# 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.

Model score | Estimated FPR |
---|---|

975 |
0.50% |

950 |
1% |

900 |
2% |

860 |
3% |

775 |
5% |

700 |
7% |

600 |
10% |