Deep Learning Credit Risk Model for Unstructured Text Analysis
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Solution Overview
Problem
Existing credit risk models overlook unstructured textual data from sources like news articles, failing to analyze semantic context, which limits their ability to accurately assess an entity's future financial events such as bankruptcy or default.
Innovation Solution
A deep learning-based credit risk model that utilizes a next-generation neural network to analyze unstructured text from various documents, including news, research, and filings, to generate document scores and default probability scores over time, integrating financial information for a comprehensive assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing credit risk models use only structured financial data, then the model structure remains simple, but the ability to assess credit risk accurately deteriorates due to overlooking unstructured textual data
Solution Approach 1:
The patent merges structured financial data with unstructured textual data from news articles into a unified credit risk assessment model. The system combines traditional financial ratios with neural network processing of textual information, allowing both data types to contribute to the final risk assessment. This integration resolves the contradiction by incorporating additional data sources to improve accuracy while managing complexity through a coordinated multi-component architecture.
Solution Approach 2:
The patent introduces an intermediary neural network layer that processes unstructured textual data and transforms it into features compatible with traditional financial data. This intermediary component bridges the gap between structured and unstructured data, enabling the system to leverage textual information from news articles without disrupting the existing financial data processing pipeline, thus improving accuracy while controlling complexity.
2Adaptability or versatility
If the model analyzes more diverse data sources including unstructured text, then the comprehensiveness of risk assessment improves, but the processing time and computational resources increase
Solution Approach 1:
The patent segments the data processing into distinct pipelines: one for structured financial data and another for unstructured textual data. The textual data pipeline processes news articles separately through neural networks, generating summarized features that are then combined with traditional financial data. This segmentation allows each data type to be processed optimally and independently, reducing overall processing time while maintaining versatility in data source utilization.
Solution Approach 2:
The system performs preliminary processing of unstructured textual data by pre-processing news articles to extract and summarize key information before integrating it with financial data. This preliminary action reduces the complexity and processing requirements of the main analysis, enabling the model to handle diverse data sources efficiently without significant time penalties.
3Measurement precision
If the model uses traditional text processing methods, then the implementation is simpler, but the ability to understand semantic context of text deteriorates
Solution Approach 1:
The patent replaces traditional mechanical text processing methods (such as bag-of-words models) with neural network-based processing. The neural networks analyze the semantic context of text in news articles by understanding relationships between words and phrases, rather than relying on simple keyword matching. This substitution dramatically improves semantic understanding while the neural network architecture manages the complexity through learned representations and patterns.
Data Source
AI summary
Systems and methods to facilitate credit risk assessment are described herein. The systems and methods described herein relate to implementing and training a credit risk model comprising a document model and a company model. The document model may be configured to read text of a document, understand long range relationships between words, phrases, and the occurrence of one or more financial events, and create a document score that indicates whether the financial events are likely to occur based on that document. A document-model-state vector may be generated that represents important features and relationships identified within each document and across a set of documents for a given entity based on the document scores. The company model may produce a sequence of default probability scores representing overall likelihoods of the occurrence of the financial events for an entity based on the document-model-state vector for documents associated with that entity.