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How Faculty Director Kai Larsen went from designing banking systems to training tomorrow’s analytics leaders

For��Faculty Director Kai Larsen, the future of business analytics presents exciting problem-solving possibilities and challenging questions about ethics. He’s been honing his expertise in analytics for over 30 years, and his unconventional journey lends a world perspective to his teachings. Through his unique professional experiences and insights on this dynamic discipline, he’s training students to drive value in the workplace.

The problem-solving possibilities of analytics

At the beginning of his analytics career, Professor Larsen served as a consultant in bringing Norwegian banking into the future. His work helped create the first Norwegian Internet banking system, as well as a central billing system—which altered the way people received bills in the mail and automated their payment process.��

“Instead of a bill per day, all bills would arrive in one envelope every two weeks,” said Larsen. “If you did nothing, then they would just get paid. So that’s sort of a case of automating an existing process and making it easier.”��

Using analytics to design automated systems was an exciting combination for Larsen, and it meant the ability to solve problems in a way that hadn’t been possible before.��

“To me, it was always about innovating processes—automating and innovating,” said Larsen. “So, can you take something people are doing and make it way easier? Analytics and artificial intelligence is just one more really cool toolset for tackling processes and problems."

An introduction to machine learning

While a consultant in Norway, Larsen began working on expert systems and automating processes that normally require human expertise. By interviewing area experts, such as loan processing officers, he was then able to create enormous collections of ‘if–then’ statements based on their knowledge. This would serve as a foundation for automating these processes with machine learning.

“If someone walked in asking for a business loan, a loan processing officer would follow up with a set of questions. At the end of those questions, that officer would then say if you could get a loan or not. So, what we would focus on there was developing systems that built that expert’s knowledge into technology. These ‘expert systems’ were expensive to build, and it was never quite clear whether automating past processes would lead to fair and correct answers.”

Shortly after his consulting work on expert systems in Norway, Larsen moved to the U.S. to pursue a PhD. At that time, he became more familiar with machine learning, its role in cutting the individual expert out of the process, and it’s relationship with the human knowledge and biases it’s created from.

Defining systems from data

In using analytics to automate processes, Larsen explained that the method relies upon past evidence, or the history of data, that’s available. That data makes it possible to then determine the guidelines, or rules, that will drive an automated system.��

“If you’ve already given out ten thousand loans, then we know how those people who received loans behaved—and we can set up a definition of success or failure,” said Larsen. “So maybe they didn’t make a payment in 60 days, or maybe they paid the whole loan on time. You can define what ‘success’ and ‘failure’ for a system looks like.”

In addition to people’s loan r