Background: The modern credit card system is critical, but it has not been fully examined to meet the unique financial needs of a constantly changing number of manufacturers and importers. Methods: An intelligent credit card system integrates the features of artificial intelligence and blockchain technology. The decentralized and unchangeable ledger of the Blockchain technology significantly reduces the risk of fraud while maintaining real-time transaction recording. On the other hand, the capabilities of AI-driven credit assessment algorithms enable more precise, effective, and customized credit choices that are specifically tailored to meet the unique financial profiles of manufacturers and importers. Results: Several metrics, including predictive credit risk, fraud detection, credit assessment accuracy, default rate comparison, loan approval rate comparison, and other important metrics affecting the credit card system, have been investigated to determine the effectiveness of modern credit card systems when using Blockchain technology and AI. Conclusion: The study of developing an intelligent scoring system for crediting manufacturers and importers of goods in Industry 4.0 can be enhanced by incorporating user adoption. The changing legislation and increasing security threats necessitate ongoing monitoring. Scalability difficulties can be handled by detailed planning that focuses on integration, data migration, and change management. The research may potentially increase operational efficiency in the manufacturing and importing industries.

Hybrid artificial intelligence optimization models combine learned behavioral signals with constrained decision-making, but their transition from prototype calibration to operational use is rarely evaluated across changing platform regimes. The Adaptive Calibration and Operational Transferability (ACOT) framework separates the parameterized decision utility from a fixed external evaluation instrument, validates calibrated configurations on unseen stochastic scenarios, and audits directional transfer regret. The framework is evaluated on a de-identified pilot dataset from a digital group-buying platform comprising 150 users, 200 products, 150 lots, 4000 behavioral events, and 500 orders. Because the observed lot records predominantly represent completed or expired states, the assignment experiments use counterfactually reconstructed pre-activation lot states rather than a complete historical replay. A corrected objective-aware heuristic restores identifiability of the relevance–completion parameter. Across 20 matched optimizer seeds, Tree-structured Parzen Estimation achieved mean validation $J=0.734518$, compared with $0.733899$ for random search and 0.730085 for expert weights, and won 15 of 20 seed-paired comparisons. The run-level BCa 95% confidence interval for the mean TPE–random difference was $[-0.000001, 0.001200]$, indicating a modest, seed-sensitive advantage. Continuous stress testing showed a non-monotonic calibration value, with the largest sampled gain occurring at an available-user fraction of 0.90. Tight-budget calibration had the lowest point-estimate worst-case transfer regret $\left(0.000720\right)$. However, hierarchical bootstrap assigned it a 55.1% probability of being the minimax source, compared with 40.1% for the base regime. The results support uncertainty-aware transfer auditing rather than assuming a universally robust calibration regime.

Group-buying platforms require the joint treatment of individual user relevance and hard operational constraints, including minimum group size, lot capacity, spending limits, product availability, and simultaneous participation limits. This paper presents Preference-Aware Hybrid Combinatorial Group Optimization (PA-HCGO), a prototype-calibrated decision framework that integrates implicit feedback preference learning with constrained user–lot assignment. The contribution is not a new recommender architecture or a new integer programming solver, but a system-level integration of learned user–lot utility and hard group-buying feasibility constraints inside the SmartBuy Connect prototype. The empirical part uses an anonymized prototype dataset containing 200 products, 150 users, 150 lots, 4000 user events, and 500 orders. The evaluation is explicitly divided into three tracks: observed temporal recommendation testing, a calibrated counterfactual pre-activation scenario, and synthetic scalability tests generated from prototype-calibrated distributions. In the observed transactional test subset, which contains 40 held-out transactional items from 36 users, the PA-PREF preference layer achieved Recall@10 = 0.3611 and NDCG@10 = 0.1319 using user–category profiles available at the end of the training interval. Although PA-PREF obtained the highest point estimate, its advantage over the content-only baseline was not statistically separable at the 95% level on this small transactional subset. The result is therefore treated as preliminary. For general engagement, the popularity baseline remained stronger, indicating that the proposed preference model is more useful for transactional intent prediction than for all activity types. In the calibrated counterfactual pre-activation scenario, PA-HCGO activated 117 of 150 lots, compared with 85 lots under independent greedy assignment. This scenario is a constructed pre-activation setting rather than an observed historical platform state. In synthetic scalability tests, the method remained computationally feasible up to 5000 users and 1000 lots, with a mean solve time of 4.7174 s in the prototype implementation. The results suggest that learned user–lot utility can improve constrained group formation, but the evidence should be interpreted as prototype-calibrated rather than as proof of industrial-scale business effectiveness.

This study considers the digital transformation of Kazakhstan’s agro-industrial complex, which has created an urgent need for scientifically grounded methods that can optimize marketing strategies under conditions of resource limitations, production seasonality, and heterogeneous consumer behavior. This study proposes a hybrid decision-support framework integrating a modified NSGA-III algorithm with machine learning techniques for optimizing digital marketing strategies in the agro-industrial complex of Kazakhstan. The model considers three objectives: maximizing channel efficiency and audience reach while minimizing marketing costs. Experimental results based on a dataset of N = 1200 observations demonstrate that the proposed approach improves the composite performance indicator by 12.4% compared to baseline single-objective optimization methods. Pareto front analysis reveals three distinct clusters of strategies, corresponding to (1) high-impact integrated digital TV strategies, (2) cost-efficient traditional channel strategies, and (3) high-risk high-return allocations. The clustering validity is confirmed by a silhouette score of 0.624, indicating strong separation between strategy groups. The results highlight the practical significance of adaptive budget allocation and demonstrate the effectiveness of combining evolutionary optimization with machine learning for decision support in complex marketing environments.

The article considers that the digital transformation of Kazakhstan's agro-industrial complex has created an urgent need for scientifically grounded methods that can optimize marketing strategies under conditions of resource limitations, production seasonality, and heterogeneous consumer behavior. Existing approaches often rely on simplified analytical models that fail to capture the nonlinear and multi-criteria relationships inherent in agricultural marketing. This study addresses that gap by proposing a hybrid decision-support framework that integrates a modified NSGA-III evolutionary algorithm with machine learning techniques to optimize the allocation of marketing budgets across digital and traditional channels. The approach incorporates three key performance criteria—channel efficiency, target audience reach, and marketing cost—while embedding sector-specific constraints related to the AIC of Kazakhstan. A comparative analysis of current digital marketing optimization practices reveals that most methods lack adaptability to sector-specific production cycles, do not sufficiently analyze trade-offs, or fail to integrate machine learning for post-optimization interpretation. The results of the hybrid model show the formation of three distinct clusters of Pareto-optimal strategies, demonstrating the need for differentiated and adaptive budget allocation patterns. The outcomes confirm that the proposed hybrid methodology provides a rigorous scientific basis for designing cost-effective and high-impact marketing strategies within the digital transformation agenda, AIC.

The agro-industrial complex of Kazakhstan faces increasing pressure to improve the efficiency of marketing activities under conditions of limited budgets, regional heterogeneity, seasonal demand, and uneven digital infrastructure. Traditional marketing planning methods often rely on expert judgment or single-objective optimization and therefore cannot adequately address the conflicting objectives of maximizing marketing effectiveness, expanding audience coverage, and minimizing campaign expenditure. This study proposes a hybrid decision-support framework that combines the Non-dominated Sorting Genetic Algorithm II (NSGA-II) with machine learning-based clustering for multi-objective marketing budget optimization in the agro-industrial sector. The model considers five major promotion channels: digital media, television, radio, print media, and events. NSGA-II is used to generate Pareto-optimal budget allocation strategies, while K-means clustering classifies the resulting solutions into interpretable strategy groups. The framework was evaluated using synthetic data and enterprise-level data reflecting marketing conditions in Kazakhstan. Results show that strategies combining digital media and television achieve the highest marketing effectiveness and audience coverage, although they require greater campaign expenditure. Radio and print media remain relevant for cost-sensitive strategies, particularly for enterprises with limited resources. Cluster analysis identified three main strategy groups: cost-efficient, balanced, and high-performance configurations. The findings confirm that marketing budget allocation in the agro-industrial sector should be treated as a multi-objective optimization problem rather than a single-criterion decision. The proposed framework provides both computational optimization and managerial interpretability, supporting data-driven, adaptive, and resource-aware marketing planning for agro-industrial enterprises in Kazakhstan.