Abstract: A system is provided for computing and tracking resource state changes using a multichain distributed register technology. In particular, the system may comprise an index distributed register that may be stored across a plurality of distributed register nodes. The index distributed register may comprise one or more resource-specific data records that may in turn make up a resource-specific distributed register originating from the index distributed register. The resource-specific distributed register may comprise an initial data record that defines the attributes of the resource, and one or more subsequent data records that may be linked with the initial data record to reflect changes in the state of the resource to be tracked. Based on the data within each of the resource-specific distributed register, the system may compute resource analysis scores and/or trigger real-time user alerts.

Abstract: The present disclosure details systems, computer program products, and methods for machine engine analysis of network interaction data to identify conflicts. This involves the activation of an intelligence engine that connects to a backend data engine to access a database of conflict of interest (COI) datasets. The intelligence engine analyzes incoming applicant data and the COI datasets to identify potential conflicts. The identified conflicts are validated using stochastic metrics via a rules engine. A quantifiable probability metric is calculated, representing the probability of conflict from both organizational and individual perspectives. Based on the potential conflict of interest, a recommendation is generated that aligns with the calculated probability. The system then provides recommendations via API or on a user interface that is communicatively coupled to the processor.

Abstract: A system is provided for an artificial intelligence-based engine for generating recommendations for resource allocation. In particular, the system may comprise an artificial intelligence module configured to access one or more data sources containing resource data associated with one or more users. Based on the resource data, the system may identify a group of analogous users with respect to a specific user. The system may then generate one or more recommendations for a resource allocation associated with the user. The one or more recommendations may then be presented on a graphical interface of a user device associated with the user. In this way, the system may provide an AI-driven method for efficient allocation of resources.