Credit scoring analytics provider Fair Isaac Corp., or FICO, has a profile of each and every customer to analyze behavioral decisions. And with its sophisticated models using machine learning and artificial intelligence, it has been able to nab money launderers and insiders faster and more effectively.

“There’s going to be very few people that will behave like a money launderer, or an insider, or something along those lines,” said Scott Zoldi (pictured), chief analytics officer of FICO. “And so by building really, really, really good models of predicting normal behavior, any deviation or a misprediction from that model could point to something that’s very abnormal and something that should be investigated.”

Zoldi spoke with Jeff Frick (@JeffFrick), host of theCUBE, SiliconANGLE Media’s mobile livestreaming studio, during the Corinium Chief Analytics Officer event in San Francisco. They discussed the newest terminology in data analytics and how machine learning is powering alternative ways to making data decisions. (* Disclosure below.)

Making data the main attraction

FICO’s autonomous analytics models look at patterns that are not normal to help nab money launderers, Zoldi explained. “We’re trying to understand behavior, ultimately. And that behavior can be manifested in terms of making a fraud decision or a credit decision. But it’s really around personalized analytics,” he said.

Another new term used around the office is “operationalizing analytics,” where one looks at all the different types of data available to aid decisions and optimize computational power. FICO breaks down operationalization into four parts, according to Zoldi: data and business decision making, sourcing data and making it available to end users, applying different analytics, and the people process.

There’s also explainable AI, focused on deconstructing machine learning models by using algorithms to identify patterns. FICO tries to understand the explanations as to why the machine scored the way it did.

“… So we can have that dialogue with the customer, and they can understand the reasons and maybe improve the outcome in the future,” Zoldi stated. “Very often, AI and machine learning will make a very different decision than we will, so it can add some level of insight to us. But we always need that human factor in there to kind of validate the reasons, the explanations, and then make sure we have that kind of human judgement that’s running alongside.”

What’s it like to be part of a company that uses data as its main core for the digital transformation? “For us it’s really exciting. … We’ve been at it for 60 years, right? And analytics is at the core of our business, [as well as] operationalizing out the data and … bringing better analytics into play.”

Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s coverage of the Corinium Chief Analytics Officer event. (* Disclosure: Fair Isaac Corp. sponsored this segment of theCUBE. Neither FICO nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE