Beyond fraud management with Digital Trust & Safety

Is your fraud management solution blocking growth? Proactively protect your customers from fraud and grow your business. It’s simple with Sift.

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Align risk and revenue with Sift

Sift powers Digital Trust & Safety and enables customers to unlock new revenue without risk. Legacy fraud management solutions look at fraud and abuse through a narrow lens. But fraud can happen at any time.

Whether it’s when a customer creates an account, makes a purchase, or posts a review, fraudsters are waiting in the wings.

With Sift, you can protect the entire customer journey from all types of online fraud and abuse with a single integrated solution while fueling explosive growth.

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Stop fraud before it happens

Take your fraud risk management strategy to the next level and proactively stop fraud, reduce manual review, and limit chargebacks. With Sift’s industry-leading machine learning, you can identify and block fraud automatically and in real time. Leverage our global network of fraud fighters and the shared knowledge of over 34,000 sites and apps using Sift to transform your fraud management system into a formidable fraud prevention solution with Digital Trust & Safety at its core.

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What is fraud management?

Fraud management is, quite literally, the management of fraud. That includes the detection, prevention, and mitigation of abuse using a set of tools, analysts, and protocols.

Tools may include rules-based fraud management software, external data providers that aid in identity verification, machine-learning platforms, and other cloud-based solutions.

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Rules-based fraud management systems

Legacy or rules-based systems rely on a pre-determined set of rules that identify behaviors and actions that the business has deemed as indicators of potentially fraudulent activity. Think of them as decision trees or if/then statements. For example, if a business was seeing fraudulent activity from a particular city or zip code, they may institute a rule that blocks orders or accounts from that specific location or send those orders or accounts to a human-led, manual-review team that can make a decision on whether to allow those interactions to proceed. Rules require a lot of investment upfront and need constant upkeep as consumer or user behavior changes, or as market conditions fluctuate.

External data providers

External data providers supplement any in-house or proprietary information a business uses to make determinations on interactions. Examples of data that may be available internally include a user’s name, billing address, shipping address, phone number, and other user-reported, personal information. External data, on the other hand, includes information not readily available within a company’s own system of record or database. Examples include verification of user-reported information like physical address, internet protocol address (IP address) confirmation, geographic and demographic data, etc. Simply put, external data providers supply information that isn’t available in a company’s database but that is crucial to making accurate decisions.

Machine-learning platforms

Unlike rules-based systems, machine learning relies on algorithms and models that are constantly updated. The more data (transactions, account logins, etc.) that is fed to the models, the more accurate they become. Machine learning helps trust and safety teams accurately and automatically determine which actions are likely fraudulent, reducing manual review, chargebacks, and false positives. Machine learning is scalable as your business grows because the more activity your model sees, the better it will be at identifying fraud for your unique business and its needs. It is the fraud prevention solution of choice for businesses of all sizes and is relied upon to tackle enterprise fraud management for some of the world’s leading companies.