Abstract: Embodiments of the invention are directed to systems, methods, and computer program products for utilizing machine learning to calculate product replacement cycle times and predict resource transfers associated with said product replacements. As such, the system allows for receipt of resource transfer datasets which are processed along with historical datasets via a machine learning engine. The system may then identify data trends and generate predictions of future resource transfers associated with product or service lifecycles. Furthermore, the system may also identify potential merchants or other third-party systems which may be associated with the predicted future transfers.

Abstract: According to certain embodiments, a routing system comprises a memory operable to store historical data associated with a plurality of previous data processing transactions and a processor operably coupled to a network interface and the memory. The processor is configured to obtain the historical data associated with the plurality of previous data processing transactions transacted using a first routing between a first computing system and a second computing system and determine a pattern based on the historical data, the pattern determined based on machine learning. The processor is further configured to associate the pattern with a second routing between the first computing system and the second computing system, determine an upcoming data processing transaction corresponding to the pattern, and use the second routing to transact the upcoming data processing transaction.

Abstract: Embodiments of the invention are directed to systems, methods, and computer program products for adaptive dynamic multi-directional resource transmissions. The invention is structured for dynamically authenticating an activity between network devices, identifying resource deficiencies associated with network devices, and deploying adapted mitigation resources at one or more network devices in real-time, and without requiring discontinuation of the activity. Specifically, the invention is structured to determine a first resource deficiency associated with the first resource. In response, the system is structured to transmit a first adapted mitigation resource of the one or more adapted mitigation resources to the first resource in a transmission direction, triggering transmission of a first resource quantity from the first resource to an entity resource on another transmission direction.

Abstract: Embodiments of the invention are directed to systems, methods, and computer program products for utilizing machine learning to calculate product replacement cycle times and predict resource transfers associated with said product replacements. As such, the system allows for receipt of resource transfer datasets which are processed along with historical datasets via a machine learning engine. The system may then identify data trends and generate predictions of future resource transfers associated with product or service lifecycles. Furthermore, the system may also identify potential merchants or other third-party systems which may be associated with the predicted future transfers.

Abstract: A system for multi-faceted resource aggregation and deployment is provided, the system comprising: a memory device with computer-readable program code stored thereon; a communication device connected to a network; and a processing device, wherein the processing device is configured to execute the computer-readable program code to: generate a user profile comprising historical user interaction data associated with a user; establish a connection to a resource location associated with the user; identify an existing resource associated with the resource location; determine an additional resource based on identifying the existing resource, wherein the additional resource is complementary to the existing resource; aggregate the existing resource and the additional resource into a multivariable resource collection; and configure a resource deployment based on the multivariable resource collection and the user profile, wherein the resource deployment is configured to complete a multi-faceted interaction using a combi

Abstract: Embodiments of the invention are directed to a system, method, or computer program product for intelligent passive touch network engine for resource touch misappropriation mitigation. In this way, the invention utilizes a multi-faceted resource decisioning machine learning application integration as data deployment across a built out passive touch network engine for deployment and integration for passive touch misappropriation identification. The passive touch network performs artificial intelligent pattern trends, locations, usages, and the like to determine potential misappropriation via supplemental resources. This is a deviation from standard data based misappropriation prevention based on utilization of supplemental resources as misappropriation identification.

Abstract: A system for multi-faceted resource aggregation and deployment is provided, the system comprising: a memory device with computer-readable program code stored thereon; a communication device connected to a network; and a processing device, wherein the processing device is configured to execute the computer-readable program code to: generate a user profile comprising historical user interaction data associated with a user; establish a connection to a resource location associated with the user; identify an existing resource associated with the resource location; determine an additional resource based on identifying the existing resource, wherein the additional resource is complementary to the existing resource; aggregate the existing resource and the additional resource into a multivariable resource collection; and configure a resource deployment based on the multivariable resource collection and the user profile, wherein the resource deployment is configured to complete a multi-faceted interaction using a combi

Abstract: A system for resource aggregation and deployment is provided. The system comprises: a memory device with computer-readable program code stored thereon; a communication device connected to a network; and a processing device, wherein the processing device is configured to execute the computer-readable program code to: generate a user profile comprising historical user interaction data associated with a user; establish a connection to a resource location associated with the user; identify an existing resource associated with the resource location; determine an additional resource based on identifying the existing resource, wherein the additional resource is complementary to the existing resource; aggregate the existing resource and the additional resource; and configure a resource deployment based on the existing resource, the additional resource, and the user profile, wherein the resource deployment is configured to complete an interaction.