Fraudulent activities severely impact many industries, such as e-commerce, social media, and financial services. Frauds could cause a significant loss for businesses and consumers. American consumers reported losing more than $5.8 billion to frauds in 2021, up more than 70% over 2020. Many techniques have been used to detect fraudsters—rule-based filters, anomaly detection, and machine learning (ML) models, to name a few.
In real-world data, entities often involve rich relationships with other entities. Such a graph structure can provide valuable information for anomaly detection. For example, in the following figure, users are connected via shared entities such as Wi-Fi IDs, physical locations, and phone numbers. Due to the large number of unique values of these entities, like phone numbers, it’s difficult to use them in the traditional feature-based models—for example, one-hot encoding all phone numbers wouldn’t be viable. But such relationships could help predict whether a user is a fraudster.

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