Abstract: Certain aspects of the present disclosure provide techniques for generating an insight, comprising: receiving a request to generate an insight for a document from a user associated with the document; receiving the document; generating a tree based on the document; parsing the tree based on a set of rules to generate a set of results associated with the tree; mapping the set of results to a subset of insight elements of a set of insight elements associated with a set of trees including the tree; providing, to a first machine-learning model, the subset of insight elements; receiving, from the first machine-learning model, an insight for the document based on the subset of insight elements; and returning the insight for the document to the user.

Abstract: A processor may receive a plurality of text samples generated by a user and identify at least one variable text element in at least one of the plurality of text samples. The processor may tokenize the at least one variable text element, thereby producing a plurality of tokenized text samples including at least one token. The processor may build a longest common substring from the plurality of tokenized text samples and add the longest common substring and the at least one token to a set of selectable user interface options specific to the user. The processor may generate a user interface comprising the set of selectable user interface options. This can include detecting a user interface context and automatically replacing the at least one token with information specific to the user interface context within the set of selectable user interface options.

Abstract: Aspects of the present disclosure provide techniques for detecting latent bias in machine learning models. Embodiments include receiving a data set comprising features of a plurality of individuals. Embodiments include receiving identifying information for each individual of the plurality of individuals. Embodiments include predicting, for each respective individual of the plurality of individuals, a probability that the respective individual belongs to a given class based on the identifying information for the given individual. Embodiments include providing, as inputs to a machine learning model, the features of the plurality of individuals from the data set. Embodiments include receiving outputs from the machine learning model in response to the inputs. Embodiments include determining whether the machine learning model is biased against the given class based on the outputs and the probability that each respective individual of the plurality of individuals belongs to the given class.

Abstract: Certain aspects of the present disclosure provide techniques for privacy preserving sharing and validation of sensitive information in a computing environment. An example method generally includes generating a hashed value of a sensitive data item. A set of modulo values is calculated for the hashed value of the first sensitive data item using a set of prime numbers between an upper bound number and a lower bound number. A request to validate the first sensitive data item is transmitted to a target computing system. The request includes the set of prime numbers and the set of modulo values. An indication of whether a match was found for each respective modulo value in the set of modulo values is received from the target computing system, and a request associated with the first sensitive data item is processed based on the indication.

Abstract: A computer-implemented method and system are provided to utilize machine learning technology to process user financial transaction data to predict a personalized payment screen architecture. A plurality of feature datasets associated with transaction data of a plurality of electronic invoices are obtained by a computing device. Each feature dataset comprises a plurality of features, a payment screen and a payment method configured to be presented on at least one payment screen. The computing device is configured to train a machine learning model with the feature datasets to produce a probability matrix with probabilities of each payment method used to pay the invoices through each payment screen. The computing device may weigh the probability matrix to generate a recommendation matrix and determine a prediction of a payment screen based on the recommendation matrix.

Abstract: Certain aspects of the present disclosure provide techniques for generating an insight, comprising: receiving a request to generate an insight for a document from a user associated with the document; receiving the document; generating a tree based on the document; parsing the tree based on a set of rules to generate a set of results associated with the tree; mapping the set of results to a subset of insight elements of a set of insight elements associated with a set of trees including the tree; providing, to a first machine-learning model, the subset of insight elements; receiving, from the first machine-learning model, an insight for the document based on the subset of insight elements; and returning the insight for the document to the user.

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: Systems and methods that may be used to allow married couples to prepare separate individual tax returns while also being able to evaluate the merits of filing a joint return in a manner that does not breach each spouse's financial privacy.

Abstract: Systems and methods for generating a credit profile based on user behavior traits are disclosed. A system may be configured to obtain a plurality of financial based interactions of a user, generate one or more behavior trait indicators based on the plurality of financial based interactions, and generate the credit profile of the user based on the one or more behavior trait indicators. A behavior trait indicator may include a self-control indicator regarding discretionary spending, an ostrich bias indicator regarding user interactions after negative news or events, or a procrastination indicator based on voluntary late payments to user accounts.

Abstract: A computer-implemented method and system are provided to utilize machine learning technology to process user financial transaction data to predict a personalized payment screen architecture. A plurality of feature datasets associated with transaction data of a plurality of electronic invoices are obtained by a computing device. Each feature dataset comprises a plurality of features, a payment screen and a payment method configured to be presented on at least one payment screen. The computing device is configured to train a machine learning model with the feature datasets to produce a probability matrix with probabilities of each payment method used to pay the invoices through each payment screen. The computing device may weigh the probability matrix to generate a recommendation matrix and determine a prediction of a payment screen based on the recommendation matrix.

Abstract: Systems and methods for recommending and automating user savings goals within a single banking account are disclosed. An example method is performed by an electronic device coupled to a machine learning model and includes training the machine learning model based at least in part on historical user data, receiving attributes of a first user, generating, using the trained machine learning model, one or more recommended savings goals for the first user based at least in part on the attributes of the first user, receiving selection of one or more of the recommended savings goals, determining, using a trained classification model, a recommended amount for the first user to periodically save, the recommended amount determined based at least in part on the attributes of the first user, and periodically allocating savings to each of the one or more selected savings goals based at least in part on the recommended amount.

Abstract: Systems and methods that may be used to provide a predictive tax loan or other monetary advance before the loan recipient (e.g., a taxpayer) prepares and files its tax return. A risk of providing a predictive tax loan or monetary advance is modeled separately from a machine learning model used to determine the anticipated tax refund amount and tax loan. The disclosed systems and methods may also predict accurate tax withholdings based on multiple machine learning models from multiple services, including non-payroll related services.

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: Certain aspects of the present disclosure provide techniques for privacy-preserving execution of a workflow in a software application. Embodiments include generally includes receiving homomorphically encrypted inputs from a client device corresponding to user-provided data needed to calculate a result for a step of a workflow in the software application. A result is calculated for the step of the workflow using the received homomorphically encrypted inputs. The calculated result is returned to the client device. The calculated result is homomorphically encrypted as a result of calculating the result using the received homomorphically encrypted inputs.

Abstract: Systems and methods for identifying businesses having complementary business cycles are disclosed. An example method may include receiving financial information for each business of a plurality of businesses, the financial information including a plurality of values of a financial indicator with respect to time, training a machine learning model to determine a preference metric for a pair of businesses based at least in part on respective values of the businesses' financial indicators, for each given business in the plurality of businesses, determining corresponding values of the preference metric, using the training machine learning model, for at least a respective subset of the plurality of businesses, and determining an optimal pairing for each business in the plurality of businesses based at least in part on the determined values of the preference metric.

Abstract: Automatic keyphrase labeling and machine learning training may include a processor extracting a plurality of keywords from at least one search query that resulted in a selection of a document appearing in a search result. For each of the plurality of keywords, the processor may determine a probability that the keyword describes the document. The processor may generate one or more keyphrases by performing processing including selecting each of the plurality of keywords having a probability greater than a predetermined threshold value for insertion into at least one of the one or more keyphrases and assembling the one or more keyphrases from the selected plurality of keywords. The processor may label the document with the keyphrase.

Abstract: A method including embedding, by a trained issue MLM (machine learning model), a new natural language issue statement into an issue vector. An inner product of the issue vector with an actions matrix is calculated. The actions matrix includes centroid-vectors calculated using a clustering method from a second output of a trained action MLM which embedded prior actions expressed in natural language action statements taken as a result of prior natural issue statements. Calculating the inner product results in probabilities associated with the prior actions. Each of the probabilities represents a corresponding estimate that a corresponding prior action is relevant to the issue vector. A list of proposed actions relevant to the issue vector is generated by comparing the probabilities to a threshold value and selecting a subset of the prior actions with corresponding probabilities above the threshold. The list of proposed actions is transmitted to a user device.

Abstract: A method including extracting data from disparate data sources. The data includes data pairs including a corresponding data point and a corresponding time associated with the corresponding data point. The method also includes extracting insights from the data at least by identifying a trend in the data pairs. The method also includes forming a model vector including the insights and an additional attribute to the insights. The additional attribute characterizes the insights. The additional attribute includes at least user feedback including a user ranking of a ranked subset of the insights from a user. The method also includes inputting the model vector into a trained insight machine learning model to obtain a predicted ranking of the insights. The method also includes selecting, based on the predicted user ranking, a pre-determined number of insights to form predicted relevant insights. The method also includes reporting the predicted relevant insights.

Abstract: System and method configured to evaluate financial transaction information and detect overdraft transaction events regardless of the financial institution associated with the event.

Abstract: Systems and methods that may be used to provide a predictive tax loan or other monetary advance before the loan recipient (e.g., a taxpayer) prepares and files its tax return. A risk of providing a predictive tax loan or monetary advance is modeled separately from a machine learning model used to determine the anticipated tax refund amount and tax loan. The disclosed systems and methods may also predict accurate tax withholdings based on multiple machine learning models from multiple services, including non-payroll related services.