Abstract
Type of the article: Research Article AbstractInvestment activities of banks are a key driver in financial sector development, yet their effectiveness largely depends on the quality of financial monitoring, which can detect, diagnose, and correct anti-money laundering and countering the financing of terrorism (AML/CFT) weaknesses. This article aims to assess the effectiveness of financial monitoring in the investment operations of Kazakhstani banks and to identify transaction-level risk indicators that can support data-driven AML/CFT supervision. The study employs a mixed methods design that combines analysis of national AML/CFT legislation and supervisory guidance with semi-structured expert interviews and case studies of three major institutions (Halyk Bank, Kaspi Bank, and ForteBank). In the quantitative component, a synthetic dataset of 1,000 investment-related transactions, calibrated to 2019–2024 statistics from the National Bank of Kazakhstan and the Committee for Financial Monitoring, is analyzed using logistic, multilevel, and Bayesian logistic regression with cross-validation and bootstrapped confidence intervals. The models achieve high predictive performance (AUC up to 0.93, test accuracy up to 93.2%, sensitivity 82.0%, specificity 94.1%) and show that higher transaction amounts, cross-border origin, SWIFT channel use, and investment-linked operations increase the odds of being flagged as suspicious by factors of roughly 1.5-3.5. Qualitative evidence reveals uneven digitalization, fragmented data integration, and capacity gaps, especially in mid-sized banks, which limit the practical implementation of these risk-sensitive tools. The results justify targeted regulatory support for advanced analytics and provide a replicable framework for strengthening investment-related financial monitoring in Kazakhstan and comparable emerging markets.