BlackRock. has been granted a patent for a method to impute missing values in Environmental, Social, and Governance (ESG) data. The method utilizes a matrix factorization model to predict missing entries and incorporates bias correction based on identified systemic missingness, enhancing the accuracy of ESG data analysis. GlobalData’s report on BlackRock 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 BlackRock, Insurance pricing automation was a key innovation area identified from patents. BlackRock's grant share as of June 2024 was 61%. Grant share is based on the ratio of number of grants to total number of patents.
Imputing missing values in esg data using prediction models
The granted patent US11983152B1 outlines a method and system for imputing missing values in Environmental, Social, and Governance (ESG) data. The process begins with receiving a data spreadsheet that contains ESG data from multiple entities. The system identifies missing data entries and utilizes a matrix factorization model to predict values for these entries based on existing data. It further assesses whether the missing entries are randomly or systemically absent by analyzing the mean and standard deviation of the predicted values. If systemic missingness is detected, the system identifies relevant ESG data descriptors that may explain this absence and calculates a bias correction term, which is then added to the predicted values to enhance accuracy.
The patent also details the technical aspects of the method, including the generation of latent matrices through a learning model, which aids in predicting the missing values. The method incorporates statistical analysis to differentiate between random and systemic missing data by estimating means and covariances of existing and missing data groups. Additionally, it includes a mechanism for generating an ESG data report that incorporates the imputed and corrected data. The system is designed to improve the reliability of ESG data analysis, which is crucial for stakeholders in making informed decisions based on comprehensive and accurate data.
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