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<p>This study is aimed at conducting a comparative evaluation of methods of machine learning to classify and predict the effectiveness in the dynamically developing electronic financial sector. Traditional methods for assessing bank efficiency and sustainability often involve subjective judgments and resource-intensive processes, which limits their adaptability and accuracy. The study carefully analyzed six artificial intelligence models: random forest, support vector machine, logistic regression, K-nearest neighbors, Naive Bayes, and gradient boosting. The classification capabilities of these models were estimated by categorizing the performance into high-performing, medium-performing, and low-performing groups using quantitative metrics such as accuracy, recall, precision and F1-score. The dataset, including key financial indicators which are assets, liabilities, return on assets, return on equity, non-performing loans, liquidity ratios, and risk-weighted assets, net income was collected from 21 banks over 6.5 years. The analysis showed that the support vector machine and logistic regression models achieved excellent forecasting accuracy exceeding 90%, demonstrating their robustness in identifying different performance classes. Random forest and gradient boosting models have achieved high results, especially in identifying between medium and top financial institutions. However, the K-nearest neighbors and Naive Bayes models faced difficulties when addressing the intricacies and inconsistencies of financial data. The findings of the research highlight the revolutionary capabilities of machine learning in developing automated predictive analysis systems in the banking sector, providing a scalable, unbiased, and data-driven alternative to traditional methods. This approach contributes to the development of algorithmic solutions for digital banking, expanding the use of artificial intelligence in the financial sector and creating the basis for the implementation of complex analytical platforms. This research lays the groundwork for deploying sophisticated analytics systems in the banking sector, ultimately leading to enhanced operational effectiveness.</p>

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Comparative Analysis of the Predictive Risk Assessment Modeling Technique Using Artificial Intelligence

  • 期刊论文

  • 发表日期：

    2025年5月15日

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  • Anel Tolkynbekova,
  • Dina Koishiyeva,
  • Alibek Bissembayev,
  • Dinargul Mukhammejanova,
  • Assel Mukasheva,
  • Jeong Won Kang

摘要

This study is aimed at conducting a comparative evaluation of methods of machine learning to classify and predict the effectiveness in the dynamically developing electronic financial sector. Traditional methods for assessing bank efficiency and sustainability often involve subjective judgments and resource-intensive processes, which limits their adaptability and accuracy. The study carefully analyzed six artificial intelligence models: random forest, support vector machine, logistic regression, K-nearest neighbors, Naive Bayes, and gradient boosting. The classification capabilities of these models were estimated by categorizing the performance into high-performing, medium-performing, and low-performing groups using quantitative metrics such as accuracy, recall, precision and F1-score. The dataset, including key financial indicators which are assets, liabilities, return on assets, return on equity, non-performing loans, liquidity ratios, and risk-weighted assets, net income was collected from 21 banks over 6.5 years. The analysis showed that the support vector machine and logistic regression models achieved excellent forecasting accuracy exceeding 90%, demonstrating their robustness in identifying different performance classes. Random forest and gradient boosting models have achieved high results, especially in identifying between medium and top financial institutions. However, the K-nearest neighbors and Naive Bayes models faced difficulties when addressing the intricacies and inconsistencies of financial data. The findings of the research highlight the revolutionary capabilities of machine learning in developing automated predictive analysis systems in the banking sector, providing a scalable, unbiased, and data-driven alternative to traditional methods. This approach contributes to the development of algorithmic solutions for digital banking, expanding the use of artificial intelligence in the financial sector and creating the basis for the implementation of complex analytical platforms. This research lays the groundwork for deploying sophisticated analytics systems in the banking sector, ultimately leading to enhanced operational effectiveness.