Feature Vector Determination for Credit Risk Modeling
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Solution Overview
Problem
Current credit risk modeling techniques overlook vast amounts of publicly available textual data and fail to analyze the semantic context, limiting their ability to provide comprehensive risk assessments.
Innovation Solution
A system and method that integrate financial accounting ratios, pricing data, ESG data, and textual data to predict credit risk events by assigning objective and predictive descriptors to documents, using feature vectors and machine learning algorithms to generate credit risk signals for events like default, bankruptcy, and equity price movements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional credit risk models use only financial accounting data and pricing data, then the modeling process is simple and manageable, but the accuracy and comprehensiveness of risk assessment is limited due to overlooking vast textual data
Solution Approach 1:
The patent combines multiple data sources including financial accounting data, pricing data, ESG data, and textual data from news articles into a unified credit risk modeling system. This integration allows the system to leverage diverse data types to improve assessment accuracy while managing complexity through systematic processing pipelines.
Solution Approach 2:
The patent introduces natural language processing and text analytics as intermediary technologies to extract meaningful information from unstructured textual data. These intermediaries transform raw text into structured features that can be integrated with traditional financial data, enabling comprehensive analysis without overwhelming system complexity.
2Loss of information
If credit risk models incorporate multiple data sources including textual data, then the comprehensiveness of risk assessment improves, but the complexity of data integration and processing increases
Solution Approach 1:
The patent segments the credit risk modeling process into distinct modules: financial data processing, pricing data processing, ESG data processing, and textual data processing. Each module handles specific data types independently before integration, reducing overall system complexity while ensuring comprehensive information utilization.
Solution Approach 2:
The patent develops a multi-functional data processing framework that can handle various data types (structured financial data, semi-structured pricing data, unstructured textual data) through unified processing mechanisms. This universal approach reduces integration complexity by applying consistent methods across diverse data sources.
3Reliability
If semantic context of text is analyzed in credit risk modeling, then the predictive capability improves, but the computational resources and processing time required increase
Solution Approach 1:
The patent applies partial text analysis by focusing on specific semantic aspects most relevant to credit risk (e.g., sentiment analysis, event detection, entity extraction) rather than comprehensive semantic parsing. This selective approach maintains predictive capability while reducing computational resource requirements.
Solution Approach 2:
The patent performs preliminary text preprocessing and feature extraction before main analysis, including tokenization, stopword removal, and initial sentiment classification. This preliminary action reduces the complexity of subsequent semantic analysis and optimizes computational resource usage during critical predictive modeling phases.
Data Source
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AI summary
Disclosed are a computer-based method and a system for determining a feature vector. The system (10) comprises a data store (32) including a first set of documents and a second set of documents. The server (12) includes a processor (14) and memory (16, 20) for storing instructions. The instructions cause the processor to provide the feature vector composed of a plurality of ranked features with associated first label values or second label values to a learning module (30).