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