Generative AI-Assisted Financial Planning and Scenario Analysis for Small-Business Decision Intelligence

Abstract

This study examined the potential contribution of generative artificial intelligence (AI)-assisted financial planning and scenario analysis to small-business decision intelligence, emphasizing forecasting accuracy, financial risk identification, managerial decision performance, and organizational readiness. A quantitative comparative research framework was developed to assess conventional statistical forecasting, machine-learning algorithms, and generative AI-assisted hybrid approaches. The analytical dataset comprised 300 enterprises and 7,200 initial financial observations, of which 6,840 were retained, representing a 95.0% retention rate. Regular generative AI utilization accounted for 46.0% of enterprises, while occasional utilization represented 32.0%. The hypothetical comparative findings indicated that the generative AI-assisted hybrid achieved a mean absolute percentage error of 7.06%, compared with 8.46% for boost and 14.36% for moving-average forecasting. The hybrid model demonstrated a coefficient of determination of 0.928 and financial distress classification accuracy of 92.2%, with an ROC-AUC of 0.948. Scenario calculation accuracy increased from 91.4% under conventional planning to 97.2% under AI-assisted planning, while managerial decision accuracy increased from 78.4% to 89.6%. Average scenario processing time decreased by 56.8%. The regression framework explained 68.4% of variation in financial decision intelligence, with forecasting accuracy demonstrating the largest standardized coefficient (β = 0.306), followed by generative AI utilization (β = 0.284). Mediation and moderation frameworks further identified forecasting accuracy, scenario reliability, financial literacy, and technological readiness as potentially important explanatory factors. Stress-scenario analysis illustrated liquidity vulnerability, with a projected current ratio of 0.96 and liquidity shortfall probability of 48.8%. Overall, the study established an integrated framework connecting generative AI, predictive financial analytics, scenario evaluation, and managerial decision-making. However, all numerical results were rather than empirically verified. Consequently, the proposed relationships required validation using observed financial records, independent predictive testing, and appropriate statistical procedures before conclusions regarding financial performance improvements could be established.