Intelligent Enterprise Financial Anomaly Detection using Association Rule Mining for Enhanced Risk Assessment

Abnormal financial data detection in companies is very important to secure financial stability, avoid fraud, and uphold conformity to regulatory requirements. Conventional auditing procedures based on rule systems and manual inspections tend to be labor-intensive, prone to error, and less capable of capturing sophisticated financial irregularities. This research therefore advocates for an intelligent mining approach with association rule algorithms to identify unusual financial patterns automatically. The suggested model uses large-scale enterprise financial data to unveil concealed correlations, identify fraudulent transactions, and pinpoint unusual financial behaviors. The system improves financial risk assessment and fraud detection with high precision using advanced data mining methodologies. Experimental findings on actual financial datasets indicate that the suggested approach has a classification accuracy of 96%, surpassing traditional methods in detecting financial anomalies with enhanced precision and recall. This paper presents a scalable and adaptive enterprise financial anomaly detection solution that supports improved financial transparency and risk reduction.