Abstract: A settlement card system includes a settlement card that is locked for authorized use with a single card specific merchant. A first computing system is operated by the card issuer and determines a transaction monetary limit and customer payment terms that differ for each subsequent, single transaction by the customer with the card specific merchant. A second computing system forwards the authorization request to the first computing system, which determines if the transaction is within the monetary limit determined for the customer, and if no, reject the transaction, if yes, accept the transaction and determine customer payment terms for that transaction. A third computing system makes payment to the authorized merchant for the transaction after receiving a payment authorization from the first computing system. The first computing system transfers a payment to the third computing system in the amount of the transaction.

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 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 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.