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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.
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