Sift connector
Automate Fraud Detection and Risk Workflows with Sift Integrations
Connect Sift's real-time fraud intelligence to your entire tech stack and act on risk signals the moment they appear.
What can you do with the Sift connector?
Sift is a digital trust and safety platform that uses machine learning to detect fraud, abuse, and account takeovers in real time. Integrating Sift with your CRM, support tools, payment processors, and data warehouses lets your fraud and risk teams respond instantly without manual review bottlenecks. With tray.ai, you can build automated workflows that route risk signals, trigger investigations, and sync fraud data across every system your business depends on.
Automate & integrate Sift
Automating Sift business process or integrating Sift data is made easy with tray.ai
Use case
Real-Time Fraud Alert Routing
When Sift scores a transaction or user event above a defined risk threshold, automatically route that alert to your fraud operations team via Slack, PagerDuty, or email. Enriching the alert with customer history from your CRM before it arrives means analysts have full context without switching tabs.
Use case
Automated Account Suspension on High-Risk Scores
Trigger account suspension or step-up authentication workflows directly from Sift score changes. When a user's fraud score crosses a threshold, tray.ai can call your identity provider or internal API to lock the account, notify the user, and log the action in your case management system simultaneously.
Use case
Chargeback and Dispute Enrichment
When a chargeback or dispute is filed in your payment processor, automatically query Sift for the original transaction's risk score and decision history, then attach that data to the dispute record in your payment platform or internal ticketing system to speed up representment.
Use case
Customer Risk Profile Sync to CRM
Keep your CRM current with Sift risk scores by syncing profile updates on a scheduled or event-driven basis. Customer success and support agents can see a user's trust score in Salesforce or HubSpot without ever logging into Sift, which makes for smarter conversations.
Use case
Fraud Data Warehouse Ingestion
Continuously stream Sift decisions, score changes, and label events into Snowflake, BigQuery, or Redshift so your data and analytics teams can build fraud trend reports, model performance dashboards, and cohort analyses without relying on manual exports.
Use case
New User Onboarding Risk Screening
When a new user registers in your application, automatically send their signup event to Sift for scoring, then conditionally trigger email verification, phone verification, or a manual review queue based on the returned risk score before the account is fully activated.
Use case
Abuse Label Feedback Loop Automation
Close the Sift model feedback loop by automatically sending confirmed fraud or abuse labels back to Sift whenever your support team resolves a case in Zendesk or Jira. Sift's machine learning stays trained on your latest confirmed ground truth without anyone writing custom scripts.
Build Sift Agents
Give agents secure and governed access to Sift through Agent Builder and Agent Gateway for MCP.
Data Source
Retrieve Transaction Risk Scores
An agent can fetch real-time fraud risk scores for specific transactions, so it can decide whether to approve, flag, or escalate payment events based on Sift's machine learning assessments.
Data Source
Look Up User Fraud Profile
An agent can pull a user's fraud history, risk signals, and behavioral data from Sift to get context when evaluating suspicious account activity or onboarding new users.
Data Source
Fetch Decision History
An agent can retrieve past fraud decisions made on accounts or orders, so it understands what actions were already taken and doesn't make contradictory or redundant calls in automated workflows.
Data Source
Query Fraud Queue
An agent can access Sift's review queues to see which orders, accounts, or transactions are currently flagged for manual review, then prioritize and route cases accordingly.
Data Source
Pull Abuse Event Data
An agent can retrieve detailed abuse event records — account takeovers, promo abuse, content violations — to feed downstream alerting, reporting, or escalation workflows.
Agent Tool
Send User Event
An agent can send behavioral events to Sift — logins, profile updates, payment attempts — keeping Sift's risk models current with the latest user activity.
Agent Tool
Apply Fraud Decision
An agent can programmatically apply block, allow, or escalate decisions to accounts, orders, or sessions in Sift, running a fully automated fraud response without anyone touching it manually.
Agent Tool
Label User or Transaction
An agent can attach fraud or non-fraud labels to specific users or transactions in Sift, feeding confirmed outcomes back into the model so future risk scoring gets more accurate over time.
Agent Tool
Create or Update Order
An agent can submit or update order events in Sift with relevant transaction details, so Sift has full context when generating risk assessments at checkout.
Agent Tool
Trigger Verification Flow
An agent can kick off identity verification or step-up authentication through Sift for high-risk users, adding friction at the exact moment suspicious behavior shows up.
Agent Tool
Flag Account for Review
An agent can escalate a suspicious account into Sift's manual review queue, handing it off to a human analyst when automated scoring isn't enough to make a confident call.
Get started with our Sift connector today
If you would like to get started with the tray.ai Sift connector today then speak to one of our team.
Sift Challenges
What challenges are there when working with Sift and how will using Tray.ai help?
Challenge
Handling Sift Webhook Volume at Scale
High-traffic platforms can receive thousands of Sift score events per minute, and custom webhook handlers built in-house often struggle with reliability, backpressure, and retry logic when downstream systems are slow or temporarily unavailable.
How Tray.ai Can Help:
tray.ai's HTTP trigger infrastructure handles high-volume webhook ingestion with built-in retry, error handling, and queue management, so every Sift event gets processed reliably without you maintaining custom infrastructure.
Challenge
Connecting Sift to Non-API Tools and Legacy Systems
Many fraud operations teams rely on internal tools, spreadsheets, or legacy case management systems with no native Sift integration, so analysts end up copying risk data between systems by hand and missing signals along the way.
How Tray.ai Can Help:
tray.ai has connectors for hundreds of tools plus flexible HTTP and database connectors, so you can bridge Sift to virtually any system in your stack, including legacy platforms, without writing bespoke integration code.
Challenge
Maintaining the Sift Label Feedback Loop Without Engineering Support
Sift's models improve when confirmed fraud decisions are labeled back through the API, but this loop is frequently broken in practice because it requires engineering effort to instrument and maintain, leaving the fraud team unable to close the feedback cycle on their own.
How Tray.ai Can Help:
tray.ai lets fraud operations teams build and own the label submission workflow themselves using a visual builder, triggering from Zendesk, Jira, or any case management tool when a case is resolved, with no engineering involvement required.
Challenge
Keeping Risk Scores Visible Across Disconnected Teams
Risk scores generated in Sift are often siloed within the fraud team, while customer support, account management, and compliance teams that could use that context have no access to it. The result is inconsistent customer treatment and missed red flags.
How Tray.ai Can Help:
tray.ai syncs Sift risk data into CRMs, support platforms, and communication tools so every team that interacts with a customer sees the same current risk context without needing Sift access or training.
Challenge
Enforcing Consistent Risk Policy Across Multiple Decision Points
Businesses often need to apply Sift risk scores at multiple touchpoints — registration, login, payment, and promotion redemption — but without a centralized orchestration layer, policy logic gets implemented inconsistently and gaps appear.
How Tray.ai Can Help:
tray.ai acts as the orchestration layer that applies your risk policy consistently at every event type, calling Sift at each touchpoint, evaluating scores against your defined thresholds, and routing to the correct downstream action every time.
Talk to our team to learn how to connect Sift with your stack
Find the tray.ai connector with one of the 700+ other connectors in the tray.ai connector library to integrate your stack.
Integrate Sift With Your Stack
The Tray.ai connector library can help you integrate Sift with the rest of your stack. See what Tray.ai can help you integrate Sift with.
Start using our pre-built Sift templates today
Start from scratch or use one of our pre-built Sift templates to quickly solve your most common use cases.
Template
Sift High-Risk Score to Slack Alert with Salesforce Enrichment
Monitors Sift webhooks for score events exceeding a configurable threshold, pulls the associated customer record from Salesforce, and posts a formatted alert to a designated Slack fraud channel including LTV, account age, and recent order history.
Steps:
- Receive Sift score webhook via tray.ai trigger
- Evaluate risk score against configurable threshold using conditional logic
- Query Salesforce for matching customer record by email or user ID
- Compose enriched Slack message with Sift score, Salesforce LTV, and account age
- Post alert to Slack fraud-ops channel with direct links to Sift console and Salesforce record
Connectors Used: Sift, Salesforce, Slack
Template
Sift Decision to Zendesk Case Creation
Automatically creates a Zendesk ticket whenever Sift returns a block or manual review decision, populating the ticket with the Sift score, device fingerprint, and IP intelligence data so the fraud team can start investigating right away.
Steps:
- Listen for Sift decision webhook with block or review outcome
- Extract risk score, device fingerprint, IP data, and user ID from payload
- Create a new Zendesk ticket with structured fraud investigation template
- Assign ticket to fraud review queue and set priority based on score severity
Connectors Used: Sift, Zendesk
Template
Chargeback Filed to Sift Score Lookup and Stripe Dispute Update
When a dispute is created in Stripe, automatically retrieves the original transaction's Sift score and decision, then updates the Stripe dispute with metadata and logs the enriched dispute record in a Google Sheet for tracking.
Steps:
- Trigger on Stripe dispute.created webhook event
- Extract payment intent ID and customer email from Stripe event
- Query Sift transaction score history using the order ID or user ID
- Update Stripe dispute metadata with Sift score and decision timestamp
- Append enriched dispute record to Google Sheets dispute tracker
Connectors Used: Stripe, Sift, Google Sheets
Template
New User Registration Risk Screening with Conditional Verification
Sends every new user signup event to Sift for scoring and then conditionally routes the user through standard activation, email verification, SMS verification, or a manual hold queue based on the returned risk band.
Steps:
- Receive new user registration event from your application webhook
- Submit user creation event to Sift and retrieve risk score
- Evaluate score against low, medium, and high risk thresholds
- Send SMS verification via Twilio for medium-risk users or welcome email via SendGrid for low-risk
- Create Jira review ticket for high-risk users and suspend account activation
Connectors Used: Sift, Twilio, SendGrid, Jira
Template
Resolved Fraud Case Label Sync from Zendesk to Sift
Whenever a Zendesk ticket tagged as confirmed fraud or confirmed legitimate is resolved, automatically submits the corresponding label event back to Sift to improve model accuracy and keep ground truth in sync.
Steps:
- Trigger when a Zendesk ticket status changes to Solved with a fraud-outcome tag
- Extract user ID, order ID, and fraud verdict from Zendesk ticket fields
- Map Zendesk verdict to Sift label type (fraud or not fraud)
- Submit label event to Sift Labels API with correct entity type and timestamp
- Update Zendesk ticket with confirmation that Sift label was successfully submitted
Connectors Used: Zendesk, Sift
Template
Sift Score Changes to Snowflake for Fraud Analytics
Streams all Sift score and decision events in real time into a Snowflake staging table, transforming and normalizing the payload so your analytics team has a continuously updated dataset for fraud trend reporting and model monitoring.
Steps:
- Receive Sift score and decision webhooks via tray.ai HTTP trigger
- Parse and normalize Sift event payload into a flat schema
- Upsert record into Snowflake staging table keyed on user ID and event timestamp
- Trigger downstream dbt transformation job or stored procedure for reporting layer refresh
Connectors Used: Sift, Snowflake