Overview - Fraud Detection Using Machine Learning


Fraud is an ongoing problem that can cost businesses billions of dollars annually and damage customer trust. Many companies use a rule-based approach 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) can provide a 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. In addition, unsupervised ML models allow us to extract knowledge from unlabeled data, flagging anomalous transactions for review.

Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes the barriers that typically slow down developers who want to use machine learning. This ability makes Amazon SageMaker applicable for a variety of use cases, including fraud detection.

To help customers more easily leverage Amazon SageMaker for real-time fraud detection, AWS offers the Fraud Detection Using Machine Learning solution. This solution automates the detection of potentially fraudulent activity, and flags that activity for review. This solution also includes an example dataset but you can modify the solution to work with any dataset.


You are responsible for the cost of the AWS services used while running this solution. As of the date of publication, the one-time cost to train the solution’s ML model in the US East (N. Virginia) Region is $01.50 for the Amazon SageMaker ml.c4.large instance. The cost to process transactions using the example dataset is approximately $0.65 per hour. Prices are subject to change. For full details, see the pricing webpage for each AWS service you will be using in this solution.