The Most Underutilized, Simple Way To Boost Predictive AI’s Value

InnovationAIThe Most Underutilized, Simple Way To Boost Predictive AI’s ValueByEric Siegel,Contributor.Forbes contributors publish independent expert analyses and insights. CEO of Gooder AI, author of “The AI Playbook” & “Predictive Analytics”Follow AuthorMay 26, 2026, 09:23am EDT--:-- / --:--This voice experience is generated by AI. Learn more.This voice experience is generated by AI. Learn more.A surprisingly easy way to multiply an AI model’s profit is to drive decisions via expected value instead of predictive scores. Here's how, illustrated with fraud detection.gettyI want to share with you what I consider to be a true breakthrough for predictive AI. It's the most underutilized – yet shockingly simple – way to multiply the value you get from your machine learning projects.Typically, when a business deploys a predictive AI model, it drives each decision with a raw model score. For example, the model might tell you there is a 30% chance a particular transaction is fraudulent. To make a decision, businesses usually compare that probability to a fixed threshold, like 50%, to decide whether to block the transaction.But here is the paradigm shift: Instead of driving decisions with the raw model score, your model deployment should drive each decision with the expected value.This very rare practice represents a straightforward "no-brainer," both technically and conceptually. Here’s how it works, using a concrete example of payment card fraud detection.The Flaw With Raw Risk ScoresImagine you are running fraud detection for a bank. Your predictive model outputs a risk score (a probability) for every single transaction.If you rely purely on the raw risk level to make each decision, you are treating a $100 transaction exactly the same as a $5,000 transaction. But a $5,000 fraudulent transaction costs your business significantly more than a $100 one. For larger transactions, the downside of undetected fraud is… larger. On the flip side, however, the...