Volume 8.
Issue 06.

Deposit-taking Savings and Credit Cooperative Organizations (SACCOs) operate within an increasingly competitive financial environment characterized by technological advancement, changing customer expectations, and heightened demand for service efficiency. Traditional operational procedures often create delays, increase costs, and reduce customer satisfaction, thereby affecting organizational competitiveness. Business Process Reengineering (BPR) has emerged as a strategic management approach capable of fundamentally redesigning organizational processes to achieve significant improvements in performance, quality, speed, and customer value. This study reviews the relationship between business process reengineering and customer satisfaction within deposit-taking SACCO organizations. Drawing upon existing literature relating to process redesign, service quality, information technology adoption, organizational performance, and customer satisfaction, the paper develops a conceptual framework illustrating how BPR influences customer experience through operational efficiency, technological integration, service quality enhancement, and organizational responsiveness. The study adopts a qualitative review methodology based on existing scholarly literature. Findings indicate that successful implementation of BPR significantly improves customer satisfaction through reduced transaction time, improved service accessibility, enhanced reliability, and increased organizational flexibility. The study further highlights the critical role of leadership commitment, technological infrastructure, employee participation, and customer-centered process design in achieving successful reengineering outcomes. The paper contributes to strategic management and cooperative finance literature by providing an integrated perspective on how SACCOs can leverage process transformation to improve customer experiences and sustain competitive advantage.

This study provides a comprehensive analysis of architectural and algorithmic approaches aimed at maintaining data confidentiality during the operation of large-scale language models. Particular attention is paid to the identification and protection of personally identifiable information under conditions of continuous user interaction with intelligent systems. Evidence on security breaches from recent years is systematised, demonstrating a sharp increase in incidents associated with information leakage through generative models. The work examines in detail the hybrid RECAP methodology, combining deterministic algorithms with context-dependent prompts, as well as approaches grounded in differential privacy and machine “defocused” learning. The analysis further addresses the trade-off between the level of protection and the quality of generated answers, including the impact of anonymization on factual accuracy and on model capabilities, which are often described as cognitive. Based on the findings, recommendations are formulated for introducing adaptive routing strategies and multi-stage data cleansing into contemporary MLOps cycles.

This study investigates the impact of social media on the development of communicative compensation strategies among B2-level EFL learners. With the increasing use of digital platforms in language education, social media offers new opportunities for students to practice and enhance their communication skills. Drawing on previous research on communicative competence and social media-assisted learning, this study examines how B2 learners utilize social media tools, specifically Telegram, to solve communication difficulties and apply compensatory strategies. The findings suggest that social media facilitates the use of strategies such as paraphrasing, synonym replacement, and contextual explanation, enabling learners to communicate more effectively despite language limitations. The study contributes to the understanding of how digital tools can support language learning and provides implications for integrating social media into EFL teaching practices.

The emergence of the multi-cloud computing environments has rapidly increased the attack surfaces of organizations, and it has created dynamic and complex cyber risk exposures that cannot be effectively captured under the traditional qualitative security assessment framework. It is suggested that this study will provide new business analytics-based model to quantify exposure to cloud cyber risks and optimize the security posture of multi-clouds based on data-driven decision-making. The study incorporates heterogeneous cloud security telemetry, such as configuration states, identity and access management (IAM) indicators, vulnerability severity scores, and threat intelligence feeds as input into a quantitative risk assessment model. The model analyzes the exposure of risks in distributed cloud infrastructures, through the use of probabilistic modeling and weighted risk scoring methods, and produces comparative risk indexes of various cloud service providers. Empirical testing, using aggregated data on publicly available cloud security benchmarking and industry threat reports, shows that the suggested model provides a significant increase in risk visibility and accuracy of risk prioritization. The analysis of the simulation shows that the exposure to high-risk misconfigurations and vulnerability is significantly reduced when organizations embrace analytics-based optimization strategies, and the level of reduction of risks is more than 30 percent in conditions of limited resources allocation. Moreover, predictive analytics allows detecting new trends of threats early, promoting more effective spending on cybersecurity. The key contribution of the study is that it will fill the gap between cybersecurity risk assessment and business analytics by proposing a scalable, quantitative framework specific to multi-cloud settings. The results give practical information to an enterprise decision-maker and allow managing security posture optimization based on organizational risk tolerance and operational limits. The study contributes to theoretical and practical insights into the quantification of cloud cyber risk, providing a solid base in future progress of cloud security governance through adaptive and intelligence-based approaches.

The increasing digitalization of insurance operations has intensified the need for resilient, scalable, and highly available claim processing systems capable of handling large transaction volumes, strict regulatory requirements, and continuous customer interaction. Traditional monolithic insurance platforms often struggle with operational bottlenecks, deployment complexity, limited scalability, and delayed claim settlement cycles. This paper presents a high-availability insurance claim processing framework based on distributed Java services, infrastructure as code (IaC), and asynchronous event streaming. The proposed framework integrates domain-driven design principles, microservices architecture, Kubernetes orchestration, Apache Kafka-based event streaming, and automated cloud-native deployment strategies to improve reliability, elasticity, and fault tolerance within insurance ecosystems. The framework employs Spring Boot-based distributed services, event sourcing mechanisms, container orchestration, infrastructure automation, and observability pipelines to support real-time claims ingestion, fraud detection, policy validation, and settlement workflows. Furthermore, the study evaluates the architectural implications of asynchronous communication, distributed data consistency, and operational resilience under high-load scenarios. The research demonstrates that event-driven microservice systems significantly improve scalability, reduce recovery times, and enhance processing throughput compared to centralized systems. However, the study also identifies challenges related to distributed transaction management, governance complexity, and operational monitoring. The findings contribute to contemporary research on enterprise modernization in financial and insurance technology by providing a technically grounded and operationally viable framework for resilient insurance claim processing infrastructures.

Microservice-based cloud applications have become the preferred architectural style for modern digital services due to their scalability, flexibility, and ease of deployment. However, the distributed nature of microservices introduces significant operational challenges, including service crashes, network failures, resource exhaustion, configuration anomalies, and dependency-related faults. These failures can propagate rapidly across interconnected services, resulting in performance degradation, service interruptions, and increased operational costs. Traditional fault management strategies are mainly dependent on predefined rules and manual action, which are not suitable for dynamic cloud environments. In view of these challenges, this study puts forward an intelligent cloud fault management framework utilizing Llama 3.1 for automatic fault detection, fault root cause analysis, and self-healing. The framework combines real-time monitoring with anomaly detection, contextual reasoning and autonomous remediation to enhance the operational resilience. Experimental evaluation demonstrates substantial improvements over conventional and semi-automated approaches, achieving 97.5% fault detection accuracy, 96.4% root cause identification accuracy, and a 95.7% self-healing success rate while reducing Mean Time to Recovery to 5.4 minutes. The findings demonstrate the capabilities of LLM to support scalable, reliable, and autonomous cloud fault managements.

The study determined the relationship between teachers’ pedagogical skills and Business studies students’ academic performance in public secondary schools in Akwa Ibom State. Three   research questions and  three  null hypotheses were raised to guide the study. One relevant theory  was  reviewed in the study. Correlational research design was used for the study. The population of the study comprised all the 714 Business Studies Teachers and 14,583 Junior Secondary three students in the 269 public secondary schools in Akwa Ibom State. Taro Yamene formula was used for selecting 343 JS3 students. Two sets of instruments were used for data collection, these include: Teachers’ Pedagogical Skills Questionnaire (TPSQ) for Business Studies and Business Studies Academic Performance Test (BSAPT) for Students. The instrument was face validated by one from Measurements and Evaluation from the Department of Psychological Foundations of Education, one expert from Business Education and one expert from curriculum Studies. The reliability of the instrument was determined using inter-item method of reliability. The TPSQ was subjected to Cronbach Alpha Analysis while the BSAPT was tested with PPMC, and the indices of .97 and .86 for TPSQ and BSAPT respectively were obtained. Pearson’s Product Moment Correlation Statistic was used to analyze the data. The r-coefficient of the PPMC was used to answer the research questions and to test the null hypotheses at 05 level of significance. Findings of the study revealed that, teachers’ communication, improvisation, time management, skills significantly relate to business studies students’ academic performance in public secondary schools in Akwa Ibom State. It was concluded that teachers’ pedagogical skills significantly relate to business studies students’ academic performance in public Secondary Schools in Akwa Ibom State.

The modern fashion industry increasingly relies on highly coordinated backstage beauty systems capable of functioning effectively under conditions of operational pressure, strict timing limitations, multicultural model diversity, and continuously changing creative requirements. In contemporary runway productions, backstage makeup artistry extends far beyond aesthetic application and functions as an integrated operational process involving visual standardization, adaptive communication, workflow synchronization, hygiene management, and rapid technical execution. The efficiency of backstage makeup coordination directly influences runway presentation quality, media representation, designer concept realization, and overall production stability during fashion events.

This article examines the operational structure of backstage makeup coordination within high-pressure fashion productions and explores the professional mechanisms required for maintaining workflow stability during large-scale runway events. The study is based on observational professional experience obtained through participation in multiple fashion productions in New York City, including Fashion4Ukraine, Young Fashion Show LLC, and Fashion Week Brooklyn. The article analyzes backstage workflow systems, communication dynamics between makeup artists and production teams, multicultural adaptation strategies, organizational challenges, and preventive operational approaches used in fast-paced runway environments.

Special attention is devoted to the standardization of makeup preparation processes for large numbers of runway models under severe time constraints. The article further investigates how backstage beauty teams adapt cosmetic techniques to diverse skin types, facial structures, lighting conditions, designer concepts, and media requirements while maintaining professional consistency and production efficiency. Additional focus is placed on operatio

nal risks associated with high-pressure beauty environments, including communication instability, sanitation concerns, product organization failures, and time-compression errors.

The findings demonstrate that backstage makeup coordination should be understood as a complex professional system requiring strategic planning, adaptive workflow methodology, technical flexibility, and interdisciplinary collaboration. The article contributes to the growing professional discourse surrounding beauty industry operational systems and highlights the increasing intellectualization and professionalization of modern runway makeup artistry.

The modernization of financial enterprise systems has accelerated significantly due to the increasing demand for real-time transaction processing, continuous scalability, operational resilience, and intelligent automation. Traditional monolithic enterprise infrastructures are increasingly unable to support the performance, elasticity, and fault tolerance required by modern mission-critical financial applications. This paper proposes a performance-optimized cloud-native enterprise ecosystem that integrates containerized microservices, Apache Kafka-based event streaming, infrastructure automation, DevOps-oriented orchestration, and intelligent data processing strategies for large-scale financial environments. The study investigates how cloud-native architectural models improve transaction throughput, operational continuity, scalability, and system responsiveness within enterprise financial infrastructures.

The proposed framework combines distributed containerized services, asynchronous communication pipelines, AI-augmented operational workflows, and infrastructure as code principles to establish resilient enterprise ecosystems capable of supporting high-frequency financial workloads. The framework also incorporates observability, security governance, automated deployment mechanisms, and scalable data engineering pipelines to optimize enterprise reliability. The research evaluates architectural performance through analytical comparison between monolithic financial infrastructures and distributed event-driven cloud-native ecosystems.

The findings indicate that Kafka-driven asynchronous architectures significantly reduce service bottlenecks, improve operational elasticity, and enhance fault isolation under high transaction volumes. Container orchestration further improves deployment stability and infrastructure utilization efficiency. However, the study also identifies limitations involving distributed consistency management, orchestration complexity, governance overhead, and operational skill requirements. The research contributes to enterprise cloud modernization literature by presenting a technically integrated model for building high-performance financial ecosystems capable of supporting intelligent automation, large-scale event processing, and mission-critical operational continuity.