Aliya Nurgaliyeva

The purpose of this study is to make a comprehensive analysis of investor perceptions in the context of green and conventional bond investments. For this purpose, a new model is presented by considering two steps. First, a criteria set is generated by considering balanced scorecard perspectives that are finance, customer, organizational effectiveness and learning and growth. After that, the neuro Quantum fuzzy M-SWARA method is considered to weight these criteria. Secondly, seven critical determinants for bond investments are identified that are coupon rates, volume, maturity, riskiness, liquidity, volatility, and tax considerations. Neuro Quantum fuzzy TOPSIS approach is employed to rank these factors. The main contribution of the study is that by combining the balanced scorecard framework and quantum-inspired decision-making techniques, this paper offers a novel and sophisticated decision-making model to understanding investor behavior. Similarly, in the proposed model, a new methodology is generated by the name of M-SWARA. In this framework, some enhancements are adopted to the SWARA technique. The weighting results indicate that meeting customer expectations is the most critical factor that affects the investor perception to make investments to the bonds. Moreover, according to the ranking results, it is concluded that coupon rates are the most important item for both conventional and green bond investors. On the other hand, with respect to the conventional bond investor, tax is the second most essential factor. However, regarding the green bond investors, volatility plays a critical role.

Acknowledgment
This research has been/was/is funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (№ AP 19679105 “Transformation of ESG financial instruments in the context of the development of the green economy of the Republic of Kazakhstan”).

Type of the article: Research Article

Abstract
Green lending growth can support bank resilience and is therefore relevant to Kazakhstan’s pathway to carbon neutrality by 2060. The study created a panel of banking years (2015–2024) and assessed the relationships between banks’ regulatory compliance, digitalization, borrowers’ ESG performance, and green loan volumes using multivariate models. The research provides short-term forecasts using compressed ARIMAX and policy scenarios. Moreover, 20 purposively selected semi-structured interviews (commercial bank executives, SME owners, customers, and policy experts) and a national survey of 850 adult bank customers / SME owners led by the author were added. Across preferred specifications, regulatory eligibility and borrower ESG are consistently positive: policy support is associated with KZT 7-9 billion more green credit per bank year, and each one-point increase in borrower ESG is associated with KZT 0.34-0.38 billion higher volumes. Digitalization is positive but model-sensitive, strengthening within-bank variation; larger banks extend more green credit, consistent with capacity advantages. The results are interpreted through three stability channels: improved screening/asset quality, portfolio tilt toward taxonomy-aligned exposures, and funding access without making solvency claims. Scenario paths suggest aggregate green lending could reach KZT 80-96 billion by 2027 under aligned policy-ESG-digital conditions; under weak support, it may stagnate near KZT 49-55 billion. Findings motivate the development of a binding taxonomy with standardized disclosures, a national ESG scorecard registry, and inclusive digital rails to enhance SME and rural uptake.

Type of the article: Research Article

Abstract
Weak risk governance and legacy non-performing loans have repeatedly destabilized banks in emerging markets, making the payoff from regulatory strengthening of internal control and internal audit a first-order policy question. This study aims to determine whether Kazakhstan’s 2019 risk-management and internal control requirements (National Bank Resolution No. 188), whose core assurance mechanism is a strengthened internal audit function, improved the financial stability of the country’s commercial (second-tier) banks, and to identify the channel of this effect. The analysis applies a continuous-treatment difference-in-differences design to an annual panel of 37 banks over 2015–2025 (287 bank-years), interacting each bank’s pre-intervention (2016–2019) weakness with the post-intervention period and measuring stability by the log Z-score. The results show that the stabilizing effect is concentrated in the asset-quality channel: banks that entered the post-2020 period with a one-standard-deviation greater non-performing-loan weakness recorded a 0.38-0.41 log-point (roughly 40-50%) larger post-intervention increase in the Z-score (p < 0.01), whereas composite pre-intervention weakness yields no robust effect under any weighting scheme. The effect emerges with a multi-year lag, becoming statistically detectable only toward the end of the sample period, and operates through capital rebuilding. Because pre-intervention weakness strongly predicts market exit (Cox hazard ratio ≈ 3.95), the estimates are a lower bound on the intervention’s full association with sector stability. The findings imply that supervisors should target credit-risk governance, loan classification, timely recognition of problem loans, and provisioning, where strengthened internal control and audit yield the largest stability gains.