hawk AI AML and Fraud Detection REST API
hawk AI anti-money laundering and fraud detection REST API for banks, payment providers, and fintech companies to monitor transactions, detect suspicious patterns, screen against sanctions lists, and manage AML investigations — combining AI-powered transaction monitoring with explainable alerts, case management, and regulatory reporting — enabling automated AML compliance, fraud prevention, and financial crime investigation through hawk AI's modern cloud-native AML and fraud platform. Enables AI agents to manage transaction monitoring for real-time AML suspicious activity detection automation, handle fraud detection for payment and account fraud pattern automation, access sanctions screening for global watchlist and OFAC check automation, retrieve alert management for AML alert triage and prioritization automation, manage case management for financial crime investigation workflow automation, handle SAR preparation for suspicious activity report compilation automation, access network analysis for connected entity and account relationship automation, retrieve explainability for alert rationale and model explanation automation, manage rule configuration for custom AML detection rule management automation, and integrate hawk AI with core banking, payment systems, and regulatory reporting for AML compliance automation.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
AML/fraud. GDPR, SOC2, AMLD6. OAuth2. EU/US. Transaction monitoring and financial crime investigation data.
⚡ Reliability
Best When
A bank, payment provider, or fintech company wanting AI agents to automate AML transaction monitoring, sanctions screening, and fraud detection with explainable AI alerts and streamlined case management through hawk AI's modern cloud-native compliance platform.
Avoid When
FINANCIAL INSTITUTION RELATIONSHIP REQUIRED: hawk AI serves licensed financial institutions; automated open-access assumption creates institutional_required; automated must be regulated financial institution or work with licensed partner. MODEL CALIBRATION REQUIRED: hawk AI models need calibration to institution's transaction patterns; automated out-of-box accuracy assumption creates high_false_positive_rate without calibration; automated must plan for model calibration and tuning period. REGULATORY EXPLAINABILITY IS REQUIRED: AML systems require explainable alerts for regulatory examination; automated black-box assumption creates examination_risk for unexplainable alert decisions; automated should use hawk AI's explainability features for all investigation decisions. CASE REVIEW IS HUMAN-REQUIRED: Regulatory guidance requires human analyst review of AML cases; automated auto-disposition assumption creates regulatory_non-compliance for automatically closed cases; automated must route cases to compliance analysts for review.
Use Cases
- • Monitoring transactions for AML suspicious activity with explainable AI alerts for compliance automation agents
- • Screening payments against global sanctions lists for real-time compliance automation agents
- • Managing AML investigation cases and preparing regulatory SAR filings for compliance operations agents
- • Detecting payment fraud patterns with AI-driven behavioral analytics for fraud prevention automation agents
Not For
- • KYC document verification (hawk AI is transaction monitoring, not identity document verification)
- • Consumer credit risk (hawk AI is financial crime/AML, not consumer credit scoring)
- • Insurance fraud (hawk AI specializes in banking/payments financial crime, not P&C insurance)
Interface
Authentication
hawk AI uses OAuth2 for AML and fraud REST API. REST API with JSON. Munich, Germany HQ. Founded 2018 by Wolfgang Berner and Tobias Schweiger. Products: Transaction Monitoring, Fraud Detection, Sanctions Screening, Case Management, SAR Filing. Raised $17M Series A. Backed by Sands Capital, Picus Capital. Used by N26, Solaris, and other European fintechs. Competes with NICE Actimize, ComplyAdvantage, and Unit21 for AML/fraud platform.
Pricing
Munich DE. Sands Capital backed. Annual subscription. N26, Solaris clients. European fintech AML specialist.
Agent Metadata
Known Gotchas
- ⚠ TRANSACTION DATA SCHEMA MUST BE COMPLETE: hawk AI requires complete transaction data for accurate monitoring; automated partial-data assumption creates degraded_detection for transactions missing counterparty, amount, or currency data; automated must ensure complete transaction data submission
- ⚠ ALERT FEEDBACK IMPROVES MODELS: hawk AI learns from analyst alert feedback; automated no-feedback assumption creates static_model_performance for deployments without systematic feedback submission; automated must implement feedback pipeline for alert dispositions
- ⚠ WATCHLIST DATA IS NOT REAL-TIME: Sanctions list updates are scheduled, not real-time; automated live-watchlist assumption creates missed_hit for newly sanctioned entities not yet in watchlist update; automated must understand watchlist update schedule
- ⚠ EXPLAINABILITY IS PER-ALERT: hawk AI provides explanations per alert; automated global-explanation assumption creates missing_rationale for individual alerts requiring specific explanation; automated must request explanation for each alert individually
- ⚠ CASE STATES FOLLOW WORKFLOW: Cases follow defined state machine (open, in review, closed, filed); automated arbitrary-state assumption creates invalid_transition for case state changes not following workflow; automated must follow hawk AI case lifecycle state machine
Alternatives
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Scores are editorial opinions as of 2026-03-07.