Abstract: Provided is a system that includes at least one processor programmed or configured to receive an XML data file, wherein the XML data file includes data associated with one or more input parameters of a machine learning model, generate a code generation template based on the data associated with one or more input parameters of the machine learning model included in the XML file, where the code generation template includes one or more keys associated with one or more parameters of a transaction aggregate for an account of a user, and generate a file of executable code based on the code generation template, wherein the file of executable code includes instructions that, when executed by at least one processor, causes at least one processor to retrieve transaction aggregate data associated with the transaction aggregate for the account of the user. A method and computer program product are also provided.

Abstract: Provided is a computer-implemented method, system, and product for providing real-time offers based on geolocation and merchant category. The method includes receiving data associated with a plurality of transactions conducted by a plurality of merchants, the plurality of merchants including a first merchant associated with at least one first merchant category, determining an area of interest based on an estimated travel time to the first merchant from the plurality of merchants, determining a subset of merchants of the plurality of merchants within the area of interest associated with the at least one first merchant category, determining real-time market activity data associated with the first merchant and each merchant of the subset of merchants, and identifying a trend of increasing and/or decreasing transactions for the first merchant and each merchant of the subset of merchants.

Abstract: Methods and systems are provided to efficiently update account profiles based on a predicted likelihood of use, including by ranking the account profiles according to the likelihood of use. The disclosed system can considerably improve the processing time to update account profiles with the most recent information available, including new access requests. An authentication platform receives a plurality of new access requests, including request data and account identifiers associated with account profiles. The request data is transmitted to a prediction engine that determines a ranking of the account identifiers based on a predicted likelihood of use during a next time interval. A profile batch scheduler retrieves a first set of access requests based on the ranking. The system updates a first set of account profiles based on the ranking, and stores the updated account profiles for use by the authentication platform.

Abstract: A method may include obtaining feature data associated with a set of feature inputs of a machine learning model, determining a probability that a subset of the feature data is concurrently used as the set of feature inputs for the machine learning model, and storing the subset of the feature data on a same cache node or server of a plurality of cache nodes or servers based on the probability.

Abstract: A method may include obtaining feature data associated with a set of feature inputs of a machine learning model, determining a probability that a subset of the feature data is concurrently used as the set of feature inputs for the machine learning model, and storing the subset of the feature data on a same cache node or server of a plurality of cache nodes or servers based on the probability.

Abstract: Cache memory requirements between normal and peak operation may vary by two orders of magnitude or more. A cache memory management system for multi-tenant computing environments monitors memory requests and uses a pattern matching classifier to generate patterns which are then delivered to a neural network. The neural network is trained to predict near-future cache memory performance based on the current memory access patterns. An optimizer allocates cache memory among the tenants to ensure that each tenant has sufficient memory to meet its required service levels while avoiding the need to provision the computing environment with worst-case scenario levels of cache memory. System resources are preserved while maintaining required performance levels.

Abstract: Provided is a computer-implemented method, system, and product for providing real-time offers based on geolocation and merchant category. The method includes receiving data associated with a plurality of transactions conducted by a plurality of merchants, the plurality of merchants including a first merchant associated with at least one first merchant category, determining an area of interest based on an estimated travel time to the first merchant from the plurality of merchants, determining a subset of merchants of the plurality of merchants within the area of interest associated with the at least one first merchant category, determining real-time market activity data associated with the first merchant and each merchant of the subset of merchants, and identifying a trend of increasing and/or decreasing transactions for the first merchant and each merchant of the subset of merchants.

Abstract: Embodiments of the invention are directed to systems and methods for utilizing a multi-tiered caching architecture in a multi-tenant caching system. A portion of the in-memory cache may be allocated as dedicated shares (e.g., dedicated allocations) that are each dedicated to a particular tenant, while another portion of the in-memory cache (e.g., a shared allocation) can be shared by all tenants in the system. When a threshold period of time has elapsed since data stored in a dedicated allocation has last been accessed, the data may be migrated to the shared allocation. If data is accessed from the shared allocation, it may be migrated back to the dedicated allocation Utilizing the techniques for providing a multi-tiered approach to a multi-tenant caching system can increase performance and decrease latency with respect to conventional caching systems.

Abstract: Provided is a computer-implemented method for providing real-time offers based on geolocation and merchant category. Transaction data for transaction from a plurality of merchants is received. A subset of merchants is determined based on the physical location of the merchants and the merchant category of the merchants. Real-time market activity data is determined for each of the merchants in the subset of merchants. A real-time offer is initiated based on comparing the market activity data of at least one merchant compared to the market activity data of a first merchant.

Abstract: Provided is a system that includes at least one processor programmed or configured to receive an XML data file, wherein the XML data file includes data associated with one or more input parameters of a machine learning model, generate a code generation template based on the data associated with one or more input parameters of the machine learning model included in the XML file, where the code generation template includes one or more keys associated with one or more parameters of a transaction aggregate for an account of a user, and generate a file of executable code based on the code generation template, wherein the file of executable code includes instructions that, when executed by at least one processor, causes at least one processor to retrieve transaction aggregate data associated with the transaction aggregate for the account of the user. A method and computer program product are also provided.

Abstract: A method, system, and computer program product for managing model updates at multiple data centers hosting a same machine learning model obtain a plurality of first feature profiles input to a first implementation of a first machine learning model and a plurality of first model states determined from processing a model input with the first implementation; determine that a first model policy associated with the first machine learning model is satisfied, based on the plurality of first model states including a set of model states defined by the first model policy and the plurality of first feature profiles including a set of feature profiles defined by the first model policy; and in response to determining that the first model policy is satisfied, provide the plurality of first model states and the plurality of first feature profiles for updating at least one second implementation of the first machine learning model.

Abstract: Described herein are a system and techniques for increasing the efficiency of generating a result set for a query. In some embodiments, the techniques may involve performing computations on a high-level element, sorting, and selecting a set of the high-level elements, and recursively repeating the process on sub-elements of the set of the high-level elements. The process may be recursively repeated until a specified level of granularity is reached. This may significantly decrease the number of computations that need to be performed, increasing the speed with which queries can be performed. In some embodiments, the process may involve identifying elements which may be highly correlated to optimal computation results and may add those elements to the result set to improve the accuracy of the result set.

Abstract: A real-time geo-intelligent aggregation engine and related methods are disclosed. The real-time geo-intelligent aggregation engine can be used to provide interaction event data corresponding to a geographic coordinate point and a boundary element to requestors or requestor computers. After receiving a request from a requestor computer, the real-time geo-intelligent aggregation engine can determine one or more geohashes corresponding to a geographic region. The one or more geohashes can be used to query a geographic resource provider database to determine a list of resource providers acting within the geographic region. The list of resource providers can be used to query a resource provider interaction database to determine interaction event data corresponding to those resource providers. The interaction event data can subsequently be provided to the requestor computer.

Abstract: Methods and systems are provided to efficiently update account profiles based on a predicted likelihood of use, including by ranking the account profiles according to the likelihood of use. The disclosed system can considerably improve the processing time to update account profiles with the most recent information available, including new access requests. An authentication platform receives a plurality of new access requests, including request data and account identifiers associated with account profiles. The request data is transmitted to a prediction engine that determines a ranking of the account identifiers based on a predicted likelihood of use during a next time interval. A profile batch scheduler retrieves a first set of access requests based on the ranking. The system updates a first set of account profiles based on the ranking, and stores the updated account profiles for use by the authentication platform.

Abstract: Methods and systems of predicting and processing cacheable AI calls are disclosed. One method includes determining, by a call prediction processing computer, that an AI computer is operating below a threshold processor usage. The method then includes requesting, by the call prediction processing computer, a set of cacheable requests from an orchestration service computer and receiving, from the orchestration service computer, the set of cacheable requests. Then the method includes sending, by the call prediction processing computer, a cacheable request from the set of cacheable requests to the AI computer. The method then includes receiving, by the call prediction processing computer, an output. The output is generated by the AI computer based on the cacheable request. Then the call prediction processing computer stores the output in a data repository.

Abstract: A method, system, and computer program product for predicting future transactions may obtain merchant data associated with a merchant; determine a geographic location associated with the merchant based on the merchant data; determine one or more other merchants within a first threshold distance of the geographic location; obtain transaction data associated with the one or more other merchants and the merchant; and predict, based on the transaction data, at least one of a future number of transactions for the merchant in a future time period and a future transaction amount for the merchant in the future time period.

Abstract: Methods and systems are provided to efficiently update account profiles based on a predicted likelihood of use, including by ranking the account profiles according to the likelihood of use. The disclosed system can considerably improve the processing time to update account profiles with the most recent information available, including new access requests. An authentication platform receives a plurality of new access requests, including request data and account identifiers associated with account profiles. The request data is transmitted to a prediction engine that determines a ranking of the account identifiers based on a predicted likelihood of use during a next time interval. A profile batch scheduler retrieves a first set of access requests based on the ranking. The system updates a first set of account profiles based on the ranking, and stores the updated account profiles for use by the authentication platform.

Abstract: Embodiments of the invention are directed to systems and methods for utilizing a cache to store historical transaction data. A predictive model may be trained to identify particular identifiers associated with historical data that is likely to be utilized on a particular date and/or within a particular time period. The historical data corresponding to these identifiers may be stored in a cache of the processing computer. Subsequently, an authorization request message may be received that includes an identifier. The processing computer may utilize the identifier to retrieve historical transaction data from the cache. The retrieved data may be utilized to perform any suitable operation. By predicting the data that will be needed to perform these operations, and preemptively store such data in a cache, the latency associated with subsequent processing may be reduced and the performance of the system as a whole improved.

Abstract: A data system may dynamically prioritize and ingest data so that, regardless of the memory size of the dataset hosted by the data system, it may process and analyze the hosted dataset in constant time. The system and method may implement a first space-efficient probabilistic data structure on the dataset, wherein the dataset includes a plurality of profile data. It may then receive update data corresponding to some of the plurality of profile data and implement a second space-efficient probabilistic data structure on the dataset including the update data. The system and method may then determine a set of non-shared profile data of the second space-efficient probabilistic data structure and prioritize the set of non-shared profile data of the second space-efficient probabilistic data structure over other profile data of the dataset for caching.

Abstract: A method, system, and computer program product for predicting future transactions may obtain merchant data associated with a merchant; determine a geographic location associated with the merchant based on the merchant data; determine one or more other merchants within a first threshold distance of the geographic location; obtain transaction data associated with the one or more other merchants and the merchant; and predict, based on the transaction data, at least one of a future number of transactions for the merchant in a future time period and a future transaction amount for the merchant in the future time period.