Content Abuse Prevention
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Prevent bad actors from posting malicious or low-quality content that can permanently damage your business’ reputation and drive good users off your site.
Save time and money by automating how you stop scammers and spammers from creating abusive content – before it goes live.
Confidently remove roadblocks to let the high-quality posts flow. Plus, the community will thrive once users know they’re guaranteed a trustworthy and abuse-free experience.
A content abuse-specific machine learning model specialized in detecting users creating abusive or malicious user-generated content across desktop and mobile - both browser and native apps.
The world’s largest network of online businesses anonymously share content abuse data and analysis to generate the largest database of fraud patterns.
Content and community managers manually review accounts and content, explore visualizations, and dig into details such as postings (listings, reviews, etc), images, email address, browsing behavior, device fingerprint, connected accounts, historical data, and more.
Content abuse decisions automatically propagate to all your existing systems. No more switching between systems to manage content and prevent fraud and abuse.
Analysts can easily configure customizable workflows directly in the console to automate fraud and abuse processes and decisions.
Custom & global machine learning models analyze thousands of spam and content abuse signals every time a user takes an action on your site or any site on our global network. Here are some examples of the data and signals we use to prevent content abuse:
Payment Fraud Prevention is powered by the Sift Science Trust Platform. One simple integration gives you access to an integrated suite of fraud and abuse products running on a single platform that includes a unified web console, automation capabilities, and real-time machine learning.
The Sift Science Trust Platform runs the world’s only machine learning system designed from the ground up to learn in real time from live events taking place on desktop and mobile apps across the world. With over 6,000 sites and apps on the platform, we collect, analyze, and learn from 100’s of millions of events each and every day.
Sift Science’s machine learning is so accurate at detecting fraud and abuse that many fraud processes and decisions can be fully automated. You can easily configure workflows to automate actions based on machine learning predictions or specific data values. Whether you want to automatically block bad users, remove friction for good ones, or assign users to manual review queues, Sift Science streamlines your fraud prevention practice.
Sift Science’s web-based console gives fraud teams the tools they need to efficiently manage their fraud management operations. Manual review queues keep analysts and managers focused on the most important cases, while data visualization tools and user-level event data provide the details needed to find even the most sophisticated fraudsters.