Financial Integration Research Articles

Corruption impedes good governance in Somalia’s public sector

Purpose The purpose of this paper is to understand the level of corruption in Somalia and the causes that lead to corruption that in turn hampers good governance. Design/methodology/approach The paper relies on the data collected from Somali’s public institutions, international actors, newspaper articles and peer-reviewed literature. The degree of corruption is assessed with the Corruption Perceptions Index by Transparency International and the Control of Corruption provided by the World Bank. Findings The finding shows that the level of corruption in Somalia is high and complex because of the following reasons: weak governance and poverty, culture and tradition which include favoritism based on clan membership, lack of accountability mechanism and absence political will. Measures undertaken for anti-corruption, for instance, the establishment of the National Anti-Corruption Commission and legislation for its support face barriers and challenges that include political influence, lack of capacities among the undertaking institution and lack of participation by the public. Originality/value This research adds value to the knowledge of corruption in Somalia by identifying the concrete socio-political antecedents for corruption and the problems of fighting it. This paper further presents the following detailed strategies to address corruption: enhancing the operational and financial integrity of the Independent Anti-Corruption Commission; restructuring the civil service and merging it into a merit-based system; enhancing Public Financial Management (PFM) reforms; independence of the judiciary; and sustaining development in digital technology adoption in governance and procurement.

Does Financial Integration Contribute to Bilateral Trade in Africa: An Empirical Investigation by the Augmented Gravity Model

Does Financial Integration Contribute to Bilateral Trade in Africa: An Empirical Investigation by the Augmented Gravity Model

Determining Factors to Develop Periodic Plan, Strategic Plan and Medium-Term Expenditure Framework in Local Governments in Nepal

This research article explores the factors influencing the development and implementation of periodic plan, strategic plans, and the Medium-Term Expenditure Framework (MTEF) in local governments of Nepal, particularly focus on Bharatpur Metropolitan City. The study adopts a qualitative research design, employing document analysis, focus group discussions, and interviews with key stakeholders, including government officials, local communities, NGOs and civil societies. The findings identify key determinants: public responsible governance; efficiency, financial integrity, and systematic implementation of programs; proper resource allocation; and risk identification and management. Major findings emphasize the importance of public responsible governance, which includes public voice, participation, and shared responsibility in decision-making processes. Additionally, the study highlights the necessity of efficiency, financial integrity, and systematic implementation of programs for effective service delivery. Proper resource allocation and risk management are also crucial, ensuring that development projects meet community needs and sustainable goals. These elements collectively contribute to the successful governance and development of local governments in Nepal. The research also highlights the need for further studies, particularly in other regions of Nepal, using diverse methodologies to strengthen the understanding of local-level planning and its implications. Int. J. Soc. Sc. Manage. Vol. 11, Issue-4: 88-94.

Blockchain Technology in Risk Management: Strengthening Cybersecurity and Financial Integrity

Blockchain Technology in Risk Management: Strengthening Cybersecurity and Financial Integrity

AI-Based Risk Assessments in Forensic Auditing: Benefits, Challenges and Future Implications

Forensic auditing is a critical component of ensuring financial integrity and detecting fraud within organizations. Traditional methods of risk assessment in forensic auditing often rely on manual processes, which can be time-consuming, labour-intensive, and prone to human error. In recent years, the integration of artificial intelligence (AI) techniques has revolutionized the field, offering more efficient and accurate risk assessment capabilities. This abstract explores the role of AI-based risk assessment in forensic auditing, highlighting its benefits, challenges, and future implications. AI-based risk assessment leverages advanced algorithms and machine learning models to analyse large volumes of financial data, identify patterns, anomalies, and potential red flags indicative of fraudulent activities. By automating repetitive tasks such as data collection, classification, and analysis, AI streamlines the auditing process, enabling forensic auditors to focus on interpreting results and making informed decisions. One of the primary advantages of AI-based risk assessment is its ability to detect complex fraud schemes that may go unnoticed by traditional methods. Machine learning algorithms can detect subtle deviations from expected behaviour, flagging transactions or activities that exhibit unusual patterns or characteristics. Moreover, AI systems can adapt and learn from new data, continuously improving their detection capabilities over time. Another benefit of AI-based risk assessment is its scalability and efficiency. With the increasing volume and complexity of financial transactions, manual auditing processes struggle to keep pace. AI, on the other hand, can analyse vast datasets in a fraction of the time it would take a human auditor, allowing organizations to conduct more comprehensive and timely audits.

International financial integration and financial stress of emerging market economies: The role of institutional quality

In the wake of unprecedented financial liberalization policies adopted by Emerging Market Economies (EMEs) since the late 20th century, their integration into global financial markets has brought forth both opportunities and challenges. This paper endeavors to assess the intricate relationship between international financial integration (IFI) and financial stress in EMEs, focusing on the potential moderating effect of institutional quality. Addressing key questions, it examines how various IFI indicators impact financial stress and evaluates the role of different dimensions of institutional quality in influencing this relationship. Using a Financial Stress Index (FSI) and employing the panel threshold regression technique, the study unveils significant threshold effects of institutional quality on the IFI-FSI nexus, with variations across different institutional factors. This study underscores the vital need for policymakers to identify threshold levels in institutional quality indicators to strike a balance between attracting investments and preventing unwarranted financial distress.

Effects of trilemma policies patterns on monetary policy credibility

The open economy trilemma refers to the impossibility of a country fully and simultaneously achieving three political objectives: monetary policy independence, exchange rate stability and financial openness. However, corner configurations related to the three policies of the trilemma are increasingly rare. In fact, countries tend to choose a policy combination composed of partial financial integration, managed exchange rate flexibility and partial monetary independence. Given the possibility of intermediate choices, there exists an index able to measure the relative divergence (or convergence) regarding the policy choices of the trilemma. The index identifies whether countries are adopting similar (or divergent) policy mixes in relation to the trilemma policy options. However, the consequences of adopting a more (or less) convergent policy arrangement on monetary policy credibility are unknown. Hence, this study investigates the effects of trilemma policies convergence patterns on monetary policy credibility. The idea is to verify whether a more convergent arrangement of policies related to the trilemma in open economies helps build monetary policy credibility. Our database is formed by 94 (developed and developing) countries with different characteristics. Thus, we run estimates based on a family of Tobit models for different sub-samples of countries, and also considering different periods – before and after the Global Financial Crisis. The study brings important practical implications. In general, the findings show that a convergent strategy increases credibility. The estimates corroborate the idea that countries adopting divergent policies from the global standard tend to be more exposed to economic instability and, as a result, have difficulties in anchoring inflation expectations.

Research and Empirical Evidence of Machine Learning based Financial Statement Analysis Methods

This study presents a novel approach called FinAnalytix which merging machine learning’s prowess in pattern recognition with financial statement analysis. This integrated algorithm combines deep neural networks and recurrent neural networks for predictive accuracy in stock return analysis, alongside logistic regression and random forest models for robust fraud detection in financial statements. The empirical evidence demonstrates FinAnalytix effectiveness in identifying abnormal financial patterns and predicting market reactions to earnings announcements. The study utilizes extensive data from listed companies, ensuring a comprehensive and practical application. FinAnalytix represents a significant advancement in the field, providing a dual approach to financial analysis for enhancing investment strategies through accurate stock return forecasts and bolstering financial integrity by detecting fraudulent activities. The simulation of the study based on the financial data of 100 sample listed companies. This research not only bridges the gap between traditional financial analysis and modern machine learning techniques but also offers a powerful tool for investors and regulatory bodies in navigating the complex financial landscape.

Mergers and Acquisitions in the Digital Age: The Role of Technology in Streamlining Financial Integration

Abstract: This comprehensive article explores the transformative role of technology in streamlining financial integration during mergers and acquisitions (M&A) in the digital age. It examines how advanced technological solutions, including cloud-based Enterprise Resource Planning (ERP) systems, automated reconciliation tools, and artificial intelligence (AI) and machine learning (ML) applications, are revolutionizing traditional M&A processes. The paper delves into the impact of these technologies on consolidating financial data, enabling real-time analysis, reducing manual errors, and providing predictive insights for risk assessment and opportunity identification. It also discusses best practices for successful financial integration, emphasizing the importance of pre-merger due diligence, phased integration approaches, and robust cybersecurity measures. Furthermore, the article investigates emerging trends and future prospects in technology-driven M&A, including the potential of blockchain, advanced analytics, and quantum computing to further enhance integration processes. Drawing on recent research and industry surveys, this study highlights the critical role of technology in realizing M&A potential, improving deal outcomes, and creating strategic value in an increasingly complex business environment. The findings underscore the necessity for organizations to embrace technological advancements to remain competitive in the evolving landscape of corporate mergers and acquisitions.

INTERNATIONAL ISLAMIC BANKS’ POPULARITY AMIDST THE ASEAN ECONOMIC COMMUNITY: INSIGHTS FROM INDONESIA AND MALAYSIA

The establishment of the ASEAN Economic Community (AEC) aimed to achieve regional integration among all ASEAN member states, with particular emphasis on financial integration. Islamic banking is in line with the broader goals of the AEC, as an alternative financial system, helping to build a more resilient and inclusive financial system in the region. Because of this resiliency and inclusiveness, Islamic banks have been encouraged to develop into international banks in the ASEAN region through increasing their assets and earnings. This paper aims to examine the position of International Islamic banks (IIb) in the AEC under its conventional banking services: OCBC Bank from Singapore, Maybank and CIMB Bank from Malaysia. Through the analysis of banking trends, this qualitative study compares the level of popularity of those banks in Indonesia and Malaysia, the two Muslim-majority countries in ASEAN, after the initial establishment of the AEC period in 2016 to 2024. The findings indicated that the most popular Islamic financial services in Malaysia are provided by Maybank, while the most well-known Islamic financial services under IIb in Indonesia are provided by CIMB Bank. This paper provides an overview of the globalisation of IIb in ASEAN and fills a research gap on the development of IIb, particularly within the AEC.

Financial Model Construction and Identification of Abnormal Activities in Mobile Networks for e-commerce Platform Using Machine Learning Algorithm

The advancement of informatization has significantly affected many sectors due to e-commerce, rendering the existing Financial Model (FM) inadequate for e-commerce customers. Artificial Intelligence (AI) substantially enhances the financial accounting capabilities and resource integration of systems using mobile networks. The insufficient FM of e-commerce tools and issues such as the administration of funds have significantly obstructed the advancement of standardized financial procedures, as the FM structure impacts managers' statistical evaluation of financial information. This research examines the financial hazards, management issues, and underlying causes of these issues inside e-commerce systems, subsequently employing AI to assess the FM operations of these websites in Peru. This research uses a Machine Learning (ML) approach to examine the clustering centers of FM information as well as the privacy aspect of the FM. This study ultimately presents ways for optimizing and constructing FMs to enhance data security and capital administration in e-commerce financing, hence fostering the continued growth of e-commerce systems using mobile networks. The experimental findings indicate that the FM’s classification value and security aspect for e-commerce systems progressively enhance under the ML. The average classification value is 1.23, whereas the average risk factor is around 1.41. AI and ML improve financial accounting requirements and the long-term growth of e-commerce systems in Peru.

Fortifying financial integrity: Advanced fraud detection techniques for business security

Fortifying financial integrity: Advanced fraud detection techniques for business security

Mapping Financial Connections: Market Integration in Emerging Economies through Graph Theory

In this study, we explore the financial and economic integration of BRICS nations (Brazil, Russia, India, China, and South Africa) and key emerging economies (Egypt, Saudi Arabia, and the UAE) using graph theory, aiming to map intersectoral connections and their impact on financial stability and market risk. The research addresses a critical gap in the literature; while political and economic linkages between nations have been widely studied, the specific connectivity between sectors within these economies remains underexplored. Our methodology utilizes eigenvector centrality and Euclidean distance to construct a comprehensive network of 106 publicly listed firms from 2013 to 2022, across sectors such as energy, telecommunications, retail, and technology. The primary hypothesis is that sectors with higher centrality scores—indicative of their interconnectedness within the broader financial network—demonstrate greater resilience to market volatility and contribute disproportionately to sectoral profitability. The analysis yielded several key insights. For instance, BHARTI AIRTEL LIMITED in telecommunications exhibited an eigenvector centrality score of 0.9615, positioning it as a critical node in maintaining sectoral stability, while AMBEV SA in the retail sector, with a centrality score of 0.9938, emerged as a pivotal player influencing both profitability and risk. Sectors led by companies with high centrality showed a 20% increase in risk-adjusted returns compared to less connected entities, supporting the hypothesis that central firms act as stabilizers in fluctuating market conditions. The findings underscore the practical implications for policymakers and investors alike. Understanding the structure of these networks allows for more informed decision making in terms of investment strategies and macroeconomic policy. By identifying the central entities within these economic systems, both policymakers and investors can target their efforts more effectively, either to support growth initiatives or to mitigate systemic risks. This study advances the discourse on emerging market integration by providing a quantitative framework to analyze intersectoral connections, offering critical insights into how sectoral dynamics in emerging economies influence global financial trends.

Higher education integrating into the “Belt and Road” economic development and its path selection, taking Kyrgyzstan as an example

Since its inception in 2013, “The Belt and Road Initiative” has become an important engine driving global economic growth. The initiative has not only promoted infrastructure construction in countries along the Belt and Road but also strengthened financial integration, unimpeded trade, people-to-people exchanges, and policy communication. In this context, higher education, as an important avenue for talent training and scientific and technological innovation, is of great significance to promoting the economic and social development of countries along the Belt and Road. By strengthening academic cooperation with Chinese universities, Kyrgyzstan can enhance its curriculum, adopt advanced teaching methods, and integrate cutting-edge research to foster more skilled labor. In addition, innovation and technology transfer through higher education partnerships can drive sustainable economic growth and diversification. This paper explores the strategic path of integrating higher education into the Belt and Road. Initiative, focusing on academic collaboration, enhancing R&D capabilities, and fostering an entrepreneurial ecosystem.

Pengaruh Audit Tenure, Kepemilikan Manajerial, dan Ukuran Perusahaan terhadap Integritas Laporan Keuangan

Purpose: This study examined the impact of audit tenure, managerial ownership, and company size on the integrity of financial statements. Methodology: The sample was selected using purposive sampling, resulting in 29 companies and 77 data points from transportation and logistics companies listed on the Indonesia Stock Exchange during the period 2020-2022. Secondary data from the annual reports were analyzed using multiple linear regression. Results: Company size affects the integrity of financial statements, whereas audit tenure and managerial ownership do not. Limitations: The study is limited by measuring audit tenure as the number of years a Public Accounting Firm has engaged with a client, going back up to six years. Additionally, only one measurement tool–accounting conservatism–was used. Contribution: The findings are expected to enhance the understanding of financial statement integrity for both internal users, such as management, and external users, such as creditors and investors, aiding in making informed decisions. Furthermore, this study contributes to the literature by expanding the determinants of financial statement integrity in transportation and logistics companies.

Influences on financial statement integrity: the role of intellectual capital, independent commissioners, and gender diversity

<span>This study aimed to determined the impact of intellectual capital, independent commissioners, gender diversity, and financial statement integrity in property and real estate subsector companies listed on the Indonesia Stock Exchange (IDX) during the period 2019-2023. Financial statement integrity was measured using market to book value (MBV). The population included property and real estate companies listed on the IDX for the period 2019-2023. Purposive sampling was used to select 8 companies meeting the criteria, providing a total of 40 observation data points. Panel data regression analysis was performed using E-views 12. Results indicate that intellectual capital, independent commissioners, and gender diversity had a simultaneous effect on financial statement integrity. However, intellectual capital and independent commissioners did not affect financial statement integrity, while gender diversity had a negative impact on financial statement integrity. Property and real estate companies should enhance financial statement integrity to increase investor confidence, strengthen market reputation, and gain sustained competitive advantage.</span>

Factors Militating against the Efficacy of the Mutual Agreement Procedure: A Comparison between South Africa, Kenya, Uganda and Ghana

The effective resolution of transfer pricing disputes has been identified as one of the primary challenges to eliminate double taxation and/or double non-taxation as well as to enhance the free flow of trade and investment. This article investigates the unique challenges faced by selected African countries, including South Africa, Kenya, Uganda and Ghana, which militate against the use of the mutual agreement procedure (MAP) as a preferred Organisation for Economic Co-operation and Development (OECD) transfer pricing dispute resolution mechanism. The research used a qualitative legal review of existing literature in the selected regimes by comparing the domestic tax law framework in these countries. Transfer pricing manipulation is a significant source of illicit financial flows of capital as identified in the research by Global Financial Integrity on Africa, which drew from Ghana, Kenya and Uganda as case studies. South Africa and Kenya are considered as the gateways to Africa with many multinational enterprises (MNEs) taking residence in them before expanding to the rest of Africa. The article focuses specifically on a number of guidelines that should be in place for the effective use of the MAP in the resolution of tax treaty disputes. It also demonstrates how the absence of guidelines, or the lack of clarity thereof, discourages both taxpayers and revenue authorities from effectively using the MAP to resolve transfer pricing disputes. The absence of guidelines has resulted in many aggrieved taxpayers opting to approach the courts as opposed to using the MAP as a mechanism to resolve tax treaty disputes. Furthermore, it highlights the importance of how resolved transfer pricing disputes eliminates double taxation. The article recommends that the same benefits enjoyed under the domestic dispute resolution process be incorporated into the MAP. It also makes recommendations on how jurisdictions with a low tax treaty network can address this challenge to enable them to effectively use the MAP as a tax treaty dispute resolution.

Financial integration and hedging and safe haven properties of metals for sovereign bonds

Previous research has shown that industrial metals outperform gold and other precious metals in terms of hedging and safe haven properties for sovereign bond holdings. We argue that the strength of these properties depends on how much the sovereign bond is globally integrated. We therefore model the hedging and safe haven properties of metals for sovereign bonds as moderated by how much bond returns are globally integrated. Our evidence from the sovereign bonds of 24 countries shows that industrial metals provide a stronger hedge and safe haven when sovereigns are more integrated, whereas precious metals do not. The inflation and exchange rates also affect the hedging properties of metals, but bond integration has the strongest economic effect. This evidence helps explain why metals are excellent hedging or safe haven assets for some sovereign bond investors but not for others.

Zero Trust Architecture: Enhancing Cybersecurity in Enterprise Networks

Zero Trust Architecture (ZTA) offers a robust approach to enhancing cybersecurity in enterprise networks, replacing the traditional perimeter-based security models. This paper examines the application, challenges, and effectiveness of ZTA in contemporary corporate environments, with a focus on hybrid and cloud infrastructures. By emphasizing key principles such as least-privileged access, continuous authentication, and network segmentation, ZTA directly addresses the security risks that organizations face today. This research includes a review of relevant literature and an analysis of case studies to explore the difficulties companies encounter when adopting ZTA, including financial costs, integration complexities, and resistance to change. The study also identifies strategies that organizations have successfully employed to overcome these obstacles, leading to improved security and operational efficiency. The findings highlight ZTA’s ability to reduce security incidents through automation and enhanced monitoring. While there are technical challenges to implementing Zero Trust, the research concludes that the framework is essential for maintaining a strong security posture. Future areas for exploration include the role of technologies like artificial intelligence and machine learning in further improving ZTA.

Assessing cybersecurity risks and prioritizing top strategies In Vietnam's finance and banking system using strategic decision-making models-based neutrosophic sets and Z number

The financial and banking system in Vietnam is currently confronted with a myriad of cybersecurity risks, a matter of paramount concern for the government, businesses, and individuals alike. These risks threaten the financial system's security, reliability, and integrity, potentially resulting in significant economic losses. This research endeavors to identify the prevailing cybersecurity risks in Vietnam's financial and banking system, assess their impact, and elucidate the interrelationships among these risks. Employing the Multi-Criteria Decision Making (MCDM) approach, the study integrates the DELPHI technique, Decision-Making Trial and Evaluation Laboratory (DEMATEL), and Combined Compromise Solution (COCOSO) methods, complemented by Neutrosophic Sets (NS) and Z-number concepts to enhance the accuracy and reliability of the findings. The research findings reveal the existence of 15 cybersecurity risks in Vietnam's financial and banking system, with Malware Infections and Supply Chain Vulnerabilities emerging as the most consequential risks. Moreover, the study identifies investing in advanced threat detection systems as the optimal strategy for mitigating cybersecurity risks in Vietnam. The results underscore the importance of addressing these critical risks to safeguard the financial infrastructure, focusing on deploying robust cybersecurity measures to enhance overall system resilience.