SoFi Technologies has been granted a patent for a system that uses machine-learning models to improve fairness metrics by training on historical data, receiving real-time data, and generating risk scores. The system includes processors and computer-readable media to perform these functions efficiently. GlobalData’s report on SoFi Technologies gives a 360-degree view of the company including its patenting strategy. Buy the report here.

According to GlobalData’s company profile on SoFi Technologies, Sensor guided flow mixing was a key innovation area identified from patents. SoFi Technologies's grant share as of April 2024 was 48%. Grant share is based on the ratio of number of grants to total number of patents.

Machine-learning model for fairness metrics and risk score generation

Source: United States Patent and Trademark Office (USPTO). Credit: SoFi Technologies Inc

A recently granted patent (Publication Number: US11928730B1) outlines a system and method for training a machine-learning model to improve fairness metrics while generating risk scores based on real-time data. The system involves processors executing computing instructions to train the model using historical data, solving maximization and minimization problems, and estimating convergence points and regularization items. The model evaluates fairness criteria and model prediction power, updating control parameters and regenerating outputs as needed to meet specified thresholds.

The patented system further includes features such as parallel processing of maximization and minimization problems, comparison of protected groups against benchmarks for fairness metrics, and outputting risk scores for credit applications based on historical and real-time credit risk data. The method involves iterative processes to ensure the uniform predicted output meets fairness criteria and model prediction power standards. Control parameters are adjusted, and outputs are regenerated when these criteria are not satisfied, all before reaching a predetermined number of iterations. This innovative approach aims to enhance the accuracy and fairness of machine-learning models in decision-making processes, particularly in sensitive areas like credit risk assessment.

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GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.