From Principles to Practice: A Cross-Sector Assessment of Responsible AI Governance Readiness

Mahmoud A. Zaher

doi:https://doi.org/10.54216/FinTech-I.050102

Journal of Sustainable Development and Green Technology | 2026

Assessing the Need for an MBA in Sustainable Economics and Finance: Evidence from Selected Higher Education Contexts in Uzbekistan

This study assesses whether there is sufficient justification for developing an MBA in Sustainable Economics and Finance in selected higher education contexts in Uzbekistan. The research is based on two surveys conducted among stakeholders and prospective candidates. The findings show that sustainability-related economic and financial competencies are viewed as increasingly relevant, while stakeholders also identify clear skills gaps in this area. Prospective candidates show a generally positive but still conditional interest in the proposed programme. The results suggest that the programme would be most viable if designed as a practical and career-oriented MBA with strong emphasis on applied skills, internships, and real-world relevance. At the same time, affordability, language accessibility, and expected career outcomes appear to be important conditions shaping demand. Overall, the study concludes that the proposed MBA has a credible foundation in the surveyed context, but its success will depend on careful programme design and alignment with labour-market needs.

groups

Gavkhar Isamutdinova mail -

Ugilshod Akhmedova mail

link https://doi.org/10.54216/JSDGT.060103

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

Details open_in_new

Journal of International Economics Research | 2026

The Evolution of the Middle Corridor and Its Implications for Kazakhstan’s International Trade in the Aftermath of the Russia–Ukraine War

After the Russian invasion of Ukraine in 2022, the role of Middle Corridor raised and this paper examines Kazakhstan’s trade turnover with ten partner countries that reflect the development prospects of the Middle Corridor. Using graphical methods, economic-statistical analysis, as well as explanatory analysis, the study identifies key factors influencing the development of the Middle Corridor. The research is based on data from the Bureau of National Statistics on the website of the Agency for Strategic Planning and Reforms of the Republic of Kazakhstan on investments and trade turnover from 2021 to 2025. This research argues that the growing interest in the Middle Corridor has influenced Kazakhstan’s economic trajectory, which, in turn, is shaping the country’s multi-vector foreign policy.

groups

Aidarov Tofik Aga-Balaevich mail -

Yusubaxmedova Durdonaxon mail -

Muhammad Eid Balbaa mail

link https://doi.org/10.54216/JIER.040201

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

Details open_in_new

American Journal of Business and Operations Research | 2026

Valuation Premium Analytics in Global Public Companies: A Cross-Sectional Study Using 2024 Public Fundamentals

This paper explores why there are listed companies that are valuing significantly higher in the market based on their asset base compared to other companies. It analyses the relationship between valuation premiums and profitability, asset efficiency, the combination of the two, the size of the firm and its loss status using a cross-section of the largest publicly traded companies in the world in 2024. The empirical design integrates the predictive analytics and hypothesis testing. During the explanatory phase, a strong ordinary least squares specification is used to model the logarithm of the market value divided by the total assets. In the predictive stage, logistic regression, random forest, and gradient boosting are used to identify firms in the top quartile of the valuation-premium distribution. The findings show that profitability and asset efficiency interaction is the most positive correlate of the valuation premium, and firm scale is the most negative correlate of relative valuation after standardization by assets. The interaction-enriched specification enhances explanatory power with significant material in comparison to an interaction-free model. The discriminatory performance of the tree-based models tends to be high in the classification phase, with random forest performing out of sample with an AUC of more than 0.93. The results of these studies indicate that valuation premium should be viewed as a combined operating-quality indicator and not as a reward to margin performance in isolation and can serve as a useful guide to screen a portfolio, benchmark a company and interpret market multiples.

groups

Saad Metawea mail -

Maha Metawea mail

link https://doi.org/10.54216/AJBOR.140205

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Financial Technology and Innovation | 2024

Supervised Machine Learning Algorithms for Equity Market Regime Classification: A Systematic Literature Review of Comparative Performance, Feature Engineering, and Generalizability (2015–2024)

The application of supervised machine learning (ML) algorithms for equity market regime classification has gained significant attention in recent years. This systematic literature review (SLR) synthesizes findings from 16 peerreviewed studies published between 2015 and 2024 to address three research questions: (1) How do supervised ML algorithms (XGBoost, Random Forest, SVM, Neural Networks, Ensemble methods) compare in accuracy, robustness, and computational efficiency for market regime classification? (2) What feature engineering approaches are most effective? (3) How generalizable are these models across different equity markets and time periods? Following PRISMA 2020 guidelines, we searched IEEE Xplore, ScienceDirect, and Springer, identifying 2953 records and including 16 studies after screening. Our findings indicate that ensemble methods (particularly Random Forest and XGBoost) and deep learning approaches (LSTM, DNN) consistently outperform traditional classifiers. Technical indicators remain the most common features, though novel approaches including event embeddings, network centrality measures, and signal decomposition show promise. Generalizability remains a challenge, with most studies focusing on developed markets. We identify gaps in cross-market validation and interpretability, providing directions for future research.

groups

Suvonkulov Abdulaziz mail -

Eugene Q. Castro mail

link https://doi.org/10.54216/FinTech-I.040101

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

Details open_in_new

Financial Technology and Innovation | 2024

Active Agent Capacity and Liquidity Discipline in Mobile Money Operations: A Business Analytics Perspective

Mobile money has evolved into a business-critical financial technology infrastructure, yet its operating strength cannot be judged from customer scale alone. A platform may report rapid growth in registered accounts, transaction value, or agent coverage while still facing service fragility when active agents do not expand at the same pace as transaction demand. This study develops a business analytics model for evaluating active agent capacity, customer activation, transaction intensity, and liquidity pressure as connected dimensions of mobile money operations. The empirical analysis uses public aggregate indicators from mobile money industry reporting and demand-side financial inclusion indicators from the Global Findex database. The model distinguishes between three managerial questions that are often combined in practice: whether customers are becoming active users, whether agents are becoming productive service points, and whether transaction value places increasing pressure on the active agent base. The results show that transaction value and transaction volume grow more rapidly than customer scale, while registered agent expansion exceeds active agent growth. Scenario analysis indicates that agent reactivation can reduce liquidity pressure, whereas customer activation without corresponding service-capacity expansion increases operational stress. The study contributes a practical measurement lens for FinTech managers, payment providers, investors, and regulators seeking to scale mobile money while maintaining reliable last-mile service capacity.

groups

Heba Moselhy mail -

Dina K. Hassan mail

link https://doi.org/10.54216/FinTech-I.040102

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

Details open_in_new

Financial Technology and Innovation | 2024

Portfolio Maturity and Product-Category Headroom in Consumer FinTech Markets

Consumer FinTech markets are commonly assessed through aggregate adoption rates, yet adoption alone does not indicate whether a market can support portfolio expansion, cross-selling, or durable customer-value creation. This paper proposes a portfolio maturity framework that separates market penetration from product-category headroom. Using a structured extract from a global consumer FinTech adoption survey, the study examines market dispersion, relative maturity, category-level adoption gaps, and tier-specific expansion opportunities. The findings show that payment and transfer services act as the principal entry point into consumer FinTech, while saving, investment, budgeting, insurance, and borrowing remain unevenly developed. High-adoption markets require strategies focused on relationship depth, ecosystem defense, retention, and responsible product broadening; lower-adoption markets require clearer value proof, trust formation, and reduction of onboarding friction. The study offers a business-oriented diagnostic approach for FinTech firms, banks, platform providers, and investors by translating adoption evidence into portfolio strategy, market-tier priorities, and risk-aware expansion choices.

groups

Ahmed Ibrahim Mokhtar mail -

Saad Metawa mail

link https://doi.org/10.54216/FinTech-I.040103

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

Details open_in_new

Financial Technology and Innovation | 2024

From Account Access to Payment Value: A Business Readiness Model for FinTech Innovation

Digital finance markets often expand through account ownership before those accounts become active sources of payment value, merchant participation and durable financial behaviour. This paper develops a business-oriented FinTech readiness model that separates access, activation, merchant conversion, stored-value behaviour and resilience. The analysis uses regional and income-group indicators from the Global Findex database to examine how account access is transformed into commercially meaningful digital payment use. The results show that account ownership alone is an incomplete measure of FinTech market opportunity. High-income economies have the strongest overall readiness, East Asia and Pacific shows strong merchant-payment conversion, Sub-Saharan Africa has a distinctive mobile-money channel, and low-income economies show large unmet activation potential. The paper contributes a practical scorecard for banks, payment firms and regulators by showing where digital finance strategy should focus: onboarding, usage activation, merchant acceptance, account-based value retention, or trust and resilience safeguards.

groups

Samandarboy Sulaymanov mail -

Durdona Davletova mail

link https://doi.org/10.54216/FinTech-I.040104

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

Details open_in_new

Financial Technology and Innovation | 2024

The FinTech Maturity Divide: Benchmarking AI Services, Open Banking, and Embedded Finance Across Financial Service Sectors

The global financial services industry is undergoing a structural transformation driven by the convergence of artificial intelligence, open application programming interfaces, digital payment infrastructure, and embedded financial services. Despite sustained investment activity, the empirical evidence base for comparing technology adoption maturity across institutional types and capability domains remains fragmented, leaving executives and policymakers without the benchmarking evidence needed for informed strategic investment decisions. This paper addresses that gap through a systematic multi-dimensional maturity study drawing on primary survey data from financial industry professionals across multiple countries and a consumer adoption survey of retail banking customers, covering five technology dimensions: artificial intelligence services, open banking and application programming interface ecosystems, digital payment infrastructure, embedded finance, and regulatory technology. Challenger banks and FinTech startups substantially outperform traditional incumbent institutions on open banking and embedded finance, while traditional institutions retain a relative advantage in regulatory technology compliance. Payment processors dominate on digital payment maturity but show the widest capability gap in artificial intelligence and embedded finance. Consumer adoption analysis reveals pronounced age-related disparities in buy-now-pay-later, cryptocurrency, and robo-advisory services with direct implications for financial inclusion strategy. Regression analysis identifies application programming interface readiness as the single strongest predictor of overall maturity, confirming that foundational data architecture investment has compounding returns across all five technology domains. The paper contributes a validated five-dimension maturity framework, a regression model of the institutional and strategic predictors of overall FinTech maturity, and ten evidence-based strategic recommendations for executives navigating the technology transformation of financial services.

groups

Davletov I. Rakhimberganovich mail -

Dusmuratov R. Davlatbayevich mail

link https://doi.org/10.54216/FinTech-I.040105

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

Details open_in_new

Financial Technology and Innovation | 2025

Explainable Artificial Intelligence for Real-Time Financial Fraud Detection: A Systematic Literature Review

Financial fraud detection systems increasingly rely on machine learning to identify suspicious transactions at scale. However, the opacity of many high-performing models raises significant concerns regarding trust, regulatory compliance, and practical deployment in real-time financial environments. Explainable Artificial Intelligence (XAI) has emerged as a promising solution to enhance transparency and accountability, yet its feasibility under real-time constraints remains unclear. This systematic literature review examines empirical studies on explainable AI approaches for financial fraud detection, with explicit focus on real-time applicability. Following PRISMA guidelines, nineteen peer-reviewed empirical studies were selected and analyzed based on fraud domain, model type, explainability technique, evaluation metrics, and evidence of real-time performance. Results show that posthoc explanation methods, particularly SHAP and LIME, dominate the literature, while intrinsic explainability and deployment-level latency reporting remain limited. Despite frequent claims of real-time applicability, only one study provides quantitative runtime evidence. The findings highlight critical gaps: absence of explanation latency evaluation, lack of deployment-oriented validation, and insufficient regulatory compliance integration. This review reveals a systematic disconnect between real-time claims and empirical evidence, establishing the need for standardized latency benchmarking in explainable fraud detection research.

groups

Ulugbek Inoyatov mail -

Eugene Q. Castro mail

link https://doi.org/10.54216/FinTech-I.050101

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

Details open_in_new

Financial Technology and Innovation | 2025

From Principles to Practice: A Cross-Sector Assessment of Responsible AI Governance Readiness

The rapid institutionalisation of artificial intelligence across financial services, healthcare, technology, and the public sector has generated a parallel proliferation of governance frameworks, ethical principles, and regulatory instruments that collectively demand organisations translate abstract values into operational practice. The gap between stated principle and enacted governance—what we term the responsible AI implementation gap—is now recognised as one of the central practical challenges in AI deployment, yet its magnitude, distribution across sectors, and organizational determinants remain poorly characterised in the empirical literature. This paper addresses that gap through a three-phase mixed-methods programme combining systematic analysis of publicly available governance frameworks, a cross-sector practitioner survey, and a governance maturity scoring exercise. Significant variation is documented across sectors on all five governance dimensions examined, with the technology sector leading on accountability and transparency, healthcare on privacy and human oversight, and the public sector on regulatory compliance readiness. Across all sectors, however, a persistent and pronounced gap exists between the governance principles that organisations formally endorse and the operational processes through which those principles are enacted: the average policy-to-practice gap across all eight governance principles assessed is consistent and substantial. Regression analysis identifies the presence of a dedicated responsible AI team as the single strongest organisational predictor of maturity, followed by staff training investment and senior executive sponsorship. The paper contributes a validated governance maturity framework, a framework coverage taxonomy for twenty-four public AI governance instruments, and six evidence-based implementation guidelines for organisations seeking to move from principle adoption to genuine operational accountability.

groups

Mahmoud A. Zaher mail

link https://doi.org/10.54216/FinTech-I.050102

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

Details open_in_new