LLM Risk Summarization System for Financial Entities
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Solution Overview
Problem
Individuals face difficulties in utilizing search engines to gather relevant qualitative information about companies, as existing search engines primarily provide repetitive and quantitative data on stock prices, lacking insight into ancillary risks and requiring users to know specific events to track, making it intractable to monitor multiple companies effectively.
Innovation Solution
An automated large language model (LLM) based risk summarization system that includes risk identification, validation, search query generation, content finding, and summarization subsystems to extract, analyze, and summarize qualitative information on financial entities, using LLMs like BART and GPT-4 to categorize risks, generate search queries, filter articles, and create summaries based on identified risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If search engines are used to gather information on companies, then quantitative data on stock prices is obtained, but qualitative information on risks and events is lost
Solution Approach 1:
The system segments the information gathering process into multiple specialized modules: risk identification subsystem extracts qualitative risk information, risk validation subsystem verifies risks against trusted sources, search query generator creates targeted queries, content finder retrieves relevant articles, and content summarizer synthesizes findings. This segmentation allows each module to focus on specific aspects of risk analysis rather than relying on general search engines.
Solution Approach 2:
The system introduces an intermediary automated risk analysis system between the user and the information sources. This intermediary automatically performs risk identification, validation, and summarization, transforming the manual process of searching and analyzing multiple sources into an automated workflow that recovers qualitative risk information without requiring manual effort.
2Measurement precision
If manual monitoring of multiple companies is performed, then comprehensive risk analysis is achieved, but time consumption increases significantly
Solution Approach 1:
The system enables self-service automated risk monitoring where the system automatically identifies risks for multiple companies, validates them against trusted sources, generates search queries, retrieves relevant content, and produces summaries without human intervention. Users can input multiple company identifiers and receive comprehensive risk analyses for all of them simultaneously, eliminating the need for manual monitoring of each company individually.
Solution Approach 2:
The system performs preliminary actions by pre-identifying potential risks for companies based on their profiles and historical data, then proactively validates these risks and gathers relevant information before users need to analyze them. This preliminary automated work reduces the time users would otherwise spend on manual research while maintaining comprehensive analysis quality.
3Quantity of substance
If general search queries are used, then broad information is retrieved, but relevant risk-specific content is diluted
Solution Approach 1:
The system applies local quality by tailoring the information retrieval process to specific risk contexts. The risk identification subsystem identifies company-specific risks, the search query generator creates customized queries for each risk type, and the content summarizer focuses on risk-relevant information. This ensures that the volume of retrieved information is optimized for each specific risk scenario rather than using a uniform broad search approach.
Solution Approach 2:
The system dynamically changes search parameters based on identified risks. The search query generator modifies query terms, filters, and scope according to the specific risks detected for each company, transforming general search parameters into risk-specific parameters. This allows the system to retrieve appropriate volumes of information with high relevance to each risk type.
Data Source
AI summary
An automated large language model (LLM) based risk summarization system and method, wherein the system includes: a latent information extraction subsystem configured to extract latent information of an entity through querying an LLM for latent knowledge related to the entity; a document information extraction subsystem configured to obtain document information related to the entity from one or more documents; a search query generator subsystem configured to formulate one or more search queries based on the latent information and the document information; a content finder subsystem configured to execute the one or more search queries to obtain search results having content indicator data; and a content summarizer subsystem configured to use the LLM or another LLM to create a summary of content associated with the content indicator data.