An Analytical-Explanatory Model for the Factors Influencing the Emergence and Development of Financial Center Cities

Abstract
The transformation of cities into financial centers has become a strategic goal in the economic development agendas of many developing countries. Nevertheless, the lack of a structured framework for identifying and prioritizing the key indicators driving this transformation often leads to inefficient policymaking and misallocation of resources. This study aims to develop an analytical-explanatory model to identify and hierarchically rank the financial indicators influencing the emergence and development of financial center cities, using the Fuzzy Delphi Method and Interpretive Structural Modeling (ISM). In the first stage, 29 financial indicators were extracted through a systematic review of the relevant literature. These indicators were then refined and validated by experts in finance, economics, and management using the Fuzzy Delphi technique. In the second stage, the ISM method was applied to analyze the interrelationships among the indicators and to construct a six-level hierarchical structure. The results reveal that indicators such as economic stability, development of technological infrastructure, quality of financial education, and the ability to attract specialized talent have the highest levels of influence and act as key driving forces. On the other hand, indicators like digital banking development, access to financial resources and credit facilities, cybersecurity, and public acceptance of fintech were found to be at lower levels, influenced by higher-level variables. The proposed model provides a structured framework for understanding the dynamics of financial center development. It can serve as a strategic tool for urban development planning, financial policymaking, and the design of national and regional roadmaps aimed at positioning cities as competitive global financial hubs.
 
Extended Abstract
Purpose: The rise of global financial centers is a defining feature of the modern economy. Cities that succeed in becoming international financial hubs benefit from greater foreign investment, skilled labor attraction, technological progress, and integration into global capital flows. For emerging economies, this transformation is both a major opportunity and a strategic necessity for long-term competitiveness.
Despite these prospects, many cities face challenges in building financial hubs. Policymakers often lack a structured framework to identify and prioritize the wide range of indicators involved. As a result, efforts frequently focus on visible outcomes, such as digital banking or capital mobility, while neglecting core enablers like institutional quality or macroeconomic stability. This misalignment leads to wasted resources and stalled financial development.
The purpose of this research is to develop a comprehensive and hierarchical model that identifies, categorizes, and explains the financial indicators influencing the transformation of cities into global financial centers. By addressing the existing lack of a systematic framework, the study seeks to a) assist policymakers in distinguishing between foundational enablers and dependent outcomes, b) provide a structured roadmap for sequencing interventions in urban financial development, and c) enhance the effectiveness of policy design and prevent misallocation of resources in emerging economies.
 
Methodology: The study adopts a mixed-method approach that integrates the Fuzzy Delphi Method (FDM) with Interpretive Structural Modeling (ISM). First, a systematic review of academic literature, international reports, and global benchmarks such as the Global Financial Centres Index and the World Bank’s financial development indicators led to the identification of 29 financial indicators across five dimensions: macroeconomic, institutional, infrastructural, financial market, and socio-technological. To refine these indicators, ten experts in finance, economics, and urban development assessed their importance and relevance, with FDM applied to consolidate expert judgments, eliminate less significant indicators, and ensure the validity of the final set. Subsequently, ISM was employed to structure the validated indicators by mapping their interrelationships and organizing them into hierarchical levels, thereby distinguishing the key enablers with the strongest systemic influence from the dependent indicators shaped by broader structural conditions.
 
Findings and Discussion: The ISM model revealed a six-level hierarchical structure of indicators.

Top-Level Enablers: Economic stability, technological infrastructure, quality of financial education, and attraction of specialized talent were identified as foundational drivers. These indicators shape an environment within which financial markets can flourish.
Intermediate Drivers: Institutional quality, legal frameworks, regulatory capacity, and policy coherence were positioned at the middle levels. They serve as mediators linking foundational enablers with market outcomes.
Dependent Outcomes: At the bottom of the hierarchy were indicators such as fintech adoption, digital banking development, cybersecurity, and access to credit. These are highly dependent on prior achievements in macroeconomic and institutional stability.

This classification underscores the importance of addressing systemic enablers before pursuing advanced financial services. For instance, efforts to promote fintech adoption may fail in environments where digital infrastructure is weak or macroeconomic volatility undermines investor confidence. Likewise, without strong legal frameworks and regulatory institutions, even advanced financial systems remain vulnerable to inefficiency.
The discussion also highlights the role of human capital and financial education as central enablers. Cities that build a skilled workforce and provide quality education in finance and technology are better positioned to sustain long-term innovation. Furthermore, technological infrastructure, including broadband penetration, digital payment systems, and data security frameworks, emerges as a prerequisite for becoming a global financial hub.
 
Conclusions and Policy Implications: The study concludes that the emergence of financial center cities is best understood as a systemic, hierarchical process rather than a collection of isolated indicators. The ISM-based model shows that foundational enablers, namely economic stability, human capital, institutional quality, and technological infrastructure, must be established before expecting outcomes in advanced financial services.
The main policy implications are as follows:

a) Sequencing of Interventions: Policymakers should prioritize macroeconomic stability, institutional quality, and infrastructural development before investing heavily in fintech and digital banking initiatives.
b) Efficient Resource Allocation: The model enables decision-makers to identify leverage points with the highest systemic influence, thereby avoiding misallocation of limited resources.
c) Capacity Building: Investments in education, talent attraction, and regulatory institutions create long-term capacity that strengthens the resilience and competitiveness of financial hubs.
d) Readiness Assessment Tool: The model can serve as a diagnostic framework for assessing the preparedness of cities aspiring to become financial centers, allowing them to benchmark their progress against international standards.

By integrating systemic thinking to practical policymaking, this study provides both a theoretical contribution and an applied roadmap for urban financial development. For emerging economies, adopting this framework can accelerate their journey toward becoming competitive players in the global financial system.