Account takeover
Evaluate login risk using customer, device, IP, session, and velocity context.
Fraud detection software for Saudi fintech
Naiza evaluates product events and returns ALLOW, REVIEW, or BLOCK outcomes for account takeover, payment fraud, card testing, abusive registrations, suspicious withdrawals, and other digital risk workflows.
Your server sends a business event with the identifiers and context available at decision time. Naiza evaluates configured rules and risk signals, returns a decision, and preserves the event trail for operations, feedback, and audit.
Submit a login, payment, registration, withdrawal, or custom event.
Apply rules, device intelligence, enrichment, and customer context.
Allow, review, or block, then send confirmed outcomes back as feedback.
Evaluate login risk using customer, device, IP, session, and velocity context.
Score transactions before fulfillment and route uncertain activity to review.
Detect repeated low-value attempts and coordinated behavior across accounts or devices.
Identify automation, disposable identities, and multi-account patterns during signup.
Apply policy and behavioral checks before funds leave the platform.
Connect repeated identifiers and device behavior across related events and customers.
Explainable, reviewable decisions
Naiza returns context that helps product and risk teams understand each result. Keep the event identifier, send investigation outcomes as feedback, and review rule impact before moving from observation to enforcement.
Practical answers for product, engineering, fraud, and risk teams evaluating a real-time fraud prevention API.
Yes. Naiza is hosted in the Kingdom of Saudi Arabia. Event data used for fraud decisions is stored and processed in KSA. See the trust page for residency language and the limits of that claim.
A fraud detection API evaluates an event such as a login, registration, payment, or withdrawal and returns a risk decision that the product can enforce or send to manual review.
Naiza supports ALLOW, REVIEW, and BLOCK outcomes. The response can also include risk context and triggered rules so product and operations teams can understand why a decision was made.
Common use cases include account takeover, payment fraud, card testing, abusive registrations, suspicious withdrawals, multi-account behavior, and coordinated fraud patterns.
Yes. Teams can begin with observation or review-oriented workflows, measure rule outcomes, and move selected rules to enforcement after validating false-positive and operational impact.