Today, hundreds of thousands of customers including top financial services organizations such as Intuit, Vanguard, Coinbase, NuData, and more, use Amazon SageMaker for machine learning (ML). The increasing growth in online transactions and the subsequent need to improve security, privacy, compliance, and governance is top of mind for financial services organizations. Financial services organizations use ML to address these needs and detect fraud, assess credit risk, and automate operational processes. SageMaker brings together a broad set of capabilities purpose-built for ML helping financial services organizations prepare, build, train, and deploy high quality ML models to support regulatory and compliance mandates and exceed the highest customer expectations.
Top use cases for Amazon SageMaker
Financial services companies are looking to automate detection of suspicious transactions and other anomalous behavior faster to strengthen customer trust. With Amazon SageMaker, you can build ML models to detect suspicious transactions before they occur and alert your customers in a timely fashion. SageMaker provides built-in ML algorithms, such as Random Cut Forrest and XGBoost, that you can use to train and deploy fraud detection models. In addition, SageMaker provides a set of solutions for fraud detection that can be deployed readily with a few clicks.
NuData Security uses Amazon SageMaker to improve detection of fraudulent attacks such as credential stuffing.
With Amazon SageMaker, Fannie Mae can develop and improve ML models to assess loans and analyze property values so lenders can make the right decisions.
Intuit uses Amazon SageMaker as part of a centralized ML platform which reduced the time required to deploy ML models by 90%.