Abstract: A transaction card system pulls past transaction data from a financial institution. A user profile model is applied to the past transaction data and customer data features and transforms the data into a user feature profile that summarizes the identity data, financial health and payment capacity of the customer. An overdraft request has current declined transaction details relating to a customer transaction using the transaction card. The computing network pulls from cache the most recent customer transaction banking data and most recent user feature profile and applies a machine learning approval model to the current declined transaction details, the most recent customer transaction data, and the most recent user feature profile, and determines whether to approve or decline the overdraft amount.

Abstract: A transaction card system pulls past transaction data from a financial institution. A user profile model is applied to the past transaction data and customer data features and transforms the data into a user feature profile that summarizes the identity data, financial health and payment capacity of the customer. An overdraft request has current declined transaction details relating to a customer transaction using the transaction card. The computing network pulls from cache the most recent customer transaction banking data and most recent user feature profile and applies a machine learning approval model to the current declined transaction details, the most recent customer transaction data, and the most recent user feature profile, and determines whether to approve or decline the overdraft amount.

Abstract: A system and method determines the creditworthiness of a consumer and issues a loan and generates a behavioral profile for that consumer. An initial set of data is acquired from the consumer that includes non-identification attributes without obtaining a full name, a credit card number, a passport number, or a government issued ID number that allows identification of the consumer. A user ID number matches the initial set of data to a physical user in a transaction database. A credit score based on the average credit among a plurality of user profiles is matched to determine a maximum credit for the consumer. A loan is credited and a behavioral profile is generated based on the consumer check-ins and location and correlating periodic location patterns to loan and transactional activities.

Abstract: A system and method determines the creditworthiness of a consumer and issues a loan and generates a behavioral profile for that consumer. An initial set of data is acquired from the consumer that includes non-identification attributes without obtaining a full name, a credit card number, a passport number, or a government issued ID number that allows identification of the consumer. A user ID number matches the initial set of data to a physical user in a transaction database. A credit score based on the average credit among a plurality of user profiles is matched to determine a maximum credit for the consumer. A machine learning model may be applied to stored consumer loan data to determine when the consumer requires an increase in the maximum allowed credit and the risk involved with increasing the maximum allowed credit.

Abstract: A system and method determines the creditworthiness of a consumer and issues a loan and generates a behavioral profile for that consumer. An initial set of data is acquired from the consumer that includes non-identification attributes without obtaining a full name, a credit card number, a passport number, or a government issued ID number that allows identification of the consumer. A user ID number matches the initial set of data to a physical user in a transaction database. A credit score based on the average credit among a plurality of user profiles is matched to determine a maximum credit for the consumer. A loan is credited and a behavioral profile is generated based on the consumer check-ins and location and correlating periodic location patterns to loan and transactional activities.

Abstract: A system and method determines the creditworthiness of a consumer and issues a loan and generates a behavioral profile for that consumer. An initial set of data is acquired from the consumer that includes non-identification attributes without obtaining a full name, a credit card number, a passport number, or a government issued ID number that allows identification of the consumer. A user ID number matches the initial set of data to a physical user in a transaction database. A credit score based on the average credit among a plurality of user profiles is matched to determine a maximum credit for the consumer. A machine learning model may be applied to stored consumer loan data to determine when the consumer requires an increase in the maximum allowed credit and the risk involved with increasing the maximum allowed credit.

Abstract: A client computing device transmits a request to link a client bank account and receives from a data provider a public access token and transmits it to a loan issuance server, which in turn, transmits it to the data provider. The loan issuance server receives a private access token and limited identity data regarding a bank account associated with the client. A credit score engine receives public data associated with the client and income and transactional data of the client bank account and applies a machine learning model to create an initial credit score that is indicative of the maximum allowed credit for the client. Based on the credit score, the loan approval server approves a loan to be distributed in an amount up to the maximum allowed credit.

Abstract: A system and method determines the creditworthiness of a consumer and issues a loan. An initial set of data is acquired from the consumer that includes non-identification attributes without obtaining a full name, a credit card number, a passport number, or a government issued ID number that allows identification of the consumer. A user ID number matches the initial set of data to a physical user in a transaction database. A credit score based on the average credit among a plurality of user profiles is matched to determine a maximum credit for the consumer. A transaction card, such as a prepaid transaction card or stored value card, is issued to the consumer having a value corresponding to the amount of the loan.

Abstract: A system and method determines the creditworthiness of a consumer and issues a loan and generates a behavioral profile for that consumer. An initial set of data is acquired from the consumer that includes non-identification attributes without obtaining a full name, a credit card number, a passport number, or a government issued ID number that allows identification of the consumer. A user ID number matches the initial set of data to a physical user in a transaction database. A credit score based on the average credit among a plurality of user profiles is matched to determine a maximum credit for the consumer. A loan is credited and a behavioral profile is generated based on the consumer check-ins and location and correlating periodic location patterns to loan and transactional activities.

Abstract: A system and method determines the creditworthiness of a consumer and issues a loan and generates a behavioral profile for that consumer. An initial set of data is acquired from the consumer that includes non-identification attributes without obtaining a full name, a credit card number, a passport number, or a government issued ID number that allows identification of the consumer. A user ID number matches the initial set of data to a physical user in a transaction database. A credit score based on the average credit among a plurality of user profiles is matched to determine a maximum credit for the consumer. A machine learning model may be applied to stored consumer loan data to determine when the consumer requires an increase in the maximum allowed credit and the risk involved with increasing the maximum allowed credit.