DBERA: A Dynamic Bayesian Network Framework for Real-Time Entrepreneurial Risk Assessment

Abstract

Entrepreneurial ventures are inherently exposed to multiple risks arising from financial uncertainty, competitive dynamics, and individual decision-making behaviors. A Bayesian network-based model provides a probabilistic framework for capturing and analyzing these interdependent risk factors. Traditional methods often rely on static financial ratios, subjective judgment, or linear risk assessment models that fail to adapt to dynamic market conditions and complex interrelationships among variables. This limits their ability to provide accurate, context-aware risk evaluation for entrepreneurs. To address these limitations, the Dynamic Bayesian-Economic Risk Analyzer (DBERA) is proposed, which integrates market volatility, competitor signals, and personal investment patterns into a unified Bayesian framework. Continuously updated conditional probability distributions enable DBERA's context-aware, real-time risk assessment for entrepreneurs. Unlike static models, it adjusts to market dynamics and uncovers new causal linkages between indicators. The model was tested using simulated and real datasets using scenario-based Monte Carlo simulations. Validation measures, including sensitivity analysis, probabilistic inference consistency, and prediction accuracy, were used to assess resilience across varied situations. Scenario planning may help politicians, bankers, and business owners assess their approaches to managing uncertainty and identify success factors. DBERA considerably improves forecast accuracy and decision-making help. The prediction power increases by 83% for competition intensity, 95% for investment pattern impact, 93% for finance availability, 96.1% for operational capacity, 97.3% for innovation index contribution, and 82.5 for environmental risk exposure. These discoveries demonstrate that DBERA is a versatile tool for reducing risk and achieving entrepreneurial success.