Emotion Recognition for Trade Monitoring Fraud Detection
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Solution Overview
Problem
Current trade monitoring systems fail to effectively detect securities fraud due to high false positive rates, inability to detect true cases of manipulation, and inability to understand the intent behind trades, as they focus on actions rather than motivations and emotional states.
Innovation Solution
The system employs emotion recognition using advanced learning algorithms and voice analytics to analyze recorded conversations, identifying sentiment and emotions like anger, excitement, or joy, and compares these to a base emotional profile to detect anomalies indicative of potential manipulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If lexicon-based search or natural language processing is used to detect patterns of conversation, then the ability to monitor trades is improved, but the false positive rate increases and true cases of manipulation remain undetected
Solution Approach 1:
The patent replaces traditional mechanical text-processing methods (lexicon-based search, basic NLP) with affective computing and emotion recognition technology. The system uses machine learning models trained on emotional patterns in voice recordings to detect manipulation, transitioning from surface-level text analysis to deep emotional state analysis, thereby reducing false positives while improving detection accuracy.
Solution Approach 2:
The patent changes the analysis parameter from textual content (words, phrases) to emotional parameters (emotion types, intensity, temporal patterns). By detecting emotions such as excitement, anxiety, or defensiveness in trader conversations, the system identifies manipulation patterns that text analysis alone cannot detect, improving both reliability and precision simultaneously.
2Reliability
If trade surveillance solutions focus on monitoring trader actions, then the ability to detect manipulation patterns is improved, but the intent behind trades remains undetected
Solution Approach 1:
The patent adds a new dimension to trade surveillance by analyzing the emotional state of traders alongside their trading actions. Instead of only monitoring what traders do (trade execution, communication patterns), the system now monitors how traders feel during these actions through voice-based emotion recognition, uncovering the motivational context that explains intent behind manipulation behaviors.
Solution Approach 2:
The patent introduces emotion recognition as an intermediary layer between trade data and manipulation detection. This intermediary component analyzes emotional patterns in conversations and connects them to trading behaviors, providing the missing link that explains why certain actions are taken and revealing intent that direct action monitoring cannot capture.
Data Source
AI summary
Trade monitoring systems and methods, and non-transitory computer readable media, include receiving a plurality of audio communications associated with a trader; scoring one or more emotions in each audio communication; creating a base emotional profile for the trader based on the scoring; receiving a current audio communication associated with the trader; scoring the one or more emotions in the current audio communication; comparing the base emotional profile to the scored one or more emotions in the current audio communication; detecting a score for an emotion in the current audio communication that is inconsistent with the base emotional profile; assigning an emotion risk score that indicates a high likelihood the trader in the current audio communication is involved in securities fraud; and generating an alert of potential securities fraud by the trader in the current audio communication.