Market Sentiment Anomaly Detection Using Gen AI Summarization
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Solution Overview
Problem
Social media platforms introduce unstructured and unnormalized data that complicate market sentiment analysis, leading to false-positive alerts and making it difficult to distinguish authentic market events from manipulations, thus posing risks to financial institutions.
Innovation Solution
A system and method using Generative Artificial Intelligence (Gen AI) to summarize social media feeds, analyze sentiment, and cross-reference it with traditional news sources to detect market sentiment manipulation, pausing transactions that show anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sentiment analysis methods are used on social media data, then analysis speed is maintained, but measurement precision deteriorates due to unstructured and unnormalized data leading to false-positive alerts
Solution Approach 1:
The patent introduces an intermediary layer (Gen AI model) between the raw social media data and the sentiment analysis process. This intermediary summarizes and structures the unstructured social media feeds into normalized formats, enabling accurate sentiment analysis while managing data complexity. The intermediary transforms chaotic social media data into structured information that can be reliably analyzed.
Solution Approach 2:
The patent replaces traditional mechanical sentiment analysis methods with Generative AI-based analysis. Instead of using conventional text processing algorithms that struggle with unstructured data, the system employs Gen AI models that can understand context, nuance, and sentiment in social media posts, significantly improving measurement precision.
2Reliability
If comprehensive social media monitoring is implemented, then detection capability improves, but loss of time increases due to processing large volumes of unstructured data
Solution Approach 1:
The patent segments the data processing workflow into distinct stages: social media feed collection, Gen AI summarization, sentiment analysis, and anomaly detection. This segmentation allows parallel processing of different data streams and enables the system to handle large volumes of data efficiently without compromising detection reliability.
Solution Approach 2:
The system performs preliminary summarization of social media feeds using Gen AI before conducting sentiment analysis. This preliminary action reduces the volume of data that needs detailed processing, enabling timely detection of market manipulation while maintaining comprehensive monitoring coverage.
3Measurement precision
If Gen AI summarization is applied to all social media feeds, then sentiment analysis precision improves, but use of energy increases due to computational requirements
Solution Approach 1:
The patent applies Gen AI summarization selectively rather than uniformly to all social media feeds. The system prioritizes feeds from identified influencers and accounts related to watched financial instruments, applying comprehensive Gen AI analysis only where necessary for detecting potential market manipulation. This partial application maintains precision while reducing overall energy consumption.
4Productivity
If real-time anomaly detection is implemented, then productivity of fraud detection improves, but device complexity increases due to multiple data sources and processing steps
Solution Approach 1:
The patent implements a universal anomaly detection module that handles multiple data sources (social media feeds, traditional news, financial instrument data) through a single integrated processing pipeline. This multi-functional approach enables real-time detection across diverse data types without requiring separate complex systems for each data source, improving productivity while managing complexity.
Data Source
AI summary
A computerized-method for detecting market sentiment manipulation that is related to financial-instruments trading. The computerized-method includes: (i) monitoring incoming alerts in an analytics-engine. (ii) retrieving data from each alert, by operating a context-extraction module; (iii) for each alert: a. collecting feeds from social-media servers based on the retrieved data by operating a social-media feeds-extraction module; b. for each feed, generating a summary by using Gen AI; and c. analyzing a social-media sentiment by providing the generated summary of each feed to the Gen-AI; (iv) for each financial instrument that has been traded in the preconfigured period, searching an anomaly between a sentiment from traditional news source and the analyzed social-media sentiment; (v) storing each anomaly in an anomalies database; and (vi) pausing each financial-transaction that has been processed and related to the financial-instrument in the anomalies database.