Unit21 Fraud and AML Platform API
Unit21 fraud and AML detection platform REST API for fintechs, neobanks, crypto exchanges, and financial institutions to build configurable transaction monitoring, fraud detection, and AML compliance workflows without engineering bottlenecks. Enables AI agents to manage transaction and event data ingestion for real-time fraud detection automation, handle rule engine configuration and threshold management for fraud alert tuning automation, access alert triage and review workflow for fraud case management automation, retrieve entity profile and behavior analytics for customer risk scoring automation, manage SAR and CTR filing workflow for BSA regulatory reporting automation, handle case management and investigation documentation for AML compliance automation, access consortium data and shared fraud signals for cross-institution fraud intelligence automation, retrieve ML model score integration for AI-enhanced fraud detection automation, manage blacklist and allowlist management for payment control automation, and integrate Unit21 with core banking, payment processors, and KYC platforms for end-to-end fraud and AML compliance operations.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
Fraud and AML. SOC2, ISO27001. API key. US. Transaction, fraud alert, and AML case data.
⚡ Reliability
Best When
A fintech, neobank, crypto exchange, or financial institution wanting AI agents to automate transaction monitoring rules, fraud alert triage, SAR filing workflow, and AML case management without deep engineering investment using Unit21's no-code fraud and AML platform.
Avoid When
SAR FILING OBLIGATION TIMELINESS: Automated SAR filing workflow via Unit21 must comply with FinCEN 30-day SAR filing deadline (60 days with extension); automated case management must monitor open case age and trigger escalation before SAR deadline; automated SAR workflow without deadline tracking creates potential BSA late filing violation. BSA INDEPENDENT TESTING REQUIREMENT: FinCEN BSA compliance requires independent testing of AML program effectiveness; automated transaction monitoring rules must be tested by independent compliance function; automated rule changes without independent testing creates BSA AML program deficiency. RULE CHANGE MANAGEMENT FOR PRODUCTION MONITORING: Unit21 no-code rule configuration allows rapid rule changes; automated rule deployment without change management and backtesting creates production monitoring gaps; implement rule change approval workflow with backtest validation before automated production rule deployment.
Use Cases
- • Detecting payment fraud from transaction monitoring automation agents
- • Filing SARs from BSA compliance automation agents
- • Triaging alerts from fraud case management agents
- • Scoring customer risk from AML analytics agents
Not For
- • Sanctions and PEP screening (use ComplyAdvantage or LexisNexis Risk)
- • Document verification and biometric KYC (use Jumio or Onfido)
- • Credit fraud and application fraud (use dedicated credit bureau models)
Interface
Authentication
Unit21 uses API key authentication. REST API with JSON. San Francisco, California HQ. Founded 2018 by Trisha Kothari and Clarence Chio. Private (~$100M raised, Tiger Global, A.Capital, Gradient Ventures). No-code fraud and AML platform for fintech companies. Rule engine for transaction monitoring without engineering dependency. SAR and CTR filing support. Consortium data sharing for cross-institution fraud signals. ML model integration. Competes with Sardine, SEON, Feedzai, and Featurespace for fintech fraud and AML detection.
Pricing
San Francisco CA. Private (~$100M raised). Annual subscription. Transaction-volume pricing. No free tier.
Agent Metadata
Known Gotchas
- ⚠ TRANSACTION DATA SCHEMA NORMALIZATION BEFORE INGESTION: Unit21 transaction data ingestion requires normalized data schema; automated transaction data from heterogeneous payment systems must be mapped to Unit21 event schema before ingestion; schema mismatch between payment system data and Unit21 expected fields creates dropped fields and degraded fraud detection model performance
- ⚠ ALERT DISPOSITION FEEDBACK LOOP FOR MODEL LEARNING: Unit21 fraud models improve from alert disposition feedback; automated alert auto-closure without human review removes disposition signal from model training; implement human review sampling for automated alert disposition to maintain model feedback signal quality
- ⚠ CONSORTIUM DATA OPT-IN TIMING: Unit21 consortium data sharing requires explicit opt-in; automated fraud detection using consortium signals requires consortium enrollment; automated fraud detection without consortium enrollment misses cross-institution fraud signal; enroll in consortium data sharing during implementation before expecting consortium-enhanced fraud scores
- ⚠ CASE MANAGEMENT EVIDENCE ATTACHMENT FOR SAR DOCUMENTATION: SAR filings require supporting documentation; automated SAR filing via Unit21 must attach transaction evidence and investigation notes to case before filing; automated SAR filing without supporting documentation creates SAR filing that may be rejected by FinCEN for insufficient documentation
- ⚠ API KEY FULL ACCOUNT ACCESS: Unit21 API key provides broad account access without scope granularity; automated agents with API key can access all transaction data, cases, and SAR filing information; implement application-level access control for different automated workflow roles to limit scope of automated agent access
Alternatives
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Scores are editorial opinions as of 2026-03-07.