The Trellance Data Blog

Robo-Analytics: Artificial Intelligence in the Credit Union of the Future

Posted by Peter Keers, PMP on Jun 12, 2017 1:01:00 PM


A recent Forbes magazine article by Randy Bean and Thomas H. Davenport notes how General Electric (GE) is making a bold transformation into a “digital industrial” company. In the past ten years, GE has taken important steps to capture massive amounts of data (massive = “Big Data”) from devices throughout the enterprise. At first, it seems GE applied conventional analytics to find ways to increase revenue, cut cost, and many other beneficial outcomes. While analytics continues to be a critical part of GE’s evolution into being a digital industrial company, GE is taking a further step forward into the emerging areas of artificial intelligence and machine learning.

According to Bill Ruh, the CEO of GE Digital and the company’s Chief Digital Officer, “It’s not enough to connect machines. You have to make your machines smarter. You need to figure out the best ways for embedding intelligence into machines and devices. Then you need to develop the best techniques for collecting the data generated by those machines and devices, analyzing that data and generating usable insights that will enable you to run your equipment more efficiently and optimize your operations and supply chains.”

What does this mean for credit unions?

Artificial Intelligence is a broad term defining machines that independently carry out tasks that formerly were considered the exclusive domain of humans. For example, a computer call AlphaGo recently beat a human champion in the ancient Chinese game of Go that is considered to be very intuitive. 

Machine learning is a branch of artificial intelligence in which computers have the ability to “learn” without being directly programmed. Such systems continually accumulate data and their internal algorithms are allowed to elaborate based on changes in data over time. AlphaGo employs machine learning as a part of its overall functioning.

For credit unions, this type of technology could:

  • Detect fraud with ever-increasing precision every time a new scam is launched by hackers.
  • Find “next best product” opportunities in more finely defined groups of members based on a wide variety of both internal and external data sources.
  • Provide the next generation of “chat bots” that interact with members in both text and voice to solve complex customer service problems.

While these technologies have a bright future, widespread use is some years away. What credit unions can do now is to become experts in current data and analytics tools and techniques. In this way they will be ready when the wave of “robo-analytics” arrives.

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Topics: Machine Learning, Digital, Artificial Intelligence