Message : Required fields No matter how much I evangelize critical strategic issues like AI governance, I am always an analytic scientist at heart. In this post I'll share key machine learning (ML) techniques we've developed at FICO to ensure monotonicity in neural networks. Monotonicity is essential to build trust in decision models used in regulated industries such as lending. Here's the connection: monotonicity helps ensure palatable (reasonable and acceptable) model behavior, an absolute requirement for gaining the confidence of lenders, customers and regulators in the machine learning models deployed in today's financial decisioning environments. What Is Monotonicity? In an analytic model, a monotonic relationship occurs when an input value is increased and the output value either only increases (positively constrained) or only decreases (negatively constrained), and vice versa. For example, if the ratio of payment to balance owed on a loan or credit card increases, we would expect that the credit risk score should improve (lower risk of default). Figure 1 shows a positively constrained monotonic relationship between these two variables.