Capital One Financial has been granted a patent for a system that utilizes machine learning to assess data compliance. The method involves analyzing compliant and non-compliant datasets to generate estimated rules, enabling the classification of unknown datasets against dynamic compliance criteria. GlobalData’s report on Capital One Financial gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Capital One Financial, Virtual banking assistant was a key innovation area identified from patents. Capital One Financial's grant share as of June 2024 was 53%. Grant share is based on the ratio of number of grants to total number of patents.
Compliance assessment using metadata and machine learning algorithms
The patent US12045265B2 describes a non-transitory computer-accessible medium that contains executable instructions for a computer hardware arrangement. This arrangement is designed to perform a series of procedures aimed at assessing data compliance. The process begins with obtaining meta-data from a compliant dataset, which includes the date it became compliant, and extracting meta-data from a non-compliant dataset. These datasets are then joined to create a combined dataset. The system also involves obtaining an unknown compliance dataset and determining its compliance status based on a set of estimated rules, which may be generated using machine learning algorithms.
Further claims detail enhancements to the initial process, including the generation of weights for each rule in the estimated set, and the application of statistical methods to classify unknown datasets as compliant or non-compliant. The system allows for the appending of additional data to the compliant dataset and re-verification of compliance, as well as the iteration of the verification process when changes in the rules occur. Additionally, it provides for the generation of a list of compliance rules and the classification of the joined meta-data against multiple sets of unknown time-variable rules, thereby offering a comprehensive framework for managing data compliance in dynamic environments.
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