Causal-LLM: A Hybrid Framework for Automated Budgetary Variance Diagnosis and Reasoning

Traditional Enterprise Resource Planning (ERP) systems excel at quantifying budgetary variances but fail to identify their root causes, leaving financial analysts with time-consuming manual investigation. We present Causal-LLM, a hybrid framework that integrates causal discovery algorithms with Large Language Models (LLMs) to automate root cause diagnosis in budgetary variance analysis. Our approach combines constraint-based causal inference to construct financial causal graphs with LLM-powered contextual reasoning to generate human-interpretable explanations. By leveraging a domain-specific Financial Causal Knowledge Graph, Causal-LLM bridges the gap between statistical correlation and genuine causation. Experimental evaluation on 240 labeled variance cases from a real-world enterprise (24 months of data) demonstrates that our framework achieves 0.87 top-1 accuracy in root cause identification (95% CI: [0.82, 0.91]), outperforming traditional statistical methods (0.68), pure LLM approaches (0.76), and standalone causal methods (0.72). The system generates actionable insights with 0.92 explainability scores (inter-rater agreement ICC=0.84), reducing investigation time from an estimated 2–4 hours (mean: 3.2 hours, based on internal workflow estimates from five senior analysts) to under 10 seconds per case. Results are demonstrated on one manufacturing enterprise; generalization to other industries requires domain-specific ontology adaptation.