AI Knowledge Graph for Derivatives Market Forecasting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The complex structure of derivatives as financial instruments poses challenges in achieving efficient position monitoring, optimized ecosystem management, and trade execution, due to inaccessibility of data, complex hierarchal reporting structures, and intricate nature of derivatives.
Innovation Solution
The implementation of an artificial intelligence technique that retrieves and processes data from government filings, generates knowledge graphs using Natural Language Processing, and applies AI algorithms to forecast future market activities and transactions involving derivatives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If human analysts manually review regulatory filings to extract intelligence on derivative instruments, then analysis accuracy can be maintained, but the scale and speed of analysis are severely limited
Solution Approach 1:
The patent replaces manual human analysis with an automated AI system that uses natural language processing and machine learning algorithms to extract intelligence from regulatory filings. This substitution enables the system to process vast quantities of unstructured data at scale while maintaining consistent analysis quality, directly resolving the contradiction between analysis scale and data accessibility.
Solution Approach 2:
The patent introduces an AI-based intermediary system that acts as a bridge between complex unstructured regulatory filings and actionable intelligence. This intermediary automatically parses, structures, and analyzes data from forms like 13F and NPORT, making previously inaccessible unstructured data readily available for analysis without requiring manual intervention.
2Loss of information
If comprehensive regulatory filings are analyzed in detail, then intelligence quality improves, but the time and resources required increase significantly
Solution Approach 1:
The patent implements preliminary automated processing of regulatory filings, using AI algorithms to pre-parse and structure data before detailed analysis is needed. The system proactively identifies relevant information, entities, and relationships in advance, so that when intelligence is required, the processed data is already organized and ready for rapid retrieval and analysis, significantly reducing both time and resource requirements.
Solution Approach 2:
The patent replaces time-consuming manual review processes with automated AI-based analysis that can process comprehensive regulatory filings instantaneously. The machine learning models are trained to identify patterns and extract intelligence quality equivalent to or exceeding human analysts, but without the time constraints, enabling simultaneous analysis of multiple filings across different time periods.
3Measurement precision
If the system processes complex hierarchical reporting structures in regulatory filings, then data accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the complex hierarchical reporting structures into manageable components, processing different sections of regulatory filings (headers, body text, tables, footnotes) through specialized AI modules. Each segment is analyzed independently with appropriate techniques, then results are integrated to maintain overall data accuracy while keeping individual processing tasks manageable and system complexity controlled.
Solution Approach 2:
The patent introduces an AI-based intermediary layer that automatically handles the complexity of hierarchical reporting structures. This intermediary uses natural language processing and entity recognition to parse complex forms like 13F and NPORT, resolving relationships between nested data elements and maintaining data accuracy without requiring the end system to directly manage the complexity of regulatory filing structures.
Data Source
AI summary
A method for using an artificial intelligence (AI) technique to forecast market activities by specific parties with respect to derivatives and other specific types of financial instruments is provided. The method includes: retrieving first information that relates to at least one form that corresponds to a government filing; generating, based on the first information, a first knowledge graph that relates to an entity, such as a commercial concern; retrieving second information that relates to historical actions performed by at least one person that is associated with the entity; and forecasting, based on the first knowledge graph and the second information, at least one proposed future transaction to be executed by the person with respect to the first entity. The forecasting is based on an application of an AI algorithm to the information retrieved from the government filing(s).