AI-Enhanced ERP Financial Intelligence Systems for Real-Time Business Analytics and Risk Decision Support

Abstract

This study examines the problem that many enterprises use ERP systems for transaction recording and financial reporting but still struggle to transform ERP-based financial data into real-time analytics, predictive intelligence, and proactive risk decision support. The purpose of the research is to assess how AI-enhanced ERP financial intelligence systems improve real-time business analytics, risk decision support, and financial decision-making effectiveness in cloud-based and enterprise ERP cases. The study used a quantitative, cross-sectional, case-based design with a valid sample of 150 ERP users, accountants, finance officers, business analysts, IT managers, internal auditors, risk officers, compliance personnel, and financial decision-makers drawn from organizations using ERP platforms such as SAP, Oracle ERP, Microsoft Dynamics, NetSuite, or similar enterprise systems. The key variables included AI-enhanced ERP financial intelligence, financial data integration, predictive analytics capability, AI-driven automation, real-time business analytics, risk decision support, and financial decision-making effectiveness. Data were collected through a structured five-point Likert-scale questionnaire and analyzed using descriptive statistics, reliability testing, Pearson correlation, and multiple regression modeling. The findings showed high agreement across all major constructs, with real-time business analytics recording the highest mean score of 4.15, followed by AI-enhanced ERP financial intelligence at 4.12, financial decision-making effectiveness at 4.10, financial data integration at 4.08, AI-driven automation at 4.05, risk decision support at 4.02, and predictive analytics capability at 3.96. Reliability was strong, with Cronbach’s alpha values ranging from 0.84 to 0.90 and an overall instrument reliability of 0.92. Correlation results confirmed significant positive relationships, including AI-enhanced ERP financial intelligence with real-time business analytics, r = 0.71, p < 0.001, real-time analytics with risk decision support, r = 0.68, p < 0.001, and AI-enhanced ERP financial intelligence with risk decision support, r = 0.66, p < 0.001. Regression results showed that AI-enhanced ERP capabilities, financial data integration, predictive analytics, and automation explained 62.4% of the variance in real-time business analytics, while AI-enhanced ERP intelligence, predictive analytics, automation, and real-time analytics explained 65.8% of the variance in risk decision support. The study implies that enterprises should strengthen AI-enabled dashboards, predictive risk models, integrated financial data governance, and automation to improve financial visibility, fraud detection, compliance monitoring, and evidence-based decision-making.