Abstract: Systems and methods are provided for transforming historical data collected in response to one or more triggering events, in order to classify textual values. Embodiments access a plurality of textual values from historical transaction data; identify one or more distinct patterns within the plurality of textual values; group the textual values based on the one or more distinct patterns, thereby forming one or more clusters; apply a similarity gauge to the textual values of each of the clusters to determine similarity or dissimilarity among the textual values of each cluster; and filter the textual values of each cluster to determine which textual values belong in each cluster, wherein the textual values that belong are cluster values. Some embodiments also remove undesired characters from the textual values, and in some cases identifying the distinct patterns includes comparing pronunciations and/or phonetics of the textual values.

Abstract: Systems and methods are provided for transforming historical data collected in response to one or more triggering events, in order to analyze resource utilization, inflow and outflow. Embodiments access inflow and outflow information corresponding to a primary node from historical data; analyze the inflow and outflow information corresponding to the primary node in order to identify one or more channels of flow out of a plurality of channels of flow, each channel connecting the primary node and a secondary node, the one or more channels of flow identified when they exhibit one or more predetermined characteristics; determine that the one or more channels of flow, based on the exhibited predetermined characteristics should be categorized into one or more categories out of a plurality of categories.

Abstract: Systems and methods are provided for transforming historical data collected in response to one or more triggering events, in order to classify textual values. Embodiments access a plurality of textual values from historical transaction data; identify one or more distinct patterns within the plurality of textual values; group the textual values based on the one or more distinct patterns, thereby forming one or more clusters; apply a similarity gauge to the textual values of each of the clusters to determine similarity or dissimilarity among the textual values of each cluster; and filter the textual values of each cluster to determine which textual values belong in each cluster, wherein the textual values that belong are cluster values. Some embodiments also remove undesired characters from the textual values, and in some cases identifying the distinct patterns includes comparing pronunciations and/or phonetics of the textual values.

Abstract: Embodiments of the invention allow an institution to obtain a more comprehensive view of its exposure to one or more entities or groups of entities and, in some cases, to use this information to identify opportunities for and/or risks to the institution. For example, embodiments of the invention involve systems and methods for: (1) selecting an entity; (2) determining exposure to the entity in isolation; (3) determining one or more related entities based on transaction data associated with the selected entity; (4) determining exposure to the one or more related entities; and (5) combining the exposure data for the selected entity and the related entities to obtain comprehensive exposure metrics for the selected entity.

Abstract: Embodiments of the invention allow an institution to obtain a more comprehensive view of its exposure to one or more entities or groups of entities and, in some cases, to use this information to identify opportunities for and/or risks to the institution. For example, embodiments of the invention involve systems and methods for: (1) selecting an entity; (2) determining exposure to the entity in isolation; (3) determining one or more related entities based on transaction data associated with the selected entity; (4) determining exposure to the one or more related entities; and (5) combining the exposure data for the selected entity and the related entities to obtain comprehensive exposure metrics for the selected entity.

Abstract: Systems, methods, computer-readable media, and apparatuses for receiving transaction data associated with a transaction between a first business entity and a second business entity are provided. Data may be extracted from the transaction data to determine a category of the transaction. In some examples, an identity of a first and/or second business entity may be determined from the transaction data and/or additional data. One or more business characteristics of the first or second business entity may be determined based on the identity of the business and the category of the transaction. Accordingly, a network of other business related or having potential to be related to the business entities may be identified (e.g., potential or current vendors, suppliers, service providers, customers, and the like).

Abstract: In certain embodiments, a system includes one or more memory modules operable to store business transaction data including expenditures and revenues for a plurality of customers of an enterprise. The system further includes one or more processing modules operable to determine, based on the business transaction data, a first customer having an amount of expenditures corresponding to an amount of revenue of a second customer. The one or more processing modules are further operable to access publicly-available financial information for the second customer that includes total revenue information for the second customer and determine, based on the accessed publicly-available financial information, whether the amount of revenue of the second customer exceeds a predetermined proportion of the total revenue of the second customer.

Abstract: In certain embodiments, a system includes one or more memory modules operable to store business transaction data for a plurality of customers of an enterprise, the business transaction data including a size classification for each of the plurality of customers. The system further includes one or more processing modules operable to determine, based on the business transaction data, a first list of customers comprising one or more of the plurality of customers that have a particular size classification. The one or more processing modules are further operable to access publicly-available financial information (including revenue information) for each of the one or more customers of the determined first list of customers and determine, based on the accessed publicly-available financial information, a second list of customers comprising one or more customers of the first list of customers that have revenue exceeding a predetermined amount.

Abstract: In certain embodiments, a system includes one or more memory modules operable to store business transaction data for a plurality of current and former customers of an enterprise. The system further includes one or more processing modules operable to access publicly-available financial information (including revenue information) for each of a plurality of entities and determine, based on the accessed publicly-available financial information, a first list of entities comprising a subset of the plurality of entities whose revenue in a first time period exceeds revenue is a second time period by more than a predetermined amount. The one or more processing modules are further operable to determine, based on the business transaction data and the determined first list of entities, a second list of entities comprising one or more entities of the first list of entities that are a selected one of current and former customers of the enterprise.

Abstract: In certain embodiments, a system includes one or more memory modules operable to store business transaction data for a plurality of customers of an enterprise, the stored business transaction data including expenditures for each of the plurality of customers of the enterprise. The system further include one or more processing modules operable to determine, based on the business transaction data, a first customer of the plurality of customers having an increase in expenditures and a second customer of the plurality of customers having an decrease in expenditures corresponding to the increase in expenditures of the first customer. The one or more processing modules are further operable to store, in the one or more memory modules, data associating the first with the second customer.