Abstract: Examples include receiving, at a system, user data related to a user of a plurality of users, the user data including user characteristics associated with the user. The system assigns the user to a group of users based at least in part on the user sharing at least one user characteristic with users in the group of users. The system uses a machine learning model for determining, at least in part, an optimal communication channel through which to send a loan offer to the user, the machine learning model having been trained to determine optimal communication channels for providing loan offers based at least on user data associated with respective users of the group of users. The system sends the loan offer to the user via at least one communication channel selected based at least on the optimal communication channel determined at least in part using the machine learning model.

Abstract: In some examples, a system may receive first merchant data for a first merchant. For instance, the first merchant data may include one or more merchant characteristics associated with the first merchant. The system may assign the first merchant to a first group of merchants based at least in part on the first merchant sharing at least one of the merchant characteristics with the first group of merchants. The system may use a machine learning model to determine a customized timing at which to provide a loan offer to the first merchant. The customized timing n ay be based at least in part on loan data associated with one or more loans previously offered to the merchants in the first group of merchants. Further, the system may send the loan offer to a computing device of the first merchant based at least in part on the customized timing.

Abstract: Techniques for optimizing capital loan offers for merchants to maximize a potential benefit of the capital loans to the merchants. A service provider may receive merchant data and/or transaction data from a merchant. The service provider may determine merchant characteristics associated with the merchant based on the merchant data and/or transaction data. The service provider may associate the merchant with a merchant group, based on the merchant characteristics. The service provider may access data corresponding to capital loans previously offered to and/or accepted by merchants in the merchant group, to determine predictive factors of successful loans. Based on the predictive factors, the service provider may generate a capital loan for the merchant with a high probability of positive impact on a merchant business. The service provider may transmit a capital loan oiler to the merchant at a time determined to maximize acceptance and optimize a growth of the merchant business.

Abstract: Embodiments of the present invention relate to systems, methods, and computer program products for assessing student prospects. More specifically, embodiments of the present invention are directed to receiving transaction data from a transaction involving a consumer, comparing the transaction data to an education position rule, and determining an education position based at least partially on a comparison of the transaction data to an education position rule.

Abstract: Apparatus and methods for providing a budget envelope credit card are provided. An apparatus according to the invention may include a budget envelope credit card software engine and a rewards software engine that provides a reward scheme for use by the budget envelope credit card engine, the rewards scheme that defines a reward frequency.