Predictive Financial Volatility Analytics Using Machine Learning Pipelines in ERP-Integrated Enterprises

Abstract

This study examined predictive financial volatility analytics using machine learning pipelines within ERP-integrated enterprises to evaluate forecasting accuracy, enterprise financial intelligence, operational monitoring capability, and predictive risk management performance across multiple industrial sectors. The study adopted a quantitative longitudinal research design using ERP-generated enterprise financial datasets collected from 126 organizations operating within banking, manufacturing, retail, healthcare, telecommunications, logistics, and energy sectors. A total of 1,248,560 transactional records covering the operational period from 2019 to 2024 were extracted from accounting systems, procurement platforms, inventory management databases, customer payment systems, operational expenditure records, and enterprise financial reporting infrastructures. Machine learning forecasting models including linear regression, decision trees, random forests, support vector machines, and recurrent neural networks were comparatively evaluated using predictive accuracy indicators such as root mean square error, mean absolute error, precision, recall, cross-validation consistency, and computational efficiency measures. The findings demonstrated that ERP-generated operational variables significantly influenced predictive financial volatility estimation across digitally integrated enterprises. Procurement cost variability produced the strongest predictive effect with a standardized beta coefficient of 0.462, followed by operational expenditure growth at 0.451 and revenue volatility at 0.428. Liquidity ratios and inventory turnover efficiency demonstrated inverse relationships with financial instability, indicating that stronger operational efficiency reduced predictive volatility outcomes. Comparative predictive analysis revealed that recurrent neural network architectures achieved the highest forecasting accuracy at 96.14%, followed by random forest models at 93.86%, while traditional regression models generated comparatively lower predictive accuracy at 79.82%. Banking and energy enterprises demonstrated the strongest predictive forecasting performance with forecasting accuracy levels of 95.72% and 94.84%, respectively, due to stronger ERP integration maturity and centralized financial synchronization systems. Statistical significance testing confirmed that all major predictive variables remained analytically significant at p < 0.05, while effect size analysis demonstrated substantial practical influence across enterprise forecasting outcomes. The study concluded that ERP-integrated machine learning pipelines significantly improved predictive financial volatility estimation, operational transparency, enterprise monitoring capability, anomaly detection performance, and quantitative decision-support efficiency across modern digitally integrated organizational environments.