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June 20, 2025The Journal of Economic Research & Business AdministrationOpen Access

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

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Authors

LKLaura KuanovaAOA.N. OtegenGKGaukhar Kenzhegulova

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Overview

Mixed-method analysis reveals AI enhances risk management in banking, suggesting ethical frameworks are needed.

Key Points

  • AI models improve prediction accuracy and classification efficiency, revolutionizing banking risk management.
  • Ensemble techniques like XGBoost and Random Forest were found to outperform traditional methods significantly.
  • A mixed-method approach combined literature review, machine learning analysis, and employee surveys from the Middle East.
  • Key challenges include data privacy and regulatory constraints affecting AI integration in finance.

Cite This Study

Kuanova et al. (2025) studied this question.

synapsesocial.com/papers/68af33efcf1dd9ea359e93d2https://doi.org/10.26577/be202515223
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  3. 3ARTIFICIAL INTELLIGENCE IN FINANCIAL RISK MANAGEMENT: PREDICTIVE ANALYTICS AND ETHICAL CONCERNS2025
  4. 4AI in Banking Risk Management and Fraud Detection in Preventing Financial Crimes and Optimizing Credit Decisions2025 · 2 citations
  5. 5Ethical and Regulatory Challenges of Deploying AI Powered Credit Management Systems in Banks2025