From variance analysis to decision intelligence: Integrating finance audit and data science to improve SME budget governance and risk-aware growth

Budget management is fundamental to Enterprise Resilience, but for many Small and Medium Enterprises (SMEs), budget variance is a threat to their resilience. This paper presents a Decision Intelligence Framework that combines classical variance analysis with cutting-edge data science to drive risk-informed growth. The research integrates Mupa et al.'s (2025) study on sustainable budgeting and Iziduh et al.'s (2021) system-wide budget management model to understand the evolution from conventional accounting to predictive decision intelligence.
We examine the use of SAP FICO and business analytics for real-time transparency (Shiwakoti, 2025) and the application of AI forecasting for financial stability (Okeke et al., 2024; Zamil, 2025). Through a study of Data-Driven Financial Optimization (Ayankoya et al., 2025) in local economies, this framework offers insights on how SMEs can buffer "variance shock" using forecasting models (Celestin & Mishra, 2025). This study, by leveraging the mathematical approach of Welekar et al. (2025) for risk assessment and the big data optimization methods of Ren (2022), offers a multi-industry approach to integrating finance and audit. The research finds that by combining these fields, SMEs can emerge from the "spreadsheet era" to achieve Decision Intelligence, in which budget governance becomes a source of strength rather than a compliance burden.