Machine Learning in Financial Risk Assessment for Investment Decisions

Abstract

XGBoost algorithm under Gradient Boosting serves as the main focus for analyzing financial risks during investment assessments. Using historical financial indicators and market performance metrics and macroeconomic variables in a dataset allows the model to deliver effective investment opportunity risk classification. XGBoost maintains a dual advantage of recognizing non-linear patterns and processing complex datasets during its successful performance assessments. The SHAP (SHapley Additive exPlanations) values provide transparent explanations to interpret feature importance and explain specific prediction results from the model. By combining XGBoost with SHAP values stakeholders can understand how the model makes classifications for risk management while meeting regulatory compliance along with building trust in AI-based financial operations. XGBoost-SHAP framework reveals itself as a dependable solution for investment risk evaluation where financial analysts and portfolio managers can achieve better insight into their uncertain decision-making process