Fraud and AML comparisons
Use these pages when you are choosing an architecture. Each comparison stays honest about what Naiza owns and what still belongs to your policy team.
In-house AML screening vs an AML API
Compare building watchlist ingestion in-house with using an AML screening API for onboarding, payments, and review workflows.
Real-time AML screening vs batch screening
Compare synchronous AML screening at onboarding and payments with overnight batch re-screening of the customer book.
Device intelligence vs IP-only fraud checks
Compare relying on IP reputation alone with device fingerprinting plus IP, velocity, and lists for account takeover and multi-accounting.
Rules engine vs black-box ML for fraud
Compare explainable fraud rules plus risk scoring with opaque machine-learning scores that operations cannot defend to a customer or auditor.
Onboarding AML screening vs ongoing monitoring
Compare one-time onboarding watchlist checks with continuous re-screening when lists update. Most programs need both.
Allowlist vs denylist in fraud prevention
A denylist blocks known-bad identifiers. An allowlist exempts known-good ones. Use both with REVIEW in the middle, not as your only controls.
Webhooks vs polling for fraud and AML decisions
Webhooks push REVIEW outcomes and screening updates to you. Polling asks the API repeatedly. Use webhooks for operations, with idempotent retries.