Each year, organizations lose tens of billions of dollars to online fraud globally. Organizations such as ecommerce companies and credit card companies use machine learning (ML) to detect online fraud. Some of the most common types of online fraud include email account compromise (personal or business), new account fraud, and non-payment or non-delivery (including card numbers compromised).
A common challenge with ML is the need for a large labeled dataset to create ML models for detecting fraud. Moreover, even if you have this dataset, you need the skill set and infrastructure to build, train, deploy, and scale your ML model to detect fraud with millions of events. In addition, you need humans to review the subset of high-risk fraud predictions to ensure that the results are highly accurate. Setting up a human review system with your fraud detection model requires provisioning complex workflows and managing a group of reviewers, which

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