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The Modeling Lifecycle—Don’t Break the Chain!
Greg Frankowiak June 19, 2019 Posted in: Blog Posts, Predictive Analytics
When it comes to advanced analytics such as building predictive models, many people immediately think about the vast amounts of data, computing horsepower needed, and very sophisticated (often mysterious) techniques applied to the data to produce the results. Without question, all of these aspects are important steps in the process. However, there are several other critical steps both before and after these that can truly make or break an advanced analytics project. We can think of this as the Modeling Lifecycle.
RPM’s Value in Continuing Education

In the spring of 2019 we attended our first industry event: the Casualty Actuarial Society’s (CAS) Ratemaking, Product and Modeling (RPM) Seminar. RPM serves as a networking event and provides continuing education for CAS members by way of numerous workshops and sessions. We both work on predictive analytics projects, which made this event relevant to our work and solidified our understanding on many of the topics we work on every day, including modeling methods, model testing and implementation. 

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The predictors in my model are all significant at the 5% level. So?
Radost Wenman September 12, 2018 Posted in: Blog Posts, Predictive Analytics

In recent years, statisticians and researchers have continued to vigorously sound the alarm on the use and abuse of p-values in clinical studies and statistical modeling in general. Look no further than the official statement of the American Statistical Association (ASA), “The ASA’s Statement on p-Values: Context, Process, and Purpose,” that was published just two years ago in response to the ever more heated debate on the confirmatory role of p-values in quantitative science and the validity of statistical inference. While many in the scientific community have generated discussions and commentaries on the misuse of p-values, the ASA’s policy statement succinctly synthesizes “several widely agreed upon principles underlying the proper use and interpretation of the p-value.” The ASA’s statement puts forth six principles aiming to guide practitioners in their search for statistically significant effects, ameliorate the problem of false discovery rates and irreproducibility of results, and thus improve on the applicability of the scientific method.

May I Have Some Neural Networks with My Insurance Data, Please?
Machine learning techniques, particularly Artificial Neural Networks (ANNs), have enjoyed an upsurge in popularity and practical applications in a myriad of disciplines.  The explosion in the variety and volume of available data, coupled with cheap data storage and fast computing power, have placed ANNs front and center in data scientists’ tool boxes. 
What Does Benford’s Law Have to Do with Insurance Fraud?
Benford’s law is not some arcane legal clause reserved only for those who are well-versed in legal jargon and procedurals, though I admit that the “law” reference in the name is a bit misleading. Benford’s law is a little-known mathematical curiosity, but if you mention it to a forensic accountant, you will immediately sense excitement rather than bewilderment. Forensic experts from the financial sector often rely on this technique to chase data anomalies and financial fraud based on the distribution of digits in the numbers they examine. Naturally, this type of digital analysis can also be extended to detecting insurance fraud, or at least red-flagging cases as suspicious and aberrant. The premise is that fraudulent data do not conform to the mathematical patterns of the law, and cases that are isolated as non-conforming should at least warrant further investigation.
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