Artificial Intelligence Driven Predictive Risk Management in Green Technology Investment - Paroli Paroli; Agung Rizky; Qurotul Aini; Dwi Cahyono; Jonathan Parker; Untung Rahardja (Pandawan, 2026)
This study explores predictive risk management in green technology investments by leveraging Artificial Intelligence (AI) to address uncertainties associated with sustainable projects. As global financial institutions and governments increasingly allocate capital toward renewable energy, smart infrastructure, and low-carbon innovation, investors face multidimensional risks, including market volatility, technological failure, and regulatory change. Therefore, this research aims to develop an AI-driven predictive framework capable of identifying, analyzing, and forecasting potential investment risks in green technology portfolios to support informed decision-making. The study employs a quantitative approach using machine learning algorithms, including Random Forest, Gradient Boosting, and Neural Networks, trained on historical financial indicators, environmental performance metrics, and policy datasets. Each algorithm is selected based on its strengths: Random Forest for robustness, Gradient Boosting for predictive accuracy, and Neural Networks for capturing complex nonlinear relationships. A comparative perspective is used to highlight their tradeoffs, followed by feature importance analysis and predictive validation through cross-validation and evaluation metrics such as accuracy, precision, and RMSE. The findings show that the proposed model improves early risk detection compared to conventional statistical models, highlighting the effectiveness of machine learning in handling complex sustainability data. Furthermore, it identifies key risk determinants and enhances predictive reliability. Consequently, integrating AI-based predictive analytics into green investment strategies can strengthen risk mitigation, improve investor confidence, and support sustainable financial decision-making.