Abstract: The system and method of the present invention expand/enhance modeling for credit risks scores. The expanded modeling system and methods discussed herein identify new credit risk related attributes using limited and/or unconventional credit data. In an aspect, the system and method enhance performance definitions to capture incremental insight, expanding the applicable consumer segments previously inaccessible.

Abstract: A method employs consumer permissioned financial data to generate an updated credit score following a consumer authorization to access to at least one demand deposit account of the consumer for use in generating the credit score for the consumer. The consumer permissioned financial data may be sourced from any one of a plurality of aggregators where the aggregators employ different data structures for the financial data. The financial data is processed to automatically normalize the data received from an aggregator by applying a common classification system across the plurality of aggregators to classify a first set of data fields; and automatically, regardless of whether an updated credit score is advantageous for the consumer and independent of any further consumer permission, generating the updated credit score using a combination of credit file data for the consumer and the financial data received from the aggregator.

Abstract: The system and method of the present invention expand/enhance modeling for credit risks scores. The expanded modeling system and methods discussed herein identify new credit risk related attributes using limited and/or unconventional credit data. In an aspect, the system and method enhance performance definitions to capture incremental insight, expanding the applicable consumer segments previously inaccessible.

Abstract: Provided are systems and methods for partitioning of segments in a consumer credit segmentation tree. Segments can be defined based on regression tree analysis.

Abstract: Methods and systems for optimal partitioning of segments in a consumer credit segmentation tree comprising defining a first attribute-based independent variable on a first tree using a primary dependent variable having two classes, defining a second attribute-based independent variable on the first tree using the primary dependent variable, defining risk tiers for the first attribute-based independent variable on the first tree using a first risk score and the primary dependent variable, defining risk tiers for the second attribute-based independent variable on the first tree using a second risk score and the primary dependent variable, superimposing the first tree structure, based on the primary dependent variable, onto a second tree, and defining profiles in the risk tiers for the second attribute-based independent variable with a profile dependent variable having two classes, completing the second tree, wherein the second tree is used to segment a population according to credit related behavior.

Abstract: Provided are methods and systems by which a data sample can be generated in order to create a true multi-Credit Reporting Agency model, eliminating the biases and variability in traditional multi-CRA models by allowing the creation of a single multi-CRA method which requires neither alignment nor translation to be applicable on all CRAs.

Abstract: Provided are methods and systems for determining score consistency.

Abstract: Characteristic normalization (or leveling) is a process that yields consistent and equitable characteristic definitions across multiple sources of credit information. This leveling ensures that when the same data is present for multiple credit sources, for example two or more credit reporting agencies, it is interpreted in the same manner, acknowledging that differences in the data itself may still be present.

Abstract: Provided are methods and systems by which a data sample can be generated in order to create a true multi-Credit Reporting Agency model, eliminating the biases and variability in traditional multi-CRA models by allowing the creation of a single multi-CRA method which requires neither alignment nor translation to be applicable on all CRAs.

Abstract: Characteristic normalization (or leveling) is a process that yields consistent and equitable characteristic definitions across multiple sources of credit information. This leveling ensures that when the same data is present for multiple credit sources, for example two or more credit reporting agencies, it is interpreted in the same manner, acknowledging that differences in the data itself may still be present.