# Connect Sift to Stripe > Automate real-time fraud scoring and payment decisioning by connecting Sift's machine learning risk signals directly to your Stripe payment workflows. **Canonical page:** https://tray.ai/connectors/sift-stripe-integrations/ **Sift connector:** https://tray.ai/connectors/sift-integrations/ **Sift documentation:** https://tray.ai/documentation/connectors/service/sift **Stripe connector:** https://tray.ai/connectors/stripe-integrations/ **Stripe documentation:** https://tray.ai/documentation/connectors/service/stripe ## Overview Sift and Stripe are a natural pairing for any business that processes online payments and takes fraud seriously. Stripe handles the mechanics of payment processing, while Sift continuously evaluates the risk behind every transaction, user action, and account event. Together, they create a closed-loop system where fraud intelligence drives payment decisions in real time, without slowing down legitimate customers. Manually reconciling fraud signals from Sift with payment actions in Stripe is slow, error-prone, and doesn't scale. By the time a fraud analyst reviews a Sift risk score and manually blocks a Stripe charge or flags an account, the damage is often already done. Integrating Sift with Stripe through tray.ai lets your fraud and payments stack operate as a unified, automated system. Risk scores from Sift can instantly trigger Stripe actions — blocking charges, issuing refunds, placing accounts under review, or escalating to your fraud team — all without human intervention. The result is fewer chargebacks, faster response to emerging fraud patterns, smoother checkout for low-risk customers, and a much more efficient fraud operations team. ## Use cases ### Real-Time Transaction Risk Scoring Before Charge Authorization Before Stripe processes a charge, send the transaction and user context to Sift for a real-time fraud score. Based on the returned risk threshold, automatically allow, flag, or block the payment in Stripe. High-risk transactions never complete, and legitimate customers don't hit any added friction. - Prevent fraudulent charges from completing before funds are captured - Reduce false positives by using Sift's ML risk model rather than static rules - Eliminate manual review bottlenecks for the vast majority of low-risk transactions ### Automated Chargeback and Dispute Evidence Submission When Stripe generates a chargeback dispute event, automatically pull the corresponding Sift risk score, device fingerprint, behavioral signals, and transaction history, then bundle them as evidence for submission through Stripe's Dispute API. Your team gets a rich, pre-assembled evidence package without manual data gathering. - Win more disputes by submitting richer, ML-backed behavioral evidence - Reduce the manual effort required to compile dispute responses - Respond to disputes faster and within Stripe's evidence submission windows ### High-Risk User Account Suspension and Payment Block When Sift raises a user's risk score above a defined threshold — say, after detecting account takeover signals or suspicious login patterns — automatically suspend the user's ability to initiate new Stripe charges or payouts. The user account gets flagged in both systems simultaneously, so enforcement stays consistent across your fraud and payments stack. - Prevent fraudulent users from making further charges the moment risk is detected - Keep Stripe account status synchronized with Sift risk decisions - Reduce the window of exposure for account takeover fraud ### Automated Refund Issuance on Confirmed Fraud When Sift confirms a transaction as fraudulent through a label or a Sift decision webhook, automatically trigger a full or partial refund via the Stripe Refunds API for the corresponding charge. This closes the loop on fraud response without a manual refund workflow and helps preserve customer trust when fraud does occur. - Resolve confirmed fraud cases faster and with less manual intervention - Reduce chargeback rates by proactively refunding before disputes are filed - Maintain customer trust with rapid, automated fraud remediation ### Stripe Payment Event Feedback Loop into Sift Send Stripe payment outcomes — successful charges, failures, refunds, and disputes — back to Sift as transaction events to continuously improve Sift's ML models with ground truth data. This feedback loop keeps Sift's fraud signals calibrated to your actual payment patterns and emerging fraud vectors on your platform. - Improve Sift model accuracy with real-time Stripe outcome signals - Ensure Sift's fraud scoring reflects your platform's actual transaction mix - Reduce both false positives and false negatives over time through continuous feedback ### New Stripe Customer Risk Assessment at Registration When a new customer is created in Stripe, automatically send a $create_account event to Sift enriched with available user profile data. Sift starts building a risk profile immediately, so when the user places their first order, a meaningful fraud score is already available rather than being generated from a cold-start context. - Eliminate cold-start fraud scoring gaps for new Stripe customers - Build Sift risk profiles proactively rather than reactively - Improve first-purchase fraud detection accuracy significantly ### Payout Fraud Detection and Stripe Payout Blocking For marketplace or platform businesses using Stripe Connect, pass payout requests through Sift before they're executed. If Sift detects anomalous payout behavior — unusually large amounts, new bank accounts, suspicious account activity — automatically delay or block the Stripe payout and route it for manual review. - Protect your platform from fraudulent seller or contractor payout abuse - Add a risk-aware gate to Stripe Connect payouts without changing your existing payout logic - Reduce financial exposure from payout fraud on marketplace platforms ## Templates ### Block Stripe Charge on High Sift Risk Score This template listens for incoming payment intent or charge creation events and routes the transaction details to Sift for real-time scoring. If Sift returns a risk score above a configurable threshold, the template automatically cancels the Stripe PaymentIntent or declines the charge before funds are captured. Connectors used: Sift, Stripe ### Sync Stripe Dispute Events to Sift and Compile Evidence Package When Stripe fires a charge.dispute.created event, this template retrieves the full Sift event history for the disputed transaction, assembles behavioral signals and risk scores, and submits them as evidence to the Stripe Dispute API while also labeling the transaction as fraudulent in Sift. Connectors used: Sift, Stripe ### Send Stripe Payment Outcomes Back to Sift as Transaction Labels This feedback loop template monitors Stripe for final payment outcomes — successful charges, failed payments, refund events, and dispute resolutions — and sends them back to Sift as transaction labels and events, keeping Sift's ML model continuously updated with ground truth outcomes. Connectors used: Sift, Stripe ### Create Sift User Profile on New Stripe Customer Registration Every time a new customer object is created in Stripe, this template automatically fires a $create_account event to Sift with all available profile attributes, so Sift starts risk profiling from day one and is ready to score transactions from the customer's very first purchase. Connectors used: Sift, Stripe ### Suspend Stripe Customer on Sift Account Takeover Decision When Sift issues an ACCOUNT_TAKEOVER decision for a user, this template immediately retrieves the associated Stripe customer record, blocks all active payment methods, cancels pending PaymentIntents, and routes the account to a fraud review queue for human investigation. Connectors used: Sift, Stripe ### Payout Risk Gate for Stripe Connect Platforms Before executing a Stripe Connect payout to a connected account, this template evaluates the payout request against Sift risk signals for the recipient account. Payouts flagged as high-risk are held automatically and routed to a manual review queue, while low-risk payouts go out immediately. Connectors used: Sift, Stripe ## Challenges Tray.ai solves ### Latency-Sensitive Real-Time Fraud Decisioning Fraud scoring has to happen fast enough to influence a payment decision before the customer completes checkout. Adding an extra API call to Sift in the payment flow risks introducing latency that hurts checkout conversion if it's not handled asynchronously or with tight SLA controls. **How Tray.ai helps:** tray.ai's low-latency workflow execution and asynchronous processing let you design fraud scoring flows that operate within the payment authorization window. You can configure timeout fallback logic so that if Sift doesn't respond within a defined threshold, the workflow defaults to a configured behavior — such as allowing the payment and flagging for post-authorization review — without breaking the checkout experience. ### Mapping User and Transaction IDs Across Sift and Stripe Sift uses its own user ID and session ID schema, while Stripe organizes data around customer IDs, charge IDs, and PaymentIntent IDs. Keeping these identifiers reliably mapped across both systems matters for accurate fraud scoring, evidence submission, and account-level decisions — and it's genuinely complex to maintain manually. **How Tray.ai helps:** tray.ai's data mapping and transformation tools let you define and maintain the ID mapping logic between Sift and Stripe in a single, centralized workflow. Using tray.ai's built-in data store or lookups, you can persist and retrieve the relationship between Stripe customer IDs and Sift user IDs across workflow executions without building custom middleware. ### Handling Sift and Stripe Webhook Volume and Reliability Both Sift and Stripe generate high volumes of webhook events, and a reliable integration depends on processing them in order, without duplication, and with proper retry handling for failed deliveries. A missed webhook — a Sift decision that never triggers a Stripe block, for instance — can mean a fraudulent transaction slips through. **How Tray.ai helps:** tray.ai provides reliable webhook ingestion with built-in retry logic, deduplication support, and error handling at every step of the workflow. You can configure dead-letter queues for failed executions and set up alerting so your team is notified immediately when a fraud-decisioning workflow fails to complete. ### Keeping Fraud Logic Consistent Across Sift Score and Stripe Actions Fraud thresholds, block rules, and review criteria often live in different places — some in Sift's console, some in custom code, some embedded in Stripe Radar rules. Without a unified integration layer, these rules can drift apart, creating gaps where a transaction is scored as high-risk in Sift but never acted on in Stripe. **How Tray.ai helps:** tray.ai lets you centralize your fraud decisioning logic in a single workflow layer that sits between Sift and Stripe. Thresholds, routing rules, and escalation logic are defined once in the tray.ai workflow and applied consistently across all transaction types, eliminating rule drift between your fraud scoring and payment systems. ### Enriching Stripe Dispute Evidence with Sift Behavioral Data Stripe's dispute evidence fields accept a defined set of data types, while Sift's behavioral signals — session data, device fingerprints, event sequences — need to be translated and formatted to fit Stripe's evidence schema. Doing this manually for every dispute is slow and inconsistent. **How Tray.ai helps:** tray.ai's data transformation tools let you build a reusable mapping between Sift's event and score response schema and Stripe's dispute evidence fields. Once built, that transformation runs automatically for every dispute event, so your evidence submissions are consistently formatted, complete, and submitted on time. ## Learn more - Intelligent Integration: https://tray.ai/platform/intelligent-ipaas/ - Merlin Agent Builder: https://tray.ai/platform/merlin-agent-builder/ - Agent Gateway for MCP: https://tray.ai/platform/agent-gateway/ - Book a demo: https://tray.ai/contact/