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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.
Reflections on the 2016 Insurance Analytics Conference
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.
Election 2016:  A Real-Life Example of the Importance of Understanding Your Model
Zach Brogadir November 22, 2016 Posted in: Blog Posts, Predictive Analytics
The 2016 Presidential Election was the most divisive of my lifetime. The candidates presented very different outlooks, sparking fierce debate among American citizens. Interestingly, it wasn’t just voters who were divided by this election. Predictive modelers were likewise involved in their own election-driven debate.
The Magic Kingdom of Analytics
Nick Gurgone March 21, 2016 Posted in: Blog Posts, Predictive Analytics
When you think of the Disney Corporation, many things come to mind: Mickey Mouse, Cinderella, magic… and analytics? At the recent Casualty Actuarial Society (CAS) Ratemaking and Product Management (RPM) Seminar in Orlando, I was fortunate to attend a presentation by Mike Akeroyd, a Managing Consultant with the Walt Disney Company. Mike demonstrated the many data collection applications and analyses that he and his team utilize to improve theme parks, optimize hotel bookings and maximize advertising effectiveness. He calls them “yield analytics” and applies them to the daily management of Disney projects. Mike’s team has statistically determined optimal outcomes for essential Disney operations, ranging from bus and shuttle schedules to the optimal order in which to ride attractions at the Magic Kingdom theme park.
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