Abstract: The disclosure includes a system and method for receiving, using one or more processors, first image data representing a first image of a document; obtaining, using the one or more processors, a first set of corner coordinates representing the corners of the document in the first image; generating, using the one or more processors, a first polygon based on the first set of corner coordinates; performing, using the one or more processors, one or more corner-based validity checks based on the first set of corner coordinates; and performing, using the one or more processors, an auto-capture.

Abstract: The disclosure includes a system and method for fraud detection in the context of a user submitting, via a client device, a photo of a photo ID and a selfie taken during a step of the verification process. The fraud detection aggregates a variety of different sources of information indicative of potential fraud, such as a liveness signal, a repeated fraudster signal, client device attributes, temporal attributes, country information, and other optional forms of information such as telephone information, IP address etc. A machine learning model may be trained to generate an aggregate fraud score and classify the aggregate fraud score into different risk categories.

Abstract: Computer systems and methods are provided for extracting information from an image of a document. A computer system receives image data, the image data including an image of a document. The computer system determines a portion of the received image data that corresponds to a predefined document field. The computer system utilizes a neural network system to assign a label to the determined portion of the received image data. The computer system performs text recognition on the portion of the received image data and stores the recognized text in association with the assigned label.