Amazon Fraud Detector
Amazon Fraud Detector is a fully managed service that uses machine learning to identify potentially fraudulent activities and accurately distinguish between legitimate and high-risk transactions. It uses your data and the same technology that Amazon uses to protect its own business from fraud.
APIs
Amazon Fraud Detector API
The Amazon Fraud Detector API provides programmatic access to create and manage detectors, models, event types, entities, labels, outcomes, rules, and variables for automated fr...
Collections
Amazon Fraud Detector API
POSTMANArazzo Workflows
Amazon Fraud Detector Author Rule
Create a DETECTORPL rule for a detector and read the detector's rules back to confirm it.
ARAZZOAmazon Fraud Detector Bootstrap Event Type
Create fraud and legit labels, define an event type that uses them, then confirm the event type exists.
ARAZZOAmazon Fraud Detector Decommission Detector
Inspect a detector's rules and then delete the detector, branching when rules still block deletion.
ARAZZOAmazon Fraud Detector Detector Pipeline
Define an event type, create a detector and a rule, then score a sample event against the detector.
ARAZZOAmazon Fraud Detector Inventory Models and Detectors
List models for an event type, then list detectors and tag a chosen detector with its model count.
ARAZZOAmazon Fraud Detector Provision Model and Detector
Define an event type, create an ML model and a detector on top of it, then confirm the detector exists.
ARAZZOAmazon Fraud Detector Score Event and Tag
Score an event against a detector and branch on the returned model score to tag the detector accordingly.
ARAZZOAmazon Fraud Detector Tag and Audit Resource
Assign tags to a Fraud Detector resource and read its tags back to confirm they were applied.
ARAZZOPricing Plans
Rate Limits
FinOps
Amazon Fraud Detector Finops
FINOPSFeatures
Automatically trains and deploys ML models using your historical transaction data without requiring ML expertise.
Returns fraud scores within milliseconds for integration into transaction approval flows.
Online Fraud Insights (OFI), Transaction Fraud Insights (TFI), and Account Takeover Insights (ATI) pre-trained model types.
DETECTORPL rule language allows writing conditional logic using model scores and event variables.
Variable importance scores explain which factors most influenced a fraud prediction.
Uses Amazon fraud experience to provide immediate predictions even with limited historical data.
Ingest historical labeled events to continuously improve model accuracy over time.
Use Cases
Score credit card and payment transactions in real-time to block fraudulent purchases.
Detect unauthorized login attempts and account compromise using behavioral signals.
Identify fraudulent new account registrations at signup to prevent synthetic identity fraud.
Flag users abusing discount codes, referral bonuses, and promotional offers.
Reduce chargeback rates by blocking high-risk transactions before they complete.
Score insurance claims for fraudulent patterns in real-time during claim submission.