Amazon Fraud Detector is a fully managed service that makes it easy to identify potentially fraudulent online activities such as online payment fraud and the creation of fake accounts.
Globally each year, tens of billions of dollars are lost to online fraud. Companies conducting business online are especially prone to attacks from bad actors who often exploit different tactics such as creating fake accounts and making payments with stolen credit cards. Companies typically use fraud detection applications to identify fraudsters and stop them before they cause costly business disruptions. However, these applications often rely on business rules that don’t keep up with the changing behaviors of fraudsters. More recent fraud detection applications have tried to use machine learning. But, they frequently use a one-size-fits-all approach based on general data sets and fraud behaviors that aren't specific to your business, which limits their accuracy.
Fraud Detector overcomes these challenges by using your data, machine learning (ML), and more than 20 years of fraud detection expertise from Amazon to automatically identify potentially fraudulent online activity so you can catch more fraud faster. You can create a fraud detection model with just a few clicks and no prior ML experience because Fraud Detector handles all of the ML heavy lifting for you.
Detect common types of online fraud
Accurately distinguish between legitimate and high-risk customer account registrations so that you can selectively introduce additional steps or checks based on risk. For example, you can setup your customer account registration workflow to require additional email and phone verification steps only for account registrations that exhibit high risk characteristics.
Spot potential fraudsters even among customers without a history of transactions. Customers who transact regularly typically use a registered account. As a result, you have a history of transactions which makes it easier to identify potential fraud. Guest checkout, on the other hand, has no historical account usage or user behavior data which makes fraud detection much harder. With Amazon Fraud Detector, you can send as little as an email and IP address from a guest checkout order to assess its potential fraud risk so you can decide whether to accept it, review it, or collect more customer details.
‘Try Before You Buy’ service abuse
Identify accounts that are more likely to abuse ‘Try Before You Buy’ programs such as fashion services that ship clothing and accessories for you to explore before sending payment. With Amazon Fraud Detector, online businesses can assess the risk of customers violating service terms and set limits on the value of goods or services provided so goods aren’t stolen or returned in a condition that violates service terms.
Online payment (Coming Soon)
Reduce online payment fraud by flagging suspicious online payment transactions before processing payments and fulfilling orders. With Amazon Fraud Detector, you can setup your checkout flow to evaluate new orders and flag suspicious ones for review prior to processing payments to reduce credit card chargebacks.
Build high quality fraud detection ML models faster
Amazon Fraud Detector provides templates that you can use to easily create ML models to identify potential fraud without writing any code. You simply upload your historical online event data such as transactions or account registrations and select the model template that matches your use case. From there, Amazon Fraud Detector automatically trains, tests, and deploys a customized fraud detection model fit to your business.
Stop bad actors at the door
Amazon Fraud Detector helps you identify fraudsters right when they create an account by predicting risk in the information they provided so that you can flag suspicious activity before they can do real harm. That's because Amazon Fraud Detector uses advanced machine learning techniques that can be applied even to the limited data provided at account creation. Amazon Fraud Detector's machine learning models can identify up to 80% more potential bad actors than traditional methods.
Built-in online fraud expertise
The pre-built machine learning model templates in Amazon Fraud Detector were developed from 20 years of experience stopping bad actors from attempting to defraud AWS and Amazon.com. For example, Amazon Fraud Detector uses models similar to those used in the AWS account sign up flow to create different account verification steps for low risk and high risk sign ups.
Give fraud teams more control
By automatically handling the complex tasks required to build, train, tune, and deploy a fraud detection model, Amazon Fraud Detector enables fraud teams to move faster. After the model is created, they can create, view, and update rules to enable actions based on model predictions without relying on others.
Get started with Amazon Fraud Detector in 5 steps
Step 1: Upload your historical fraud datasets to Amazon S3
Step 2: Select from pre-built fraud detection model templates
Step 3: The model template uses your historical data as input to build a custom model. The model template inspects and enriches data, performs feature engineering, selects algorithms, trains and tunes your model, and hosts the model
Step 4: Create rules to either accept, review, or collect more information based on model predictions
Step 5: Call the Amazon Fraud Detector API from your online application to receive real-time fraud predictions and take action based on your configured detection rules. For example, an ecommerce application can send an email and IP address and receive a fraud score as well as the output from your rule (e.g., review)
Vacasa is the largest full-service vacation rental management company in North America, with more than 23,000 vacation homes in 17 countries serving over 2 million guests per year.
“Since our founding, we have used technology to enable our local teams to focus on caring for homes and guests, while maximizing revenue for vacation home owners,” said Eric Breon, founder and CEO of Vacasa. “We’re excited about the introduction of Fraud Detector because it means we can more easily use advanced machine learning techniques to accurately detect fraudulent reservations. Protecting our ‘front door’ from potential harm enables us to focus on making the vacation rental experience seamless and worry-free.”