AI-Driven Predictive Analytics for Financial Risk Management
DOI:
https://doi.org/10.51976/ex981g64Keywords:
AI, credit scoring, financial risk management, fraud detection, machine learning, predictive analyticsAbstract
In the rapidly evolving financial landscape, effective risk management has become paramount to ensuring organizational sustainability and growth. The application of Artificial Intelligence (AI) in predictive analytics offers significant advantages in identifying, assessing, and mitigating various financial risks. This research paper explores the integration of AI-driven predictive models in financial risk management, emphasizing their role in improving forecasting accuracy, identifying emerging risks, and enhancing decision-making processes. By leveraging machine learning algorithms, such as decision trees, neural networks, and ensemble methods, financial institutions can detect patterns and trends within historical data, which often remain undetected by traditional methods. Furthermore, AI models are capable of analyzing vast amounts of real-time data from diverse sources, including market trends, macroeconomic indicators, and internal financial reports, providing more comprehensive insights into potential risks. The paper delves into several AI techniques used in financial risk management, including supervised learning for credit scoring, anomaly detection for fraud prevention, and reinforcement learning for portfolio optimization. Moreover, AI can automate routine risk assessment tasks, allowing financial institutions to focus on more strategic decision-making. The study also highlights the challenges and limitations of implementing AI models, including data quality concerns, model interpretability, and regulatory compliance. Despite these challenges, the benefits of AI in financial risk management are undeniable, as it provides the tools necessary to predict and manage risk proactively rather than reactively. Additionally, the paper discusses the ethical implications of AI in finance, including data privacy and algorithmic bias, stressing the importance of maintaining transparency and accountability in AI model development and deployment. It concludes with a future outlook on the continued evolution of AI in financial risk management, emphasizing the need for ongoing research and development to refine models, improve accuracy, and mitigate potential risks associated with AI usage in this critical domain.
References
[1] G. Harshitha, S. Kumar, and A. Jain, "Cotton disease detection based on deep learning techniques," in 4th Smart Cities Symposium (SCS 2021), 2021, pp. 496-501.
[2] S. Kumar, A. Jain, and A. Swathi, "Commodities price prediction using various ML techniques," in 2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS), 2022, pp. 277-282.
[3] S. Kumar, E. G. Rajan, and "Enhancement of satellite and underwater image utilizing luminance model by color correction method," Cognitive Behavior and Human Computer Interaction Based on Machine Learning Algorithm, pp. 361-379, 2021.
[4] D. Ghai, and S. Kumar, "Reconstruction of wire frame model of complex images using syntactic pattern recognition."
[5] Saha, B., Aswini, T., & Solanki, S. (2021). Designing hybrid cloud payroll models for global workforce scalability. International Journal of Research in Humanities & Social Sciences, 9(5).
[6] Saha, B., & Kumar, M. (2020). Investigating cross-functional collaboration and knowledge sharing in cloud-native program management systems. International Journal for Research in Management and Pharmacy, 9(12).
[7] Biswanath Saha, A., Kumar, L., & Biswanath Saha, A. (2019). Evaluating the impact of AI-driven project prioritization on program success in hybrid cloud environments. International Journal of Research in All Subjects in Multi Languages (IJRSML), 7(1), 78-99.
[8] Biswanath Saha, A. K., & Biswanath, A. K. (2019). Best practices for IT disaster recovery planning in multi-cloud environments. Iconic Research and Engineering Journals (IRE), 2(10), 390-409.
[9] Saha, B. (2019). Agile transformation strategies in cloud-based program management. International Journal of Research in Modern Engineering and Emerging Technology, 7(6), 1-16.
[10] Biswanath, S., Saha, A., & Chhapola, A. (2020). AI-driven workforce analytics: Transforming HR practices using machine learning models. International Journal of Research and Analytical Reviews, 7(2), 982-997.
[11] Biswanath, M. K., & Saha, B. (2020). Investigating cross-functional collaboration and knowledge sharing in cloud-native program management systems. International Journal for Research in Management and Pharmacy, 9(12), 8-20.
[12] Jain, A., & Saha, B. (2020). Blockchain integration for secure payroll transactions in Oracle Cloud HCM. International Journal of New Research and Development, 5(12), 71-81.
[13] Biswanath, S., Solanki, D. S., & Aswini, T. (2021). Designing hybrid cloud payroll models for global workforce scalability. International Journal of Research in Humanities & Social Sciences, 9(5), 75-89.
[14] Saha, B. (2021). Implementing chatbots in HR management systems for enhanced employee engagement. Journal of Emerging Technologies and Innovative Research, 8(8), 625-638.
[15] Jain, A. K., Saha, B., & Jain, A. (2022). Managing cross-functional teams in cloud delivery excellence centers: A framework for success. International Journal of Multidisciplinary Innovation and Research Methodology (IJMIRM), 1(1), 84-107.
[16] Saha, B. (2023). Robotic Process Automation (RPA) in onboarding and offboarding: Impact on payroll accuracy. IJCSPUB, 13(2), 237-256.
[17] Agarwal, R., & Saha, B. (2024). Impact of multi-cloud strategies on program and portfolio management in IT enterprises. Journal of Quantum Science and Technology, 1(1), 80-103.
[18] Singh, N., Saha, B., & Pandey, P. (2024). Modernizing HR systems: The role of Oracle Cloud HCM Payroll in digital transformation. International Journal of Computer Science and Engineering (IJCSE), 13(2), 995-1027.
[19] Jayaraman, Srinivasan, and Anand Singh. "Best Practices in Microservices Architecture for Cross-Industry Interoperability." International Journal of Computer Science and Engineering 13.2 (2024): 353-398.
[20] S. Kumar, E. G. Rajan, and "A study on vehicle detection through aerial images: Various challenges, issues and applications," in 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), 2021, pp. 504-509.
[21] D. Ghai, and S. Kumar, "Reconstruction of simple and complex three dimensional images using pattern recognition algorithm," Journal of Information Technology Management, vol. 14, no. Special Issue: Security and Resource Management challenges for Internet of Things, pp. 235-247, 2022.
[22] S. Gowroju, and S. Kumar, "IRIS based recognition and spoofing attacks: A review," in 2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART), 2021, pp. 2-6.
[23] D. Ghai, and S. Kumar, "Object detection and recognition using contour based edge detection and fast R-CNN," Multimedia Tools and Applications, vol. 81, no. 29, pp. 42183-42207, 2022.
[24] S. Kumar, A. Jain, D. Ghai, S. Achampeta, and P. Raja, "Enhanced SBIR based Re-Ranking and Relevance Feedback," in 2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART), 2021, pp. 7-12.
[25] K. Lakhwani, and S. Kumar, "Knowledge vector representation of three-dimensional convex polyhedrons and reconstruction of medical images using knowledge vector," Multimedia Tools and Applications, vol. 82, no. 23, pp. 36449-36477, 2023.
[26] D. Ghai, S. Kumar, M. P. Kantipudi, A. H. Alharbi, and M. A. Ullah, "Efficient 3D AlexNet architecture for object recognition using syntactic patterns from medical images," Computational Intelligence and Neuroscience, vol. 2022, no. 1, 2022.
Published
Issue
Section
License
Copyright (c) 2025 International Journal of Advance Research and Innovation(IJARI, 2347-3258)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Fringe Global Scientific Press publishes all the papers under a Creative Commons Attribution-Non-Commercial 4.0 International (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/) license. Authors have the liberty to replicate and distribute their work. Authors have the ability to use either the whole or a portion of their piece in compilations or other publications that include their own work. Please see the licensing terms for more information on reusing the work.
