Machine learning (ML) has become a huge part of our day-to-day lives. At Sift, we apply ML to detect fraud and abuse on the internet, enabling our customers to slash fraud losses and unlock incredible growth. And in true Sift spirit, we aim to be transparent about how our technology works.
What is a Sift Score?
If you run Trust and Safety operations for one of the 34K+ websites protected by Sift, you’re familiar with the “Sift Score.” This number, on a scale of 0–100, indicates the likelihood that a given interaction (whether that’s creating an account, posting content, or making a purchase) will be fraudulent.
But if you’re a consumer, what’s your Sift Score anyway? And can you “look up” your score? The answer is: Sift doesn’t score people. Instead, Sift scores user interactions on a specific site for a specific type of fraud.
How is a Sift Score calculated?
Sift uses machine learning algorithms to predict the likely outcome of an interaction on a website or app. Each of Sift’s products is designed to detect a specific kind of fraud or abuse. Each product has its own set of ML models, and generally speaking, each of Sift’s customers has their own product-specific model. For instance, suppose Customer X uses Payment Protection to prevent credit card fraud and Account Defense to prevent account takeover. In this situation, Customer X has two bespoke ML models. The result is the most accurate prediction for our customers and their customers.
When a user accesses Customer X’s site, Sift runs thousands of signals through each bespoke model, analyzing things like time of day, characteristics of email addresses, and number of attempted logins. Customer X only sees the signals that they have shared with Sift in their custom model. Sift then calculates the probability of fraud (the Sift Score) for that specific interaction. For any other interaction with the site, Sift reassesses the probability of fraud.
This calculation has to happen fast enough to matter. Logins, payments, and account changes all occur in real time, so Sift scores and returns a result in milliseconds rather than minutes, fast enough to inform a decision before the interaction is even complete.
You can start to see how the Sift Score is situational. It changes for each interaction, based on information that Sift has at that moment, and is unique to Customer X’s environment. Sift Scores are never shared across customers because each customer’s ML model is different.
Why isn’t there simply a score for every user?
Sift isn’t in the business of scoring people, but rather, our objective is to make the best predictions of outcomes for behaviors on the internet. To do that, we’ve learned from our data that the best way to predict fraud and abuse is to assess specific interactions.
To illustrate this with an example, let’s consider the case of account takeover (ATO), in which a fraudster hijacks a legitimate user’s account. In this situation, the user’s account is completely valid. Evaluating the person owning the account tells us nothing about the ATO. On the other hand, the details of the login itself, such as the time of day, unknown browser type, or failed login attempts reveal the suspicious nature of the interaction. By evaluating the signals associated with the interaction, Sift enables customers to assess whether that account’s actions were initiated by the legitimate owner or a fraudster.
This same interaction-based approach is what lets Sift connect fraud across different parts of a business. An account takeover today can turn into a fraudulent payment tomorrow and a fake review the day after, all using the same compromised identity. Because Sift scores the interaction rather than the person, it can trace that identity’s activity across products and flag the full pattern, instead of treating each event as an unrelated, isolated incident.
How do Sift customers use Sift Scores?
It’s entirely situational and at the discretion of the customer. Consider the Sift Score as an insight into user activity on a customer’s website. It’s up to our customers’ Trust and Safety teams to determine what subsequent action to take and what to convey to their end-users. Ultimately, a Sift Score is a situational assessment that enables our customers to take appropriate actions for their users and business.
In practice, the score usually feeds directly into automated Workflows that customers configure themselves. A low score can let an interaction pass through with no added friction, a middle-of-the-road score can route to a step-up challenge or a manual review queue, and a high score can trigger an automatic block, all without an analyst having to review every case by hand. This lets friction scale with actual risk: trusted users move through checkout, login, or signup without noticing any extra steps, while only the interactions carrying real risk get an added challenge, like a one-time passcode or a manual verification step.
But there are limits to what our customers can do with Sift Scores. Sift customers are contractually prohibited from using our services outside of fraud detection and prevention. Customers are not allowed to use our services as a factor in establishing an individual’s creditworthiness or eligibility for credit, insurance, housing, or employment; or in any way that facilitates discrimination.
Read more about Sift Scores, and how they can help to prevent account takeover.
