Design of Machine Learning Models for Financial Risk Assessment and Investment Decision Support in the Financial Sector
Keywords:
Financial Risk Assessment, Machine Learning, Investment Decisions, Portfolio Optimization, Credit RiskAbstract
Accurate financial risk assessment is critical for investment decision-making and portfolio management in today's volatile markets. This paper presents the design of machine learning models specifically tailored for financial risk assessment and investment decision support. Our approach integrates ensemble methods, time series forecasting, and sentiment analysis to evaluate credit risk, market risk, and operational risk comprehensively. The models are trained on historical financial data and validated using stress testing scenarios. Results indicate superior performance compared to traditional risk models, with area under ROC curve values exceeding 0.92 for credit risk prediction and portfolio optimization achieving 15% higher risk-adjusted returns. The research provides financial institutions with advanced tools for navigating complex investment landscapes.
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