Intelligent Loan Processing System: Integrating Agentic Artificial Intelligence and Advanced Ensemble Learning for Enhanced Credit Decision-Making

Abstract

The digital transformation of banking has accelerated the adoption of artificial intelligence (AI) and machine learning (ML) technologies in loan processing and credit risk assessment. This paper presents a comprehensive framework that integrates Agentic AI autonomous, goal-driven AI agents with advanced ensemble learning methods to revolutionise the end-to-end loan origination process. Research explores how autonomous AI agents can orchestrate complex loan workflows while advanced ensemble methods, including XGBoost, LightGBM, CatBoost, and hybrid stacking ar chitectures, enhance predictive accuracy beyond traditional Random Forest approaches. This analysis demonstrates that combining agentic orchestration with sophisticated ensemble techniques addresses key challenges in modern banking processing efficiency, predictive accuracy, explainability, and regulatory compliance. The proposed framework achieves significant improvements in loan approval rates (approximately 6% increase) and classification metrics (F1-scores exceeding 85%) while maintaining transparency through integrated explainability tools. This research contributes to the field by demonstrating how intelligent systems can transform financial services while addressing ethical considerations of fairness, bias mitigation, and responsible AI deployment.