Al-Kindi Center for Research and Development (KCRD) (E-Journals)
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Early Detection Of Financial Crisis: Analysis Of Insurance Companies On The Indonesian Stock Exchange
No full textThis research aims to analyze the influence of Liquidity, Cash Flow, Institutional Ownership, Profits, and Independent Commissioners on Financial Distress in Insurance Companies. The unit of analysis is 15 insurance companies listed on the Indonesia Stock Exchange for the 2018-2021 period. The independent variables used in this research are Liquidity which is measured using the current ratio (CR), Cash Flow which is measured through operating activities, Institutional Ownership which is measured through the percentage of shares owned by institutional parties, Profit which is measured using profit before tax (EBT), and Independent Commissioners which are measured using the percentage of the number of independent commissioners. Meanwhile, the dependent variable is Financial distress which is measured using ICR (Interest Coverage Ratio). The sample collection method used by researchers is purposive sampling. The analytical method used is multiple linear regression analysis. The results of this research show that Liquidity, Cash Flow, Institutional Ownership, Profits, and Independent Commissioners have a significant influence on Financial Distress conditions
Remittances and Federal Government Spending: Evidence from Mexico
No full textWe develop an empirical analysis of the effect of remittances on the size and composition of government spending. Using data from 28 years in Mexico, we show that remittances increase the provision of public goods and social security transfers. We also find evidence that the proportion of social spending in relation to programmable spending falls as remittances increase in Mexico. This evidence shows that remittances not only affect the size but also the composition of government spending changing who benefits the most from public spending in Mexico
The Role of Language in Digital Marketing Strategies in Saudi Arabia
No full textThis study examines the impact of using the Arabic language and Arabic-rooted expressions in digital marketing strategies for foreign products in the Gulf countries. By conducting a comprehensive literature review, the research highlights the significant role of linguistic and cultural adaptation in enhancing consumer engagement, fostering brand loyalty, and demonstrating cultural sensitivity. Key findings reveal that the use of Arabic in marketing content creates a stronger emotional connection with consumers, making the advertising more relatable and trustworthy. Additionally, incorporating Arabic into marketing strategies positively influences brand loyalty, as consumers perceive brands that respect their cultural identity more favorably. The effectiveness of bilingual advertising, which balances the use of Arabic and English, is also emphasized, catering to both cosmopolitan and traditional market segments. Case studies of successful campaigns in the automotive and luxury goods industries illustrate the practical benefits of localized marketing strategies. This study provides valuable insights for marketers seeking to optimize their strategies and succeed in the culturally rich and diverse markets of the Gulf region, offering recommendations for future research on the long-term impacts of these approaches
Essential Knowledge for English Student Teachers: Core Competencies and Pedagogical Skills Needed for Effective Classroom Teaching
No full textThis study aimed to identify the essential knowledge and teaching competencies English student teachers should possess before beginning their in-school teaching practice. The study involved 24 English student teachers from the College of Teacher Education at Phranakhon Rajabhat University. All participants were in their 4th year of study and were actively engaged in their practicum during the academic year 2024. The data was collected using a Google form survey, designed based on the suggestions of three education specialists. The results of the Google form questionnaire were interpreted, categorized and tabulated on computer sheets and were calculated into the statistical values, Mean. The findings revealed that the participants demonstrated the highest confidence in integrating technology into English teaching, with a mean score of 4.34, reflecting their preparedness to utilize digital tools to enhance instruction. Confidence in pronunciation and phonetics (mean 4.27) and classroom management techniques (mean 4.20) also ranked highly, indicating their readiness to model accurate language and maintain an effective learning environment. Moreover, the highest-rated competencies include Classroom Management and Technology Integration (mean 4.37), highlighting confidence in managing classroom environments and integrating digital tools to support learning
Training Generative AI to Ingest Logs and Detect Anomalies in Large-Scale Applications
No full textGenerative AI has transformed log analysis in large-scale distributed applications, offering unprecedented capabilities for anomaly detection and operational intelligence. This transformation addresses the exponential growth of log data generated by modern systems, which traditional approaches struggle to process effectively. Large language models and specialized AI architectures demonstrate exceptional accuracy in identifying anomalous patterns across heterogeneous log formats while significantly reducing false positives and manual configuration requirements. Natural language processing techniques enable semantic understanding of unstructured logs, while unsupervised learning models detect novel anomalies without requiring pre-labeled training data. Time-series forecasting provides critical predictive capabilities, enabling proactive intervention before performance degradations impact users. Commercial observability platforms have integrated these technologies to deliver measurable improvements in operational efficiency, security posture, and resource optimization across financial, healthcare, and e-commerce sectors. Despite implementation challenges including model drift, explainability deficits, and privacy concerns, organizations that successfully deploy AI-driven log intelligence achieve substantial returns on investment through faster incident resolution, enhanced security, and improved customer experiences. As these technologies continue to mature, they promise to transform log data from an overwhelming operational burden into a strategic asset for maintaining system health and optimizing performance
AI-Augmented DevOps for Application Modernization: Transforming Software Development and Operations
No full textAI-augmented DevOps has emerged as a transformative paradigm for application modernization efforts, bridging the gap between traditional operational processes and modern software development needs. By integrating artificial intelligence capabilities throughout the DevOps lifecycle, organizations can overcome legacy system challenges while accelerating innovation cycles. Intelligent coding assistants enhance developer productivity and code quality, while AI-powered CI/CD pipelines optimize deployment processes through predictive algorithms and automated remediation. Advanced monitoring systems leverage machine learning for anomaly detection and predictive maintenance, significantly reducing downtime and operational costs. The organizational impact extends beyond technical improvements to include cultural shifts, skills development requirements, and structured implementation frameworks tailored to varying maturity levels. As organizations navigate digital transformation journeys, AI-DevOps integration offers a structured approach to balancing innovation with stability, ultimately delivering modernized applications with improved quality, enhanced security, and optimized total cost of ownership
The Future of Data Platforms: AI-Driven Automation and Self-Optimizing Systems
No full textThe evolution of data platforms is entering a new era characterized by AI-driven automation and self-optimizing capabilities that address the unprecedented challenges of exponential data growth. As organizations struggle with increasingly complex data ecosystems, traditional management approaches are becoming inadequate. This document presents how next-generation data platforms leverage artificial intelligence to transform data operations through four key innovations: metadata intelligence serving as the nervous system of modern platforms; self-healing data pipelines that autonomously detect and resolve issues; predictive resource optimization that anticipate computational needs before they arise; and embedded governance frameworks that make compliance an integral rather than external function. These advancements collectively shift data management from reactive to proactive paradigms, enabling organizations to derive more excellent value from their information assets while reducing manual intervention and operational costs
Event-Driven Integration: Real-Time Data Flow in the Digital Age
No full textEvent-Driven Architecture (EDA) represents a transformative paradigm in enterprise integration, enabling organizations to process and respond to information in real-time. As digital ecosystems become increasingly interconnected, traditional request-response models prove inadequate for handling the volume and velocity of modern data flows. This article explores how EDA fundamentally reshapes business responsiveness through instantaneous data processing capabilities, examining its core architectural principles and implementation strategies. The integration framework provided by SAP Business Technology Platform, particularly its Event Mesh component, offers a comprehensive solution for enterprises seeking to leverage event-driven integration. Through a detailed evaluation of industry-specific applications, including manufacturing IoT implementations, financial services risk management, retail customer experience optimization, and healthcare monitoring systems, the article demonstrates how event-driven integration delivers measurable improvements in operational efficiency, customer engagement, and competitive advantage. By adopting EDA principles, organizations can effectively navigate the demands of data-intensive business environments while maintaining system resilience and scalability in an increasingly real-time digital landscape
AI-Driven Enterprise Cloud Solutions: Balancing Innovation, Privacy, and Ethics for Sustainable Business Transformation and Stakeholder Trust
No full textThis article explores the complex interplay between artificial intelligence, data privacy, and ethical considerations within enterprise cloud solutions. As organizations increasingly adopt AI-powered CRM, ERP, and automation systems, they face the dual challenge of driving business innovation while safeguarding societal trust. The discussion examines how companies can implement enhanced data privacy compliance mechanisms, detect and mitigate algorithmic bias, and establish robust governance frameworks. By analyzing current regulatory landscapes, technical solutions, and organizational strategies, the article provides a comprehensive examination of how businesses can balance operational efficiency with ethical responsibility. This article contributes valuable insights for technology leaders, compliance officers, and executives seeking to harness AI\u27s transformative potential while upholding principles of transparency, fairness, and privacy in an increasingly data-driven business environment
Causal Machine Learning for Intervention Analysis in AML Systems: Beyond Correlation to Causation in Financial Crime Detection
No full textThis article explores the paradigm shift from correlation-based to causation-based machine learning approaches in Anti-Money Laundering (AML) systems. We examine how causal machine learning enables more effective intervention analysis in financial crime detection, reducing false positives while increasing detection accuracy. Integrating causal inference frameworks with traditional ML methods provides financial institutions with more interpretable models that better withstand regulatory scrutiny and adapt to evolving criminal strategies. This paper presents theoretical foundations, implementation methodologies, and case studies demonstrating the practical advantages of causal approaches in AML systems