Browsing by Author "Zhumashev B."

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    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.