Abstract: Aspects described herein pertain to the use of an adaptive and tunable processing system and method to provide a risk assessment and compliance analysis, but are not so limited. An adaptive and tunable processing system of an embodiment adaptively determines an amount of compliance and/or risk based on one or more quantification variables, including one or more of a litigation category variable, a licensing category variable, and/or a consumer sentiment category variable, to generate a stratified score that quantifies an amount of compliance and/or risk associated with a particular entity. The system of an embodiment uses one or more adaptive filters tuned to output values corresponding to an amount of compliance and/or risk associated with use of a particular entity to perform a service or services.

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.

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: Aspects described herein pertain to the use of an adaptive and tunable processing system and method to provide a risk assessment and compliance analysis, but are not so limited. An adaptive and tunable processing system of an embodiment adaptively determines an amount of compliance and/or risk based on one or more quantification variables, including one or more of a litigation category variable, a licensing category variable, and/or a consumer sentiment category variable, to generate a stratified score that quantifies an amount of compliance and/or risk associated with a particular entity. The system of an embodiment uses one or more adaptive filters tuned to output values corresponding to an amount of compliance and/or risk associated with use of a particular entity to perform a service or services.

Abstract: According to one example embodiment, there is provided a method and an apparatus to evaluate the credit of a healthcare patient. The example embodiment provides methods and computer systems programmed to use multiple variables that are known about a patient prior to a service being rendered to segment the patient population into finer grained groupings. These finer grained groupings allow financial factors, such as a credit score, to be a more accurate predictor. Also, according to another example embodiment, the model is not a generic model for all patients, but the variables and their parameters are specific to a particular healthcare organization's or facility's patient population. This creates a custom model that further enhances its predictiveness.

Abstract: According to one example embodiment, there is provided a method and an apparatus to evaluate the credit of a healthcare patient. The example embodiment provides methods and computer systems programmed to use multiple variables that are known about a patient prior to a service being rendered to segment the patient population into finer grained groupings. These finer grained groupings allow financial factors, such as a credit score, to be a more accurate predictor. Also, according to another example embodiment, the model is not a generic model for all patients, but the variables and their parameters are specific to a particular healthcare organization's or facility's patient population. This creates a custom model that further enhances its predictiveness.

Abstract: According to one example embodiment, there is provided a method and an apparatus to evaluate the credit of a healthcare patient. The example embodiment provides methods and computer systems programmed to use multiple variables that are known about a patient prior to a service being rendered to segment the patient population into finer grained groupings. These finer grained groupings allow financial factors, such as a credit score, to be a more accurate predictor. Also, according to another example embodiment, the model is not a generic model for all patients, but the variables and their parameters are specific to a particular healthcare organization's or facility's patient population. This creates a custom model that further enhances its predictiveness.