Harnessing AI-Enhanced Financial Statement Analytics for Intelligent Resource Management

This study developed an artificial-intelligence-enhanced financial statement analytics framework to modernize enterprise resource management. It was undertaken to address the limits of static ratio analysis and the lack of closed-loop, interpretable links between prediction, decision, and financial governance. The approach integrated eXtensible Business Reporting Language data, conference-call sentiment, and network topology through a light gradient boosting machine–financial bidirectional encoder representations from transformers–graph sample and aggregate fusion model, embedded in a dual-loop system that fed insights to decision modules and used performance and governance signals for adaptive retraining. Empirical tests on A-share manufacturers showed improved cash-flow and capital expenditure forecasting, lower restatement risk, higher return on investment, and measurable gains in financing cost, investment accuracy, and governance alignment. These results indicated that artificial-intelligence–financial-statement closed-loop analytics can deliver actionable, transparent financial management and support faster, evidence-based strategic decisions.