Innovative approaches to risk management in startup financing

Abstract

The study examines contemporary approaches to forecasting investment risks in startups operating under conditions of economic turbulence and limited access to capital. Classical statistical methods remain essential for establishing baseline risk assessments, yet their predictive accuracy declines as market dynamics and technological shifts intensify. Particular attention is given to ensemble machine-learning algorithms and deep-learning architectures capable of capturing nonlinear relationships, temporal patterns, and network interdependencies within the startup ecosystem. The analysis highlights the importance of integrating Explainable AI methods, monitoring data-drift phenomena, and employing scenario analysis to ensure transparency and robustness of risk-forecasting models. The findings demonstrate the potential of digital analytical tools to enhance risk assessment quality and support adaptive financial strategies for startups, with specific relevance to the Ukrainian context.