This article considered the problem of determining the creditworthiness of an enterprise operating in the field of trade and services. The assessment of the creditworthiness of borrowers, particularly small businesses, needs to be more careful: the level of development of small enterprises and their specific activities must be considered, as well as the uncertainty in obtaining any financial result. A method for assessing the creditworthiness of enterprises (trade and services) is proposed, based on the use of the mathematical apparatus of the theory of fuzzy sets. This article analyzes the indicators of industry and regional specifics, indicators of the activity of a small enterprise, and financial and economic indicators typical for the service sector and trade. The rules on the basis of which decisions are made are formed in the form of logical formulas containing parameters. In its most general form, one parameter is predicted, called the creditworthiness index, which varies from 0 to 1 and has a natural interpretation. On the basis of the proposed method, examples of calculating the assessment of the creditworthiness of enterprises operating in the field of trade and services are given. The proposed scientific approach can be used as a basis for creating expert decision support systems for lending to small businesses.

This article provides an overview of modern machine learning methods in the context of their active use in credit scoring, with particular attention to the following algorithms: light gradient boosting machine (LGBM) classifier, logistic regression (LR), linear discriminant analysis (LDA), decision tree (DT) classifier, gradient boosting classifier and extreme gradient boosting (XGB) classifier. Each of the methods mentioned is subject to careful analysis to evaluate their applicability and effectiveness in predicting credit risk. The article examines the advantages and limitations of each method, identifying their impact on the accuracy and reliability of borrower creditworthiness assessments. Current trends in machine learning and credit scoring are also covered, warning of challenges and discussing prospects. The analysis highlights the significant contributions of methods such as LGBM classifier, LR, LDA, DT classifier, gradient boosting classifier and XGB classifier to the development of modern credit scoring practices, highlighting their potential for improving the accuracy and reliability of borrower creditworthiness forecasts in the financial services industry. Additionally, the article discusses the importance of careful selection of machine learning models and the need to continually update methodology in light of the rapidly changing nature of the financial market.

This study investigates the integration of machine learning (ML) techniques into football analytics to predict player skills and optimize tactical decisions. A dataset of over 150,000 professional match actions from various leagues and seasons was analyzed using deep neural networks, convolutional neural networks (CNNs), and gradient boosting machines (GBM) algorithms on biometric, contextual, and match data. The valuing actions by estimating probabilities (VAEP) metric indicated scores from +1.8 to +3.0 for key players, enabling detailed performance evaluation. CNN models achieved up to 91% precision, 88% recall, and a receiver operating characteristic – area under the curve (ROC-AUC) of 0.94, confirming their effectiveness in predicting player actions and contributions. Injury risk prediction using eXtreme gradient boosting (XGBoost) reached an F1-score of 0.87 and a ROC-AUC of 0.92, offering actionable insights for injury prevention and optimal player rotation. The findings highlight artificial intelligences (AI)’s capacity to support individualized preparation, tactical adjustments, and cost-effective recruitment strategies. While computational demands and data quality remain challenges, the results demonstrate the transformative potential of AI in modern football, providing a practical framework for data-driven decision-making to enhance team performance and strategic planning

The growing number of spam messages in digital communication highlights the urgent need for effective spam detection systems, particularly for languages that lack sufficient digital resources, such as Kazakh. This research aims to develop a machine learning-based approach tailored for spam detection in Kazakh messages, utilizing various text preprocessing techniques and methods to enhance model performance. The primary objective of this study is to evaluate the effectiveness of the Multinomial Naive Bayes algorithm in classifying spam and non-spam messages within a dataset composed of 200 manually labeled samples. The methodology involves several essential steps, including data collection, preprocessing to clean and normalize the text, and feature extraction to transform the messages into a suitable format for analysis. The findings reveal that the proposed model achieves an impressive accuracy rate of 95%, demonstrating its potential for effective spam detection in the Kazakh language. This work significantly contributes to addressing the existing gap in spam detection resources specifically designed for the Kazakh-speaking community. The practical implications of the results are considerable, as they can inform the development of more sophisticated spam filtering systems, thereby enhancing user experience and security in digital communications. Moreover, theoretical significance lies in its contribution to the fields of natural language processing and machine learning, encouraging further research and development of algorithms and techniques applicable to underrepresented languages. The study outlines text processing steps to enhance spam detection accuracy in Kazakh messages improving machine learning models' ability to identify patterns.

Investment assessment in the agricultural sector remains a complex problem owing to uncertainty, incomplete information, and the heterogeneous nature of economic and environmental data, particularly in developing economies. This study proposes a multi-agent hybrid model to evaluate the investment attractiveness of Kazakhstan’s agro-industrial complex. The methodology integrates fuzzy logic, artificial neural networks, and optimization algorithms into a modular multi-agent architecture. The model is based on international datasets from the OECD and FAO covering the period 2010–2023, including Producer Support Estimate (PSE), Agri-Environmental Indicators (AEI), Agricultural Outlook projections, and foreign direct investment (FDI) inflows. The data were normalized using the min–max method and transformed into fuzzy representations through triangular membership functions to capture uncertainty and expert judgment. Specialized agents perform data acquisition, preprocessing, factor analyses, and prediction tasks. Neural networks optimized using genetic algorithms and particle swarm optimization were trained on fuzzy-transformed indicators to generate an integrated investment attractiveness index ranging from zero to one. The experimental results demonstrate high predictive performance, with MAE = 0.083, ROC-AUC = 0.87, and F1-score = 0.81, confirming the robustness and stability of the proposed approach. The results indicate that combining fuzzy logic for interpretability, neural networks for predictive accuracy, and multiagent systems for scalability significantly improves investment assessment under uncertainty. The proposed framework can be practically applied by policymakers, investors, and agricultural stakeholders to support transparent, adaptive, and evidence-based investment decision-making in Kazakhstan’s agro-industrial sector.