OPTIMIZING RISK MANAGEMENT IN DIGITAL BANKS USING AI ALGORITHMS
DOI:
https://doi.org/10.5281/zenodo.21278656Ключевые слова:
AI, digital banking, risk management, machine learning, credit scoring, fraud detection, regulatory compliance, cybersecurity.Аннотация
This research presents a comprehensive analysis of the integration and optimization of Artificial Intelligence algorithms within the risk management frameworks of digital banks. As the financial sector moves toward autonomous value units, the limitations of traditional, linear risk assessment methodologies have become pronounced. By leveraging advanced machine learning architectures, including ensemble models, artificial neural networks, and natural language processing, digital banks can significantly enhance their predictive accuracy across credit, market, and operational risk domains.Библиографические ссылки
Ahsan, M., et al. (2022). Interactions between artificial intelligence technology and cybersecurity. Journal of Financial Resilience.
Areo, G. (2025). Exploring the Role of AI in Enhancing Fraud Prevention and Detection Mechanisms in Digital Banking. International Journal of Digital Finance.
Brown, M. (2024). Influence of Artificial Intelligence on Credit Risk Assessment in Banking Sector. Fintech Research Review.
Faisal, S. M., Khan, W., & Ishrat, M. (2025). Al and Financial Risk Management: Transforming Risk Mitigation With Al-Driven Insights and Automation. Advances in Financial Technology.
Josyula, P., et al. (2023). The potential of AI to support financial risk governance. Journal of Economic Research.
Vaithilingam, S. (2022). Privacy and data security in Al-based banking. Journal of Digital Sovereignty.
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