Text Analysis Credit Risk Evaluation Method
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Solution Overview
Problem
In the P2P network loan industry, there is a lack of effective credit risk evaluation methods, leading to systematic risks, illegal fund-raising, and transaction frauds due to inadequate supervision and certification systems, with existing financial services failing to satisfy the diversified needs of small and medium enterprises and individuals.
Innovation Solution
A credit risk evaluation method and apparatus based on text analysis that extracts language features from borrower text descriptions using machine learning techniques to predict repayment behavior, integrating these features into an evaluation model to determine a credit score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional financial features are used for credit risk evaluation, then the evaluation can be performed, but the acquisition cost is high and the efficiency is low
Solution Approach 1:
The patent replaces traditional mechanical credit evaluation methods (manual financial analysis) with text analysis technology. By analyzing text data such as loan application descriptions, borrower profiles, and communication records, the system automatically extracts features and evaluates credit risk, substituting the manual mechanical process with an automated information processing system that achieves both high accuracy and efficiency
Solution Approach 2:
The patent changes the evaluation parameters from traditional financial indicators (income, assets, credit history) to text-based linguistic features (word choice, sentence structure, emotional tone, readability). This parameter transformation enables the system to evaluate credit risk through natural language processing, achieving rapid automated assessment while maintaining reliability through sophisticated text analysis algorithms
2Productivity
If text analysis is used to evaluate credit risk, then the acquisition cost is reduced and efficiency is improved, but the measurement precision of traditional financial features may be compromised
Solution Approach 1:
The patent employs a composite evaluation approach that integrates multiple text analysis techniques (linguistic feature extraction, sentiment analysis, readability assessment) combined with traditional financial features. This composite methodology creates a robust credit evaluation system that maintains measurement precision by leveraging the strengths of both text-based and traditional financial indicators, achieving accurate credit scoring through diversified data sources
Solution Approach 2:
The text analysis system is designed to perform multiple evaluation functions simultaneously: assessing repayment willingness through sentiment analysis, evaluating financial literacy via readability measurement, and detecting potential fraud through linguistic pattern recognition. This multi-functional approach ensures comprehensive credit assessment with high precision while maintaining evaluation efficiency
3Reliability
If comprehensive text analysis is performed to improve credit risk assessment accuracy, then the evaluation quality improves, but the system complexity increases
Solution Approach 1:
The patent segments the credit evaluation process into distinct modular components: text preprocessing module, linguistic feature extraction module, sentiment analysis module, readability assessment module, and credit scoring module. Each module performs a specific function and can be independently optimized or replaced, reducing overall system complexity while maintaining comprehensive evaluation accuracy through structured modular architecture
Data Source
AI summary
Aspects of the disclosure provide an information processing apparatus that includes interface circuitry and processing circuitry. The interface circuitry is configured to obtain a text authored by a person. The processing circuitry is configured to analyze the text to obtain measurements of language features of the person, input the measurements of the language features into an evaluation model that is trained to predict a score as a function of the language features, determine a specific score for the person based on the evaluation model and output the specific score of the person for predicting a behavior of the person.