Abstract: A device and method for processing data records to train a machine learning model and modify data records based on predictive scoring are disclosed. Historical data may be partitioned into a training set and a validation set, with dimensionality reduction applied to the training set to create a minimum feature set. The trained model may predict outcomes and generate predictive scores for data records. A device may modify data records by updating parameters based on the predictive scores and may monitor performance metrics associated with the model. Updated parameters and predictive scores may be stored in a secure repository and displayed via a user interface. Systems and non-transitory computer-readable media storing instructions for executing these operations may also be disclosed.

Abstract: In some implementations, a device may receive a request associated with a data record, the request including a first set of input for a first machine learning model, and the first set of input defining a first set of values associated with a first set of parameters that correspond to the data record. The device may obtain, from a data storage device, a second set of values associated with a second set of parameters that correspond to the data record. The device may determine, using the first machine learning model, a score for the data record based on the first set of input and the second set of values, wherein the first machine learning model is trained to receive the first set of input and the second set of values and produce, as output, the score.

Abstract: In some implementations, a device may receive a request associated with a data record, the request including a first set of input for a first machine learning model, and the first set of input defining a first set of values associated with a first set of parameters that correspond to the data record. The device may obtain, from a data storage device, a second set of values associated with a second set of parameters that correspond to the data record. The device may determine, using the first machine learning model, a score for the data record based on the first set of input and the second set of values, wherein the first machine learning model is trained to receive the first set of input and the second set of values and produce, as output, the score.

Abstract: A device receives a request for information regarding a debt resolution plan available for a delinquent account. The request may include a first input indicating a payment amount, a second input indicating a payment frequency, and a third input indicating a payment start date. The device obtains account data associated with the delinquent account and determines, using a model, a score for the delinquent account based on the first input, the second input, the third input, and/or the account data. The score predicts a likelihood that the delinquent account will charge off within a predetermined time period. The device determines a plurality of plan parameters for an accelerated charge off plan when the score satisfies a threshold. The device transmits the plurality of plan parameters, receives an enrollment request, enrolls the delinquent account in the accelerated charge off plan, and performs one or more actions based on the enrolling.

Abstract: A device receives a request for information regarding a debt resolution plan available for a delinquent account. The request may include a first input indicating a payment amount, a second input indicating a payment frequency, and a third input indicating a payment start date. The device obtains account data associated with the delinquent account and determines, using a model, a score for the delinquent account based on the first input, the second input, the third input, and/or the account data. The score predicts a likelihood that the delinquent account will charge off within a predetermined time period. The device determines a plurality of plan parameters for an accelerated charge off plan when the score satisfies a threshold. The device transmits the plurality of plan parameters, receives an enrollment request, enrolls the delinquent account in the accelerated charge off plan, and performs one or more actions based on the enrolling.