With all the hype around artificial intelligence, many of our customers are asking for some proof that AI can get them better results in areas where other kinds of analytics are already in use, such as credit risk assessment. With 25 years of experience with AI and machine learning under our belt, we can certainly provide that proof. My colleague Scott Zoldi blogged recently about how we use AI to build credit risk models. In this post, I'd like to drill into one of the examples he gave, to show some of the explorations we're doing to make sure we get the full power of machine learning without losing the transparency that's important in the credit risk arena. A traditional credit risk scorecard model generates a score reflecting probability of default, using various customer characteristics as inputs to the model.