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Item Open Access Banking System(Suleyman Demirel University, 2007) Kurdaeva V; Sihimbaev DThis paper explores the structure, functions, and key principles of the modern banking system, with particular attention to Kazakhstan’s financial sector. The banking system plays a vital role in the economy by channeling funds from savers to borrowers, ensuring the efficient flow of capital, and supporting economic growth. Based on Frederic S. Mishkin’s model, the study highlights that loans constitute the major portion of external funding worldwide, with banks serving as the primary financial intermediaries. The paper discusses the composition of commercial bank balance sheets, detailing their assets and liabilities, and explaining how banks generate profit through asset transformation and interest rate differentials. Furthermore, it outlines the four core principles of bank management — liquidity management, asset management, liability management, and capital adequacy management — emphasizing how these ensure stability and profitability. The Kazakhstani banking system, while still developing, reflects global patterns but faces challenges of liquidity, credit risk, and diversification. The paper concludes that strengthening regulation, improving transparency, and fostering innovation are key to enhancing the efficiency and resilience of Kazakhstan’s banking sector.Item Open Access EXPLORING THE IMPACT OF MACHINE LEARNING ON KYC COMPLIANCE COSTS AND CUSTOMER EXPERIENCE(СДУ хабаршысы - 2023, 2023) Sattarbek A. ; Zhumashev B.; Parmanov S.Abstract. The Know Your Customer (KYC) compliance process is a critical requirement for financial institutions to prevent money laundering, fraud, and terrorist financing. Machine learning algorithms have the potential to improve the efficiency and accuracy of KYC compliance checks. In this study, we explored the effectiveness of several classification algorithms for KYC compliance checks using a dataset with 3000 rows collected from a famous banking system in Kazakhstan. We compared the performance of four commonly used algorithms: Decision Tree, Random Forest, Logistic Regression, and Support Vector Machines. Our results showed that all four algorithms achieved high accuracy rates, with Random Forest performing the best, achieving an accuracy rate of 92.1%. These findings suggest that machine learning algorithms can effectively classify KYC checks, with Random Forest being the most effective algorithm in our study. This study provides further evidence of the potential of machine learning for KYC compliance checks in the banking industry, but also highlights the need for ongoing monitoring and validation of machine learning models and concerns about explainability and transparency.