Abstract: Artificial-intelligence computer-implemented processes and machines predict whether personal data may be present in structured software based on metadata field(s) contained therein. Natural language processing preprocesses input strings corresponding to the metadata field(s) into normalized input sequence(s). Individual characters in the sequence(s) are embedded into fixed-dimension vectors of real numbers. Bidirectional LSTM(s) or other machine-learning algorithm(s) are utilized to generate forward and backward contextualization(s). Neural network output(s) are provided based on element-wise averaging or feed forwarding based on the contextualization(s) in order to predict whether one or more value fields corresponding to the metadata field(s) may contain personal data.

Abstract: A computing platform may generate requests for log data stored on computing systems and may communicate the requests to the computing systems. The computing platform may receive the log data from the computing systems and may utilize the log data to generate records interrelating different data-access requests indicated by the log data. The computing platform may analyze the records to identify corresponding requests made by a user to multiple different computing systems and may determine an interrelationship between the corresponding requests. The computing platform may generate data indicating the multiple different computing systems and the interrelationship between the corresponding requests and may communicate the data to a computing device associated with at least one of the multiple different computing systems.

Abstract: A computing platform may generate requests for log data stored on computing systems and may communicate the requests to the computing systems. The computing platform may receive the log data from the computing systems and may utilize the log data to generate records interrelating different data-access requests indicated by the log data. The computing platform may analyze the records to identify corresponding requests made by a user to multiple different computing systems and may determine an interrelationship between the corresponding requests. The computing platform may generate data comprising a graphical depiction of the multiple different computing systems and the interrelationship between the corresponding requests and may communicate the data to a computing device associated with at least one of the multiple different computing systems.

Abstract: A computing platform may generate requests for log data stored on computing systems and may communicate the requests to the computing systems. The computing platform may receive the log data from the computing systems and may utilize the log data to generate records interrelating different data-access requests indicated by the log data. The computing platform may analyze the records to identify corresponding requests made by a user to multiple different computing systems and may determine an interrelationship between the corresponding requests. The computing platform may generate data indicating the multiple different computing systems and the interrelationship between the corresponding requests and may communicate the data to a computing device associated with at least one of the multiple different computing systems.

Abstract: In systems and methods, account data associated with one or more account of a first financial institution are received and the account data is stored in a storage device; triggers are identified based on the account data, where the triggers include one or more transactions; external account activity of one or more accounts associated with a second financial institution is determined based on the one or more transactions; and a product recommendation is provided to a user associated with one or more accounts of the first financial institution based on the identified external activity. The systems and methods further provide determining a shift in internal account usage of the one or more accounts associated with the first financial institution based on the one or more transactions.

Abstract: Aspects of the present disclosure are directed to methods and systems for macroeconomic indication, including electronically selectively mapping a plurality of merchant classification data to sub-sector economic data for payment card transactions classification. The method may electronically receive a plurality of retail sales data based on the payment card transactions classification over a predetermined period of time to define an economic time series dataset; electronically adjust the economic time series dataset based on an autoregressive integrated moving average; and electronically transform the economic time series dataset after the adjusting step by using linear regression to define a predefined time period percentage change in the periodic data.

Abstract: Systems, methods, and computer program products are provided for customer-defined financial institution account management rules associated with linked financial institution accounts. The methods, systems and computer program products herein described allow the customer to define rules that maximize the customer's rate of return and/or minimizing the likelihood of insufficient funding of an account leading to a withdrawal in excess of account balance. Thus, poor utilization of cash in terms of excessive balances in non-interest bearing accounts or low-interest bearing accounts is minimized by automatically transferring, based on a customer-defined rules, the funds to a higher rate of return account. Additionally, managing a primary payment account, such as a checking or bill account, is addressed so as to ensure that funds are sufficient to cover the customer's expenditures.

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: Systems, methods and computer program products are provided for optimizing social networks. In the systems and methods, account data associated with a user account is received and the account data is stored in a storage device. Pluralities of networking opportunities are identified based on the account data. Furthermore, based on the account data triggers may be detected indicating a user's interest in one or more social networking opportunities. The system then offers the user, based at least partially on the detected trigger, the one or more social networking opportunities.

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 and methods for monitoring trigger data quality are provided herein. The systems and methods detect and report whether the current trigger counts are normal or flawed in real time. The systems and methods monitor the triggers to determine the accuracy, completeness, domain of values, and format of the trigger data. In the system and methods, account data associated with the one or more accounts is received and stored in a storage device; the account data is segregated into one or more periods of time; triggers associated with the one or more periods of time are identified based on transactions that occur during the one or more periods of time; a total transaction count for each of the triggers is calculated; and control limits are determined based on the transaction count for each of the triggers.

Abstract: A method of one embodiment facilitates the evaluation of reward choices. A plurality of reward parameters are received by an interface, each reward parameter associated with a user account of a user, and the plurality of reward parameters are stored by a memory. The interface receives information associated with a seller. A processor then automatically selects a user account based on the plurality of reward parameters and the information associated with the seller, and the interface communicates information indicating the selected user account.

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 and methods for producing and maintaining account related triggers are provided herein. The systems and methods may be utilized for risk mitigation. Account triggers may be executed to determine account activity indicative of a risk event. Once a risk event is identified, the financial institution may determine an appropriate action to mitigate foreseeable risk due to the risk event.

Abstract: Aspects of the disclosure relate to assisting credit account holders during economic hardship. More specifically, aspects of the disclosure relate to systems and methods for providing a rebate of at least a portion of a finance charge applied to a credit account. Unlike certain prior art systems and methods, embodiments disclosed herein may benefit both the credit account holder as well as the financial institution that extends the credit account. In one embodiment, it may be determined that several credit accounts meet a threshold of probability of having a charge-off. In certain embodiments, the number of accounts that receive a rebate may be limited to a certain percentage of credit accounts with a non-zero balance.

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.

Abstract: Systems, methods, and computer program products are provided for optimizing amounts collected in an offset action where a creditor attaches funds from a debtor's deposits account to satisfy overdue debt payments. In operation, embodiments of the present invention leverage a deposits account's transaction history to detect a deposit pattern for the account and, based on the detected pattern, predict the date of the next large deposit to the attachable account. An offset associate, using the present invention, can then schedule the offset action to occur on the expected date of the next large deposit.

Abstract: Systems and methods for retaining users in their existing relationship with a financial institution are provided herein. Based on account data, comparisons of past and current transactional activity are made and a change in account usage is determined. In the systems and methods, account data associated with one or more account are received and the account data is stored in a storage device; a first trigger group comprising one or more transactions occurring during a first period of time and a second trigger group comprising one or more transactions occurring during a second period of time are identified based on the account data; the first trigger group and the second trigger group are compared; and a decrease in account activity for each of the one or more accounts is determined in response to the comparison of the first trigger group and second trigger group.

Abstract: Systems and methods for increasing transaction depth and account breadth are provided herein. In the systems and methods, account data associated with one or more account are received and the account data is stored in a storage device; a first trigger group comprising a number of transactions occurring during a first period of time and a second trigger group comprising a number of transactions occurring during a second period of time based on the account data are identified based on the account data; the first trigger group and the second trigger group are compared; and an increase in account activity for each of the one or more accounts is determined in response to the comparison of the first trigger group and second trigger group.

Abstract: Systems and methods for monitoring trigger data quality are provided herein. The systems and methods detect and report whether the current trigger counts are normal or flawed in real time. The systems and methods monitor the triggers to determine the accuracy, completeness, domain of values, and format of the trigger data. In the system and methods, account data associated with the one or more accounts is received and stored in a storage device; the account data is segregated into one or more periods of time; triggers associated with the one or more periods of time are identified based on transactions that occur during the one or more periods of time; a total transaction count for each of the triggers is calculated; and control limits are determined based on the transaction count for each of the triggers.