Abstract: This application relates to apparatus and methods for identifying fraudulent transactions. The embodiments may employ machine learning processes to detect fraudulent activity. In some examples, a computing device determines customer data and device data for a customer and device involved in a transaction. The customer data may include previous transactions by the customer, and the device data may include previous transactions involving the device. The computing device generates features based on the customer data and the device data, and applies one or more machine learning models to the generated features to generate a trust score. The trust score is indicative of how likely a transaction is to be fraudulent. In some examples, the transaction is not allowed if the trust score is beyond a threshold. In some examples, the computing device trains the machine learning models based on customer data and device data for a plurality of customers and devices.

Abstract: An online concierge system schedules pickers (shoppers) to fulfill orders from users. During periods of peak demand, the system increases compensation to shoppers to encourage more to participate, thereby reducing missed orders. The system determines an optimal multiplier to increase compensation based on predictive models of supply and demand and then applying an optimization algorithm to search different hyperparameters that affect how the models generate the multipliers. The system selects the optimal multipliers for different time periods and locations. The system may further present the multipliers being offered during future time periods and enable users to activate reminder alerts for select periods. The offers may be presented in a ranked list using a model trained to infer likelihoods of the user accepting participation and/or setting a reminder notification.

Abstract: This application relates to apparatus and methods for identifying fraudulent transactions. The embodiments may employ machine learning processes to detect fraudulent activity. In some examples, a computing device determines customer data and device data for a customer and device involved in a transaction. The customer data may include previous transactions by the customer, and the device data may include previous transactions involving the device. The computing device generates features based on the customer data and the device data, and applies one or more machine learning models to the generated features to generate a trust score. The trust score is indicative of how likely a transaction is to be fraudulent. In some examples, the transaction is not allowed if the trust score is beyond a threshold. In some examples, the computing device trains the machine learning models based on customer data and device data for a plurality of customers and devices.

Abstract: This application relates to apparatus and methods for identifying fraudulent transactions. The embodiments may employ machine learning processes to detect fraudulent activity. In some examples, a computing device determines customer data and device data for a customer and device involved in a transaction. The customer data may include previous transactions by the customer, and the device data may include previous transactions involving the device. The computing device generates features based on the customer data and the device data, and applies one or more machine learning models to the generated features to generate a trust score. The trust score is indicative of how likely a transaction is to be fraudulent. In some examples, the transaction is not allowed if the trust score is beyond a threshold. In some examples, the computing device trains the machine learning models based on customer data and device data for a plurality of customers and devices.

Abstract: This application relates to apparatus and methods for identifying fraudulent transactions. The embodiments may employ machine learning processes to detect fraudulent activity. In some examples, a computing device determines customer data and device data for a customer and device involved in a transaction. The customer data may include previous transactions by the customer, and the device data may include previous transactions involving the device. The computing device generates features based on the customer data and the device data, and applies one or more machine learning models to the generated features to generate a trust score. The trust score is indicative of how likely a transaction is to be fraudulent. In some examples, the transaction is not allowed if the trust score is beyond a threshold. In some examples, the computing device trains the machine learning models based on customer data and device data for a plurality of customers and devices.

Abstract: This application relates to apparatus and methods for identifying fraudulent transactions. The embodiments may employ machine learning processes to detect fraudulent activity. In some examples, a computing device determines customer data and device data for a customer and device involved in a transaction. The customer data may include previous transactions by the customer, and the device data may include previous transactions involving the device. The computing device generates features based on the customer data and the device data, and applies one or more machine learning models to the generated features to generate a trust score. The trust score is indicative of how likely a transaction is to be fraudulent. In some examples, the transaction is not allowed if the trust score is beyond a threshold. In some examples, the computing device trains the machine learning models based on customer data and device data for a plurality of customers and devices.

Abstract: Systems and methods for training a machine learning model are disclosed. A new machine learning model for a new system is trained using portions of source data used to train a well-established machine learning model that solves a different but related problem compared to the new machine learning model. Features required to train the new machine learning model may be compared to features in data samples of the source data to determine the portion of source data that can be used as training data to train the new machine learning model. The training data may then be used to train the new machine learning model without requiring a large set of training data that is unavailable for the new system.