Maplebear Inc.

Benjamin Peyrot

Camille Van Horne

Sean Cashin

Maksim Golivkin

Benjamin Knight

Djordje Gluhovic

Alice Han

Rohit Turumella

An online concierge system for fraud detection in customer orders fulfilled by a picker. A picker client device, with an installed picker application, sends a first list of items related to a fulfilled customer order. Separately, a transaction log containing a second list of items purchased is received from the retailer's inventory system. These lists are compared to identify any unmatched items. A pretrained fraud detection model, trained on historical data with labeled instances of fraud or non-fraud, is applied to the unmatched items to assess the likelihood of fraud. If this likelihood surpasses a predefined threshold, the item is flagged as a fraudulent instance. This determination is then sent to an auditor client device for further action.

Classifying fraud instances in completed orders

An online concierge system requests an image of a receipt of an order from a picker after the picker fulfills the order at a store. The online concierge system performs image processing on the image of the receipt and uses machine learning and optical character recognition to determine a tax amount paid for the order and a confidence score associated with the tax amount. The online concierge system may use the machine learning model for segmenting extracted text in the image of the receipt into tokens. The online concierge system may then determine at least one token associated with a tax item and the tax amount associated with the tax item. The online concierge system communicates the tax amount to the store for reimbursement based on the tax amount and the confidence score.

Optical scanning using receipt imagery for automated tax reconciliation

An online system receives a completed order of items fulfilled by a picker and a transaction log of the completed order from a retailer where the completed order was fulfilled. The online system compares the completed order of items to the transaction log of the completed order to identify one or more unmatched items. The online system generates, at an auditor client device, a user interface that shows the unmatched items and an option to mark each unmatched item as a fraud instance. The online system receives an indication of a fraud instance for one of the unmatched items from an auditor via the user interface. The online system adds a value of the fraud instance to a fraud total of the picker. If the online system determines that the fraud total for the picker exceeds a threshold, the fraud module deactivates the picker's account.

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San Francisco, CA, United States of America

Benjamin Peyrot