Fair East Publishers: E-Journals
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Modelling the impact of climate change on Nigeria’s agricultural sector: A nonlinear modelling approach
No full textClimate change is posing increasing concerns to agricultural output and food security worldwide. This study explores the asymmetric influences of climatic conditions on Nigeria's agricultural output using yearly time series data from 1986 to 2022. This is based on the fact that climate change manifestations in the agriculture sector come in the form of an increase or decrease in climatic variables such as rainfall or temperature. Therefore, the use of an autoregressive distributed lag (ARDL) approach models the nonlinear relationships between temperature, rainfall and agricultural output. Rainfall and temperature indices capture climate variability relative to baseline levels. The model quantifies both immediate and lagged effects of increasing/decreasing rainfall and temperatures on sectoral performance. Cointegration tests confirm long-run equilibrium associations. The estimated asymmetric error correction model reveals rising temperatures and declining rainfall significantly hamper agricultural GDP in the short run. A 1% temperature increase reduces output by N5.1 billion whereas a 1% fall in rainfall lowers it by N9.7 billion. Long-run climate sensitivities also indicate rainfall variability critically constrains productivity. The negative rainfall coefficients agree with agronomic evidence that water stress and droughts diminish yields. Contrastingly, temperature impacts fade over time. Based on the findings of the study, it therefore recommends the development and promotion of heat and drought-resistant crop and livestock varieties to counter the negative impacts of rising temperatures and declining rainfall on agricultural productivity.
Keywords: Climate Change, Agricultural Sector, Nonlinear, Autoregressive Distributed Lag (ARDL) Model
Beyond the credit score: The untapped power of LLMS in banking risk models
No full textTraditional credit scoring models have long been the cornerstone of risk assessment in banking. However, these models often rely on limited, structured data and fail to capture the nuanced behavioral and contextual signals embedded in unstructured information. This review explores the transformative potential of large language models (LLMs) in enhancing banking risk models beyond conventional credit scoring. By leveraging their advanced natural language processing capabilities, LLMs can analyze diverse sources such as transaction narratives, customer communications, social media sentiment, and financial news to extract deeper insights into borrower behavior and creditworthiness. The paper examines how LLMs can improve risk prediction accuracy, enable more inclusive credit assessments, and uncover latent risk factors, particularly in underbanked populations. It also discusses the technical, ethical, and regulatory challenges of integrating LLMs into financial systems, including model interpretability, bias mitigation, and compliance with data privacy laws. Through a comprehensive synthesis of current research, emerging use cases, and industry developments, this review highlights the untapped potential of LLMs to redefine risk modeling in the modern banking landscape.
Keywords: AI Compliance, Regulatory Challenges, Model Development, Implementation Strategies, Risk Management
Privacy and Data Protection: Balancing Security and User Rights
No full textIn an era of rapid technological advancement and increasing digital interconnectedness, the balance between security and user rights has emerged as a critical issue. Privacy and data protection are at the forefront of this discourse, driving the need for robust frameworks that safeguard individual privacy while ensuring the security of digital systems. This paper explores the evolving landscape of privacy and data protection, emphasizing the challenges and strategies for achieving a balance between security imperatives and user rights. Data protection regulations, such as the General Data Protection Regulation (GDPR) in the European Union and the California Consumer Privacy Act (CCPA) in the United States, have set stringent standards for how organizations collect, store, and use personal data. These regulations aim to empower individuals with greater control over their personal information and mandate rigorous compliance measures for businesses. The implementation of such regulations has highlighted the necessity of adopting privacy-by-design principles, where privacy considerations are integrated into the development of technologies and business practices from the outset. Balancing security and user rights requires navigating complex trade-offs. Enhanced security measures, including encryption and stringent access controls, are essential for protecting data against breaches and unauthorized access. However, these measures must be carefully designed to avoid infringing on user rights and freedoms. Transparency and accountability are crucial in building trust between users and organizations. By ensuring clear communication about data collection practices and providing mechanisms for users to exercise their rights, organizations can foster a culture of trust and compliance. Technological innovations, such as artificial intelligence and blockchain, offer promising avenues for enhancing both security and privacy. AI-driven analytics can help detect and mitigate security threats while respecting user privacy through techniques like differential privacy. Blockchain technology can provide decentralized and transparent data management solutions that enhance security and user control over personal data. In conclusion, the interplay between privacy and data protection in the digital age demands a nuanced approach that prioritizes both security and user rights. By adhering to regulatory frameworks, embracing privacy-by-design, and leveraging advanced technologies, organizations can navigate this complex landscape and achieve a sustainable balance that benefits both individuals and society at large.
Keywords: Privacy, Data Protection, Balancing, Security, User Rights
Predictive modelling and spatial flow analysis of United States of America crude oil imports
No full textThe global energy system significantly dependent on crude oil and it is also a major driver of the transportation industry and petrochemical production. By 2040, oil and gas will likely account for over half of the universal energy mix due to increasing demands in various countries of the world, despite developing interest in renewable energy. The United States is a major importer of crude oil due to the role it plays in the country’s economy and energy requirements. Nevertheless, economic uncertainty and geopolitical tensions, such as the battle between Russia and Ukraine, weaken global oil markets. Hence, the need for countries to be able to understand how their supply would be affected. In order to enhance how countries can predict their future supply from different sources, this study evaluates the predictive performance of two machine learning (ML) models: Random Forest (RF), and Support Vector Regression (SVR) in relation to the commonly used Linear Regression (LR). Data of crude oil import from Iraq, Saudi Arabia, Venezuela, Mexico, Canada and Russia into the USA from 1973 to 2023 was obtained for the study from Energy Information Administration. The data were subjected to clearing. Afterwards, 80% of the data was trained while 20% were used to test the predictive performance of the three models by predicting the import flows from 2024 to 2033. Metrics used for the test were root-mean-squared error (RMSE) and mean absolute error (MAE). Maps were used to visualise the flow of the crude oil imports from each country based on the data and the prediction of the three models. With an RMSE of 259.35 and an MAE of 169.17, Random Forest scored better than the other models, showing balanced geographical flows and high predicted accuracy from important importers like Saudi Arabia and Canada. On the other hand, due to their difficulties with nonlinear dynamics, SVR (RMSE: 568.04, MAE: 365.99) and Linear Regression (RMSE: 538.02, MAE: 384.77) performed poorly. Random Forest's ability to forecast import volumes and optimize trade routes was confirmed by spatial flow maps. The result suggest that energy security and supply chain resilience can be improved by incorporating ML models and geographical analysis into energy planning.
Keywords: Crude Oil, Machine learning, Random Forest, Linear Regression, Support Vector Regression
Access to Credit for SMEs in Rural Vietnam
Get PDFThis study addresses the important question of why rural small and medium enterprises in Viet Nam face significant challenges in accessing formal credit and explores barriers to financial inclusion. This study fills a gap in previous studies by focusing specifically on small and medium enterprises in rural Vietnam, which are important for economic development but are disadvantaged by geographical and institutional constraints. The study was conducted to understand these challenges, evaluate the effectiveness of current financial policies and programs, and propose practical solutions to improve access to credit for small and medium enterprises in rural Vietnam. Using a qualitative approach, the study collected data from semi-structured interviews with 18 rural small and medium enterprises owners, 12 financial experts, and 10 policy makers across Vietnam. The findings suggest key barriers such as lack of collateral, high interest rates, limited financial literacy, inefficient bureaucracy and limited presence of financial institutions in rural areas. Although government initiatives and microfinance models show potential, their scope and impact are limited by many operational and cognitive issues. The results underscore the need for targeted interventions, such as simplifying the lending process, expanding financial literacy programs, and strengthening community-based financing mechanisms. Improving access to credit for rural small and medium enterprises plays an important role in promoting inclusive economic growth, poverty alleviation, and addressing regional inequalities in Vietnam.
Keywords: Small and Medium Enterprises, Access to Credit, Rural Vietnam
AI-driven intelligent document processing for banking and finance
Get PDFThe banking and finance industry is buried in paperwork—loan applications, compliance reports, risk assessments, and fraud investigations. Manual processing and outdated automation slow operations, increase costs and expose institutions to compliance risks (Vaultedge, 2023). AI-driven Intelligent Document Processing (IDP) is changing this by automating document workflows, accelerating approvals, and enhancing fraud detection.
AI-powered IDP integrates machine learning, NLP, and RPA to reduce verification times, reduce errors, and strengthen compliance monitoring. Banks using AI-driven document automation process loan approvals 70% faster, improve fraud detection rates by 50%, and lower compliance costs by 40% (Rajput et al., 2025).
This paper explores real-world applications of AI in banking document processing, highlighting efficiency gains, challenges, and future potential. As financial institutions move toward self-learning AI models, IDP is set to become a critical driver of speed, accuracy, and security in banking operations.
Keywords: AI-Driven Document Processing, Banking Automation, Fraud Detection, Regulatory Compliance, Machine Learning in Finance, Robotic Process Automation (RPA), Intelligent Workflow Optimization
Advances in cybersecurity strategies for financial institutions: A focus on combating E-Channel fraud in the Digital era
Get PDFIn the digital era, financial institutions are increasingly vulnerable to sophisticated cyber threats, particularly e-channel fraud, which poses significant risks to financial stability, customer trust, and regulatory compliance. This paper explores the multifaceted nature of e-channel fraud, including its various forms such as phishing, malware, and account takeovers, and examines recent trends that highlight the evolving tactics of cybercriminals. The discussion extends to advanced cybersecurity strategies that financial institutions can deploy to combat these threats. These strategies encompass the adoption of cutting-edge technologies like artificial intelligence, machine learning, blockchain, and biometrics, which enhance fraud detection and secure transaction processes. Additionally, the paper emphasizes the importance of behavioral analytics and real-time monitoring systems in identifying and mitigating fraudulent activities. Organizational measures and best practices are also examined, underscoring the need for comprehensive cybersecurity policies, robust employee training and awareness programs, and active collaboration with other financial entities, regulatory bodies, and cybersecurity firms. By implementing these recommendations, financial institutions can fortify their defenses against e-channel fraud, ensuring the integrity of their operations and maintaining customer confidence.
Keywords: E-channel Fraud, Cybersecurity Strategies, Financial Institutions, Artificial Intelligence, Behavioral Analytics
Virtual assistants and AI in customer service: A review of technological advancements and business impacts
No full textThis paper provides a comprehensive review of the technological advancements and business impacts associated with the integration of Virtual Assistants (VAs) and Artificial Intelligence (AI) in customer service. As organizations increasingly leverage these technologies to enhance customer interactions, it becomes imperative to understand the evolving landscape and its implications. The review begins by exploring the evolution of virtual assistants, tracing their roots from rule-based systems to the current sophisticated AI-driven models. It delves into the underlying technologies such as natural language processing, machine learning, and sentiment analysis that empower these virtual assistants to comprehend and respond to user inquiries with human-like efficiency. Furthermore, the paper investigates the transformative impact of VAs and AI on various aspects of customer service, including improved response times, personalized interactions, and the ability to handle complex queries. The analysis extends to the integration of virtual assistants across multiple channels, ranging from chatbots on websites to voice-activated assistants on smart devices, providing a seamless and omnichannel customer experience. The business impacts of adopting VAs and AI in customer service are assessed, focusing on efficiency gains, cost reduction, and enhanced customer satisfaction. Case studies and real-world examples illustrate how leading organizations across industries have successfully deployed these technologies to streamline their customer support processes and gain a competitive edge in the market. Challenges and considerations associated with implementing virtual assistants and AI in customer service are also discussed, including issues related to privacy, security, and the ethical use of customer data. The paper concludes with insights into future trends, highlighting the potential advancements in VAs and AI that may further revolutionize the customer service landscape. This comprehensive review serves as a valuable resource for businesses, researchers, and practitioners seeking to understand the current state of virtual assistants and AI in customer service and their potential implications for the future.
Keywords: Virtual Assistants, Artificial Intelligence (AI), Customer Service, Technological Advancements, Business Impacts, Chatbots
The impact of financial crime regulations on corporate governance: A critical analysis of U.S. SEC, PCAOB, and global anti-fraud policies
No full textFinancial crimes such as fraud, corruption, and misreporting continue to threaten corporate integrity, investor confidence, and the stability of global financial markets. In response, regulatory bodies in the United States—particularly the Securities and Exchange Commission (SEC), the Public Company Accounting Oversight Board (PCAOB), and landmark legislation such as the Sarbanes-Oxley Act (SOX) and the Foreign Corrupt Practices Act (FCPA)—have implemented robust frameworks to strengthen corporate governance and fraud prevention. This paper critically analyzes how these U.S. financial crime regulations have transformed corporate oversight mechanisms, improved accountability, and shaped global compliance standards. It also examines how international anti-fraud policies increasingly align with U.S. practices, fostering cross-border regulatory synergy. Through a review of legal frameworks, enforcement cases, and corporate responses, this study highlights the positive influence of these regulations on internal controls, board governance, and ethical conduct in multinational corporations. The paper concludes by offering policy recommendations to enhance compliance effectiveness and promote ethical business practices in a dynamic global environment.
Keywords: Financial Crime Regulations, Corporate Governance, Sec Compliance, PCAOB Oversight, Sarbanes-Oxley Act (Sox), Anti-Fraud Policies, Forensic Accounting, Regulatory Compliance, Risk Management, Global Financial Transparency
The Role of Urban Design in Affordable Housing Development: Creating Livable, Inclusive Communities
No full textThis review paper explores the critical role of urban design in the development of affordable housing, with a focus on creating livable and inclusive communities. It examines the impact of key urban design elements such as site planning, density optimization, and community integration on the quality of life in affordable housing projects. The paper also discusses the importance of balancing social, economic, and environmental sustainability in these developments. Through the analysis of design principles, the review identifies challenges and opportunities in implementing effective urban design strategies for affordable housing. The findings highlight the need for innovative approaches, policy support, and community engagement to overcome obstacles and ensure the successful integration of affordable housing into the urban fabric. The paper concludes with recommendations for future directions in urban design that prioritize inclusivity, sustainability, and the overall well-being of residents.
Keywords: Urban design, Affordable housing, Livable communities, Sustainability, Community integration, Policy support