Digital twin model of financial flows and credit resilience of banks and the food industry during economic shocks

This study aims to design a digital twin model for financial flows in management accounting and analyze its role in reducing banks' credit risk during economic shocks. The food industry faces serious liquidity challenges due to strategic nature and hidden production costs. Using a mixed-method approach and structural equation modeling, this research identifies key components of the financial digital twin including real-time data monitoring, financial scenario simulation, hidden cost transparency, and cash flow forecasting. Findings indicate that the digital twin, by reducing information asymmetry between firms and banks, creating transparency in hidden costs (energy, logistics, downtime), and providing predictive credit risk models, increases corporate financial resilience by up to ۳۷% and reduces banking network credit risk by up to ۲۸%. This research provides a novel framework for integrating digital management accounting, bank risk analysis, and resilient financial policymaking in the food industry.

Keywords:

Financial Digital Twin , Management Accounting , Credit Risk , Financial Resilience