Protect and serve: fraud fighting finds a partner in machine learning

GitHub cat logo pumpkin carving
Flickr/Nick Quaranto

October is one of my favorite months of the year in Oregon, where I live. I call it “pumpkin patch season” and the colors are magnificent, as the season changes. For many of my friends, October weekends are spent squarely rooted in front of the TV watching football. And in case it isn’t penciled in your calendar – it’s also National Cyber Security Awareness Month (NCSAM).

Celebrate cybersecurity?

Developed by the National Cyber Security Alliance and the U.S. Department of Homeland Security, NCSAM seeks to unite industry and government organizations in the goal of providing Americans a safer and more secure online experience. Now in its 14th year, the month feels more relevant than ever, given the almost-daily breaches reported in the news.

You may ask, “How does one celebrate NCSAM?” Good question – there are no costumes or candy involved, so it isn’t like Halloween, but it does have great possibilities as a community event – especially if you’re in the financial services community.

Easy and secure — can I really have both?

While most consumer-facing businesses understand the responsibility of protecting their customers, businesses face a significant hurdle when trying to provide both a safe and friendly experience. One industry in particular is finding this conundrum confounding – financial services. As financial institutions (FI’s) like banks and credit unions or insurance companies work to protect their customers’ sensitive information, they are searching for solutions that won’t compromise customer service quality – essentially, trying to find the balance between providing low-friction experiences for users and proper risk mitigation for the business.

Machine learning is a viable option for these organizations. Because machine learning solutions are able to learn from previous observations and make inferences about future behavior, their value in fraud-fighting ability becomes inherent.

Machine learning is being funded by FIs

According to a recent report from iovation and Aite Group, more financial institutions (FIs) than ever are looking to machine learning for improving fraud mitigation and customer experience. Sixty eight percent of North American FI’s cite machine learning analytics as a high priority investment over the next few years. The high alert state of threat today is likely the new normal for organizations and consumers, so early adopters of solutions that have machine learning incorporated will not only be able to reduce fraud, but will also have a major advantage over their competitors.

 A good example of the struggle these FIs are facing is the positive omni-channel approach to customer engagement which brings both opportunity and risk. While it’s great there are now multiple points of interaction where consumers can touch their accounts (ATMs, call centers, email) here’s the rub – this opens up more attack vectors for hackers to exploit as well.

Broaden your perspective to really leverage machine learning

While the adoption of machine learning analytics is a step in the right direction for FIs, embracing a broader fraud fighting solution, if possible, is their best bet. By taking advantage of customer data and applying advanced machine learning techniques, FIs can create insights that not only prevent fraud, but provide more convenient and applicable authentication methods for customers.

As much as I support (and even celebrate) NCSAM, it’s my hope that it won’t be needed much longer. Instead, I envision a world in which organizations and individuals will learn to incorporate cyber-awareness into their daily lives, and hopefully, stay one step ahead of hackers and fraudsters. Until then, I’m eager to see where machine learning takes us. Data is our new currency, so let’s keep it out of hackers’ pockets.

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