Integrating Time-Series Econometric Forecasting with Risk-Based Business Process Auditing: An Empirical Framework for JSc Navoiyazot

Modern industrial conglomerates operating within the capital-intensive chemical sector encounter complex operational, geological, and market uncertainties that necessitate an integrated approach to corporate governance, enterprise risk mitigation, and internal control execution. This study establishes a novel, dual-engine empirical framework that directly links quantitative time-series econometric forecasting with qualitative Risk-Based Internal Audit (RBIA) and Business Process Auditing (BPA) workflows to protect structural enterprise stability. Utilizing longitudinal enterprise financial data from JSC Navoiyazot alongside macro-level sectoral indicators from Uzbekistan's chemical industry, we model enterprise quarterly net profit dynamics to construct an automated quantitative trigger engine for internal control engagements. This article examines the mismatch between the internal audit planning processes of JSC Navoiyazot, a large state-owned industrial enterprise, and the International Standards for the Professional Practice of Internal Auditing (IIA Standards). Using the Business Process Improvement (BPI) methodology and Business Process Model and Notation (BPMN), the study analyses the current “As-Is” process and develops a target “To-Be” model. The proposed model incorporates risk-profile assessment, end-to-end working-paper documentation, peer-review procedures, and artificial intelligence (AI) functionality to automate anomaly detection. The findings indicate that transition to the “To-Be” model can improve audit quality, reduce residual risks, and optimise labour requirements.