Abstract: Automated assistance program qualification and enrollment are provided. An assistance program qualification and enrollment (APQE) system determines whether a user qualifies for an assistance program that offers assistance with an ongoing transaction record with a service provider. The APQE system generates a set of questions based on qualification data (used to make a qualification determination) that are not included in collected user data and financial data about the user. The user's qualification is based on an evaluation of the user data, financial data, and user-provided responses to the questions. If the user qualifies for the assistance program, an option is provided to the user to enroll in the assistance program. If additional information is needed to complete an enrollment process for the assistance program, an interface is provided that enables the user to provide the data for completing the process.

Abstract: Automated assistance program qualification and enrollment are provided. An assistance program qualification and enrollment (APQE) system determines whether a user qualifies for an assistance program that offers assistance with an ongoing transaction record with a service provider. The APQE system generates a set of questions based on qualification data (used to make a qualification determination) that are not included in collected user data and financial data about the user. The user's qualification is based on an evaluation of the user data, financial data, and user-provided responses to the questions. If the user qualifies for the assistance program, an option is provided to the user to enroll in the assistance program. If additional information is needed to complete an enrollment process for the assistance program, an interface is provided that enables the user to provide the data for completing the process.

Abstract: Predictive model development, training, evaluation, and selection are provided for enabling more-accurate evaluations of consumers. Aspects of an evaluation system use machine learning techniques to train models based on training datasets and known outputs provided by one or more service providers (e.g., pieces of demographic data and historical transaction data). The predictive models are developed against the training datasets to optimize the predictive models to correctly predict an output (e.g., a consumer propensity) for the given inputs. When a consumer seeks services from a service provider, the service provider provides pieces of demographic data and ongoing transactions data to the evaluation system. A most-accurate predictive model is selected based on known data elements, and a propensity score is calculated indicative of a likelihood of settlement by the consumer. Results are communicated with the service provider such that informed decisions can be made.

Abstract: Improvements to the distribution and handling of electronic transactions across a distributed system are provided herein. A user interface is provided to a user responsible for one or more ongoing transactions or plans. The user interface is populated with data related to the user and the ongoing transactions. The user interface enables the user to group and manipulate various ongoing transactions and plans, and is populated with data based on the ongoing transactions, the user's historical transactional data, and a profile for the user based on similar users.