Machine Learning Models for Qualitative Financial Data
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Solution Overview
Problem
Conventional machine learning models struggle with subjective data in financial risk assessment, particularly in bond rating predictions, due to the difficulty in quantifying, contextualizing, and interpreting subjective factors like investor sentiment and market psychology.
Innovation Solution
The development of methods and systems for training machine learning models to evaluate relative default risk using both qualitative and quantitative features, involving data preprocessing, feature extraction, and conversion of qualitative features into numerical representations for combined input into a machine learning model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional ML models are used with quantitative features only, then model simplicity and ease of operation are maintained, but measurement precision and reliability of credit rating predictions deteriorate due to inability to capture subjective factors
Solution Approach 1:
The patent introduces an intermediary layer that converts qualitative features into quantitative representations through embedding vectors. This mediator enables the ML model to process subjective information without directly increasing model complexity, as the conversion is performed in a separate preprocessing stage rather than within the core model architecture.
Solution Approach 2:
The patent segments the data processing pipeline into distinct stages: qualitative feature extraction, quantitative feature collection, and model training. By separating the handling of subjective and objective data into different processing modules, the system manages complexity systematically while improving prediction accuracy through comprehensive feature integration.
2Measurement precision
If qualitative features are converted to quantitative representations, then measurement precision improves, but loss of information may occur during the conversion process
Solution Approach 1:
The patent transforms qualitative features into quantitative representations by mapping them to embedding vectors in a higher-dimensional space. This dimensional transformation preserves semantic relationships and contextual nuances while enabling mathematical processing, effectively converting subjective information without losing essential meaning.
Solution Approach 2:
The system changes the parameter representation of qualitative features from categorical or textual forms to numerical embedding vectors. This parameter transformation maintains the semantic information while making the data suitable for quantitative analysis, preserving the essence of subjective factors in a format that ML models can process accurately.
3Adaptability or versatility
If both qualitative and quantitative features are integrated, then adaptability and comprehensiveness improve, but device complexity and difficulty of detecting and measuring increase
Solution Approach 1:
The patent creates a universal feature processing framework that handles both qualitative and quantitative data through a unified embedding representation system. This multi-functional approach allows the same processing infrastructure to accommodate diverse data types, improving adaptability while managing complexity through standardization.
Solution Approach 2:
The system performs preliminary conversion of qualitative features into quantitative embedding vectors before they enter the main ML modeling process. This preliminary action simplifies subsequent processing by transforming all features into a consistent quantitative format, reducing the complexity of detecting and measuring different feature types during model training.
Data Source
AI summary
Systems and methods of generating rating indicators for a portfolio of financial assets are described. A machine learning model is trained using a training data set that includes one or more qualitative features and one or more first quantitative features to generate an output predictor of the performance of the portfolio. The qualitative features are converted into quantitative features before being used as input to the machine learning model. Input features are generated for a new portfolio of financial assets whose rating indicator is to be generated, and fed into the trained machine learning model to generate a new output predictor for the new portfolio of financial assets. The rating indicator for the new portfolio of financial assets is determined based at least on the generated new output predictor.