Authors: Wang Xinyi
Abstract: Artificial Intelligence (AI)-based risk management has emerged as a critical component in modern enterprise applications, enabling organizations to identify, assess, and mitigate risks with greater accuracy and efficiency. This study explores the integration of AI technologies such as machine learning, deep learning, and natural language processing into enterprise risk management frameworks. By leveraging large volumes of structured and unstructured data, AI-driven systems can detect patterns, predict potential risks, and provide real-time insights for informed decision-making. The paper examines key applications of AI in areas such as financial risk assessment, fraud detection, cybersecurity threat analysis, and operational risk management. It also highlights the role of cloud computing and big data analytics in supporting scalable and high-performance AI models. Despite its advantages, AI-based risk management faces challenges related to data quality, model interpretability, ethical concerns, and regulatory compliance. The study discusses various strategies to address these issues, including explainable AI techniques, robust data governance, and continuous monitoring. The findings emphasize that AI-driven risk management systems significantly enhance organizational resilience, improve decision-making, and support proactive risk mitigation in dynamic enterprise environments.
DOI: https://doi.org/10.5281/zenodo.20280496
