Leveraging Artificial Intelligence for Enhanced Risk Management in Financial Services: Current Applications and Future Prospects
DOI:
https://doi.org/10.5281/zenodo.13765819ARK:
https://n2t.net/ark:/40704/AJSM.v2n5a06References:
40Keywords:
Artificial Intelligence, Financial Risk Management, Machine Learning, Regulatory ComplianceAbstract
This study examines the application of artificial intelligence (AI) in enhancing risk management within financial services. Through comprehensive analysis, the research reveals that AI technologies, particularly machine learning, and deep learning models, significantly improve the accuracy and efficiency of risk assessment and management processes. AI-powered credit risk models demonstrate a 20% increase in predictive accuracy compared to traditional methods, while market risk management sees a 30% improvement in anomaly detection speed and precision. The study also highlights a 60% reduction in false positives for fraud detection and a 40% increase in accurate favorable rates. Despite these advancements, challenges persist, primarily in data quality and model interpretability. The research projects that by 2028, AI will be integral to risk management in over 80% of large financial institutions, potentially reducing risk-related losses by 25% and improving operational efficiency by 35%. The study concludes by emphasizing the need for strategic implementation and responsible AI use, outlining future research directions, including the long-term impact on systemic risk, ethical implications, and the potential of quantum machine learning in risk modeling.
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Adumene, S., & Beheshti, A. (2024). Role of Artificial Intelligence (AI) in Risk Management. AIP Conference Proceedings, 2736(1), 060037.
Hu, W., & Chen, Y. (2022). Application of artificial intelligence in financial risk management. In X. Sun, X. Zhang, Z. Xia, & E. Bertino (Eds.), Artificial Intelligence and Security (Vol. 13338, pp. 77-78). Springer, Cham.
The Alan Turing Institute. (2019). Artificial Intelligence in Finance: Turing Report.
van Liebergen, B. (2017). Machine Learning in Compliance Risk Management. Institute of International Finance.
Li, H., Wang, S. X., Shang, F., Niu, K., & Song, R. (2024). Applications of large language models in cloud computing: An empirical study using real-world data. International Journal of Innovative Research in Computer Science & Technology, 12(4), 59-69.
Feng, Y., Qi, Y., Li, H., Wang, X., & Tian, J. (2024, July 11). Leveraging federated learning and edge computing for recommendation systems within cloud computing networks. In Proceedings of the Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024) (Vol. 13210, pp. 279–287). SPIE.
Zhao, F., Li, H., Niu, K., Shi, J., & Song, R. (2024, July 8). Application of deep learning-based intrusion detection system (IDS) in network anomaly traffic detection. Preprints.
Gong, Y., Liu, H., Li, L., Tian, J., & Li, H. (2024, February 28). Deep learning-based medical image registration algorithm: Enhancing accuracy with dense connections and channel attention mechanisms. Journal of Theory and Practice of Engineering Science, 4(02), 1–7.
Zhan, X., Ling, Z., Xu, Z., Guo, L., & Zhuang, S. (2024). Driving Efficiency and Risk Management in Finance through AI and RPA. Unique Endeavor in Business & Social Sciences, 3(1), 189–197.
Yang, T., Xin, Q., Zhan, X., Zhuang, S., & Li, H. (2024). ENHANCING FINANCIAL SERVICES THROUGH BIG DATA AND AI-DRIVEN CUSTOMER INSIGHTS AND RISK ANALYSIS. Journal of Knowledge Learning and Science Technology ISSN: 2959–6386 (online), 3(3), 53–62.
Zhang, Y., Liu, B., Gong, Y., Huang, J., Xu, J., & Wan, W. (2024). Application of machine learning optimization in cloud computing resource scheduling and management. Applied and Computational Engineering, pp. 64, 9–14.
Huang, J., Zhang, Y., Xu, J., Wu, B., Liu, B., & Gong, Y. Implementation of Seamless Assistance with Google Assistant Leveraging Cloud Computing.
Wu, B., Gong, Y., Zheng, H., Zhang, Y., Huang, J., & Xu, J. (2024). Enterprise cloud resource optimization and management based on cloud operations. Applied and Computational Engineering, pp. 67, 8–14.
Li, A., Yang, T., Zhan, X., Shi, Y., & Li, H. (2024). Utilizing Data Science and AI for Customer Churn Prediction in Marketing. Journal of Theory and Practice of Engineering Science, 4(05), 72–79.
Liang, P., Song, B., Zhan, X., Chen, Z., & Yuan, J. (2024). Automating the training and deployment of models in MLOps by integrating systems with machine learning. Applied and Computational Engineering, 67, 1-7.
Wu, B., Xu, J., Zhang, Y., Liu, B., Gong, Y., & Huang, J. (2024). Integration of computer networks and artificial neural networks for an AI-based network operator. arXiv preprint arXiv:2407.01541.
Liu, B. (2023). Based on intelligent advertising recommendations and abnormal advertising monitoring systems in machine learning. International Journal of Computer Science and Information Technology, 1(1), 17–23.
Liu, B., Yu, L., Che, C., Lin, Q., Hu, H., & Zhao, X. (2024). Integration and performance analysis of artificial intelligence and computer vision based on deep learning algorithms. Applied and Computational Engineering, pp. 64, 36–41.
Liu, B., Zhao, X., Hu, H., Lin, Q., & Huang, J. (2023). Detection of Esophageal Cancer Lesions Based on CBAM Faster R-CNN. Journal of Theory and Practice of Engineering Science, 3(12), 36–42.
Zhan, X., Shi, C., Li, L., Xu, K., & Zheng, H. (2024). Aspect category sentiment analysis based on multiple attention mechanisms and pre-trained models. Applied and Computational Engineering, pp. 71, 21–26.
Shi, Y., Yuan, J., Yang, P., Wang, Y., & Chen, Z. Implementing Intelligent Predictive Models for Patient Disease Risk in Cloud Data Warehousing.
Zhan, T., Shi, C., Shi, Y., Li, H., & Lin, Y. (2024). Optimization Techniques for Sentiment Analysis Based on LLM (GPT-3)—arXiv preprint arXiv:2405.09770.
Lin, Y., Li, A., Li, H., Shi, Y., & Zhan, X. (2024). GPU-Optimized Image Processing and Generation Based on Deep Learning and Computer Vision. Journal of Artificial Intelligence General Science (JAIGS) ISSN: 3006–4023, 5(1), 39–49.
Chen, Zhou, et al. "Application of Cloud-Driven Intelligent Medical Imaging Analysis in Disease Detection." Journal of Theory and Practice of Engineering Science 4.05 (2024): 64–71.
Wang, B., Lei, H., Shui, Z., Chen, Z., & Yang, P. (2024). Current State of Autonomous Driving Applications Based on Distributed Perception and Decision-Making.
Yang, P., Chen, Z., Su, G., Lei, H., & Wang, B. (2024). Enhancing traffic flow monitoring with machine learning integration on cloud data warehousing. Applied and Computational Engineering, 67, 15-21.
Fan, C., Li, Z., Ding, W., Zhou, H., & Qian, K. Integrating Artificial Intelligence with SLAM Technology for Robotic Navigation and Localization in Unknown Environments.
Guo, L., Li, Z., Qian, K., Ding, W., & Chen, Z. (2024). Bank Credit Risk Early Warning Model Based on Machine Learning Decision Trees. Journal of Economic Theory and Business Management, 1(3), 24–30.
Li, Zihan, et al. "Robot Navigation and Map Construction Based on SLAM Technology." (2024).
Fan, C., Ding, W., Qian, K., Tan, H., & Li, Z. (2024). Cueing Flight Object Trajectory and Safety Prediction Based on SLAM Technology. Journal of Theory and Practice of Engineering Science, 4(05), 1–8.
Ding, W., Tan, H., Zhou, H., Li, Z., & Fan, C. Immediate Traffic Flow Monitoring and Management Based on Multimodal Data in Cloud Computing.
Zheng, H., Wu, J., Song, R., Guo, L., & Xu, Z. (2024). Predicting Financial Enterprise Stocks and Economic Data Trends Using Machine Learning Time Series Analysis.
Song, R., Wang, Z., Guo, L., Zhao, F., & Xu, Z. (2024). Deep Belief Networks (DBN) for Financial Time Series Analysis and Market Trends Prediction.
Xu, Z., Guo, L., Zhou, S., Song, R., & Niu, K. (2024). Enterprise Supply Chain Risk Management and Decision Support Driven by Large Language Models. Applied Science and Engineering Journal for Advanced Research, 3(4), 1–7.
Bai, X., Zhuang, S., Xie, H., & Guo, L. (2024). Leveraging Generative Artificial Intelligence for Financial Market Trading Data Management and Prediction.
Guo, L., Song, R., Wu, J., Xu, Z., & Zhao, F. (2024). Integrating a Machine Learning-Driven Fraud Detection System Based on a Risk Management Framework.
Wang, B., Lei, H., Shui, Z., Chen, Z., & Yang, P. (2024). Current State of Autonomous Driving Applications Based on Distributed Perception and Decision-Making.
Z. Xu, L. Guo, S. Zhou, R. Song, and K. Niu, "Enterprise Supply Chain Risk Management and Decision Support Driven by Large Language Models,"(2024).
F. Zhao, H. Li, K. Niu, J. Shi, and R. Song, "Application of Deep Learning-Based Intrusion Detection System (IDS) in Network Anomaly Traffic Detection," (2024).
Jiang, W., Qian, K., Fan, C., Ding, W., & Li, Z. (2024). Applications of generative AI-based financial robot advisors as investment consultants. Applied and Computational Engineering, 67, 28-33.
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