Abstract: A computer-implemented method for risk assessment and providing refinements to credit risk analysis based on a variety of information, including information voluntarily contributed by an applicant. The method may comprise performing a risk analysis based on a first set of information available in at least a first credit information data source and receiving a second set of information, in response to determining that the analysis provides a first result that is unfavorable to the applicant. The second set of information may be unavailable in the at least first credit information data source, and the second set of information may be retrievable from at least a secondary data source after the applicant's informed interaction with a computer-implemented interface configured to verify an authenticated approval by the applicant to provide access to information associated with at least one of the applicant's financial accounts.

Abstract: A system and method to analyze the difference between a consumer's baseline credit score at one point in time and the consumer's credit score at a later point in time, and process the score difference to determine the factors in the consumer's credit profile that most explain the score difference. Systems and methods provide a mechanism for generating and formatting a record, which can be displayed on a display device, to represent those main factors of score difference.

Abstract: Data can be accessed from a plurality of disparate data sources from at least one database. A plurality of test models can be automatically built by a model building engine. Each test model can have predetermined predictive variables. A final set of predictive variables can be determined by a variable selector from the predetermined predictive variables in the plurality of test models by comparing the predictive power of the predictive variables across the plurality of test models. A master dataset can be generated from the disparate data sources. A master model can be built from the master dataset. The master model can combine the final set of predictive variables from the plurality of disparate data sources. The master model can characterize a quantitative estimate of the probability that an entity will display a defined behavior. Related apparatus, systems, techniques, and articles are also described.

Abstract: Data can be accessed from a plurality of disparate data sources from at least one database. A plurality of test models can be automatically built by a model building engine. Each test model can have predetermined predictive variables. A final set of predictive variables can be determined by a variable selector from the predetermined predictive variables in the plurality of test models by comparing the predictive power of the predictive variables across the plurality of test models. A master dataset can be generated from the disparate data sources. A master model can be built from the master dataset. The master model can combine the final set of predictive variables from the plurality of disparate data sources. The master model can characterize a quantitative estimate of the probability that an entity will display a defined behavior. Related apparatus, systems, techniques, and articles are also described.

Abstract: Data can be accessed from a plurality of disparate data sources from at least one database. A plurality of test models can be automatically built by a model building engine. Each test model can have predetermined predictive variables. A final set of predictive variables can be determined by a variable selector from the predetermined predictive variables in the plurality of test models by comparing the predictive power of the predictive variables across the plurality of test models. A master dataset can be generated from the disparate data sources. A master model can be built from the master dataset. The master model can combine the final set of predictive variables from the plurality of disparate data sources. The master model can characterize a quantitative estimate of the probability that an entity will display a defined behavior. Related apparatus, systems, techniques, and articles are also described.

Abstract: Data is received that characterizes at least one of credit, financial, and demographic data for a consumer. Thereafter, estimated income is determined for the user. Using the estimated income and the data, a second income level for the consumer is determined also using a confidence interval model and a pre-defined confidence threshold Ci. The second income level for the consumer is less than the determined estimated income and is determined such that actual income for the consumer is Ci % likely to exceed the second income level. Data can then be provided that characterizes the second income level. Related apparatus, systems, techniques and articles are also described.

Abstract: The current subject matter provides models that enable a projection of credit scores at a specified future date as well as an estimation of a date when a credit score will reach a certain level. Related apparatus, systems, techniques and articles are also described.

Abstract: A system and method is provided that provides tools to consumers to help consumers understand their credit scores and how to take action to improve their credit scores. A system and method for each of and for a combination of a score estimating tool, a best action simulation tool, an easy error correction tool, and a score improvement tool are provided.