Explainable rules vs black-box ML
A rules engine lets you write conditions you can show to operations: velocity, device, lists, amount. Black-box ML returns a score without a stable reason code. Most teams need scores and rules; they should not ship a model they cannot explain.
What auditors and customers ask
“Why was this payment reviewed?” needs a reason, an event id, and the data that fired. A 0.81 score with no features is hard to defend. Naiza returns ALLOW, REVIEW, or BLOCK with rule context so investigators and support share one story.
Where scoring still helps
Risk scoring and device scores are useful ranking signals. Naiza includes scoring next to the rule engine. The point of this comparison is not “never score.” It is “do not hide the decision in a model nobody can inspect.”
When Naiza is the better fit
Choose Naiza when you want API-first events, Saudi data residency, and decisions you can tune without a data-science queue. If you already run a mature in-house model, keep it and use Naiza rules as a policy layer and AML screening as the watchlist layer.
Frequently asked questions
Short answers written so search and answer engines can cite them.
Are fraud rules worse than machine learning?
Not for explainability. Rules win when you must tell a customer or regulator why an action was held. Models can rank risk but still need a policy layer.
Does Naiza use machine learning?
Naiza provides risk scoring and an adaptive rule engine. Product pages describe scoring and device intelligence. Do not infer an undocumented black-box model.
How should we roll out new rules?
Shadow or REVIEW-first, measure false positives, then BLOCK. That path is documented in Naiza tutorials on launch posture.