Systems and methods for enrichment of data relating to consumer credit collateralized debt and real property and utilization of same to maximize risk prediction

摘要

Systems and methods for enrichment of data associated with risk prediction. Data may be enriched by incorporating one or more aspects of consumer credit, collateralized debt, mortgage, real property, and loan data for use in systems, products, and methods relying on risk prediction models to maximize the effectiveness of the risk prediction. A time-series of consumer credit data may be provided to provide historical context to data. In a particular application, enriched data is leveraged to predict occurrence of an event relating to underlying assets of a structured security, such as a mortgage-backed security. Other systems and methods are disclosed.

主权项

1. A computer system for matching underlying assets of a security to consumer credit data in connection with risk prediction associated with valuation and pricing of the security, the system comprising:
a computer processor in selective communication with a requesting computer to receive an initial set of data associated with the security, the initial set of data comprising records of the underlying assets without sufficient consumer identification data for each consumer associated with each of the underlying assets; a computer readable medium accessible by the processor; a database in communication with the processor, the database containing a plurality of credit data files stored thereon, each of the plurality of credit data files associated with a respective consumer; and software stored on the computer readable medium for executing the steps of:
identifying data in data fields of each of the records from the initial set of data that match data in data fields associated with the plurality of credit data files associated with the respective consumers, wherein at least one data field associated with the records from the initial set of data is matched to a differently named data field associated with the plurality of credit data files;for each of the matched data fields, assigning a weighted point score corresponding to a quality of the match;for each of the records from the initial set of data, determining an identity of a consumer associated with the plurality of credit data files that corresponds to a highest point score of the assigned point scores; andassigning the identity of the identified consumer to the respective record from the initial set of data;providing a prediction of an occurrence of an event relating to the underlying assets of the security based on the identity of the identified consumer and a time series credit score associated with the identified consumer's credit history over a predetermined period of time and at a predetermined time interval to permit an updated valuation and pricing of the security.