AI-Driven Wealth Advisory: Machine Learning Models for Personalized Investment Portfolios and Risk Optimization

Abstract

This study develops and evaluates an integrated machine learning framework for personalized wealth advisory services that optimizes portfolio allocation while incorporating individual risk profiles, financial goals, and behavioral preferences. We employ a hybrid architecture combining deep reinforcement learning with ensemble methods Random Forest, XGBoost, and LSTM networks to analyze historical market data spanning 2008 to 2022, investor characteristics from a sample of 15,000 individuals, and comprehensive macroeconomic indicators. The framework integrates Modern Portfolio Theory with behavioral finance principles and implements dynamic risk assessment through conditional value-at-risk optimization. The proposed AI-driven system demonstrates superior performance metrics: a 23.4% improvement in risk-adjusted returns (Sharpe ratio: 1.84 versus 1.49 for traditional advisory approaches), 31% reduction in portfolio volatility, and 89.3 % accuracy in risk tolerance classification. The personalization engine successfully adapts to changing market conditions with an average rebalancing efficiency of 94.7%. Component analysis reveals that sophisticated risk profiling, return prediction via LSTM-Attention networks, and reinforcement learning optimization each contribute meaningfully to final performance. Stress testing during major market crises demonstrates superior downside protection, with maximum drawdowns averaging 4.5 percentage points lower than traditional benchmarks. This research contributes a novel multi-agent learning architecture that bridges the gap between algorithmic portfolio optimization and human-centric financial advisory, providing empirical evidence for AI’s role in democratizing sophisticated wealth management services while maintaining interpretability and regulatory compliance through SHAP-based explainability mechanisms.