Abstract: An artificial intelligence (AI) system for guiding a user interaction in a phone call or chat session. The system includes a computer running an AI algorithm, such as a machine learning algorithm, which is trained to recognize patterns in user interaction dialog which lead to satisfactory outcomes for the user. The system may operate in a completely autonomous mode, or the system connect a human agent in the loop. The algorithm adaptively guides the dialog to achieve a favorable outcome based on the current status of the dialog—including identifying a next question to ask, information to provide, or an action to take. The algorithm is trained via supervised learning using real dialog transcriptions from past user interactions which have been supplemented with decision points and outcomes. After deployment, update training may be performed on the algorithm using data captured by the system after the user interactions.

Abstract: An artificial intelligence (AI) system for guiding a user interaction in a phone call or chat session. The system includes a computer running an AI algorithm, such as a machine learning algorithm, which is trained to recognize patterns in user interaction dialog which lead to satisfactory outcomes for the user. The system may operate in a completely autonomous mode, or the system connect a human agent in the loop. The algorithm adaptively guides the dialog to achieve a favorable outcome based on the current status of the dialog—including identifying a next question to ask, information to provide, or an action to take. The algorithm is trained via supervised learning using real dialog transcriptions from past user interactions which have been supplemented with decision points and outcomes. After deployment, update training may be performed on the algorithm using data captured by the system after the user interactions.

Abstract: A system for improving distributed network data flow efficiency by using a machine learning model to engage a reduced set of unknown objects is disclosed. The system includes at least one processor, a communication interface communicatively coupled to the at least one processor, and a memory device storing executable code that, when executed causes the at least one processor to receive interaction data corresponding to interactions between known objects and unknown objects, develop an interaction map, develop a profile for each unknown object, process each unknown object using a machine learning algorithm to generate a reduced set of unknown objects most likely to reciprocate and identify properties of each unknown object, and trigger a communication to each of the reduced set of unknown objects.

Abstract: A number of variations may include a mobile user device and back-end server system for remotely authorizing a user of a user mobile device proximate a point of interaction system for real-time transmission of contemporaneous interaction information from the point of interaction system to the user mobile device prior to its periodic deletion by the point of interaction system, the back-end server system non-collocated with both the user mobile device and the point of interaction system.

Abstract: A number of variations may include a mobile user device and a back-end server system for remotely authorizing a user of a user mobile device proximate a point of interaction system for real-time transmission of contemporaneous interaction information from the point of interaction system to the user mobile device prior to its periodic deletion by the point of interaction system, the back-end server system non-collocated with both the user mobile device and the point of interaction system.

Abstract: An artificial intelligence (AI) system for guiding a user interaction in a phone call or chat session. The system includes a computer running an AI algorithm, such as a machine learning algorithm, which is trained to recognize patterns in user interaction dialog which lead to satisfactory outcomes for the user. The system may operate in a completely autonomous mode, or the system connect a human agent in the loop. The algorithm adaptively guides the dialog to achieve a favorable outcome based on the current status of the dialog—including identifying a next question to ask, information to provide, or an action to take. The algorithm is trained via supervised learning using real dialog transcriptions from past user interactions which have been supplemented with decision points and outcomes. After deployment, update training may be performed on the algorithm using data captured by the system after the user interactions.

Abstract: An artificial intelligence (AI) system for guiding a user interaction in a phone call or chat session. The system includes a computer running an AI algorithm, such as a machine learning algorithm, which is trained to recognize patterns in user interaction dialog which lead to satisfactory outcomes for the user. The system may operate in a completely autonomous mode, or the system connect a human agent in the loop. The algorithm adaptively guides the dialog to achieve a favorable outcome based on the current status of the dialog—including identifying a next question to ask, information to provide, or an action to take. The algorithm is trained via supervised learning using real dialog transcriptions from past user interactions which have been supplemented with decision points and outcomes. After deployment, update training may be performed on the algorithm using data captured by the system after the user interactions.

Abstract: A data analyzer associated with an entity determines patterns and/or sequences in user data. The analyzer may provide businesses with feedback on spatiotemporal patterns in user habits. In certain embodiments, the analyzer determines the frequency of a sequence of purchases made at a first entity immediately followed by purchases made at a second entity. In another embodiment, the analyzer determines trends in user purchases made during the weekday versus those that are made during the weekend. The results of the analysis may be used in a variety of ways, including, but not limited to merchant/consumer prospecting, and targeted promotions.

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: Transaction data is processed using a store number that is associated with a merchant. By analyzing sales of a merchant by store, a financial institution can better assess the financial health of a merchant. The financial institution can subsequently evaluate different value propositions that may be offered to the merchant or a consumer. The store numbers are extracted from the transaction entries so that a performance metric can be determined by store locations of the merchant. Consequently, the financial institution can determine the financial health of the merchant based on individual stores rather than on the total sales of the merchant. Different value propositions can be offered by the financial institution to a merchant or a consumer of the merchant based on an analysis of the transaction data. Transaction data for different merchants and different geographic areas can be compared to identify potential customers for the financial institution.

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.

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 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: Aspects of this disclosure relate to a computer configured to identify potential customers which may include a processor and memory storing computer executable instructions that, when executed, cause the computer to perform a method for identifying potential customers for a first bank, by electronically receiving data regarding the first bank, electronically receiving data regarding a second bank. Further, based on the electronically received data the method may further include identifying locations of the first bank, identifying locations of the second bank, comparing the locations of the first bank with the locations of the second bank, determining locations of the second bank that are within a predetermined distance of at least one location of the first bank, determining an amount of funds in a set of deposit accounts in each location of the second bank, and determining if the amount of funds in each location of the second bank is more than a predetermined amount.

Abstract: Aspects of this disclosure relate to a computer configured to identify potential customers which may include a processor and memory storing computer executable instructions that, when executed, cause the computer to perform a method for identifying potential customers for a first bank, by electronically receiving data regarding the first bank, electronically receiving data regarding a second bank. Further, based on the electronically received data the method may further include identifying locations of the first bank, identifying locations of the second bank, comparing the locations of the first bank with the locations of the second bank, determining locations of the second bank that are within a predetermined distance of at least one location of the first bank, determining an amount of funds in a set of deposit accounts in each location of the second bank, and determining if the amount of funds in each location of the second bank is more than a predetermined amount.

Abstract: Systems, methods, and computer program products are provided for providing a consumer with information about the balance of the consumer's credit or debit account after a transaction, such as a purchase transaction, is made using the credit or debit account. In one embodiment, the issuing bank attaches a number representing the balance of the consumer's credit or debit account and/or other information about the balance to the authorization code when authorizing a purchase transaction so that, when a vendor provides the consumer with a purchase receipt that has the authorization code on the receipt, the balance information is automatically included on the receipt with the authorization code. In this way, the consumer can keep track of spending by knowing the total balance of the consumer's credit and/or debit account and/or other balance information after each credit or debit card purchase.

Abstract: Apparatus and methods for processing a loan application. The apparatus and methods may involve accessing a financial institution customer account that includes a plurality of transactions; identifying those transactions that belong to a predetermined transaction category; and summarizing the transactions. Summary information may be presented as part of an income and expenses flow report that at least partially characterizes the account or the applicant. A financial institution representative may query a database for the summary information and, optionally, initiate an underwriting process based on the loan application and the summary information.