Document history
The following table describes important changes in Amazon Fraud Detector User Guide. We also update the Amazon Fraud Detector User Guide frequently to address the feedback that you send us.
| Change | Description | Date |
|---|---|---|
Amazon Fraud Detector introduces new variable types and a datatype you can use to extract useful information. | June 5, 2023 | |
The Event orchestration makes it easy for you to send events to AWS services for downstream processing, using Amazon EventBridge. | May 30, 2023 | |
The Lists resource enables you to reference a set of values such as IP addresses or email addresses, as part of a rule. Use lists in a rule to allow or deny access or a transaction. | February 14, 2023 | |
The Data Models Explorer provides insights into the data elements required by Amazon Fraud Detector to create your fraud detection model. Use data models explorer before you prepare your event dataset. | December 15, 2022 | |
Use Account takeover insights (ATI) model to detect accounts that are compromised through malicious takeovers, phishing, or from credentials being stolen. | July 21, 2022 | |
Updated the introductory chapter with additional information about Amazon Fraud Detector | April 11, 2022 | |
Enable enrichment of some of the raw data you provide to boost performance for the models that use these data elements and that were trained before February 8, 2022. | February 8, 2022 | |
Use opt-out policies to opt out of having your event data used to develop or improve the quality of Amazon Fraud Detector. | January 6, 2022 | |
Create policies to prevent a third-party or a cross-service entity from manipulating an entity with permissions to act on its behalf to gain access to resources in your account. | December 6, 2021 | |
Use the guidance provided in Create event dataset to prepare and gather data for training your model. | November 22, 2021 | |
Use Prediction explanations to get insight into how each event variable impacted your model's fraud prediction scores. | November 10, 2021 | |
Use information in Troubleshoot training data issues to help diagnose and resolve issues you might see in Amazon Fraud Detector console when you train your model. | October 11, 2021 | |
Use Transaction fraud insights (TFI) model to detect online or card-not-present transaction fraud. | October 11, 2021 | |
Store your event data in Amazon Fraud Detector and use the stored data to later train your models. By storing event data in Amazon Fraud Detector, you can train models that use auto-computed variables to improve performance, simplify model retraining, and update fraud labels to close the machine learning feedback loop. | October 11, 2021 | |
Use Model variable importance to gain insight into what is driving performance of your model up or down and which of your model variables contribute the most. And then tweak your model to improve overall performance. | July 9, 2021 | |
Use AWS CloudFormation to manage your Amazon Fraud Detector resources. | May 10, 2021 | |
Use Batch predictions to get predictions for a set of events that do not require real-time scoring. | March 31, 2021 | |
Rework of Get started and other sections | July 17, 2020 | |
Initial release | December 2, 2019 |
