AWS announces model variable importance for Amazon Fraud Detector

Tuesday 13 July 2021 10:46 CET | News

AWS has announced that Amazon Fraud Detector now includes model variable importance values to provide customers more insight into their fraud detection machine learning (ML) model’s performance.

With the new variable importance functionality, Amazon Fraud Detector provides customers a ranked list of model inputs (variables/model features) based on their relative importance to the model’s performance.

By leveraging this information, businesses can better understand their ML models. This information assists in iteratively improve model performance and make more informed decisions, according to the official press release.

First announced in preview at re:Invent 2019 and becoming generally available in 2020, Amazon Fraud Detector is a fully managed service powered by ML that automatically trains, tests, and deploys custom fraud detection ML models based on a customer’s historical fraud data, helping identify potentially fraudulent online activities in milliseconds, the announcement concludes.

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Keywords: machine learning, fraud detection, Amazon, fraud prevention
Categories: Securing Transactions | Digital Identity, Security & Online Fraud
Countries: United States
This article is part of category

Securing Transactions