HR Risk Management and Predictive Analytics: Leveraging Digital Twin Technology in Banking

Digital Twin Technology (DTT) has emerged as a transformative force in the banking sector, offering enhanced predictive analytics and improved risk management strategies, including Human Resource (HR) Risk Management. However, the relationship between DTT implementation, Risk Management Effectiveness (RME), Human Resource Risk Management (HRRM), and Operational Efficiency (OE) remains underexplored. This study addresses this gap by examining the impact of DTT on RME, HRRM, and OE within the banking industry. Using Structural Equation Modeling (SEM) with SmartPLS version 4.0, we analyzed data collected through structured questionnaires from 128 professionals in the banking sector. The results reveal that DTT significantly enhances both RME and HRRM, with HRRM playing a crucial role in mitigating risks related to workforce performance and regulatory compliance. In turn, these improvements in RME and HRRM positively influence OE. These findings underscore the importance of integrating HR risk factors into predictive analytics models powered by DTT to optimize banking risk management and operational processes. However, challenges related to integrating DTT into existing systems and aligning with HR risk mitigation strategies must be addressed to realize its full potential. The study concludes with recommendations for future research and practical implications for banking institutions aiming to leverage DTT for improved operational outcomes and HR risk mitigation.