Risk Management in Hospitality: Machine Learningbased Approaches for Crisis Prediction and Response

This research paper highlights the use of ML-based approaches to the hotel industry’s crisis prediction and response processes. We investigate the potential of predictive analytics to enhance risk management strategies and operational resilience by examining historical data and various external sources. Our extensive literature analysis explains important theoretical frameworks like the resource-based view (RBV) and agility theory, which might lead to predictive analytics improving organizational resilience and competitive advantage. We analyses hypothetical data using analytical methodologies and mathematical expressions and give practical insights as part of our research. The results illustrate the tremendous merit of ML-based approaches in boosting guest satisfaction and organizational efficiency as well as optimizing resource utilization. This study is relevant to anyone who works in the area of hospitality or plans to do so in the future. We recommend that thoughtful and data-based solutions be implemented to address the contemporary world’s complexity. All in all, this health care administration project contributes to an increased awareness of hotel management and may inform future success in an ever more digital planet.