Much like with auto insurers in the early 20th Century, it’s only been recently that the insurance industry has realized how significant—in both size and importance—cyber exposures are. Further, this significance will likely continue to increase for the foreseeable future, as businesses continue to become more technology-driven and hostile actors continue to find new ways to commit cybercrimes that steal from and disrupt businesses.  

During the recent Pinnacle University event, I discussed the growing challenges of the cybersecurity industry in today’s evolving technological world. The importance of advanced measures and practices and how actuarial modeling techniques can be applied to the cybersecurity industry were also discussed. 

This past March, a ransomware attack caused government operations in Atlanta to come to a complete standstill. It left courts unable to process warrants, residents unable to pay bills online, and travelers unable to use airport Wi-Fi. Attacks like these have become the rule rather than the exception for businesses, with former FBI director Robert Mueller saying it best: “…there are only two types of companies: those that have been hacked and those that will be hacked.” Everyone is a target, so the seeming inevitability of becoming a hacking victim has now made risk management strategies more important than ever.

The recent Equifax breach of 143 million individuals’ personal data was announced September 7, 2017. The hack included social security numbers, birth dates, addresses, driver’s license numbers and might also include credit card numbers. According to Privacy Rights Clearinghouse, “The number of people impacted and the sensitivity of the breached information may make this the most significant data breach ever.”

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.