Abstract: Disclosed embodiments may include a system configured to perform digital image analysis. The system may receive transaction data and image data associated with a user. The system may identify, from the transaction data, first travel feature(s). The system may identify, from the image data via computer vision, second travel feature(s). The system may train a machine learning model (MLM) to generate trip recommendation(s) for the user based on the first travel feature(s) and the second travel feature(s). The system may determine, via the trained MLM, whether at least a first trip recommendation of the trip recommendation(s) exceeds a predetermined threshold indicating a likelihood the user will be interested in the first trip recommendation. Responsive to determining the first trip recommendation exceeds the predetermined threshold, the system may provide the first trip recommendation to the user.

Abstract: Disclosed herein are system, method, and computer program product embodiments for training and deploying a machine learning model to generate an assessment of risks and mitigations in response to a novel initiative request. After generating labeled data from a corpus of prior risk assessments, a machine learning model may be trained to programmatically generate a risk assessment in response to a novel initiative request. The system may provide for centralized control over the creation, review, and approval of initiative requests. The system may further analyze consumer-facing applications deployed by an organization and train the machine learning model to algorithmically determine consumer-facing applications potentially affected by an initiative request.

Abstract: Disclosed herein are system, method, and computer program product embodiments for training and deploying a machine learning model to generate an assessment of risks and mitigations in response to a novel initiative request. After generating labeled data from a corpus of prior risk assessments, a machine learning model may be trained to programmatically generate a risk assessment in response to a novel initiative request. A risk auditor may subsequently review the risk assessment generated using the machine learning model to provide various feedback reflecting the accuracy of the risks and mitigations. The machine learning model may then be retrained based on the feedback provided by the risk auditor to provide more accurate risks and mitigations in response to future initiatives.