Location
Kumasi, Ghana & Virtual
Start Date
6-8-2026 12:30 PM
End Date
6-8-2026 1:00 PM
Description
Start-up enterprises operate in uncertain and rapidly changing financial environments characterized by volatile cash flows, limited historical data, and high exposure to financial risk. Conventional financial forecasting and risk assessment methods are often static and assumption-driven, limiting their effectiveness in supporting managerial decision making. Within the Decision Support Systems (DSS) domain, this study analyses the effect of AI-driven DSS on financial forecasting, risk assessment and managerial decision making among start-up enterprises in the Harare Central Business District, Zimbabwe. The study adopts a quantitative approach using structured questionnaires administered to founders, financial managers, and senior decision makers. Data were analysed using SPSS, applying descriptive statistics, correlation and regression analysis to examine relationships between AI-based forecasting accuracy, risk assessment capability and decision-making effectiveness. Key variables include cash flow forecasting, revenue prediction, burn rate monitoring, liquidity risk identification and perceived decision quality. Findings indicate that AI-driven forecasting significantly improves predictive accuracy under financial volatility. AI-based risk assessment also enhances early identification of liquidity constraints and financial distress. Regression results further show that AI-driven DSS capabilities significantly improve managerial financial decision making. The study provides empirical evidence that AI-enabled decision support strengthens financial planning, risk management, and decision quality in start-up enterprises, offering practical value for founders, managers, and investors seeking improved financial resilience and sustainable growth.
Included in
Business Analytics Commons, Management Information Systems Commons, Technology and Innovation Commons
AI-Driven Financial Forecasting, Risk Assessment and Decision Making in Start Up Enterprises
Kumasi, Ghana & Virtual
Start-up enterprises operate in uncertain and rapidly changing financial environments characterized by volatile cash flows, limited historical data, and high exposure to financial risk. Conventional financial forecasting and risk assessment methods are often static and assumption-driven, limiting their effectiveness in supporting managerial decision making. Within the Decision Support Systems (DSS) domain, this study analyses the effect of AI-driven DSS on financial forecasting, risk assessment and managerial decision making among start-up enterprises in the Harare Central Business District, Zimbabwe. The study adopts a quantitative approach using structured questionnaires administered to founders, financial managers, and senior decision makers. Data were analysed using SPSS, applying descriptive statistics, correlation and regression analysis to examine relationships between AI-based forecasting accuracy, risk assessment capability and decision-making effectiveness. Key variables include cash flow forecasting, revenue prediction, burn rate monitoring, liquidity risk identification and perceived decision quality. Findings indicate that AI-driven forecasting significantly improves predictive accuracy under financial volatility. AI-based risk assessment also enhances early identification of liquidity constraints and financial distress. Regression results further show that AI-driven DSS capabilities significantly improve managerial financial decision making. The study provides empirical evidence that AI-enabled decision support strengthens financial planning, risk management, and decision quality in start-up enterprises, offering practical value for founders, managers, and investors seeking improved financial resilience and sustainable growth.
