Financing of Eco-innovations: Sources and Trends in Kazakhstan

Entrepreneurship and Sustainability Issues is a peer-reviewed journal which publishes original research papers and case studies

Investments form the basis for high-quality economic growth by ensuring renewal of production capacities and improvement of technologies and processes, thereby increasing labor productivity. Investment research is key to understanding its impact on the country’s economic advancement and more effective government policymaking to stimulate investment activity. The purpose of this paper is to study the relationship between fixed capital and R&D investments and labor productivity growth, as well as to determine the optimal level of these investments in determining the greatest effect on labor productivity growth. We use a regression model and the least squares method to empirically analyze data for seven countries for the period between 1997 and 2022. We calculate estimated values using SPSS and Python. The results show a certain impact of fixed assets and R&D investments on labor productivity growth. However, the payoff varies across the countries under study. Furthermore, despite its relatively small volumes, investment in R&D brings a greater effect on productivity growth than investment in fixed capital. With other factors remaining constant, the calculated optimums for investments in fixed assets and R&D show maximum points of growth in labor productivity.

Entrepreneurship and Sustainability Issues is a peer-reviewed journal which publishes original research papers and case studies

Type of the article: Research ArticleAbstractInvestment 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.