Hawk AML vs Tazama

Hawk AML is an AI- and rules-based platform for financial institutions seeking to detect suspicious activity and reduce unnecessary alerts. Its AML tools combine customer due diligence, watchlist screening, and transaction monitoring. Monitoring covers money-laundering typologies across client, product, and transaction types, and supports ACH, BACS, card, check, P2P, SEPA, SWIFT, and wire channels. Alerts include an auditable rationale to support investigations. Case management spans AML, fraud, and screening, with alert queues, assignment, investigative workflows, and audit trails. Teams can configure rules and test them in a sandbox; APIs support real-time transaction checks and customer AML risk scoring. Hawk lists SaaS, VPC, and on-premises deployment options and says the platform can connect with existing core banking and data management systems. It describes its solutions as intended for financial institutions and regulated entities, including banks, payment firms, and fintechs. Hawk says it complies with data protection regulations including EU GDPR and CCPA, and uses organization-wide technical controls and processes to protect information. Pricing is on request; prospective customers are directed to request a demo.

Tazama is an open-source platform for monitoring transactions in real time for fraud and money-laundering typologies. It evaluates current transaction information together with historical participant behavior, applies configurable rules and thresholds, and can block high-risk payments or send alerts for investigation. Rule Studio gives analysts visual rule building, transaction replay, and review before deployment. Connection Studio and DEMS map and ingest message formats, including ISO 20022 payment messages. Investigators manage alerts through triage, assignment, investigation, filing, and closure in a case system that records decisions. BIAR combines detection and case results in a lakehouse with dashboards and calibration notebooks. Studio changes, case actions, triage decisions, and workflow transitions are audited. Docker Compose supports evaluation on one machine; production guides cover on-premises Kubernetes, EKS, GKE, and AKS. The software has no license charge, but hosting, implementation, and support require resources.