.

Description: Alternative sources of learning available for free or at low cost if you wish to round out your F&F education about machine learning, artificial neural networks, genetic algorithms and the millennia of biological advances we draw from when we use these techniques.

Description: Discusses how 'insignificant' changes in assumptions can have very significant affects.

Description: Arthur David Snell promotes and briefly discusses several articles in this May 2004 Issue. Article from CompAct, May, 2004, Issue No.17.

Description: In An Introduction to Incremental Learning, by Qiang Wu and I describe how machine learning is a natural enhancement to predictive analytics through its many useful tools to derive meaning from a lot of otherwise unusable data such as handwriting. Again, there are formulas involved; but we have tried to keep them understandable to the actuary who isn’t already familiar with stochastic gradient descent, perceptrons, and principal component analysis. Incremental learning can help you refine the estimations you derive from more traditional actuarial methods.

Description: This abstract describes a presentation that introduces new methods such as Experimental Mathematics, Genetic Algorithms, Predictive Modeling, Deterministic Chaos, Fractal Geometry, Behavioral Economics, Cellular Automata and other topics in Complexity Sciences.

Description: The first annual SOA Predictive Analytics Symposium was held in Chicago and exceeded all expectations in attendance, presentation quality, and relevant content.