The article presents a case study on the implementation and evaluation of the effectiveness of an adaptive business process management system (BPM) for small and medium-sized businesses (SMEs), integrating machine learning (ML), serverless architecture and cybersecurity measures. The research is based on the previous works of the author [1, 2], where the methods of designing automated information systems (AIS) were analyzed and a conceptual model of the system was developed. The relevance of the topic is due to the growing need for automation in SMEs in developing economies such as Kazakhstan and Russia, where traditional ERP/CRM systems are often ineffective due to high costs and low adaptability. The goal is to evaluate the ROI of the system through implementation in two fictional but realistic cases (retail and services) using data modeling for privacy.The methodology includes the collection of metrics before and after implementation (process processing time, costs, security level), A/B testing, and log analysis. Python with TensorFlow for predictive analysis ML models, AWS Lambda for serverless components, and OWASP threat assessment tools were used. The results show a 25-30% reduction in costs, a 35-40% improvement in adaptability due to ML, and an increase in cybersecurity through vulnerability detection. Ethical aspects include data anonymization and compliance with GDPR-like standards. The findings confirm the benefits of the system for SMEs, with recommendations for scaling and discussion of limitations such as reliance on cloud services.

Climate change is increasing the frequency of hydrological extremes such as floods, requiring more effective approaches to build community preparedness alongside traditional flood risk management. This study develops and evaluates a serious-game-based flood risk education intervention in Kazakhstan and examines whether post-intervention protective behavioral intention can be predicted from gameplay, hydrological-literacy, and governance-attitude data. A cross-sectional, post-intervention survey of 60 participants from flood-, GLOF-, and drought-exposed regions of Kazakhstan was analyzed using descriptive/inferential statistics and three machine-learning classifiers (logistic regression, random forest, multilayer perceptron) to predict protective behavioral intention and to identify its strongest correlates. The participants showed good knowledge about immediate responses in flood events: 91.7% were able to identify the appropriate evacuation procedures, and the mean hydrological-literacy score was 5.13 out of 7. There was relatively less knowledge about longer-term mitigation measures, especially the cost effectiveness of nature-based flood protection (56.7%). Most participants agreed with the introduction of measures for flood management using nature (58.6%) and mandatory disaster insurance (87.7%), and 81.0% indicated that they planned to take preparedness measures after playing. The predictive models with the highest discrimination were logistic regression and random forest, with an area under the receiver operating characteristic curve (AUC) of 0.81 each, while the multilayer perceptron (MLP) had an AUC of 0.75. Importantly, both machine-learning and conventional statistical analyses revealed that perceived scenario fidelity was the strongest and most consistent predictor of protective behavioral intention. These results suggest that serious-game-based flood risk education provides a useful context for examining hydrological knowledge and generating behavioral data for predictive modeling of preparedness. The framework combines experiential learning, flood-governance assessment, and machine-learning-based behavioral prediction, while the findings indicate an association between perceived scenario realism and preparedness-related behavioral intention. These results provide a basis for further investigation of data-driven disaster risk education in Kazakhstan and other settings facing similar hydrological hazard risks.