Abstract: Systems and methods for using a machine learning model for alignment with a rule set. In some aspects, the system receives a training dataset. The system trains a machine learning model based on the training dataset. The system processes, using a real-time resource processing system, a first resource access request, a first initial account state, and a first subsequent account state from a real-time data stream and generates a first approval determination. The system applies the machine learning model to the real-time data stream to generate a first confidence score that the new resource access request was executed correctly. Based on the first confidence score, the system determines that there is a discrepancy between the first approval determination by the real-time resource processing system and an expected approval of the machine learning model. Based on the discrepancy and a runtime explainability vector, the system adjusts the real-time resource processing system.

Abstract: In some implementations, a device may present, on a user interface, a depiction of a map of a region, a user interface layer, over the map, that indicates a state of relationships between a plurality of entities and a plurality of territories, and information indicating one or more metrics for the plurality of territories based on the state of relationships. The device may detect a user interaction with a user interface element that indicates a new association between an entity represented by the user interface element and a territory, the new association resulting in a new state of relationships between the plurality of entities and the plurality of territories. The device may present, on the user interface, the user interface layer that indicates the new state of relationships, and the information indicating the one or more metrics for the plurality of territories based on the new state of relationships.