Data Governance Framework Research Articles

The Impact of Information Technology On Data-Driven Decision Making In Human Resource Management

In the contemporary landscape of human resource management (HRM), the integration of information technology (IT) has revolutionized data-driven decision-making processes. This shift is characterized by the increased reliance on data analytics, cloud computing, and advanced software applications that enable HR professionals to harness vast amounts of data effectively. As organizations strive for agility and competitiveness, the role of IT in HRM has become pivotal, influencing various aspects such as recruitment, performance management, employee engagement, and talent development. The first major impact of IT on data-driven decision-making in HRM is the enhancement of recruitment processes. Traditional hiring methods often relied on subjective assessments and limited candidate data. However, IT solutions, such as Applicant Tracking Systems (ATS) and AI-driven recruitment tools, have enabled HR managers to analyze candidates’ qualifications, experiences, and fit with organizational culture based on data-driven metrics. This not only improves the quality of hires but also reduces time-to-fill positions and enhances overall recruitment efficiency. Performance management has evolved significantly due to the integration of IT. Performance appraisal systems now utilize real-time data analytics to track employee performance against key performance indicators (KPIs). This allows HR managers to make informed decisions about promotions, training needs, and workforce planning. The implementation of continuous feedback mechanisms, facilitated by IT platforms, fosters a culture of transparency and engagement, enabling employees to take ownership of their development. IT has transformed employee engagement strategies through the use of data analytics. Organizations now leverage employee surveys and feedback tools that provide insights into employee satisfaction and engagement levels. By analyzing this data, HR professionals can identify trends, address concerns proactively, and implement targeted initiatives that enhance workplace culture and retention rates. Such data-driven approaches ensure that decisions are aligned with employee needs and organizational objectives. The role of IT in talent development cannot be overstated. Learning Management Systems (LMS) and data analytics tools provide HR managers with the capability to assess skill gaps and monitor training effectiveness. By analyzing learning outcomes and employee progress, organizations can tailor development programs that align with both individual career paths and organizational goals. This strategic alignment is crucial for fostering a skilled workforce capable of meeting future challenges. Despite these benefits, the reliance on IT in HRM also poses challenges, particularly regarding data privacy and security. As organizations collect and analyze large volumes of employee data, they must navigate complex regulations and ethical considerations to protect sensitive information. This underscores the need for robust data governance frameworks that ensure compliance while fostering a culture of trust within the organization. In conclusion, the impact of information technology on data-driven decision-making in human resource management is profound. By leveraging data analytics and advanced IT tools, organizations can enhance their recruitment processes, improve performance management, boost employee engagement, and foster talent development.It is essential for HR professionals to address the challenges of data privacy and security to maximize the benefits of IT in HRM. As technology continues to evolve, the synergy between IT and HRM will play a critical role in shaping the future of work.

Intelligent Data Governance Frameworks : A Technical Overview

This comprehensive article explores the transformative potential of AI-driven data governance frameworks in addressing modern data management challenges. The article examines the limitations of traditional governance methods in the face of exponential data growth, regulatory complexities, and diverse data sources. It delves into the architecture, core components, and benefits of intelligent governance systems that leverage advanced AI technologies such as machine learning, natural language processing, and reinforcement learning. The article highlights significant improvements in operational efficiency, compliance accuracy, and data quality achieved through AI-driven solutions. Furthermore, it investigates future directions, including the integration of explainable AI, federated learning, and blockchain technology, while acknowledging the challenges that need to be addressed for widespread adoption.

The role of data governance in enhancing cybersecurity resilience for global enterprises

Data governance plays a critical role in enhancing cybersecurity resilience for global enterprises. In the face of increasingly sophisticated cyber threats, coupled with rising regulatory demands such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), enterprises must implement robust data governance frameworks to safeguard data integrity, availability, and confidentiality (Ramirez et al., 2008). Effective data governance involves a combination of policies, procedures, and technologies that align with an organization’s overall cybersecurity strategy (Khatri & Brown, 2010). This paper provides a comprehensive analysis of the critical intersection between data governance and cybersecurity, emphasizing the link between governance structures, risk management processes, and technological innovations in mitigating evolving cyber threats (Weber et al., 2009). Additionally, we explore the essential role of data stewardship, stakeholder responsibilities, and the deployment of advanced technologies, such as artificial intelligence (AI) and machine learning (ML), in fostering organizational resilience (Arora & Pedersen, 2017). Through case studies and best practices, the study presents a conceptual framework that enables global enterprises to adapt to the fast-changing cybersecurity landscape while maintaining compliance with international data regulations. By addressing key elements such as access control, data classification, and real-time monitoring, this research underscores how comprehensive data governance frameworks serve as a foundation for enhanced cybersecurity resilience in today’s digital economy (Da Veiga & Eloff, 2007).

Navigating the ethical and governance challenges of ai deployment in AML practices within the financial industry

The rapid deployment of Artificial Intelligence (AI) in Anti-Money Laundering (AML) practices within the financial industry presents significant ethical and governance challenges that must be navigated effectively. As financial institutions increasingly adopt AI technologies to enhance their AML efforts, concerns regarding data privacy, algorithmic bias, and transparency emerge. This review explores the ethical implications of AI in AML and offers governance strategies to mitigate risks while ensuring compliance with regulatory frameworks. One of the primary ethical challenges in deploying AI for AML is the potential for algorithmic bias. AI systems trained on historical data may inadvertently perpetuate existing biases, leading to discriminatory practices in transaction monitoring and customer profiling. This raises serious concerns about fairness and equity in the financial sector. Addressing algorithmic bias requires the implementation of rigorous testing and validation processes to ensure AI systems function impartially across diverse populations. Data privacy is another critical issue. The extensive data collection required for effective AML monitoring raises questions about the protection of sensitive customer information. Financial institutions must establish robust data governance frameworks that prioritize privacy and comply with regulations such as the General Data Protection Regulation (GDPR). Ensuring transparency in how data is used and providing clear communication to customers about data practices is essential for building trust. Effective governance frameworks are crucial in navigating these ethical challenges. Financial institutions should adopt a multi-disciplinary approach that includes ethical guidelines, compliance measures, and risk management strategies. Establishing oversight committees can help ensure that AI deployment aligns with ethical standards and regulatory requirements. Furthermore, ongoing training for employees on the ethical use of AI in AML can foster a culture of responsibility and accountability. This review highlights the need for a balanced approach to AI deployment in AML, emphasizing the importance of ethical considerations and governance structures. As the financial industry continues to evolve, addressing these challenges will be essential for maintaining trust, ensuring compliance, and leveraging AI’s potential to enhance AML practices effectively.

Artificial Intelligence And Big Data For Enhancing Public Health Surveillance And Disease Prevention: A Systematic Review

This systematic review explores the transformative role of Artificial Intelligence (AI) and Big Data analytics in enhancing public health outcomes, focusing on key areas such as disease surveillance, resource allocation, and personalized preventive healthcare. In the wake of increasing healthcare challenges, the integration of AI technologies and Big Data offers unprecedented opportunities for improving health monitoring, early disease detection, and strategic resource management. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive review was conducted across multiple databases, resulting in an initial pool of 450 articles. After applying rigorous inclusion and exclusion criteria, a total of 90 high-quality studies were systematically analyzed. The findings demonstrate that AI models, particularly those leveraging machine learning, significantly enhance the early detection of outbreaks and optimize healthcare resource allocation, especially during health crises like the COVID-19 pandemic. Additionally, the use of predictive analytics in personalized preventive healthcare has shown promise in reducing the burden of chronic diseases by identifying at-risk populations and tailoring interventions based on individual risk profiles. However, challenges related to data quality, standardization, and ethical concerns continue to hinder the widespread adoption of these technologies. The review emphasizes the need for interdisciplinary collaboration and robust data governance frameworks to fully realize the potential of AI and Big Data in public health. This study not only highlights current advancements but also identifies gaps in research, offering insights into future directions for integrating AI-driven solutions to strengthen public health systems globally.

The Privacy-Centric Data Pipeline : Strategies for Implementing Robust Security Measures in Data Engineering

This article presents a comprehensive framework for addressing data privacy and security challenges in modern data engineering environments. As organizations grapple with ever-increasing volumes of sensitive data and an evolving regulatory landscape, data engineers face complex challenges in safeguarding information throughout its lifecycle. We explore key components of a robust data protection strategy, including regulatory compliance, technical safeguards, and organizational practices. Our analysis covers critical areas such as encryption methodologies, access control mechanisms, data masking techniques, and lifecycle management policies. We also examine the role of data governance frameworks and the importance of continuous employee training in maintaining a secure data ecosystem. By synthesizing current best practices and emerging trends, this article provides data engineers and organizations with actionable insights for implementing effective privacy and security measures. Our findings underscore the need for a holistic approach that integrates technical solutions with strong governance practices to ensure data integrity, confidentiality, and compliance in an increasingly data-driven world.

Implementing data governance in financial systems: Strategies for ensuring compliance and security in multi-source data integration projects

This study explores the complexities of implementing data governance within financial systems, focusing on strategies to ensure compliance and security in multi-source data integration projects. The primary aim was to identify challenges and establish a comprehensive framework to enhance data integrity, confidentiality, and regulatory adherence in the financial sector. Using a mixed-methods approach, the research included an extensive literature review, case studies, and expert interviews to gain insights into current practices, challenges, and emerging trends in data governance. Key findings reveal that while multi-source data integration offers significant opportunities for enhanced decision-making, it also presents substantial risks, including data breaches and regulatory non-compliance. The study underscores the pivotal role of data governance frameworks in mitigating these risks through the establishment of clear data policies, the implementation of robust cybersecurity measures, and the promotion of a compliance-focused organizational culture. Additionally, the analysis highlights the necessity for harmonized global data privacy laws to address the diverse regulatory environments that financial institutions operate within. The research concludes that effective data governance is crucial for financial institutions to safeguard their data assets, ensure regulatory compliance, and maintain stakeholder trust. Recommendations include the adoption of advanced technologies like artificial intelligence and blockchain to enhance data governance capabilities, the provision of ongoing employee training to foster a culture of compliance, and the implementation of dynamic compliance monitoring systems to keep pace with evolving regulatory demands. By embracing these strategies, financial institutions can develop a resilient data governance framework that not only ensures compliance and security but also drives operational excellence, competitive advantage, and sustained trust in the increasingly digital financial landscape. Keywords: Data Governance, Financial Systems, Multi-Source Data Integration, Regulatory Compliance, Cybersecurity, Data Privacy.

Designing cost-effective data solutions: Best practices from multi-million dollar projects involving cross-functional teams and diverse data sources

This paper explores the design and implementation of cost-effective data solutions, focusing on multi-million dollar projects that involve cross-functional teams and diverse data sources. The study aims to identify best practices and technological advancements that enhance data management efficiency, ensuring scalability, security, and compliance. Employing a comprehensive review of existing literature and case studies, the paper examines the challenges and opportunities in integrating technologies such as AI, machine learning, cloud computing, and blockchain. Key findings highlight the importance of cross-functional collaboration in addressing data management complexities and the adoption of agile methodologies to enhance adaptability and efficiency. The study also underscores the significance of advanced data integration techniques and the application of circular economy principles to promote sustainability in data management. The conclusion provides actionable recommendations for organizations seeking to optimize their data solutions, emphasizing the need for strategic investment in emerging technologies and a robust data governance framework. By offering insights into future trends and research directions, this paper contributes to the ongoing discourse on developing cost-effective, secure, and sustainable data solutions in an increasingly digitalized world. Keywords: Cost-Effective Data Solutions, Cross-Functional Teams, Data Integration, Cybersecurity, Artificial Intelligence, Circular Economy Principles

Emergent Data Governance Framework In Nigeria

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Effects of artificial intelligence on financial reporting accuracy

Intelligence (AI) has emerged as a transformative force in modern financial reporting, promising to revolutionize accuracy and efficiency across various industries. This study delves into the effects of AI on financial reporting accuracy, addressing critical questions surrounding its implementation, challenges, and best practices. Through a comprehensive investigation, the research aims to provide valuable insights to guide organizations in leveraging AI effectively while maintaining the integrity of their financial reporting practices. The main objective of this study is to explore the effects of artificial intelligence on the accuracy of financial reporting, examining both the benefits and challenges associated with its integration into organizational processes. Specific Objectives; To analyze how various AI technologies influence the accuracy of financial data and reporting in organizations, to explore the challenges and limitations faced by organizations when integrating AI into their financial reporting systems, to assess the importance of human oversight in ensuring the accuracy of AI-generated financial reports, to develop best practices for organizations to enhance the accuracy of financial reporting when using AI technologies. This study employed a mixed-methods approach, combining qualitative and quantitative data collection techniques to comprehensively explore the effects of AI on financial reporting accuracy. Quantitative data was gathered through surveys distributed to accountants, finance professionals, auditors, and personnel from manufacturing and tourism sectors across various industries. The surveys focused on assessing perceptions of AI's impact on financial reporting accuracy and included Likert-scale questions to gauge agreement levels. Qualitative data was obtained through in-depth interviews with selected participants to gain deeper insights into their experiences and perspectives regarding AI technologies in financial reporting. Thematic analysis was applied to interview transcripts to identify recurring themes and patterns related to AI's effects on accuracy. Participants were informed about the study's purpose and their rights, including confidentiality and anonymity. Informed consent was obtained prior to data collection, ensuring ethical standards were adhered to throughout the research process. Survey responses indicated a generally positive perception of AI's impact on financial reporting accuracy, with a majority of respondents acknowledging improvements in efficiency and error reduction. However, challenges such as data security concerns and the need for skilled personnel were highlighted as significant barriers to AI integration. Human oversight emerged as a crucial factor in validating AI-generated outputs, emphasizing the complementary role of human judgment alongside technological advancements. The findings underscored AI's potential to enhance financial reporting accuracy through advanced data analytics and automation. Key recommendations include investing in comprehensive training programs for staff, integrating AI with human expertise, implementing robust data governance frameworks, conducting regular audits of AI systems, and engaging stakeholders throughout the integration process. In conclusion, this study provided valuable insights into how AI technologies can improve the accuracy of financial reporting while addressing challenges and emphasizing the importance of human oversight. By adopting recommended best practices, organizations can maximize the benefits of AI in financial reporting, paving the way for more reliable and informed decision-making in the digital age. This research contributes to the growing body of knowledge on AI's impact on financial practices, offering practical recommendations for organizations aiming to leverage technology effectively in their financial reporting processes.

Accelerating a learning public health system: Opportunities, obstacles, and a call to action.

Public health systems worldwide face increasing challenges in addressing complex health issues and improving population health outcomes. This experience report introduces the concept of a Learning Public Health System (LPHS) as a potential solution to transform public health practice. Building upon the framework of a Learning Health System (LHS) in healthcare, the LPHS aims to create a dynamic, data-driven ecosystem that continuously improves public health interventions and policies. This report explores the definition, benefits, challenges, and implementation strategies of an LPHS, highlighting its potential to revolutionize public health practice. This report employs a comparative analysis approach, examining the similarities and differences between an LPHS and an LHS. It also identifies and elaborates on the potential benefits, challenges, and barriers to implementing an LPHS. Additionally, the study investigates promising national initiatives that exemplify elements of an LPHS in action. An LPHS integrates data from diverse sources to inform knowledge generation, policy development, and operational improvements. Key benefits of implementing an LPHS include improved disease prevention, evidence-informed policy-making, and enhanced health outcomes. However, several challenges were identified, such as interoperability issues, governance concerns, funding limitations, and cultural factors that may impede the widespread adoption of an LPHS. Implementation of an LPHS has the potential to significantly transform public health practice. To realize this potential, a call to action is issued for stakeholders across the public health ecosystem. Recommendations include investing in informatics infrastructure, prioritizing workforce development, establishing robust data governance frameworks, and creating incentives to support the development and implementation of a LPHS. By addressing these key areas, public health systems can evolve to become more responsive, efficient, and effective in improving population health outcomes.

Data-driven decision-making in public health: The role of advanced statistical models in epidemiology

This paper critically examines the transformative role of data-driven decision-making in public health, focusing on the integration of advanced statistical models in epidemiology. As the volume and complexity of health data increase, leveraging predictive analytics, machine learning, and real-time data integration has become essential for improving public health outcomes. The study explores how these technologies have shifted public health strategies from reactive to proactive approaches, particularly in areas such as disease surveillance, chronic disease management, and health equity. Through comprehensive analysis, the paper identifies key advancements, such as hybrid models that combine traditional epidemiological frameworks with AI, and the integration of multi-modal data sources that enhance predictive accuracy. The findings emphasize the potential of these models to optimize resource allocation, address health disparities, and provide timely interventions. However, challenges such as data quality, algorithmic bias, and the ethical implications of model transparency are highlighted as critical issues requiring ongoing research. The study concludes that for these models to be effectively adopted, there must be a balance between technological innovation and ethical considerations. Recommendations include the need for interdisciplinary collaboration, improved data governance frameworks, and the development of more inclusive models that are generalizable across diverse populations. This research underscores the necessity of combining robust analytical tools with ethical frameworks to enhance the reliability and equity of public health interventions.

The impact of business analytics on healthcare operations: A statistical perspective

This study examines the transformative potential of business analytics within the healthcare sector, highlighting its wide-ranging applications, the challenges it faces, and potential future developments. The primary goal is to evaluate how advanced analytical tools can enhance healthcare outcomes, improve decision-making, and optimize operational efficiency in healthcare organizations. Through an in-depth literature review, this study critically analyzes existing research and real-world case studies to identify the key trends and obstacles associated with implementing business analytics in healthcare. The findings reveal that business analytics plays a crucial role in personalizing patient care, enabling predictive modeling, and streamlining healthcare operations. These tools support more informed decision-making, leading to better patient outcomes and more efficient resource utilization. However, significant challenges hinder the widespread adoption of business analytics, such as difficulties in integrating data across diverse systems, concerns about patient data privacy, and the need for a cultural shift towards data-driven decision-making within healthcare institutions. In conclusion, while integrating business analytics in healthcare presents substantial benefits, it requires a comprehensive and multifaceted approach. This approach should include technological advancements, the establishment of robust data governance frameworks, and significant organizational changes. The study recommends that healthcare organizations invest in training programs to enhance data literacy among healthcare professionals, develop policies to promote interoperability across different data systems, and carefully address the ethical implications of data usage. Future research should focus on the long-term impacts of business analytics in healthcare, with a particular emphasis on the ethical challenges that may arise. Ensuring these powerful tools are used responsibly will be essential to improving healthcare delivery.

Optimizing data management in healthcare: Lessons from clinical trials and beyond

This study comprehensively explores the optimization of data management in healthcare by drawing on lessons learned from clinical trials and extending these insights to broader healthcare contexts. The study's purpose is to examine the challenges, strategies, and technological innovations that are essential for effective data management in modern healthcare systems. Utilizing a detailed literature review, the study identifies key methodologies from clinical trials that have successfully enhanced data integrity, accuracy, and security, such as the implementation of electronic data capture (EDC) systems and rigorous data governance frameworks. These methods are crucial in managing the increasing volume and complexity of healthcare data. The study's findings reveal that big data analytics and artificial intelligence (AI) are pivotal in transforming healthcare data management, enabling personalized and precise patient care. However, these technologies also introduce challenges related to data security, interoperability, and ethical considerations, particularly concerning patient privacy and the transparency of AI-driven decision-making processes. In conclusion, the study emphasizes the need for continuous innovation and improvement in healthcare data management practices. Recommendations include the integration of blockchain technology, cloud computing, and real-time data from wearable devices to address existing challenges and enhance data management capabilities. The study advocates for a balanced approach that prioritizes patient privacy, ethical governance, and regulatory compliance to ensure that technological advancements contribute positively to patient outcomes and healthcare efficiency.

A critical review of machine learning applications in supply chain risk management

This article examines the transformative role of Machine Learning (ML) in Supply Chain Risk Management (SCRM), emphasizing its ability to enhance risk prediction and mitigation through real-time, data-driven insights. It demonstrates how ML applications like demand forecasting, inventory optimization, supplier risk assessment, and fraud detection improve supply chain resilience by analyzing large datasets to identify patterns, predict risks, and recommend proactive actions. However, the article also highlights key challenges, including data quality, availability, and privacy issues, which limit the effectiveness of ML models. Integrating ML with legacy systems poses additional technical and financial difficulties, particularly for smaller businesses. High implementation costs and scalability constraints further hinder widespread adoption. Ethical concerns, such as data bias and privacy, still need to be explored, raising questions about responsible ML use in supply chains. Future research should address these gaps by improving data governance frameworks for accuracy and privacy, developing scalable ML models suited to various supply chain environments, and exploring synergies with technologies like blockchain and the Internet of Things (IoT). These advancements will help realize ML’s full potential, fostering more agile, transparent, and resilient supply chains capable of navigating complex global risks. By tackling these challenges, ML can shift SCRM from reactive to proactive, ensuring long-term operational continuity and a competitive edge in the evolving global market.

Data-driven decision making in higher education to ensure quality education: a bibliometric review study

In the evolving landscape of higher education, data-driven decision-making has emerged as a crucial strategy for enhancing quality education. This bibliometric review study examines the existing literature on data-driven decision-making within higher education to outline its impact on the quality of education. This study employs a thorough bibliometric analysis to gather and analyze scholarly articles from major databases. It identifies main trends, influential authors, and key research themes in the field. The findings reveal that there is increasing focus on leveraging data analytics, learning analytics, and educational data mining to inform policy, teaching methods, and administrative strategies. The study also highlights the challenges and opportunities associated with implementing data-driven approaches, such as data privacy concerns and the need for robust data governance frameworks. By synthesizing current research, this review provides a holistic understanding of how data-driven decision-making can be strategically employed to ensure and enhance the quality of education in higher institutions. This paper not only contributes to the academic discourse by mapping the intellectual landscape but also offers practical insights for educators, policymakers, and administrators aiming to foster a data-informed educational environment. Keywords: data-driven decision, university, higher education, quality assurance, quality education, university management, bibliometric analysis.

Content, controls, audience: contextualizing de-identification as a novel approach to enabling cross-sectoral data linkages

BackgroundCanadian privacy laws often distinguish personal health information (PHI) from non-health data. A recent legislative designation permits the Institute for Clinical Evaluative Sciences (ICES) cross-sectoral data linkages and analyses of health and non-health data but requires a discrete environment for each data type and only one environment to conduct research and analytics. It further stipulates that only de-identified, non-health data may enter the PHI environment where research and analytics are conducted, an added complication since data linkages necessarily require some identifiability. In order to consolidate its health and non-health data, then, ICES requires novel approaches to data governance and de-identification. ObjectiveTo establish a comprehensive data governance framework that optimizes cross-sectoral data linkages and classifies identifiability as existing on a spectrum dependent on content, controls and audience. ApproachOperating under legislative and regulatory definitions of de-identification that contemplate what is “reasonably foreseeable in the circumstances,” ICES is working with its privacy regulator to create a data governance framework and de-identification guidelines for its health and non-health data. ResultsWhile still awaiting regulatory approval, ICES will be a first-of-its-kind data integration unit whose data governance model is formally approved by its privacy regulator and an industry leader in novel approaches to de-identification and cross-sectoral data linkages. ConclusionsIdentifiability is determined by context, and data may be simultaneously identifiable and de-identified, depending on its contextual factors. ImplicationsICES’ approach to data governance and de-identification can offer important insights to other research and analytics-based organizations facing legislative barriers to cross-sectoral data linkages.

BIG DATA IN CREDIT RISK MANAGEMENT: A SYSTEMATIC REVIEW OF TRANSFORMATIVE PRACTICES AND FUTURE DIRECTIONS

This systematic review examines the profound impact of big data analytics on credit risk management in financial institutions, highlighting both its transformative benefits and the associated challenges. By integrating a wide range of real-time and diverse data sources—such as customer behavior, market trends, and macroeconomic indicators—financial institutions have significantly enhanced the accuracy, efficiency, and predictive power of their credit risk models. The findings reveal that institutions employing big data analytics have achieved substantial reductions in default rates, with improvements of up to 30% over traditional risk assessment methods. However, the adoption of big data also presents considerable challenges, particularly in ensuring data privacy and security, navigating complex regulatory environments, and overcoming technical hurdles related to data integration, storage, and processing. These issues necessitate robust data governance frameworks and significant investments in IT infrastructure. Despite these challenges, big data is expected to play an increasingly central role in credit risk management, offering early adopters a strategic advantage through enhanced risk assessment and decision-making capabilities. This review provides critical insights for financial institutions, policymakers, and researchers, emphasizing the need for ongoing innovation and adaptation to fully harness the potential of big data in this field.

BIG DATA IN CREDIT RISK MANAGEMENT: A SYSTEMATIC REVIEW OF TRANSFORMATIVE PRACTICES AND FUTURE DIRECTIONS

This systematic review examines the profound impact of big data analytics on credit risk management in financial institutions, highlighting both its transformative benefits and the associated challenges. By integrating a wide range of real-time and diverse data sources—such as customer behavior, market trends, and macroeconomic indicators—financial institutions have significantly enhanced the accuracy, efficiency, and predictive power of their credit risk models. The findings reveal that institutions employing big data analytics have achieved substantial reductions in default rates, with improvements of up to 30% over traditional risk assessment methods. However, the adoption of big data also presents considerable challenges, particularly in ensuring data privacy and security, navigating complex regulatory environments, and overcoming technical hurdles related to data integration, storage, and processing. These issues necessitate robust data governance frameworks and significant investments in IT infrastructure. Despite these challenges, big data is expected to play an increasingly central role in credit risk management, offering early adopters a strategic advantage through enhanced risk assessment and decision-making capabilities. This review provides critical insights for financial institutions, policymakers, and researchers, emphasizing the need for ongoing innovation and adaptation to fully harness the potential of big data in this field.

Acceptance of artificial intelligence technologies in business management, finance, and e-commerce: factors, challenges, and strategies

This research investigates the comprehensive acceptance of artificial intelligence (AI) in business management, finance, and e-commerce, focusing on the factors driving its adoption, the obstacles encountered, and strategies for enhancing integration. AI technologies have transformed these sectors, delivering exceptional efficiencies, predictive analytics, and personalized customer experiences. However, their acceptance is influenced by various factors, including technological readiness, organizational culture, and perceived benefits. In business management, AI improves decision-making processes, optimizes operations, and fosters innovation. Financial institutions utilize AI for risk management, fraud detection, and personalized banking services, while the e-commerce sector gains from AI through enhanced customer service, dynamic pricing, and inventory management. Despite these benefits, challenges such as data privacy concerns, high implementation costs, and resistance to change impede widespread adoption. Additionally, ethical considerations and the need for regulatory compliance add layers of complexity. This paper identifies key strategies to address these challenges, such as promoting a culture of innovation, investing in AI education and training, and developing robust data governance frameworks. Strategic partnerships and collaborations with AI experts and tech firms are also essential for navigating the AI landscape. By comprehensively addressing these factors and challenges, businesses can unlock AI's full potential, driving sustainable growth and competitive advantage. This study contributes to understanding AI acceptance in critical sectors, providing a roadmap for successful AI implementation and emphasizing the importance of strategic planning and stakeholder engagement.