Sardine AI Fraud and Compliance REST API
Sardine AI fraud detection and compliance REST API for neobanks, crypto exchanges, BNPL providers, and fintech companies to prevent account fraud, payment fraud, and money laundering using device intelligence, behavioral biometrics, and AI-powered risk signals — enabling automated user risk scoring, transaction fraud prevention, AML suspicious activity monitoring, and crypto compliance through Sardine's fintech-specialized fraud and compliance platform. Enables AI agents to manage device intelligence for device fingerprint and behavioral biometric automation, handle user risk scoring for real-time user fraud risk assessment automation, access transaction screening for payment fraud and AML screening automation, retrieve crypto compliance for blockchain transaction risk screening automation, manage case management for fraud and AML investigation automation, handle ACH fraud for bank account takeover and ACH debit fraud automation, access chargeback intelligence for dispute prediction and prevention automation, retrieve network intelligence for connected account and device network graph automation, manage rules engine for custom fraud rule configuration automation, and integrate Sardine with neobanks, crypto platforms, and payment processors for real-time fraud prevention automation.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
Fraud prevention. SOC2, GDPR. API key. US/EU. Behavioral biometric and transaction fraud data.
⚡ Reliability
Best When
A neobank, crypto exchange, BNPL provider, or fintech company wanting AI agents to prevent fraud using device intelligence and behavioral biometrics, comply with crypto AML requirements, and manage fraud cases through Sardine's fintech-native fraud and compliance platform.
Avoid When
SDK REQUIRED FOR BEHAVIORAL DATA: Sardine behavioral biometrics require JS/mobile SDK for data collection; automated server-side assumption creates missing_behavioral_signal without client SDK; automated must deploy Sardine client SDK for behavioral intelligence. CALIBRATION TAKES TIME: Sardine models need data accumulation to calibrate effectively; automated instant-accuracy assumption creates higher_false_positives during initial deployment period; automated must plan for model calibration period. CRYPTO COMPLIANCE REQUIRES TRAVEL RULE INTEGRATION: Crypto AML requires Travel Rule compliance for transactions over threshold; automated compliance-included assumption creates regulatory_gap without Travel Rule data exchange; automated must integrate Travel Rule solution. RULE TUNING IS ONGOING: Fraud rules need continuous tuning as attack patterns evolve; automated set-and-forget assumption creates outdated_rules for evolving fraud patterns; automated must implement ongoing rule review and tuning process.
Use Cases
- • Preventing account takeover and payment fraud for neobank and fintech fraud automation agents
- • Screening crypto transactions for AML and sanctions compliance for crypto exchange automation agents
- • Detecting ACH and bank account fraud using device and behavioral signals for payment fraud automation agents
- • Monitoring user behavioral biometrics for continuous authentication fraud prevention agents
Not For
- • Enterprise bank AML (Sardine is fintech/neobank-focused, NICE Actimize/Featurespace better for large banks)
- • Physical identity document verification (Sardine is digital behavioral/device, not ID document OCR)
- • Traditional insurance fraud (Sardine specializes in fintech/payments/crypto, not P&C insurance)
Interface
Authentication
Sardine uses API key for fraud and compliance REST API. REST API with JSON. San Francisco, CA HQ. Founded 2020 by Soups Ranjan and Aditya Goel (ex-Coinbase fraud team). SDKs: JavaScript, iOS, Android, Python. Raised $75M Series B. Backed by a16z, Andreessen Horowitz Growth, Experian Ventures, Google Ventures. Specializes in fintech, neobank, crypto, BNPL fraud. Competes with Socure, SEON, and Unit21 for fintech fraud prevention.
Pricing
San Francisco CA. a16z backed. Per-call pricing. Fintech/crypto fraud specialist. $75M Series B.
Agent Metadata
Known Gotchas
- ⚠ SESSION MUST BE INITIATED BEFORE EVENTS: Sardine uses session-based event model; automated event-without-session assumption creates orphaned_events for events sent without active session; automated must initiate session before sending behavioral events
- ⚠ DEVICE FINGERPRINT IS SESSION-LINKED: Sardine device intelligence is tied to sessions initiated via SDK; automated server-side fingerprint assumption creates incomplete_device_signal without SDK session; automated must deploy SDK for device intelligence
- ⚠ CUSTOMER ID IS PERSISTENT IDENTIFIER: Sardine tracks fraud history by customer ID; automated random-ID assumption creates no_history for customers assigned different IDs across sessions; automated must use stable, consistent customer identifier
- ⚠ RISK SCORES ARE ASYNCHRONOUS FOR ENRICHED ANALYSIS: Real-time scores are available immediately, enriched scores take longer; automated single-score assumption creates latency_confusion for pipeline expecting both score types simultaneously; automated must understand real-time vs enriched score timing
- ⚠ CRYPTO SCREENING REQUIRES BLOCKCHAIN DATA: Crypto transaction screening needs wallet address and transaction hash; automated account-only assumption creates incomplete_screening for crypto flows without on-chain transaction data; automated must pass blockchain transaction metadata
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Scores are editorial opinions as of 2026-03-07.