Predictive Analytics and AI-Based Forecasting Models for Loan Default and Portfolio Optimization

Abstract

The adoption of predictive analytics and artificial intelligence (AI) in financial risk management has changed the way institutions evaluate creditworthiness and its lending portfolios are managed. This paper, entitled “Predictive Analytics and AI-Based Forecasting Models for Loan Default and Portfolio Optimization,” focuses on high-level development and using of advanced machine learning algorithms to predict loan default rates as well as optimize portfolio performance. We use historical financial, behavioral and transactional data to build models (logistic regression, random forest, XGBoost, deep neural networks), which improve predictive performance for early risk identification. The AI-driven forecasting was achieved by the use of dynamic feature engineering, real-time anomaly detection and explainable AI (XAI) methods to make sure that it is not only interpretable but also compliable with regulation. In addition, portfolio optimization is performed by AI driven allocation strategies including reinforcement learning and evolutionary algorithms that balance risk and return. The results show that the hybrid AI models are superior to traditional statistical methods in terms of prediction precision, default rate lowering and portfolio robustness under market turmoil. This effort adds to the emerging literature on financial technology by providing a scalable, transparent and data-driven method for credit risk management and sustainable financial choice.