AI-Driven Financial Risk Management and Security in Complex Financial Systems

The rapid evolution of financial systems has prompted the need for more advanced and efficient risk management and security strategies. With the increasing complexity of financial markets and transactions, traditional methods are no longer sufficient to address emerging threats and challenges. This Research Topic seeks to bring together cutting-edge research on the application of Artificial Intelligence (AI) in financial risk management and security. By focusing on AI-driven techniques such as machine learning, deep learning, and anomaly detection, this collection aims to highlight the transformative potential of AI in improving the resilience and efficiency of financial systems. Through this curated collection, we invite contributors to explore how AI can reshape the landscape of financial risk assessment, fraud detection, and overall security, offering novel insights and solutions that are crucial for the future of the financial industry. This Research Topic aims to address the pressing need for innovative and effective AI-driven solutions in the realm of financial risk management and security. As financial systems become more complex, traditional methods struggle to cope with the scale and intricacies of modern financial data, making them vulnerable to risks such as fraud, cyberattacks, and inaccurate risk assessment. The goal of this collection is to highlight how AI, particularly machine learning, deep learning, and graph-based techniques, can be leveraged to address these challenges. By exploring novel AI methods for fraud detection, credit risk modeling, anomaly detection, and overall system security, this Research Topic aims to provide actionable insights and methodologies that can be implemented by financial institutions to improve their operational resilience. We invite contributions that focus on real-world applications, case studies, and experimental results that demonstrate the effectiveness of AI in enhancing the integrity, security, and efficiency of financial systems, thereby offering practical solutions for the evolving landscape of financial risk management. This Research Topic invites contributions on a broad range of AI applications in financial risk management and security. We are particularly interested in submissions that explore, but are not limited to, the following areas:- AI-driven fraud detection and prevention techniques in financial transactions- Machine learning and deep learning algorithms for credit risk assessment and scoring- Anomaly detection in financial systems using AI methods- Graph neural networks (GNNs) for fraud detection and financial network analysis- AI applications for regulatory compliance and anti-money laundering- Predictive modeling for market risk and volatility management- AI for securing financial data and protecting against cyber threats- Case studies of AI implementations in real-world financial institutionsWe welcome original research, review articles, and case studies that present novel AI methodologies, as well as practical applications that improve the resilience and security of financial systems. Manuscripts should focus on empirical results, new techniques, or in-depth analysis of AI’s impact on financial risk management and security. Topic editors: Yu Xie, Guanjun Liu, Honghao Zhu Source: https://www.frontiersin.org/research-topics/79304/ai-driven-financial-risk-management-and-security-in-complex-financial-systems