Fraud and AML comparisons

Compare integration approaches, operational workflows, and the Naiza capabilities that support each architecture.

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 vs Batch AML 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 vs Ongoing AML Monitoring

Compare one-time onboarding watchlist checks with continuous re-screening when lists update. Most programs need both.

Allowlist vs Denylist Explained

Compare allowlists and blocklists for fraud prevention. Naiza combines trusted exceptions, confirmed abusive identifiers, risk scores, and REVIEW decisions.

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.