Optimizing Risk Management in Digital Banks Using AI Algorithms

This paper examines the optimization of risk management in digital banks through the application of artificial intelligence and machine learning algorithms. It analyzes the transition from traditional, largely linear risk assessment frameworks to adaptive and data-driven systems capable of processing real-time and multimodal financial information. Particular attention is given to credit risk assessment, AI-based credit scoring, fraud detection, cybersecurity, regulatory compliance, and operational resilience. The study discusses the use of ensemble models, neural networks, natural language processing, alternative data, and automated learning mechanisms to improve predictive accuracy and accelerate risk identification. It also highlights the importance of explainability, human oversight, ethical governance, transparency, and regulatory compliance in the deployment of AI-based risk management systems. The paper argues that sustainable digital banking requires a balance between predictive performance, technological scalability, governance, and institutional trust.

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