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.

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. 

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.

Last month I had the opportunity to attend the 2016 Insurance Analytics Conference (IAC), a two-day event which was held in New Orleans, Louisiana. The IAC is a unique forum that attracts some of the most prominent figures in the insurance analytics arena. They gather together to share their success stories using data science and to advocate for the universal adoption of descriptive, predictive and prescriptive modeling as holistic approach in analyzing insurance-related data.