Fraud is an ongoing problem that can cost businesses billions of dollars annually and damage customer trust. Many companies use rule-based approaches to detect fraudulent activity where fraud patterns are defined as rules. But, implementing and maintaining rules can be a complex, time-consuming process because fraud is constantly evolving, rules require fraud patterns to be known, and rules can lead to false positives or false negatives.

Machine learning (ML) provides a much more flexible approach to fraud detection. ML models do not use pre-defined rules to determine whether activity is fraudulent. Instead, ML models are trained to recognize fraud patterns in datasets, and the models are self-learning which enables them to adapt to new, unknown fraud patterns.

Key Takeaways:

1. Learn about different supervised and unsupervised methods to build fraud models.
2. Train models on Amazon SageMaker using different algorithms like XGBoost, Random cut forest, Autoencoders
3. Deploy models using Amazon SageMaker