Abstract: A modelling platform including at least one processor in communication with a memory device and a payment processor is provided. The at least one processor is configured to retrieve subsets of data from a transaction history database, derive training data sets from the subsets, apply model input data fields of each training data set as inputs to one or more machine learning models, and apply a machine learning algorithm to adjust parameters of the one or more machine learning models. The at least one processor is also configured to upload at least one trained machine learning model to an operational predictive model module, apply a stream of real-time authorization request messages as inputs to the at least one trained machine learning model, and transmit, to the payment processor in real-time or near real-time, values of at least one output obtained by applying the stream.

Abstract: An integrated record management (IRM) computing device is configured to receive and store incentive structures and a record of a credited amount to be credited to a pre-paid debit account associated with a user. The computing device is also configured to receive a first authorization request message for a transaction with a merchant, and compare a transaction amount of the transaction to a credited amount of funds identified in the record. When the transaction amount is greater than the credited amount of funds, the computing device modifies the first authorization request message to generate a second authorization request message and transmits the second authorization request message to an issuer of another payment account associated with the user for authorization processing. When the transaction amount is less than or equal to the credited amount of funds, the computing device initiates a pre-paid debit transaction with the merchant and excluding the issuer.