A groundbreaking study led by L.A. Kuanova of Al-Farabi Kazakh National University in Almaty, Kazakhstan, has shed light on how artificial intelligence (AI) is transforming risk management in the banking sector. Published in the journal *Хабаршысы. Экономика сериясы* (Bulletin. Economic Series), the research explores the potential of AI to revolutionise financial stability, operational resilience, and regulatory compliance.
Kuanova’s study, which combines machine learning analysis of open banking datasets with a survey of 200 bank employees in the Middle East, reveals that AI-driven models—particularly ensemble methods like XGBoost and Random Forest—significantly outperform traditional risk assessment techniques. These advanced algorithms enhance credit scoring, fraud detection, and regulatory adherence, offering banks a more proactive and data-driven approach to risk management.
“AI is not just an incremental improvement—it’s a paradigm shift,” Kuanova said. “By leveraging machine learning, banks can predict risks with far greater accuracy, reducing financial losses and improving decision-making.”
The research highlights key challenges, including data privacy concerns, model interpretability, and regulatory constraints, which could slow AI adoption. However, the findings also suggest that AI-driven models have the potential to reshape financial governance by enabling real-time, predictive risk assessments.
For the banking sector, this means a future where AI doesn’t just support decision-making but actively mitigates risks before they escalate. The study provides strategic recommendations for financial institutions and policymakers, urging them to adopt ethical and secure AI frameworks to ensure sustainable innovation.
As banks increasingly integrate AI into their operations, Kuanova’s work offers a roadmap for navigating the complexities of this transformation. By addressing ethical and regulatory concerns, financial institutions can harness AI to build more resilient and adaptive risk management systems.
The study’s findings could have broader implications beyond banking, influencing other high-stakes industries where risk management is critical. In sectors like energy, where financial stability and operational resilience are paramount, AI-driven risk assessment could lead to more efficient resource allocation and reduced exposure to market volatility.
Kuanova’s research underscores the need for collaboration between technologists, regulators, and financial institutions to ensure that AI is deployed responsibly. As AI continues to evolve, its role in safeguarding financial health will only grow, making this study a crucial contribution to the ongoing debate about the future of risk management.