Description: The well-known examples of predictive modeling “successes” are numerous. Perhaps less widely recognized is the myriad of ways in which a predictive model can fail to perform as expected, often due to misconceptions or misrepresentations on the part of the analyst. This article, which is intended for a general actuarial audience, focuses on three such pitfalls. By understanding these three common pitfalls, readers may guard against them within their own work, teams, and organizations.

Description: The artcle looks at the most important features necessary to conduct a meaningful quantitative analysis. The article also discusses how predictive analytics should be used as a tactic to anwer question and not an end in of itself.

Description: Reflections on SOA delphi study released Oct 2005

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

Description: It is about using actuarial judgement to validate the predictive model.

Description: Discussion about the need for, and approach to, measuring organizational expertise ton ensure the learning investment is resulting in expertise growth.