Sift Digital Trust & Safety Suite
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Sift Digital Trust & Safety Suite
Additional Info
| Company (that provides the nominated product / solution / service) | Sift |
| Website | http://www.sift.com |
| Company size (employees) | 100 to 499 |
| Type of solution | Software |
Overview
Sift’s mission is to help everyone trust the Internet. This starts by empowering businesses of all sizes to protect themselves from fraud and abuse while growing revenue. Some of the largest digital companies in the world— including Twitter, Airbnb, DoorDash and Wayfair—trust Sift to prevent fraudsters from buying items with stolen payment information, taking over user accounts, and scamming other customers, all while reducing friction for legitimate customers. With sophisticated technology, a global community of fraud-fighters, and a commitment to long-term partnership, Sift helps its customers gain a competitive advantage in their markets.
Sift’s Digital Trust & Safety Suite is powered by real-time machine learning and assesses risk of all live events taking place across the global network of customers. With over 34,000 sites and apps that use the DT&S Suite, the solution collects, analyzes, and learns from hundreds of millions of positive and suspicious events each day. Sift produces real-time risk scores for every interaction along the user journey, including account creation, logins, orders, content posting, and any custom events so customers can make instant and accurate decisions about the legitimacy of user actions. Sift’s global machine-learning models are customized for each customer’s specific requirements and use case, and customers can use the full DT&S Suite via a single integration and the web-based Sift Console.
Products include:
Payment Protection – Proactively stops fraudulent chargebacks and protects revenue
Streamlines operations and reduces manual review
Grows revenue with decreased false positives and increased conversions
Account Defense – Blocks fake accounts and fraudulent sign ups
Stops account takeover attempts and secures legitimate user accounts
Reduces friction for legitimate users
Content Integrity – Reduces fraudulent content like spam and scams
Safeguard a company’s brand and creates better user experiences
Allows trust and safety professionals to work efficiently and automate comments/reviews.
Additional Information
- Unlike competitors, Sift uses its ensemble of machine learning models to detect multiple types of fraud (payment fraud, fraudulent content, account takeover, promotion abuse, and fake accounts) while identifying legitimate users in real time. It provides the highest accuracy in fraud prevention for the industry. For example, B2B shipping API company Shippo experienced a dramatic drop in the number of fraudulent accounts and chargebacks; plus a 0% false positive rate on flagged orders. Sift also aided online fashion marketplace Poshmark which experienced a 60-70% reduction in spam content, creating a safer and more trustworthy environment for their users
- Rather than taking a myopic approach towards stopping fraud, Sift has observed how bad actors adopt different channels and vectors to steal from businesses, and created a full platform for stopping them. Importantly, Sift’s machine learning-powered global data model benefits from the company’s massive network effects: the more customers that Sift has, the smarter its models become. By looking beyond fraud to understand the digital experience holistically, the company created a whole new approach to designing user experiences that doesn't treat risk and revenue as mutually exclusive.
- Sift’s products are flexible and can serve as either the primary fraud tool or as an input to a larger, layered approach. Customers can access their data and results through APIs and the customizable web-based Sift Console. The Console gives trust and safety teams of all sizes a tool to investigate fraud patterns, automate decisions, and analyze business performance. It also supports capabilities for more protection and growth with apps such as multi-factor authentication and false positive experimentation.
