Financial exclusion remains a significant barrier to economic empowerment for billions worldwide, limiting access to essential financial services such as credit, savings, insurance, and payment systems.1 This divide disproportionately affects low-income populations, rural communities, and marginalized groups.2 Fintech (financial technology) has emerged as a transformative force, leveraging innovation to deliver financial services more efficiently, affordably, and accessibly.3 This article provides a comprehensive review of the role of fintech in bridging this financial access gap and fostering economic empowerment. Drawing from extant literature, we analyze how various fintech solutions—including mobile money, digital banking, peer-to-peer lending, and robo-advisors—address the traditional barriers to financial inclusion. While acknowledging challenges such as the digital divide, data privacy concerns, and regulatory complexities, this review highlights fintech's profound potential to democratize financial services, stimulate entrepreneurship, and improve overall economic well-being for underserved populations globally.

In the evolving landscape of financial technology, credit scoring systems must adapt to increasingly complex data environments to ensure accurate and fair lending decisions. Traditional credit models rely heavily on structured data such as income, credit history, and debt ratios, but often fall short in assessing borrowers with limited credit footprints or non-traditional financial behaviors. This study proposes a hybrid deep neural network (DNN) model that integrates structured financial indicators with unstructured textual data—including customer service interactions, financial news, and social media sentiment—to enhance credit risk prediction. We collected and preprocessed a multimodal dataset comprising over 100,000 loan profiles, developed a bi-directional LSTM architecture for text processing, and fused it with structured data via a deep learning framework. Our model was evaluated against benchmark algorithms including logistic regression, random forest, XGBoost, and single-input DNNs. Experimental results show that the hybrid DNN significantly outperforms traditional models, achieving an accuracy of 87% and an AUC-ROC of 0.91. These findings underscore the potential of multimodal deep learning in transforming credit scoring systems, improving model precision, and expanding financial inclusion. The proposed model offers a scalable and robust framework for future credit evaluation tools in data-rich financial ecosystems.

Small and medium-sized enterprises play a decisive role in national economies by generating employment, fostering innovation, and contributing to economic resilience. Despite their importance, SMEs consistently face structural constraints related to finance, human resources, and knowledge management that limit their growth and long-term sustainability. The academic literature has traditionally examined these constraints in isolation, focusing separately on working capital management, human resource management, or intellectual capital. This fragmented approach fails to capture the interconnected nature of operational finance, people management, and knowledge-based resources in shaping SME performance. Drawing exclusively on established theoretical and empirical studies, this article develops an integrated analytical framework that links working capital management practices with strategic human resource management and intellectual capital development in SMEs.

The study synthesizes foundational and contemporary research on working capital policies, liquidity behavior, cash conversion cycles, and financing constraints, highlighting how financial decisions directly influence operational risk and investment capacity. At the same time, it incorporates insights from strategic human resource management and family business literature to demonstrate how human resource practices affect productivity, decision-making quality, and organizational adaptability. Intellectual capital theory is used to bridge these domains by explaining how human, structural, and relational capital mediate the relationship between financial management and firm performance.

Using a qualitative, theory-driven methodology based on comparative and integrative analysis of prior empirical findings, the article identifies consistent patterns showing that SMEs with aligned financial, human, and intellectual capital strategies outperform those that treat these functions independently. The results emphasize that efficient working capital management is not merely a technical financial exercise but a strategic process deeply embedded in managerial capabilities, employee competencies, and organizational learning mechanisms. The discussion elaborates on theoretical implications, contextual limitations, and future research directions, particularly in developing and transition economies.

By offering a holistic perspective grounded in established literature, this article contributes to SME research by advancing an interdisciplinary understanding of performance drivers. It provides scholars with a conceptual synthesis and offers policymakers and practitioners a foundation for designing integrated support mechanisms that enhance SME sustainability, competitiveness, and long-term value creation.