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Sift: Digital Fraud Prevention and Risk-Based Authentication

Sift helps fraud and compliance teams identify and prevent digital threats. It is designed for businesses managing account takeovers and payment fraud using a global data network.

At a glance

Category
Security
Best for
Ecommerce stores, Financial services providers, Digital commerce platforms, Fintech companies, Enterprise-level digital businesses
Pricing
Pricing was not clearly available from the provided evidence. Buyers should confirm current pricing on the vendor website.
Key use cases
Preventing Account Takeover (ATO), Reducing Payment Fraud, Managing Chargeback Disputes, Combating Content Scams, Enhancing Internal Risk Models
Official website
sift.com
Screenshot of Sift website

Sift is a cybersecurity platform designed to detect and prevent digital fraud across the customer journey. It uses a global data network and machine learning to provide risk scores in real time, which helps businesses decide whether to allow or block specific transactions and account activities.

The software is intended for ecommerce stores, financial services, and enterprise companies. It supports teams such as IT operations, product managers, and cybersecurity professionals who work to reduce chargebacks and protect user accounts.

Beyond full platform implementation, Sift offers a specific API for companies that have already built internal risk models but need external data context to support their detection accuracy.

Buyers should confirm if the platform's automation and rules-based decisioning align with their existing manual review workflows and compliance requirements, such as PSD2 or PSD3.

Key Features

Digital Fraud Detection

Identifies fraudulent patterns and threats across digital interactions using AI-powered models.

Risk-Based Authentication

Evaluates the risk level of a user session to determine the appropriate authentication challenge.

Real-Time Decisioning

Provides risk scores and actions to allow, block, or review transactions.

Account Takeover Protection

Designed to detect and prevent unauthorized access to user accounts.

Payment and Chargeback Protection

Identifies fraudulent payments and supports the dispute process to help reduce chargeback losses.

Sift Score API

Adds external risk scores and signals from a global consortium to internally-built risk models.

Case Management and Reporting

Tools for tracking fraud cases and generating reports on prevention efforts.

Use Cases

Preventing Account Takeover (ATO)

Identifying attacks where fraudsters attempt to gain control of legitimate user accounts.

Reducing Payment Fraud

Detecting fraudulent transactions at the point of sale to help protect revenue.

Managing Chargeback Disputes

Prioritizing high-value chargebacks and building responses to help mitigate losses.

Combating Content Scams

Identifying and blocking spam and phishing schemes within a digital platform.

Enhancing Internal Risk Models

Using the Sift Score API to add global network context to a company's own custom fraud engine.

Best For

Ecommerce storesFinancial services providersDigital commerce platformsFintech companiesEnterprise-level digital businesses

Pricing

Pricing was not clearly available from the provided evidence. Buyers should confirm current pricing on the vendor website.

FAQ

What does Sift do?

Sift is a platform that uses AI and a global data network to detect and prevent digital fraud, including payment fraud, account takeovers, and content scams.

Who is Sift designed for?

It is designed for digital businesses, particularly those in ecommerce, financial services, and enterprise sectors, as well as fraud and compliance teams.

Can Sift work with an existing internal fraud model?

Yes, Sift offers a Score API designed to add external risk context and signals to internally-built risk models.

How does Sift handle chargebacks?

Sift provides Dispute Management tools to help teams prioritize high-value chargebacks and build responses to reduce losses.

Source category: Security

Source subcategory: Cybersecurity

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