RiskWise: An Explainable Behavioral Intelligence Framework for Detecting Risky Investor Behavior in Simulated Trading using Machine Learning
Abstract
Individual retail investors often exhibit emotional trading patterns—fear of missing out, panic selling, revenge trading, and overconfidence—that systematically erode long-term performance. Despite the availability of sophisticated analytical tools on modern trading platforms, few systems offer direct feedback on the user's own behavioral tendencies. RiskWise is an explainable framework designed for paper trading that shifts analytical emphasis from asset forecasting to behavioral self-assessment. The system translates raw trading actions into structured behavioral events, derives a rich set of behavioral features, and employs an XGBoost classifier to label each action with a behavioral state. SHAP-based explanations render the model's decisions transparent, enabling personalized recommendations and behavioral risk scoring. Core elements include behavioral profiling, a behavioral DNA representation, simulationdriven training data, and a modular architecture that integrates behavioral finance, explainable AI, and machine learning. The platform is educational and non-investment-advisory, aiming to cultivate disciplined investing habits without compromising user autonomy. This document details the framework's design, feature engineering, learning methodology, and evaluation plan.
