Model scores

Amazon Fraud Detector generates model scores differently for different model types.

For Account Takeover Insights (ATI) models, Amazon Fraud Detector uses only aggregated value (a value calculated by combining a set of raw variables) to generate the model score. A score of -1 is generated for the first event of a new entity, indicating an unknown risk. This is because for a new entity, the values used for calculating the aggregate will be zero or null. Account Takeeover Insights (ATI) model generates model scores between 0 and 1000 for all subsequent events for the same entity and for existing entities, where 0 indicates low fraud risk and 1000 indicates high fraud risk. For ATI models, the model scores are directly related to the challenge rate (CR). For example, a score of 500 corresponds to an estimated 5% challenge rate whereas a score of 900 corresponds to an estimated 0.1% challenge rate.

For Online Fraud Insights (OFI) and Transaction Fraud Insights (TFI) models, Amazon Fraud Detector uses both aggregated value (a value calculated by combining a set of raw variables) and raw value (the value provided for the variable) to generate the model scores. The model scores can be between 0 and 1000, where 0 indicates low fraud risk and 1000 indicates high fraud risk. For the OFI and TFI models, the model scores are directly related to the false positive rate (FPR). For example, a score of 600 corresponds to an estimated 10% false positive rate whereas a score of 900 corresponds to an estimated 2% false positive rate. The following table provides details of how certain model scores correlate to estimated false positive rates.