In The State of Insurance Fraud Technology (2019), the most recent report published by the Coalition Against Insurance Fraud (CAIF) and the SAS Institute, nearly 75% of survey participants experienced a rise in fraudulent claims in the past three years. This represents a disturbing 11% increase since 2014. The survey was based on 84 primarily property and casualty insurers. None of the participating insurers indicated that fraud had decreased significantly during the same time frame. As a consequence of the rise in fraudulent claims, insurers are moving away from traditional formulaic business rules and red flags for identifying fraud. Instead, insurers are enriching their data analytics arsenals to include more sophisticated tools, methods and means to investigate fraud.  

At Pinnacle Actuarial Resources, we focus on innovation and thought leadership with a goal to make sure our clients remain ahead of the technological, economic and related forces shaping our profession and the insurance industry. Artificial intelligence (AI) and machine learning are two of those forces.

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.