Abstract: One or more embodiments provide for a method. The method includes receiving a document including text. The method also includes receiving different definitions of a position in the document of an identifier-value pair within the text. The identifier-value pair includes an identifier identifying an information type in the text and a value including an entry of the information in the text. The method also includes determining which of the different definitions correctly point to the position to form a number of correct definitions. Each of the different definitions correctly points to the position when the identifier-value pair is found on a first attempt using a definition of the different definitions to search for the identifier-value pair. The method also includes, responsive to the number of correct definitions at least matching a threshold value, issuing a command to perform data scraping on the document to extract the value of the identifier.

Abstract: Certain aspects of the present disclosure provide techniques for automatic form detection and completion. When a form is first identified by systems and methods disclosed herein, an image of the form is acquired and the form is uniquely identified. The image of the form is presented to a user, who clicks on areas of the form image and provides data into the areas so chosen. The coordinates of the user input in the form image, data provided by the user, and attributes of that data, are stored. Once a sufficient number of users, determined by a confidence interval threshold, have used the form in this manner, the user data input is clustered around user defined input coordinates. From the user data, a data schema of the user input is determined, and the data schema is assigned to the related user defined input coordinates.

Abstract: Systems and methods that may be configured to implement a nearest neighbor-based bank account validation process that may be used with electronic payments, transactions and or services.

Abstract: A processor may generate a plurality of vectors from an original text by processing the original text with at least one unsupervised learning algorithm. Each of the plurality of vectors may correspond to a separate portion of a plurality of portions of the original text. The processor may determine respective segments to which respective vectors belong. The processor may minimize a distance between at least one vector belonging to the segment and a known vector from among one or more known vectors and applying a label of the known vector to the segment.

Abstract: Transactions include text fields, such as description fields. Transactions are extracted from financial institutions using web-scraping extraction. In the process of extracting transactions, errors can be introduced into text fields, such as the inclusion of a space within a word or the removal of a space between words. A statistical approach is applied to the text fields. When two alternative text fields are presented, the alternative that statistically includes more common tokens, such as unigrams and bigrams, is chosen as the correct alternative. The incorrect alternative is replaced by the correct alternative in the text field.

Abstract: A computer-implemented method is provided to preforming re-categorization of financial transactions. The re-categorization is implemented by a server computing device which receives the financial transactions associated with a merchant and a first category. The server computing device receives user inputs that are each associated with re-categorizing a financial transaction from the first category to one or more other categories. Based at least in part on a count of the first category and counts of the one or more other categories, the server computing device determines a set of normalized ratios for the first category and the one or more other categories with respect to a total number of respective financial transactions received. The server computing device determines a second category corresponding to a minimum value in the set of the normalized ratios for each financial transaction associated with the merchant.

Abstract: One or more embodiments includes storing a group email message in multiple email mailboxes to obtain multiple stored email messages, receiving, for a first stored email message in the stored email messages, an updated label from a first user computing device, detecting, in response to receiving the updated label, that the stored email messages matches the first stored email message, and storing the updated label with at least a subset of the stored email messages. The method further includes transmitting, with the updated label and to a second user computing system, a second stored email message in the stored email messages.

Abstract: A method including receiving a data structure describing transactions between electronic user accounts associated with users. A relationship graph is constructed from the data in the data structure. The relationship graph has nodes representing entities described in the transactions. The relationship graph has edges representing connections between the nodes. The method also includes clustering groups of nodes within the nodes to form clusters among the nodes. The edges are labeled as relationships types. Labeling is performed by receiving, as input to a machine learning model, a vector having attributes representing the clusters, the nodes, and the edges. Labeling is also performed by outputting, from the machine learning model, probabilities. Each of the probabilities corresponds to a corresponding probability that an edge in the edges represents a relationship type between two nodes in the nodes. Labeling is also performed by labeling, based on the output, the edges as the relationship types.

Abstract: Known fraudulent invoice data, including defined and known fraudulent invoice feature data, is used to train a machine learning-based fraudulent invoice detection model to generate a fraudulent invoice score for invoices indicating a determined probability that a given invoice is fraudulent. The machine learning-based fraudulent invoice detection model is then used to generate a fraudulent invoice score for subsequent invoices before those invoices are paid by, and in some cases before the invoices are provided to, the parties being asked to pay the invoices. The fraudulent invoice score for the subsequent invoice is then used to determine if the subsequent invoice should be passed on to the parties being asked to pay the invoices for payment, or if one or more protective actions should be taken.

Abstract: Customer transaction data is processed to determine transaction locations for transactions, including transactions whose locations are not initially known. The transaction location data is then utilized to identify merchants that are mobile merchants, and the mobile merchant locations are periodically recalculated and tracked. Customer transaction data is further utilized to identify relationships between mobile merchants and customers of those mobile merchants. Merchant and customer data is also analyzed to identify potential customers of mobile merchants, and data related to the mobile merchants is provided to current and potential customers of those mobile merchants.

Abstract: Machine learning-based anomaly detection methods are used to identify a change in a user's streaming transaction data. If a threshold level of change in the user's transaction data is detected, the user is then identified as potentially having experienced a life event. Then, after a user is identified has having potentially experienced a life event, individual user transactions are processed and analyzed to determine the specific life event the user has most likely experienced. The user is then identified as having experienced the identified specific life event. This information is then used to customize the interactions between the user and the data management system such as questions asked of the user, forms or displays provided to the user, or offers made to the user.

Abstract: Test deposit mechanisms used by financial institutions to link accounts are used to identify undisclosed accounts associated with users of a data management system. The potential existence of undisclosed accounts is determined based on the assumption that the presence of test deposit transactions in user account data is a strong indication that an undisclosed user account exists. Using this assumption, transaction data from user accounts disclosed to a user data management system is scanned to identify test deposit transactions listed in the transaction data. If test deposit transactions are identified, the user of the data management system is queried regarding the existence of the undisclosed user account. If the user confirms the existence of the undisclosed account, the formally undisclosed account is added to a set of disclosed user accounts with the data management system.

Abstract: An embodiment of the method of processing communication data to identify one or more themes within the communication data includes identifying terms in a set of communication data, wherein a term is a word or short phrase, and defining relations in the set of communication data based on the terms, wherein the relation is a pair of terms that appear in proximity to one another. The method further includes identifying themes in the set of communication data based on the relations, wherein a theme is a group of one or more relations that have similar meanings, and storing the terms, the relations, and the themes in the database.

Abstract: Systems, methods, and media for the automated removal of private information are provided herein. In an example implementation, a method for automatic removal of private information may include: receiving a transcript of communication data; applying a private information rule to the transcript in order to identify private information in the transcript; tagging the identified private information with a tag comprising an identification of the private information; applying a complicate rule to the tagged transcript in order to evaluate a compliance of the transcript with privacy standards; removing the identified private information from the transcript to produce a redacted transaction; and storing the redacted transcript.

Abstract: Systems, methods, and media for the automated removal of private information are provided herein. In an example implementation, a method for automatic removal of private information may include: receiving a transcript of communication data; applying a private information rule to the transcript in order to identify private information in the transcript; tagging the identified private information with a tag comprising an identification of the private information; applying a complicate rule to the tagged transcript in order to evaluate a compliance of the transcript with privacy standards; removing the identified private information from the transcript to produce a redacted transaction; and storing the redacted transcript.

Abstract: Certain aspects of the present disclosure provide techniques for predicting a location based on transaction record data. An example technique includes obtaining a first set of transaction records and determining a merchant associated with each transaction record. The example further includes based on the merchant, determining and appending a branch identifier to each transaction record associated with the merchant to generate a first set of extended transaction records. The example further includes creating a consumption graph based on the first set of extended transaction records and determining an estimated location based on the consumption graph. The example further includes determining a precise point location based on the estimated location.

Abstract: Systems and methods of automated ontology development include a corpus of communication data. The corpus of communication data includes communication data from a plurality of interactions and is processed. A plurality of terms are extracted from the corpus. Each term of the plurality is a plurality of words that identify a single concept within the corpus. An ontology is automatedly generated from the extracted terms.

Abstract: Certain aspects of the present disclosure provide techniques for predicting a location based on transaction record data. An example technique includes obtaining a first set of transaction records and determining a merchant associated with each transaction record. The example further includes based on the merchant, determining and appending a branch identifier to each transaction record associated with the merchant to generate a first set of extended transaction records. The example further includes creating a consumption graph based on the first set of extended transaction records and determining an estimated location based on the consumption graph. The example further includes determining a precise point location based on the estimated location.

Abstract: A method of zoning a transcription of audio data includes separating the transcription of audio data into a plurality of utterances. A that each word in an utterances is a meaning unit boundary is calculated. The utterance is split into two new utterances at a work with a maximum calculated probability. At least one of the two new utterances that is shorter than a maximum utterance threshold is identified as a meaning unit.

Abstract: Systems and methods of automated ontology development include a corpus of communication data. The corpus of communication data includes communication data from a plurality of interactions and is processed. A plurality of terms are extracted from the corpus. Each term of the plurality is a plurality of words that identify a single concept within the corpus. An ontology is automatedly generated from the extracted terms.