Fraud plagues many online businesses and costs them billions of dollars each year. Financial fraud, counterfeit reviews, bot attacks, account takeovers, and spam are all examples of online fraud and malicious behaviors.
Although many businesses take approaches to combat online fraud, these existing approaches can have severe limitations. First, many existing methods aren’t sophisticated or flexible enough to detect the whole spectrum of fraudulent or suspicious online behaviors. Second, fraudsters can evolve and adapt to deceive simple rule-based or feature-based methods. For instance, fraudsters can create multiple coordinated accounts to avoid triggering limits on individual accounts.
However, if we construct a full interaction graph encompassing not only single transaction data but also account information, historical activities, and more, it’s more difficult for fraudsters to conceal their behavior. For example, accounts that are often connected to other fraudulent-related nodes may indicate guilt by association. We can also combine weak signals from

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