Gaukhar Kodasheva
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Actual problems of development of the banking sector in the economy of Kazakhstan
Gaukhar Kodasheva , Nadezhda Parusimova , Madina Rispekova , Aigul Uchkampirova doi: http://dx.doi.org/10.21511/bbs.12(3-1).2017.10Banks and Bank Systems Volume 12, 2017 Issue #3 pp. 257-268
Views: 2681 Downloads: 916 TO CITE АНОТАЦІЯThe article deals with topical issues to develop the banking sector in Kazakhstan as their condition assessment, weaknesses, strengths, problems and basic ways of development of Kazakhstan’s second-tier banks in the current environment, these issues are discussed in this article and determine the relevance of the material presented. The need to address the main problems in the development of the banking sector is due to the fact that it is represented as a fundamental element of the financial system. Moreover, under the modern conditions, it is subject to the impact of financial globalization, crisis phenomena in the world economy, the growth of uncertainty in the world financial market, which determines a number of negative consequences for the stable development of banking activities. Effective functioning of the banking system allows ensuring the sustainable economic development of any state, as the banking sector participates in the redistribution of funds and financing of the real sector of the country’s economy. At the present stage of the development, the issues of dealing with the key problems of the development of the banking sector acquire special relevance on a global scale, since the financial crisis has revealed the shortcomings of the current system of banking regulation and supervision. In this regard, in the crisis conditions, the state intervention in regulation of banking activities has intensified, the role of risk management in commercial banks has increased, the requirements to the bank’s capital, and the quality of assets has increased. Volatility and instability of the world financial markets require the search for new approaches in the implementation of banking activities to maintain sustainable development, increase margins in the banking business, which determines the relevance of this study. The main results of the research show the influence of external and internal factors that inhibit the development of banking activity.
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What drives central bank digital currency implementation? A machine-learning analysis using support vector machines and SHAP explainability
Zhanat Khishauyeva
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Diana Sitenko
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Vitaliia Koibichuk
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Arsen Petrosyan
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Gaukhar Kodasheva ,
Ekaterina Dmitrieva
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Kseniіa Mohylna
doi: http://dx.doi.org/10.21511/bbs.21(1).2026.13
Type of the article: Research Article
Abstract
Central bank digital currency (CBDC) programs have rapidly shifted from experimentation to policy-critical infrastructure decisions, yet countries show strikingly uneven progress from research to pilots and implementation. This study aims to identify and explain the key structural, macroeconomic, technological, and ecosystem-related factors that differentiate CBDC initiatives advancing to pilot or implementation stages from those remaining in early research or being discontinued across countries worldwide. Using 161 CBDC projects across 109 countries (as of December 2024) and 10 project-, public interest-, technology-, and macroeconomic indicators, we estimate a Support Vector Machines classifier with GridSearchCV (5-fold) tuning and interpret the results using Shapley Additive exPlanations explainability. The raw outcome distribution was strongly imbalanced (83.85% in the early/cancelled class), so ADASYN balancing was applied, producing 270 observations with equal class shares and an 85/15 train–test split (229/41). The optimized SVM (RBF; C = 10, gamma = 10) achieved 93.90% cross-validated accuracy and 0.88 accuracy on the test set, indicating strong predictive performance on unseen data. Test-set metrics show an informative error profile: for class 1 (advanced projects), recall = 1.00 and F1 = 0.89, while for class 0 (early/cancelled), precision = 1.00 with recall = 0.75 (macro/weighted F1 = 0.88), implying that the model identifies all advanced projects but may misclassify around one-quarter of early/cancelled cases. SHAP ranks the strongest drivers as use-case direction, inflation, crypto adoption ranking, CBDC-related research output, and international participation, with mixed/wholesale projects, higher inflation, stronger scientific attention, and greater international involvement generally increasing the likelihood of advancement.
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