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.
| Dimension | Rules | Black-box ML | Where Naiza fits |
|---|---|---|---|
| When to use | You must tell ops why a hold happened | Ranking risk after a policy layer exists | ALLOW / REVIEW / BLOCK with rule context |
| Failure mode | Brittle rules with no velocity or device | A score nobody can defend to a customer | Risk scores prioritize review; rule context explains the decision |
| Evaluation | Configured conditions | Model inference | Configurable rules evaluated on product events |
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
Naiza combines risk scoring with named rules. Scores help prioritize the review queue; rule context explains the action so investigators can review and tune the control.
When Naiza is the better fit
Choose Naiza for configurable fraud rules, device context, AML screening, and Saudi data residency. Connect risk scores and rules to event decisions and investigator workflows through its APIs and dashboard.
Frequently asked questions
Answers about screening, risk signals, and Naiza integration.
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.
How does Naiza combine risk scoring and rules?
Naiza evaluates events with an adaptive rule engine and risk scoring. Use named rules for ALLOW, REVIEW, or BLOCK decisions and scores to prioritize investigation, with device and customer context supporting the review.
How should we roll out new rules?
Shadow or REVIEW-first, measure false positives, then BLOCK. That path is documented in the Naiza best-practices guide.