Artificial intelligence-based risk management for the banking sector: impact and challenges

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Mixed-method analysis reveals AI enhances risk management in banking, suggesting key ethical challenges.

AI-driven models can significantly improve credit scoring and fraud detection, transforming traditional risk management practices.
Findings indicate that ensemble models like XGBoost outperform traditional techniques in prediction accuracy and efficiency.
The research utilized a mixed-method approach, including a survey of 200 bank employees and analysis of open banking datasets.
Challenges identified include data privacy, model interpretability, and regulatory constraints that may limit AI's integration.

Kuanova et al. (2025) studied this question.

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View Full Paper

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Authors

Mixed-method analysis reveals AI enhances risk management in banking, suggesting key ethical challenges.

AI-driven models can significantly improve credit scoring and fraud detection, transforming traditional risk management practices.
Findings indicate that ensemble models like XGBoost outperform traditional techniques in prediction accuracy and efficiency.
The research utilized a mixed-method approach, including a survey of 200 bank employees and analysis of open banking datasets.
Challenges identified include data privacy, model interpretability, and regulatory constraints that may limit AI's integration.

Kuanova et al. (2025) studied this question.

View Full Paper

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