Uber Technologies, Inc.

Square, Inc.

Melissa Dalis

Emre Demiralp

Brian Boates

Eoin O'Mahony

John Mark Nickels

Danhua Guo

Lior Seeman

Chaoxu Tong

Hyung Jin Kim

En Yu

Xiangyu Sun

Rensheng Zhou

A network system can communicate with user and provider devices to facilitate the provision of a network-based service. The network system can identify optimal service providers to provide services requested by users. The network can utilize context data in matching service providers with users. In particular, the network system can determine, based on context data associated with a user, whether to perform pre-request matching for that user. A service provider who is pre-request matched with the user can be directed by the network system to relocate via a pre-request relocation direction. When the user submits the service request after the pre-request match, the network system can either automatically transmit an invitation to the pre-request matched service provider or can perform post-request matching to identify an optimal service provider for the user.

Forecasting requests based on context data for a network-based service

An automated purchase transaction analyzes a purchase transaction using a probability model to determine a probability that the transaction is fraudulent. If the probability exceeds a threshold, the transaction may be manually reviewed to determine whether to freeze the account associated with the transaction. When introducing the model, the threshold may be set to a relatively high value so that a small number of transactions are submitted for manual review. After a period of time, the observed freeze rate resulting from manual reviews is compared to a target freeze rate. The threshold is then adjusted upwardly or downwardly to decrease the difference between the observed and target freeze rates.

Regulating fraud probability models

An automated purchase transaction service implements a two-phase analysis to identify suspect transactions and to freeze merchant accounts associated with certain of the suspect transactions. In a first analysis phase, a transaction is analyzed using a first predictive model to determine a probability that the transaction is fraudulent. If the probability exceeds a first threshold, the transaction is further analyzed in a second phase. In the second analysis phase, the transaction is analyzed using a second predictive model to determine a probability that manual review by a human analyst will result in freezing the associated account. If the probability exceeds a second threshold, the transaction is automatically frozen. If the probability does not exceed the second threshold, the transaction is submitted to a human analyst for manual review.

Fraud detection and transaction review

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Melissa Dalis