Sift + Stripe
Stop Fraud Before It Costs You: Integrate Sift with Stripe on tray.ai
Automate real-time fraud scoring and payment decisioning by connecting Sift's machine learning risk signals directly to your Stripe payment workflows.
Why integrate Sift and Stripe?
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.
Automate & integrate Sift & Stripe
Use case
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.
Use case
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.
Use case
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.
Use case
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.
Use case
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.
Use case
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.
Use case
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.
Get started with Sift & Stripe integration today
Sift & Stripe Challenges
What challenges are there when working with Sift & Stripe and how will using Tray.ai help?
Challenge
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 Can Help:
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.
Challenge
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 Can Help:
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.
Challenge
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 Can Help:
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.
Challenge
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 Can Help:
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.
Challenge
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 Can Help:
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.
Start using our pre-built Sift & Stripe templates today
Start from scratch or use one of our pre-built Sift & Stripe templates to quickly solve your most common use cases.
Sift & Stripe Templates
Find pre-built Sift & Stripe solutions for common use cases
Template
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.
Steps:
- Trigger on Stripe PaymentIntent creation event via webhook
- Send transaction, user, and device data to Sift Score API for real-time fraud evaluation
- Evaluate Sift risk score against configured thresholds (e.g., block if score > 80)
- Cancel or confirm the Stripe PaymentIntent based on Sift risk decision
- Log the decision and notify the fraud team if a block is executed
Connectors Used: Sift, Stripe
Template
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.
Steps:
- Trigger on Stripe charge.dispute.created webhook event
- Retrieve corresponding Sift events and risk score for the disputed charge ID
- Format Sift behavioral evidence, device data, and risk scores into Stripe evidence fields
- Submit evidence package to Stripe Disputes API within the submission window
- Apply a fraud label to the transaction in Sift to improve future model accuracy
Connectors Used: Sift, Stripe
Template
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.
Steps:
- Trigger on Stripe webhook events: charge.succeeded, charge.failed, charge.refunded, charge.dispute.closed
- Map Stripe event type to the corresponding Sift label or event type
- Enrich Sift event payload with Stripe charge metadata, user ID, and outcome details
- POST the labeled transaction event to the Sift Events API
- Log sync status and flag any failed event submissions for retry
Connectors Used: Sift, Stripe
Template
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.
Steps:
- Trigger on Stripe customer.created webhook event
- Extract user ID, email, name, metadata, and creation timestamp from Stripe customer object
- Map Stripe customer fields to Sift $create_account event schema
- POST the $create_account event to the Sift Events API
- Log the created Sift profile reference and handle any mapping errors
Connectors Used: Sift, Stripe
Template
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.
Steps:
- Trigger on Sift Decision webhook for ACCOUNT_TAKEOVER category
- Retrieve the associated Stripe customer object using the matched user ID
- Detach or disable all active payment methods on the Stripe customer record
- Cancel any open or pending Stripe PaymentIntents for the customer
- Create a fraud review task in your case management or ticketing system and notify the fraud team
Connectors Used: Sift, Stripe
Template
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.
Steps:
- Trigger on Stripe payout.created or transfer.created event for connected accounts
- Send recipient account details, payout amount, and behavioral context to Sift for risk scoring
- Evaluate returned Sift risk score against payout risk threshold
- If high-risk, place the Stripe payout on hold and create a manual review task
- If low-risk, confirm payout release and log the Sift score alongside the Stripe payout record
Connectors Used: Sift, Stripe