AI Trading Model Integrating Biodata to Prevent Impulsive Decisions
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Solution Overview
Problem
Human traders' physical and mental states can lead to impulsive decisions in financial product transactions, resulting in incorrect trading requests and potential losses, as their emotional and mental conditions are not effectively considered in existing trading systems.
Innovation Solution
A financial product transaction method using an artificial intelligence model that incorporates biodata, such as EEG and fNIRS data, to diagnose the trader's state and determine appropriate processing modes for transaction requests, thereby preventing incorrect trades and maximizing profits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional trading systems are used without considering trader state, then trading speed and simplicity are maintained, but trading accuracy and reliability deteriorate due to impulsive decisions from poor physical or mental conditions
Solution Approach 1:
The patent introduces an AI model as an intermediary between the trader and the trading system. This intermediary receives trader state data (biodata, transaction history, financial data), processes it through machine learning algorithms, and outputs processed trading signals. The AI model acts as a mediator that filters out impulsive decisions while maintaining the trader's ultimate control, thus improving reliability without requiring complete system redesign.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring trader state through biodata collection and using this information to adjust trading recommendations. The AI model analyzes current trader conditions and provides real-time feedback on whether to execute or modify trading orders, creating a closed-loop system that adapts to trader state changes and prevents impulsive decisions.
2Measurement precision
If biodata collection and AI analysis are added to trading systems, then trading accuracy improves, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing trader biodata, transaction history, and financial data in structured formats before actual trading decisions are needed. The AI model is pre-trained on historical data to recognize patterns and make rapid predictions. This preparation allows the system to quickly analyze current trader states without performing complex computations in real-time during critical trading moments.
Solution Approach 2:
The patent applies partial action by selectively analyzing only the most relevant aspects of trader state rather than processing all available data equally. The AI model identifies and focuses on key biomarkers and data patterns that most strongly correlate with impulsive trading behavior, ignoring less relevant information. This selective processing reduces computational burden while maintaining high detection precision for critical trading decisions.
3Speed
If real-time biodata monitoring is implemented, then trading responsiveness to trader state improves, but system cost and infrastructure requirements increase
Solution Approach 1:
The patent makes the monitoring infrastructure universal by using multi-functional devices that can collect various types of biodata (EEG, fNIRS, heart rate, sweat analysis) through a single integrated system. The same hardware and software platform serves multiple purposes: monitoring physical state, detecting mental conditions, and providing trading recommendations. This approach reduces overall infrastructure complexity compared to having separate specialized systems for each monitoring function.
Solution Approach 2:
The system implements self-service by having the trader wear portable biodata collection devices (such as headbands or wearables) that automatically monitor their physiological state without requiring manual intervention. The devices continuously collect data in the background and transmit it to the processing system, eliminating the need for manual data entry or complex manual monitoring procedures while maintaining real-time responsiveness.
Data Source
AI summary
Provided are: a decision-making method using an artificial intelligence model, a server, and a computer program. A decision-making method using an artificial intelligence model according to various embodiments of the present invention comprises the steps of: acquiring a transaction request for a financial product from a trader; determining a processing mode for the acquired transaction request based on data about the trader, and outputting a transaction signal corresponding to the determined processing mode for the transaction request.