EXPLORING THE IMPACT OF MACHINE LEARNING ON KYCCOMPLIANCE COSTS AND CUSTOMER EXPERIENCE - Sattarbek, Alisher; Zhumashev, Bekzat; Parmanov, Serikzhan (Journal of Emerging Technologies and Computing, 2024)
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 KYCcompliance checks using a dataset with 3000 rows collected from a famousbanking system in Kazakhstan. We compared the performance of fourcommonly used algorithms: Decision Tree, Random Forest, LogisticRegression, and Support Vector Machines. Our results showed that all fouralgorithms 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.