Businesses can lose billions of dollars each year due to malicious users and fraudulent transactions. As more and more business operations move online, fraud and abuses in online systems are also on the rise. To combat online fraud, many businesses have been using rule-based fraud detection systems.
However, traditional fraud detection systems rely on a set of rules and filters hand-crafted by human specialists. The filters can often be brittle and the rules may not capture the full spectrum of fraudulent signals. Furthermore, while fraudulent behaviors are ever-evolving, the static nature of predefined rules and filters makes it difficult to maintain and improve traditional fraud detection systems effectively.
In this post, we show you how to build a dynamic, self-improving, and maintainable credit card fraud detection system with machine learning (ML) using Amazon SageMaker.
Alternatively, if you’re looking for a fully managed service to build customized fraud detection models without

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