Abstract: A processor may receive user interaction data of a user for a plurality of electronically-presented offers. The processor may generate a plurality of labels, the generating comprising generating a label for each respective offer according to a comparison of the quality of the user interactions of the respective offer to the frequency of the user interactions of the respective offer. Each label may be a positive label or a negative label. The processor may determine whether the generating produced both positive and negative labels. The processor may select one of a plurality of available ML models, wherein a two-class ML model is chosen in response to determining that the generating produced both positive and negative labels and a one-class ML model is chosen in response to determining that the generating did not produce both positive and negative labels. The selected ML model may be trained and/or may be used to process user profile data and provide recommendations.

Abstract: A processor may receive user interaction data of a user for a plurality of electronically-presented offers. The processor may generate a plurality of labels, the generating comprising generating a label for each respective offer according to a comparison of the quality of the user interactions of the respective offer to the frequency of the user interactions of the respective offer. Each label may be a positive label or a negative label. The processor may determine whether the generating produced both positive and negative labels. The processor may select one of a plurality of available ML models, wherein a two-class ML model is chosen in response to determining that the generating produced both positive and negative labels and a one-class ML model is chosen in response to determining that the generating did not produce both positive and negative labels. The selected ML model may be trained and/or may be used to process user profile data and provide recommendations.

Abstract: A processor may receive user interaction data of a user for a plurality of electronically-presented offers. The processor may generate a plurality of labels, the generating comprising generating a label for each respective offer according to a comparison of the quality of the user interactions of the respective offer to the frequency of the user interactions of the respective offer. Each label may be a positive label or a negative label. The processor may determine whether the generating produced both positive and negative labels. The processor may select one of a plurality of available ML models, wherein a two-class ML model is chosen in response to determining that the generating produced both positive and negative labels and a one-class ML model is chosen in response to determining that the generating did not produce both positive and negative labels. The selected ML model may be trained and/or may be used to process user profile data and provide recommendations.

Abstract: A method including generating, by a state engine from data describing behaviors of users in an environment external to the state engine, an executable process. An agent executes the executable process by determining, from the data describing the behaviors of the users, a problem of at least some of the users, and selects, based on the problem, a chosen action to alter the problem. At a first time, a first electronic communication describing the chosen action to the at least some of the users is transmitted. Ongoing data describing ongoing behaviors of the users is monitored. A reward is generated based on the ongoing data to change a parameter of the agent. The parameter of the agent is changed to generate a modified agent. The modified agent executes the executable process to select a modified action. At a second time, a second electronic communication describing the modified action is transmitted.

Abstract: This disclosure relates to systems and methods for constructing a customized debt reduction plan for a user. In some implementations, a customized debt reduction system obtains a plurality of financial attributes of the user and a plurality of other users, where the plurality of financial attributes are indicative of credit card debt, and identifies users from the plurality of other users who successfully repaid their credit card debt based on their respective financial attributes and one or more repayment techniques that resulted in successful repayment of their credit card debt. The customized debt reduction system correlates the plurality of financial attributes of the user with the plurality of financial attributes of a number of the identified users and determines a personalized score for the user, using a trained machine learning model, based on the correlation to determine a customized debt reduction plan for the user based on the personalized score.

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 processor may obtain financial data for a user. The processor may process the financial data to generate feature data indicative of at least one feature. The processor may compare the at least one feature to at least one threshold value to determine that the user has a cognitive bias affecting a financial preference of the user and associated with the at least one threshold value. The at least one threshold value may denote a threshold for membership in a cluster of unlabeled users having the cognitive bias. In response to the comparing, the processor may identify a change applicable to a financial account of the user. The change may be associated with the cognitive bias. The processor may automatically cause the change to be implemented by a network-accessible financial service.

Abstract: This disclosure relates to systems and methods for constructing a customized debt reduction plan for a user. In some implementations, a customized debt reduction system obtains a plurality of financial attributes of the user and a plurality of other users, where the plurality of financial attributes are indicative of credit card debt, and identifies users from the plurality of other users who successfully repaid their credit card debt based on their respective financial attributes and one or more repayment techniques that resulted in successful repayment of their credit card debt. The customized debt reduction system correlates the plurality of financial attributes of the user with the plurality of financial attributes of a number of the identified users and determines a personalized score for the user, using a trained machine learning model, based on the correlation to determine a customized debt reduction plan for the user based on the personalized score.

Abstract: Systems and methods that may be used to provide personalized financial nudges to users of a financial service that may be used to further the users' savings intentions (e.g., a savings goal, an emergency fund, etc.). The disclosed systems and methods may increase user interactivity with the financial service and the services it offers by providing personalized nudges that are based on, among other things, an evaluation of various behavioral economics principles. A machine learning recommendation system may be used to fit and output different nudges to users in a personalized way to maximize their savings' intentions.

Abstract: A processor may receive user interaction data of a user for a plurality of electronically-presented offers. The processor may generate a plurality of labels, the generating comprising generating a label for each respective offer according to a comparison of the quality of the user interactions of the respective offer to the frequency of the user interactions of the respective offer. Each label may be a positive label or a negative label. The processor may determine whether the generating produced both positive and negative labels. The processor may select one of a plurality of available ML models, wherein a two-class ML model is chosen in response to determining that the generating produced both positive and negative labels and a one-class ML model is chosen in response to determining that the generating did not produce both positive and negative labels. The selected ML model may be trained and/or may be used to process user profile data and provide recommendations.

Abstract: A method may include obtaining, over a network from a financial institution and using login credentials of a user, transactions corresponding to the user, calculating average transaction amounts for merchants based on the transactions, generating a spending propensity score for a category using a harmonic mean of amounts in a subset of the transactions corresponding to the category, generating, for the category, spending match scores between the spending propensity score and the average transaction amounts for the merchants, and recommending a merchant to the user using the spending match scores.

Abstract: A processor may obtain financial data for a user. The processor may process the financial data to generate feature data indicative of at least one feature. The processor may compare the at least one feature to at least one threshold value to determine that the user has a cognitive bias affecting a financial preference of the user and associated with the at least one threshold value. The at least one threshold value may denote a threshold for membership in a cluster of unlabeled users having the cognitive bias. In response to the comparing, the processor may identify a change applicable to a financial account of the user. The change may be associated with the cognitive bias. The processor may automatically cause the change to be implemented by a network-accessible financial service.

Abstract: Certain aspects of the present disclosure provide a method of establishing a mapping between users of different applications to enable them to use contact information to initiate data exchange with each other.

Abstract: A processor may obtain data indicating a user's financial health. The data may include personality data not directly related to finances. The processor may analyze the data to identify a change applicable to a financial account of the user. The change may be configured to improve the user's financial health. The processor may automatically cause the change to be implemented by a network-accessible financial service.

Abstract: A method including receiving a website text item of the website, where the website text item is categorized by a text category and a website element category, identifying, multiple text items of multiple websites relating to multiple businesses in an industry, searching the multiple text items using the website element category to identify multiple comparable text items, parsing the website text item to generate multiple keywords, searching the multiple comparable text items using the multiple keywords to identify a comparable text item, where the comparable text item includes a conversion rate corresponding to a percentage of transactions completed by multiple website visitors accessing the comparable text item, identifying, a modified conversion text item from the multiple comparable text items, where the modified conversion text item has a modified conversion rate that exceeds the conversion rate, generating, a conversion improved website including the modified conversion text item.

Abstract: A method including identifying a business category of a business related to a user, obtaining multiple website analytics data items from multiple websites related to multiple business in the business category, evaluating the multiple website analytics data items to generate multiple website templates for the user, providing the multiple website templates to the user, receiving a selection from the user of a website template from the multiple website templates, receiving website content for a user website from the user, evaluating the multiple website analytics data items and the website content to generate multiple recommendations for the user website, providing the multiple recommendations for the user website to the user, receiving multiple recommendation approvals of the multiple recommendations for the user website from the user, and generating the user website based on the website content, the website template, and the multiple recommendation approvals.

Abstract: A method including receiving a website text item of the website, where the website text item is categorized by a text category and a website element category, identifying, multiple text items of multiple websites relating to multiple businesses in an industry, searching the multiple text items using the website element category to identify multiple comparable text items, parsing the website text item to generate multiple keywords, searching the multiple comparable text items using the multiple keywords to identify a comparable text item, where the comparable text item includes a conversion rate corresponding to a percentage of transactions completed by multiple website visitors accessing the comparable text item, identifying, a modified conversion text item from the multiple comparable text items, where the modified conversion text item has a modified conversion rate that exceeds the conversion rate, generating, a conversion improved website including the modified conversion text item.