Experian Information Solutions, Inc.

Vantagescore Solutions, LLC

Luz M Torrez

Adam Thomas Kornegay

Hua Hu

Sherry Harper

Marie Conlin

Embodiments of a system and method are described for generating a finance attribute. In one embodiment, the systems and methods retrieve raw tradeline data from a plurality of credit bureaus, retrieve industry code data related to each of the plurality of credit bureaus, determine one or more tradeline leveling characteristics that meet at least one pre-determined threshold, and generate a finance attribute using the selected leveling characteristics.

System and method for generating a finance attribute from tradeline data

One embodiment of a system for automatically generating code for attributes includes a credit bureau data store that stores credit bureau convention information and an attribute generation system that communicates with the credit data store to detect rules associated with respective credit bureaus. The system may include an input module which receives, from a user system, a document comprising pseudo-code defining attributes including credit bureau specific logic; an error checking module which automatically performs error checking and provides error correcting information on the document; an attribute verification module which parses the document to identify attributes and automatically retrieves rules associated with the respective credit bureaus; a document generation module which generates an attribute specification document based on the retrieved rules as applied to the attributes in the document; and a code generation module which generates an executable code set based on the retrieved plurality of attributes and credit bureau specific rules.

Automatic generation of code for attributes

One embodiment of a system for automatically generating code for attributes includes a credit bureau data that store stores credit bureau convention information and an attribute generation system that communicates with the credit data store to detect rules associated with respective credit bureaus. The system may include an input module which receives, from a user system, a document comprising pseudo-code defining attributes including credit bureau specific logic; an error checking module which automatically performs error checking and provides error correcting information on the document; an attribute verification module which parses the document to identify attributes and automatically retrieves rules associated with the respective credit bureaus; a document generation module which generates an attribute specification document based on the retrieved rules as applied to the attributes in the document; and a code generation module which generates an executable code set based on the retrieved plurality of attributes and credit bureau specific rules.

One embodiment of a system for automatically generating code for attributes includes a credit bureau data that store stores credit bureau convention information and an attribute generation system that communicates with the credit data store to detect rules associated with respective credit bureaus. The system may include an input module which receives, from a user system, a document comprising pseudo-code defining attributes including credit bureau specific logic; an error checking module which automatically performs error checking and provides error correcting information on the document; an attribute verification module which parses the document to identify attributes and automatically retrieves rules associated with the respective credit bureaus; a document generation module which generates an attribute specification document based on the retrieved rules as applied to the attributes in the document; and a code generation module which generates an executable codeset based on the retrieved plurality of attributes and credit bureau specific rules.

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.

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Fountain Valley, CA, United States of America

Luz M Torrez