Abstract: This application relates to apparatus and methods for identifying fraudulent payment transfers. In some examples, a computing device determines payment transfer initiation features, and payment transfer reception features, based on previous payment transfer data. The computing device may train a machine learning fraud detection model with the payment transfer initiation features, and may train a machine learning fraud detection model with the payment transfer reception features. Once trained, the computing device may employ the machine learning fraud detection models to identify fraudulent payment transfers. For example, the computing device may determine whether a payment transfer is fraudulent when the payment transfer is initiated. Assuming the payment transfer is allowed, the computing device may determine whether the payment transfer is fraudulent when the payment is being received.

Abstract: According to some embodiments of the present disclosure, a computer-implemented method for generating machine-learned models on behalf of an organization is disclosed. In embodiments, the method includes receiving a contact list, generating a respective individual profile, generating a plurality of analytical insights, presenting the analytical insights, receiving a set of attribute-specific filters, filtering the individual profiles, training a machine-learned model, determining a measure of predictive power of the machine-learned model based on a testing dataset and the machine-learned model, receiving a cutoff level from a user, and deploying the machine-learned model on behalf of the organization.

Abstract: This application relates to apparatus and methods for identifying fraudulent payment transfers. In some examples, a computing device determines payment transfer initiation features, and payment transfer reception features, based on previous payment transfer data. The computing device may train a machine learning fraud detection model with the payment transfer initiation features, and may train a machine learning fraud detection model with the payment transfer reception features. Once trained, the computing device may employ the machine learning fraud detection models to identify fraudulent payment transfers. For example, the computing device may determine whether a payment transfer is fraudulent when the payment transfer is initiated. Assuming the payment transfer is allowed, the computing device may determine whether the payment transfer is fraudulent when the payment is being received.

Abstract: This application relates to apparatus and methods for identifying fraudulent payment transfers. In some examples, a computing device determines payment transfer initiation features, and payment transfer reception features, based on previous payment transfer data. The computing device may train a machine learning fraud detection model with the payment transfer initiation features, and may train a machine learning fraud detection model with the payment transfer reception features. Once trained, the computing device may employ the machine learning fraud detection models to identify fraudulent payment transfers. For example, the computing device may determine whether a payment transfer is fraudulent when the payment transfer is initiated. Assuming the payment transfer is allowed, the computing device may determine whether the payment transfer is fraudulent when the payment is being received.

Abstract: An intelligent peer-to-peer fundraising campaign platform is provided for managing charitable and for profit campaigns. The platform includes capabilities for creating and managing campaigns by supporters, retrieving person data for supporters and leads for the campaign, relationship data and other attributes including affinity data, donation history data, potential and financial data and storing such data in the form of social graph. The campaign platform also helps supporters of a campaign to identify leads, send personalized communication to the leads for enlisting support to the campaign, determine donor outcomes with respect to each personalized communication sent to the lead; and updating or reinforcing a donor prediction model based on the donor outcome.

Abstract: An intelligent campaign platform is provided for managing non-profit campaigns. In embodiments, the platform is configured to obtain data relating to a number of individuals and resolve the identities of the individuals using multiple data sources to improve lead prioritization, donor prediction models accuracy, and/or identification of potential contributors to the campaigns. In embodiments, the identity resolution may include querying a first attribute data collection with a unique identifier of the respective lead to obtain a first set of attributes of the respective lead; querying a second attribute data collection with the unique identifier of the lead to obtain any other unique identifiers associated with the lead and querying the first attribute data collection with the one or more other unique identifiers to obtain a second set of attributes of the respective lead.

Abstract: The present disclosure relates to a campaign management platform that manages campaigns on behalf of an organization. In embodiments, a method includes receiving organizational data from the organization that indicates donor outcome data and attributes of previous donors to previous campaigns of the organization. The method includes determining random individual data of a plurality of random individuals including attributes thereof. The method also includes generating training data based on the donor outcome data and the random individual data and training a machine-learned donor prediction model based on the training data that is trained to receive a set of attributes of an individual and determine a likelihood that the individual will donate to the campaign based on the set of attributes of the individual, where the donor prediction model may be leveraged to determine whether a lead of the campaign is likely to contribute to a campaign of the organization.

Abstract: This application relates to apparatus and methods for identifying fraudulent payment transfers. In some examples, a computing device determines payment transfer initiation features, and payment transfer reception features, based on previous payment transfer data. The computing device may train a machine learning fraud detection model with the payment transfer initiation features, and may train a machine learning fraud detection model with the payment transfer reception features. Once trained, the computing device may employ the machine learning fraud detection models to identify fraudulent payment transfers. For example, the computing device may determine whether a payment transfer is fraudulent when the payment transfer is initiated. Assuming the payment transfer is allowed, the computing device may determine whether the payment transfer is fraudulent when the payment is being received.

Abstract: An intelligent peer-to-peer fundraising campaign platform is provided for managing charitable and for profit campaigns. The platform includes capabilities for creating and managing campaigns by supporters, retrieving person data for supporters and leads for the campaign, relationship data and other attributes including affinity data, donation history data, potential and financial data and storing such data in the form of social graph. The campaign platform also helps supporters of a campaign to identify leads, send personalized communication to the leads for enlisting support to the campaign, determine donor outcomes with respect to each personalized communication sent to the lead; and updating or reinforcing a donor prediction model based on the donor outcome.

Abstract: A system for campaign management employing a high volume web search and predictive analytic capabilities has been devised. A campaign control module is used to accept commands, parameters, and data defining a fundraising campaign and use those commands to instruct operation of the system to maximize participation while maintaining the enthusiasm of participants to continue to support the cause though regular contact. Web searches retrieve participant contact, financial, and social media post information which is then predictively analyzed to maximize the impact of individualized messages sent and automatically maintain the integrity of each participant's data store record.