The patent by the Federal Home Loan Mortgage details a computer-implemented method for assessing data quality in electronic business records. It involves evaluating consistency metrics over time, analyzing business outcomes, and predicting future results based on established relationships between data quality and outcomes. GlobalData’s report on Federal Home Loan Mortgage gives a 360-degree view of the company including its patenting strategy. Buy the report here.

According to GlobalData’s company profile on Federal Home Loan Mortgage, Virtual banking assistant was a key innovation area identified from patents. Federal Home Loan Mortgage's grant share as of June 2024 was 89%. Grant share is based on the ratio of number of grants to total number of patents.

Evaluating data quality in electronic business records

Source: United States Patent and Trademark Office (USPTO). Credit: Federal Home Loan Mortgage Corp

The granted patent US12039597B1 outlines a computer-implemented method and system for assessing the relationship between data quality metrics of electronic business records. The method involves reading a first data quality metric from a database, which is derived from consistency data evaluated against specific business rules. The processor analyzes the outcomes of a set of business records associated with this data metric and generates a relationship that is stored in the database. Users can request predictions for future outcomes of additional business records, which are generated based on the established relationship and a second data quality metric. This prediction process includes fitting a curve to the data reflecting the relationship between the first data quality metric and the outcomes of the initial records.

Additionally, the patent describes operations that allow for grouping business records based on their data quality metrics, correlating data volatility metrics to data quality, and identifying sources of error within the data sets. The system is designed to respond to user requests for evaluating data quality metrics and predicting future outcomes based on established correlations. The method emphasizes the importance of analyzing changes in data quality metrics over time, particularly in relation to different business rules, to enhance the accuracy of predictions regarding future business record outcomes. This innovative approach aims to improve data-driven decision-making processes in electronic business environments.

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