Built for regulated industries
Regulated industries need models whose predictions a human can actually follow, not a black-box prediction explained after the fact. The problem is that standard interpretable models, a basic logistic regression, a shallow decision tree, sacrifice real accuracy to get that clarity, and standard tools like scikit-learn use basic heuristics for feature selection that settle for a locally optimal model, leaving real accuracy on the table.
iQ ML solves this with the same engine behind iQ Xtreme: optimal, systematic feature selection instead of a locally optimal shortcut, closing the gap between interpretable and black-box accuracy.
Credit scoring and credit risk
Meet Basel III and IFRS 9 interpretability requirements without giving up accuracy.
Fraud detection
Flag suspicious transactions with a model your team can actually audit.
Healthcare decision support
Recommendations a clinician can follow and justify, not just trust.
Time series forecasting
Understand which variables actually drive the forecast, not just the number itself.
