The study, which analyzes panel data from 39 countries over the period 2016 to 2023, takes an interdisciplinary approach to a question that has become increasingly urgent amid rising global fragmentation. Wars, sanctions, trade disputes, and diplomatic ruptures do not merely dominate headlines; they reshape the plumbing of the global financial system. The researchers set out to quantify how this turbulence interacts with foreign investment ecosystems to shape financial inclusion outcomes in both developing and developed economies. Their dataset draws on some of the most authoritative sources available in the field, including the World Bank Global Findex Database, the IMF Financial Access Survey, the World Governance Indicators, the Geopolitical Risk Index, and UNCTAD investment data. By combining these sources, the authors constructed a picture of how digital financial inclusion evolves from year to year under shifting political and economic conditions.

Methodologically, the paper is notable for its careful handling of the short-run dynamics that most cross-country studies tend to smooth over. The researchers applied a first-difference fixed-effects panel approach, which examines how changes in digital financial inclusion from one period to the next respond to changes in the explanatory variables, effectively controlling for all time-invariant country characteristics. To guard against the biases that plague dynamic panel models, particularly those arising from the correlation between lagged dependent variables and country-specific error terms, they employed dynamic generalized method of moments estimation together with LSDVC bias-corrected robustness checks. These techniques are essential when the dependent variable is persistent, meaning that past levels of digital financial inclusion strongly predict current levels, and when the number of countries is modest relative to the number of time periods.

Perhaps the most striking finding of the study concerns persistence itself. The authors report that, beyond institutional quality and conditional macroeconomic factors, the persistence of digital financial inclusion over time is the main, if not the only, driver of the phenomenon in their short-run panel. In plain terms, countries that were already advancing digital financial access tended to keep advancing, while those lagging behind found it difficult to break out of their trajectory within the eight-year window. This inertia has profound implications. It suggests that digital financial inclusion behaves less like a policy dial that governments can simply turn up and more like a cumulative process in which early momentum, network effects, and established digital infrastructure compound over time. For countries seeking to accelerate inclusion, the message is sobering: waiting has a cost, because the gap between leaders and laggards tends to reproduce itself.

Foreign direct investment emerges as a second central character in the story, but with an important twist. The study finds that FDI exerts a significant positive effect on digital financial inclusion once its interaction with geopolitical risk is properly accounted for. This statistical nuance matters. In models that ignore the interaction, the apparent effect of foreign investment may be distorted, because the benefit of FDI is not constant across political environments. Instead, the researchers show that the positive effect of foreign investment weakens as geopolitical risk rises. Capital, technology, and expertise flowing across borders appear to nurture digital financial ecosystems most effectively in stable environments, while in turbulent ones that same investment delivers a diminished boost. The finding echoes a broader theme in development economics: foreign capital is not a substitute for stability but a complement to it.

The geographic pattern of these effects adds another layer of significance. The study finds that the FDI effect is concentrated in non-OECD economies, meaning that developing countries stand to gain the most from foreign investment in digital finance when conditions are favorable, and to lose the most when geopolitical risk surges. This asymmetry maps directly onto the global development agenda. The authors connect their findings to Sustainable Development Goal 8 on decent work and economic growth, SDG 9 on industry, innovation and infrastructure, SDG 10 on reduced inequalities, and SDG 16 on peace, justice and strong institutions. Digital financial inclusion sits at the intersection of all four: it enables economic participation, depends on digital infrastructure, determines who shares in growth, and requires trustworthy institutions to function.

Why should geopolitical tension interfere with something as seemingly domestic as whether a farmer can open a mobile wallet? The mechanisms are both direct and indirect. Directly, geopolitical crises can disrupt cross-border payment corridors, trigger capital flight, complicate compliance regimes through sanctions, and deter the foreign banks and fintech firms that often supply the technology and capital behind digital financial services. Indirectly, elevated risk raises uncertainty for households and firms alike, discouraging the formalization of savings and transactions that digital inclusion represents. The Geopolitical Risk Index used in the study captures the frequency of conflict, terrorism, and interstate tension as reported in major newspapers, providing a quantifiable proxy for the climate of uncertainty in which financial decisions are made. When that climate darkens, the study suggests, the machinery of inclusive digital finance grinds more slowly.

The institutional dimension of the analysis reinforces this interpretation. By disaggregating the World Governance Indicators and testing their individual and joint effects, alongside multicollinearity diagnostics, the researchers examined whether specific aspects of governance, such as regulatory quality, rule of law, or government effectiveness, carry distinct weight in shaping digital financial inclusion. The emphasis on institutions in their findings aligns with a growing consensus that technology alone cannot deliver financial inclusion. A mobile payment app requires a regulatory framework that permits agent networks, identity systems that verify customers, consumer protection rules that build trust, and courts that enforce contracts. Where these institutional foundations are weak or destabilized by political turmoil, digital finance struggles to take root regardless of how sophisticated the underlying technology may be.

For policymakers in developing economies, the study’s implications are pointed. First, because digital financial inclusion is highly persistent, early and sustained investment in digital ecosystems is likely to pay compounding dividends, while delays entrench exclusion. Second, attracting foreign direct investment remains a viable strategy for building inclusive digital finance, but its returns are contingent on managing geopolitical exposure. Countries that diversify their investment partnerships, strengthen governance, and insulate their financial infrastructure from external shocks are better positioned to capture the benefits of foreign capital even in a fragmented world. Third, the link to SDG 16 suggests that resilient governance is not merely a political aspiration but an economic prerequisite for equitable financial access. The authors argue that demonstrating how geopolitical instability constrains inclusive digital finance highlights the importance of resilient governance and sustainable digital ecosystems for achieving fair and widespread financial access.

The research arrives at a moment when the forces it measures are intensifying. Geopolitical fragmentation has deepened since the early 2020s, and the digitalization of finance has accelerated in parallel, making the question of how the two interact one of the defining policy puzzles of the decade. By showing that the benefits of global integration, in the form of foreign investment, are conditional on political stability, the study challenges both techno-optimists who believe digital finance will spread regardless of circumstances and skeptics who dismiss foreign investment as irrelevant to inclusion. The reality, the data suggest, is more conditional and more contingent. Digital financial inclusion can be a powerful engine of equitable growth, but its engine runs on a fuel mixture of persistent momentum, sound institutions, and foreign investment, and geopolitical risk drains all three. In a world where political shocks are increasingly frequent, building financial systems that can withstand them may be as important as building the systems themselves.

Subject of Research: The effect of geopolitical risk and foreign direct investment on digital financial inclusion across developing and developed economies

Article Title: Geopolitical risk and digital financial inclusion in short-run panel dynamics

Article References: Sharma, M., Bhardwaj, N., & Singh, M. (2026). Geopolitical risk and digital financial inclusion in short-run panel dynamics. Discover Sustainability. https://doi.org/10.1007/s43621-026-04514-w

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04514-w

Keywords: digital financial inclusion, geopolitical risk, foreign direct investment, panel data, dynamic GMM, financial access, sustainable development goals, governance indicators, developing economies, fintech, World Bank Findex, economic growth

At the heart of the volume lies a simple but consequential observation: rapid advances in artificial intelligence, blockchain, cloud computing, the Internet of Things and data analytics are reshaping the way financial institutions operate and deliver services. These technologies are not incremental improvements to legacy systems. They represent a structural shift in how value is stored, transferred, priced and protected. The book argues that alongside opportunities for greater efficiency, transparency and innovation, the same technologies are creating new challenges related to cybersecurity, regulation, ethics and digital trust, and that understanding both sides of the ledger is essential for anyone working in or studying modern finance.

Artificial intelligence and machine learning receive particular attention throughout the volume. In banking, these techniques now underpin credit scoring, algorithmic trading, customer service automation and, increasingly, the detection of financial fraud. Machine learning models can identify anomalous patterns across millions of transactions in real time, flagging suspicious behavior that would be invisible to manual review. Yet the contributors also confront the harder questions that come with algorithmic decision-making: how to ensure that automated credit decisions are fair, how to explain model outputs to regulators and customers, and how to maintain accountability when machines make choices that were once the preserve of human judgment. Ethical decision-making in AI-driven finance is treated as a first-order concern rather than an afterthought.

Blockchain and distributed ledger technologies form another pillar of the analysis. By allowing transactions to be recorded on tamper-evident, shared ledgers, blockchain promises reductions in settlement times, reconciliation costs and counterparty risk. The book situates these capabilities within the wider movement toward digital payments and tokenized assets, exploring how distributed architectures could reshape everything from cross-border remittances to trade finance. At the same time, the contributors acknowledge the regulatory ambiguity that still surrounds crypto-assets and smart contracts, and the technical hurdles, including scalability and interoperability, that stand between pilot projects and production-scale deployment.

Cloud computing and the Internet of Things extend the transformation beyond software into infrastructure. Cloud platforms give banks elastic computing power and enable the rapid deployment of new services, while connected devices generate streams of data that can inform insurance pricing, asset monitoring and hyper-personalized financial products. The volume’s treatment of hyper-personalized banking is especially timely: financial institutions can now tailor offers, advice and risk assessments to individual customers with a granularity that was unimaginable a decade ago. But personalization at this scale depends on vast data collection, which in turn raises questions about privacy, consent and the security of the cyber-physical systems that link physical devices to financial networks.

Cybersecurity and digital trust emerge as connecting threads across the entire book. As financial services migrate to digital channels, the attack surface for fraud and cybercrime expands in parallel. The contributors examine financial fraud detection as both a technical and an institutional problem, one that requires not only sophisticated analytics but also investor confidence, robust governance and coordinated regulatory oversight. The book’s chapters on risk management consider how institutions can build resilience into systems that are increasingly interconnected, automated and dependent on third-party cloud providers, and how regulators can keep pace with technologies that evolve faster than the rulebooks written for earlier eras.

Regulation itself is a major focus. The volume devotes sustained attention to fintech regulation, exploring the tension between fostering innovation and protecting consumers, markets and financial stability. Regulatory sandboxes, licensing regimes for digital-only banks and evolving standards for data protection all feature in the discussion. The contributors argue that the jurisdictions that strike the right balance between experimentation and oversight are likely to become hubs for financial innovation, while overly rigid frameworks risk pushing activity into less transparent channels. The book also connects regulation to ethics, emphasizing that trust is the fundamental asset of any financial system and that digital transformation succeeds only when customers believe their money and their data are safe.

Sustainability adds a further dimension to the analysis. Chapters on green banking examine how technology can support the transition to a lower-carbon economy, from digital processes that reduce paper and energy consumption to analytics that measure the environmental exposure of loan portfolios. The book positions green banking not as a niche marketing exercise but as a strategic imperative, driven by investor demand, regulatory pressure and the growing recognition that climate risk is financial risk. This perspective links the technological themes of the volume to the broader societal questions that will define the next decade of finance.

For portfolio management and investment services, the book surveys how data analytics and AI are changing the way assets are selected, allocated and monitored. Algorithmic strategies, alternative data sources and automated advisory platforms are democratizing tools that were once available only to large institutions, while also concentrating new forms of systemic risk when many market participants rely on similar models. The contributors consider what these developments mean for investor confidence, market efficiency and the responsibilities of asset managers in an era when machines execute a growing share of trading volume.

By bringing together conceptual analysis, contemporary research and practical perspectives, Reshaping Banking and Financial Services with Disruptive Technologies offers a comprehensive understanding of the digital transformation now underway in finance. The editors, Monica Gupta, Priya Jindal and Piyush Gupta, have assembled contributions spanning fintech, financial technology, digital transformation and the evolving banking landscape, making the volume relevant to researchers, financial-sector practitioners, policymakers and postgraduate students alike. The book is published by Bentham Science Publishers and carries the DOI 10.2174/97988988175341260101. As financial institutions worldwide grapple with the pace of technological change, the volume arrives as a timely map of both the promise and the peril of disruptive innovation in banking, a field in which the next disruption is always closer than it appears.

Subject of Research: The impact of disruptive technologies such as AI, blockchain and cloud computing on banking and financial services

Article Title: Reshaping banking and financial services with disruptive technologies

Article References: Reshaping banking and financial services with disruptive technologies. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: banking, fintech, artificial intelligence, blockchain, cloud computing, Internet of Things, data analytics, cybersecurity, financial regulation, digital payments, green banking, risk management

The study, published on 8 October 2026, takes aim at a deceptively simple question: why do so many AI initiatives in financial technology stumble, stall or fail outright, even when the underlying algorithms work as designed? The authors’ answer is that failures rarely stem from technical defects alone. Instead, they emerge from overlooked interdependencies across legal, technical, economic and human dimensions — interdependencies that most adoption strategies treat in isolation, if at all.

To capture these interactions systematically, the researchers developed a framework they call SETEH, short for Systemic–Ethical–Technical–Economic–Human. The model treats AI adoption not as a single engineering challenge but as a coupled system in which regulatory compliance, ethical governance, systemic risk, human psychology and information technology infrastructure jointly determine whether deployment succeeds. Each dimension feeds back into the others: a compliance failure can amplify human distrust, which in turn degrades the quality of data and oversight that technical systems depend upon.

The empirical core of the research is an analysis of 40 fintech case studies, examined through the resilience lens that the SETEH framework provides. By tracing how organizations navigated regulatory requirements, ethical dilemmas, infrastructure constraints and workforce anxieties, the authors were able to identify recurring patterns that separate durable AI integrations from fragile ones. The case-based approach grounds the framework in observed practice rather than purely theoretical modeling, giving the findings an evidentiary weight that conceptual papers on AI governance often lack.

Among the clearest findings is the centrality of regulatory adaptability. The study concludes that adaptive regulation and robust oversight are essential to mitigate three interlocking hazards: decision opacity, market concentration and cyber risk. Opacity arises when machine learning models produce consequential financial decisions — loan approvals, trading positions, fraud flags — that neither customers nor regulators can meaningfully interrogate. Market concentration emerges when only the largest institutions can afford the data, computing power and compliance apparatus that sophisticated AI demands, tilting the competitive field. Cyber risk compounds both, as AI-driven systems expand the attack surface of financial infrastructure and become targets in their own right.

Ethical governance, the researchers find, is not a soft add-on but a hard operational requirement. Where firms establish genuine ethical oversight, transparency improves, stakeholder uncertainty declines, and — critically — human resistance to AI diminishes. The study highlights distrust and job insecurity as the dominant psychological barriers among financial-sector employees. Workers who fear replacement or who cannot see how automated systems reach their conclusions tend to disengage, circumvent or quietly sabotage new tools. Ethical frameworks that make decision processes legible and that address employment concerns directly therefore function as adoption accelerants, not bureaucratic friction.

This human dimension is where the study’s resilience perspective departs most sharply from conventional adoption literature. Drawing on a long tradition of socio-technical research — the lineage of studies showing that technology and social organization must be designed together — the authors treat psychological factors as first-class variables alongside IT infrastructure and regulatory compliance. An AI system embedded in a workforce that does not trust it is, in the study’s framing, a system operating under chronic stress, vulnerable to failure modes that no amount of technical redundancy can eliminate.

The economic dimension of the framework adds further nuance. Sustainable AI adoption, the authors argue, requires that economic incentives align with legal and ethical constraints rather than compete against them. Firms pressured to deploy AI rapidly for competitive advantage may underinvest in oversight infrastructure, creating latent systemic risk that only materializes under market stress. The case evidence suggests that organizations which internalize compliance and ethics costs early — rather than treating them as external impositions — build adoption pathways that survive regulatory scrutiny and market turbulence alike.

Cybersecurity threads through the analysis as a persistent cross-cutting threat. The financial sector has long been a prime target for cyberattacks, and the study situates AI within that contested environment in two directions at once: AI strengthens defenses through improved fraud detection and anomaly monitoring, yet simultaneously introduces new vulnerabilities through adversarial manipulation, data poisoning and the concentration of critical decision-making in opaque models. Managing this duality, the authors contend, requires governance structures that treat security, compliance and ethics as a single integrated problem rather than separate silos handled by different departments.

The broader significance of the research lies in its contribution of a structured, empirically grounded model for what resilient AI adoption actually looks like in global finance. Rather than offering a checklist, the SETEH framework provides an analytical map of the terrain — showing regulators where adaptability matters most, showing firms where ethical investment pays operational dividends, and showing technologists why human factors cannot be engineered away. As AI’s role in allocating capital, assessing risk and executing trades continues to expand, the study’s central message grows more urgent: the sustainability of intelligent finance will be decided not by the power of the algorithms, but by the strength of the legal, ethical and human systems that surround them.

Subject of Research: Governance and sustainability of artificial intelligence adoption in global financial technology

Article Title: Artificial intelligence in global finance: legal compliance, ethical conflicts, and human resistance shaping sustainability

Article References: Surya, S., & Maduletty, A. K. (2026). Artificial intelligence in global finance: legal compliance, ethical conflicts, and human resistance shaping sustainability. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-026-05124-0

Image Credits: AI Generated

DOI: 10.1007/s12652-026-05124-0

Keywords: artificial intelligence, fintech, AI governance, legal compliance, ethics, SETEH framework, cybersecurity, human resistance, regulatory adaptability, systemic risk, sustainability, financial technology

The research team set out to examine how green finance affects banking stability, measured by the widely used Z-score, across the five BRICS countries over the period from 2005 to 2024. The Z-score is a standard barometer of financial soundness that combines a bank’s profitability, its capitalization, and the volatility of its returns; a higher Z-score signals a lower probability of insolvency, because the bank has a larger buffer of earnings and equity relative to the risks it is taking. Rather than assuming that more green finance is always better, the authors explicitly tested whether the relationship bends. Their approach is a direct response to earlier studies that treated green finance, financial technology adoption, and financial depth as separate, one-at-a-time influences on stability, without ever estimating the curvature of the relationship or asking how the different drivers interact with one another.

Methodologically, the study relies on a two-step system generalized method of moments estimator, a workhorse of modern panel econometrics that is designed to handle the stubborn realities of macro-financial data: variables that move together over time, feedback loops between the outcome and its determinants, and unobserved country-specific characteristics that could otherwise contaminate the estimates. The researchers estimated both a linear specification and a quadratic specification, the latter adding the square of the green finance indicator as well as interaction terms between green finance and two moderators: FinTech development and financial depth. This quadratic design is what allows the study to speak directly about thresholds rather than merely about directions of effect. The panel, however, contains only five countries, and the authors are candid that the generalized method of moments results should be read as indicative rather than definitive, a caveat they reinforce by cross-checking against a battery of second-generation unit root, cointegration, and structural-break tests.

The headline finding is the inverted U. In the quadratic model, the linear term for green finance is positive, with a coefficient of 0.042, meaning that at low and moderate levels, expanding green finance is associated with greater banking stability. But the squared term is negative, at minus 12.603 multiplied by ten to the power of minus six, which mathematically guarantees that the benefit eventually peaks and then reverses. Solving the quadratic equation places the unconditional turning point at roughly 1666 units of the green finance indicator, a level slightly above the sample mean. In plain terms, most BRICS countries in the sample still sit on the upward-sloping portion of the curve, where green finance and bank stability reinforce each other, but the marginal benefit of each additional unit of green finance shrinks as the indicator climbs, and beyond the turning point it would turn negative.

What elevates the study beyond a simple curve-fitting exercise is the role of the two moderators. Both interaction terms, green finance combined with FinTech development and green finance combined with financial depth, are positive and statistically significant. The practical consequence is that the turning point is not a fixed number etched into the economy; it moves. As FinTech adoption and financial depth increase, the threshold shifts to the right, from roughly 2400 to 3870 units across the scenarios the authors examined. In other words, a country with a sophisticated digital financial sector and deep, liquid markets can absorb considerably more green finance before the stability benefits begin to erode. The threshold, the authors show, is best expressed as a function of the two moderators rather than as a single universal constant, which is a genuinely novel contribution to a literature that has mostly reported point estimates.

The causal architecture of the system adds another layer of complexity. Dumitrescu-Hurlin panel causality tests indicate two-way causality between banking stability and each of its determinants, including green finance itself. This means the relationship is not a one-way street in which green finance simply acts upon banks; healthier, more stable banking systems are also better positioned to extend green credit and absorb the risks of novel green projects. Such bidirectional feedback is exactly the kind of dynamic that simple regression models miss and that the system GMM framework, with its use of internal instruments, is built to accommodate. It also carries a policy implication: interventions that strengthen banks may indirectly accelerate green finance, creating a virtuous cycle, at least while the economies remain on the favorable side of the curve.

The structural-break tests tell their own story about when the relationship shifted. The main ruptures in the data are dated to the 2008 to 2011 window, coinciding with the global financial crisis and its aftermath, and to 2021, a period marked by the pandemic-era surge in digital finance and the intensification of climate policy commitments across the BRICS bloc. Dating these breaks matters because it suggests that the green finance and stability relationship is not timeless; it has been reshaped by systemic shocks and by the accelerating digitalization of financial services. The authors used Stata 17 and Python to run their estimation programs, and the study was conducted without dedicated external funding, with the authors declaring no competing interests.

Why would green finance eventually strain stability at high levels? The study does not overclaim a single mechanism, but the inverted U-shape is consistent with several plausible dynamics that economists have long discussed. Early green investments may diversify bank portfolios away from carbon-intensive exposures and attract favorable regulatory treatment, both of which support the Z-score. But as green lending grows very large, concentration risk can re-emerge in a new form: banks become heavily exposed to a class of assets whose valuations depend on subsidies, technology costs, and policy continuity. Meanwhile, the comparability of green finance and FinTech indicators across countries remains unverified, a limitation the authors flag explicitly, since different national statistical systems may measure the underlying flows in different ways.

The policy message that emerges is deliberately conditional. Green finance appears compatible with banking stability at the levels observed in most BRICS countries today, which is reassuring for governments that have staked significant political capital on green industrial strategies. But the marginal benefit shrinks at high levels of green finance, and the room for safe expansion depends on the digital and financial infrastructure that accompanies it. For policymakers in Brasília, Moscow, New Delhi, Beijing, and Pretoria, the practical takeaway is that green finance strategy cannot be designed in isolation; it must be sequenced alongside investments in FinTech adoption, payments infrastructure, credit registries, and capital market depth. Pushing green lending past the threshold without building that supporting architecture could, on the evidence of this study, begin to erode the very stability that sustainable finance is meant to protect.

For the broader research community, the study opens as many questions as it answers. A five-country panel, however carefully instrumented, cannot settle these questions definitively, and the authors themselves present their GMM results as indicative. Extending the threshold framework to larger panels of emerging economies, verifying the cross-country comparability of green finance and FinTech series, and probing the specific balance-sheet channels through which the turning point operates are all natural next steps. What the study already delivers is a conceptual upgrade: the debate over whether green finance helps or harms financial stability is, on this evidence, asking the wrong question. The right question is where a country sits on the curve, and how far its digital and financial development can push that curve outward before the benefits begin to bend back on themselves.

Subject of Research: The nonlinear relationship between green finance and banking stability in BRICS countries, moderated by FinTech development and financial depth

Article Title: Green finance influences financial stability through nonlinear dynamics in BRICS countries with FinTech and financial development as complementary drivers

Article References: Jarallah, G., & Gabsi, F. B. (2026). Green finance influences financial stability through nonlinear dynamics in BRICS countries with FinTech and financial development as complementary drivers. Discover Sustainability. https://doi.org/10.1007/s43621-026-04791-5

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04791-5

Keywords: green finance, financial stability, BRICS, FinTech, Z-score, nonlinear dynamics, panel GMM, financial development, banking systems, threshold effects, sustainable finance, emerging economies

The technical architecture of the e-CNY helps explain why it can serve inclusion goals that conventional digital payments cannot. Unlike decentralized cryptocurrencies, the digital renminbi is a direct liability of the central bank, issued through a two-tier system in which the PBOC distributes the currency to commercial banks and licensed operators, who then place it in the hands of consumers and businesses. This structure preserves the role of existing financial institutions while giving the central bank a unified, state-backed digital cash instrument. The e-CNY is designed to pay for consumer retail and service transactions, civil servants’ wages, utility bills, and company-to-company exchanges, and it has been rolled out through large-scale pilots in cities including Shenzhen, Suzhou, and Shanghai, including lottery-style giveaways such as a three-million-yuan distribution in Suzhou that introduced millions of residents to the new wallet technology.

One of the most technically distinctive features of the e-CNY is its support for offline payment. In trials documented in Shenzhen, users could complete transactions in a ‘touch and pay’ mode even when neither device had network connectivity, using near-field communication between hardware wallets. This matters enormously for inclusion because network coverage, smartphone ownership, and battery life are precisely the barriers that exclude many vulnerable users from mobile payment systems. The PBOC and its corporate partners extended this logic to dedicated hardware: a ‘hard wallet’ card program was launched so that people without smartphones at all could hold and spend digital yuan, and Huawei’s Mate 40 series shipped with a built-in hardware wallet for the currency. Mu Changchun, the head of the PBOC’s digital currency research institute, has publicly framed the goal as providing an inclusive and easy-to-use digital central bank currency, a commitment the study treats as central rather than incidental to the project.

The rural dimension of this strategy is where the study’s findings become most vivid. China’s countryside has historically been underserved by bank branches, a gap that the Postal Savings Bank of China, with its vast network of rural outlets, has long worked to narrow. Pilot reporting from 2021 described tests of the digital yuan in villages, and analysts noted the introduction of a phone-free e-RMB card explicitly aimed at helping the rural poor. The policy context is the CCP’s longstanding ‘sannong’ agenda, referring to agriculture, farmers, and the rural countryside, which has made rural finance a recurring political priority. By pairing the e-CNY with card-based hardware and offline functionality, the PBOC effectively built a payment instrument that works for a farmer without a smartphone just as well as for an urban professional with the latest device.

Elderly citizens form the second major target group, and the design choices here are equally revealing. China’s aging population includes millions of people who find smartphone apps confusing or inaccessible, a problem that became acute as the country’s dominant private payment platforms made daily commerce increasingly app-dependent. The e-CNY’s hardware wallets, simple card interfaces, and offline modes offer a state-backed alternative that does not require downloading an application, managing a password, or maintaining an internet connection. The study emphasizes that the PBOC directed this design orientation during the pilot phase itself, meaning inclusion was not an afterthought bolted onto a finished product but a requirement shaping the engineering from the start. That is a notable departure from the pattern in many countries, where central banks treat inclusion as one consideration among many, and where private fintech innovation tends to serve the connected and creditworthy first.

Why would the world’s second-largest economy invest so heavily in a retail CBDC when it already has near-universal private mobile payments? Chin’s analysis points to a combination of motivations. Domestically, the e-CNY gives the state a payment rail it controls, reducing dependence on a small number of private platform companies and providing a public option in digital money. Internationally, Chinese policymakers and analysts have framed the project in terms of currency competition in the digital era and the long-term prospect of renminbi internationalization, although the study notes that the link between a retail CBDC and international currency status remains debated among Chinese economists themselves. There is also a standards dimension: China has proposed global rules for central bank digital currencies, positioning itself as a rule-shaper at the frontier of the technology rather than a follower.

The international spillovers are already visible. Hong Kong began allowing the digital yuan to be used in local shops in 2024, marking the first time the e-CNY crossed a border for everyday retail use. Chinese technology firms with deep telecommunications infrastructure across Africa have been identified by analysts as potential channels for digital currency adoption on the continent, and commentators have described China as leading Africa’s digital currency race. The Bank for International Settlements and the International Monetary Fund have both published extensive research on CBDCs as a potential new tool in the financial inclusion toolkit, and the e-CNY now serves as the most extensive real-world test case for the propositions in that literature. Agustín Carstens, the BIS general manager, has argued that the design of digital currencies touches on the very nature and soul of money, and China’s experiment is providing the empirical evidence for that debate.

The study also documents the scale and pace of adoption. PBOC governor Yi Gang reported in 2023 that e-CNY transaction volumes were seeing strong momentum, and by that point the pilots had expanded to dozens of cities across multiple provinces, covering payroll disbursements, transit, retail, and government services. The corporate ecosystem involved is broad: more than twenty companies were identified as participants in the underlying DC/EP development effort, spanning state banks, telecommunications operators, and technology firms. This public-private division of labor, in which the central bank owns the currency and the core ledger while partners build wallets, acceptance networks, and hardware, is itself a technical and institutional innovation that other central banks studying CBDC design are watching closely.

Still, the research is careful about limits and open questions. Rural adoption faces real challenges, including digital literacy gaps, merchant acceptance in low-density areas, and the persistence of cash preferences among some users, issues that Chinese industry reporting on the state of the e-CNY in rural areas has highlighted. The study does not claim that the digital yuan has solved financial exclusion, only that the PBOC has directed its design toward that goal with unusual seriousness. Whether offline cards, hardware wallets, and two-tier distribution can overcome the entrenched economics of rural banking remains to be seen, and the interplay between the e-CNY and the dominant private payment platforms is still evolving.

What the research ultimately establishes is that the global frontier of central bank digital currency is no longer theoretical. China has moved from concept to mass pilots to cross-border retail use, and it has done so with an explicit inclusion mandate for the elderly, the rural poor, and the unbanked. For the more than one hundred central banks now weighing their own digital currencies, the e-CNY offers the first large-scale demonstration that a CBDC can be engineered not just for efficiency or control, but as a deliberate instrument for bringing the financially marginalized into the formal economy. How well that engineering holds up in the villages and among the aging millions it was designed to serve will shape the next decade of monetary innovation worldwide.

Subject of Research: China's digital renminbi (e-CNY) central bank digital currency and its role in financial inclusion

Article Title: China’s ‘digital renminbi’ (e-CNY) as financial inclusion: the global frontier of central bank digital currency

Article References: Chin, G. T. (2025). China’s ‘digital renminbi’ (e-CNY) as financial inclusion: the global frontier of central bank digital currency. Global Public Policy and Governance, 5(1), 63-81. https://doi.org/10.1007/s43508-025-00112-0

Image Credits: AI Generated

DOI: 10.1007/s43508-025-00112-0

Keywords: central bank digital currency, digital renminbi, e-CNY, People's Bank of China, financial inclusion, China, offline payments, hardware wallet, rural finance, unbanked, monetary policy, fintech

Researchers led by Mohamed M. Mostafa of the Institute of Business Administration in Karachi, together with colleagues at the University of Manchester, Lamar University, and Texas Southern University, assembled a corpus of over one million user-generated reviews of open banking applications drawn from the Google Play Store. Rather than reading a sample and extrapolating, the team applied natural language processing at full scale, using two complementary computational techniques: structural topic modeling and sentiment analysis. The goal was to map what consumers actually talk about when they review these apps, how those themes relate to one another, and how strongly users feel about each of them.

Structural topic modeling is a statistical framework that treats every document, in this case every review, as a mixture of latent themes. The method, developed by political scientists and statisticians including Margaret Roberts, Brandon Stewart, and Dustin Tingley, improves on the classic Latent Dirichlet Allocation approach by allowing researchers to correlate topics with each other and with document-level metadata. The team also ran LDA, the workhorse algorithm of topic modeling, which assumes each document draws its words from a probability distribution over topics, and each topic a distribution over words. By comparing the two models and evaluating topic coherence, the researchers could identify dominant themes in the review corpus with statistical confidence rather than intuition.

The analysis surfaced a set of recurring topics that will feel familiar to anyone who has abandoned a banking app in frustration: customer service, data privacy, and digital support ranked among the most prominent. But the study went beyond simply listing themes. By mapping the inter-relationships between topics, the researchers showed how concerns cluster together, revealing the structure of consumer perception rather than just its surface. That structural view matters because it suggests that fixing one complaint in isolation may not resolve the underlying dissatisfaction if it is tightly linked to other grievances.

The sentiment analysis operated at multiple levels of granularity. At the coarsest level, the team measured overall polarity, classifying reviews as positive, negative, or neutral. They then drilled down to specific emotional tones, drawing on a tradition of emotion mining that includes lexicon-based methods such as SentiWordNet and the crowdsourced word-emotion association lexicon developed by Saif Mohammad and Peter Turney, as well as the psychoevolutionary theory of emotion proposed by Robert Plutchik. The result is a layered picture: two reviews may both be negative overall, but one may express anger about a failed transaction while the other expresses anxiety about data misuse, and those distinctions carry very different implications for how a provider should respond.

The technical pipeline also had to contend with the messy reality of app-store language. Reviews are short, informal, saturated with emojis, and often sarcastic, all of which challenge standard text-mining tools. The researchers’ decision to combine topic modeling with fine-grained emotion analysis reflects a broader trend in computational linguistics, where single-method studies are increasingly seen as insufficient for capturing the complexity of user-generated content. Prior work on mobile banking apps in countries from Morocco to Saudi Arabia and Turkey has used similar techniques, but the sheer scale of this corpus, over a million reviews, makes the new study the first of its kind for open banking applications specifically.

The findings arrive at a critical moment. Open banking’s promise depends on consumer consent: users must agree to share their financial data with third parties before the ecosystem can function at all. The study’s authors frame persistent issues of trust, data privacy, usability, and system reliability as the principal brakes on adoption, and their empirical results give those abstract categories concrete texture. When a million users write about privacy, they are not voicing a vague philosophical worry; they are describing specific experiences with specific apps, and the language they use reveals which aspects of data handling provoke the strongest emotional reactions.

For financial institutions and FinTech developers, the study offers a scalable framework for listening to customers continuously rather than episodically. Traditional satisfaction surveys capture a snapshot of opinion from a self-selected sample; app-store reviews capture a running stream of feedback from the people who actually use the product. The authors position this kind of user-centered data analytics as a strategic tool for enhancing engagement, mitigating user churn, and fostering adoption through transparency and targeted service design. In other words, the same reviews that public researchers mine for insight are, in principle, a free and constantly updating diagnostic instrument for the industry itself.

Regulators have reasons to pay attention too. The study explicitly aims to bridge the gap between regulatory ambition and consumer perception, and its methods could be adapted to monitor how sentiment shifts as new rules take effect. The literature on open banking spans questions of competition, credit access, and financial inclusion, from research on how customer data access enables FinTech entry to analyses of open banking’s impact on lending in emerging economies. Understanding the emotional register of consumer complaints adds a missing behavioral dimension to those policy debates, one that could help explain why legally mandated data sharing has not automatically translated into mass consumer uptake.

The research also contributes to a growing body of work that integrates computational linguistics with behavioral finance theory, treating text as data in the sense championed by Justin Grimmer and Brandon Stewart. That methodological lineage comes with caveats that the authors acknowledge through their careful model evaluation: topic models are sensitive to preprocessing choices, and coherence metrics must be interpreted with care. Still, the study demonstrates that when these tools are applied rigorously to a large, publicly available corpus, they can turn the noise of a million app reviews into a structured, actionable map of consumer sentiment. As open banking matures, the voice of the user, captured at scale and decoded by machines, may prove to be the most important dataset in the room. The data underlying the analysis is publicly available via the Google Play Store API, allowing other researchers to replicate and extend the approach.

Subject of Research: Consumer perceptions of open banking applications analyzed through topic modeling and sentiment analysis of user reviews

Article Title: Analyzing consumer reviews on open banking apps: insights from topic modeling and sentiment analysis

Article References: Mostafa, M. M., Feizollah, A., Sargsyan, G., & Shetty, S. (2026). Analyzing consumer reviews on open banking apps: insights from topic modeling and sentiment analysis. Neural Computing and Applications, 38(17), Article 727. https://doi.org/10.1007/s00521-026-12402-7

Image Credits: AI Generated

DOI: 10.1007/s00521-026-12402-7

Keywords: open banking, topic modeling, sentiment analysis, natural language processing, structural topic model, Latent Dirichlet Allocation, FinTech, data privacy, consumer trust, mobile banking apps, customer satisfaction, text mining

The research team, led by Manal Ali Almarashdah of Jadara University together with Ayman Abdalmajeed Alsmadi of Al Zaytoonah University of Jordan and Mohammad Ali Al-Afeef of Al Al-Bayt University, grounded their work in two complementary theoretical traditions: the resource-based view and dynamic capability theory. The resource-based view holds that valuable, rare and hard-to-imitate resources — such as an agile strategy process or a digitally committed leadership team — form the raw material of competitive advantage. Dynamic capability theory explains how firms sense opportunities, seize them and continuously reconfigure resources in fast-moving environments. In the study’s framework, strategic agility, digital orientation and innovation governance act as strategic resources, while FinTech capability maturity serves as the dynamic capability that converts those resources into measurable outcomes.

FinTech capability maturity is defined as an organization’s accumulated ability to systematically integrate, implement and continuously improve its use of financial technology — a progression that runs from basic digital adoption through data-driven innovation to fully integrated financial ecosystems. The authors argue that this construct fills a gap in the literature, which has often examined FinTech from the standpoint of customer adoption or technological implementation without identifying the organizational capability through which managerial decisions translate into performance. By positioning maturity as the central mediating capability, the study offers a unified explanation of how leadership actions produce long-term value in turbulent digital financial markets.

To test their model, the researchers surveyed 255 qualified informants working in Jordanian banks, financial institutions and FinTech organizations. Respondents included senior managers, digital transformation specialists, innovation managers, IT professionals, compliance officers and risk managers — all selected through purposive sampling to ensure direct knowledge of their organizations’ digital strategies and FinTech initiatives. The questionnaire contained 24 items across six constructs, adapted from previously validated measurement scales and reviewed by three academic experts before distribution. The sample size exceeded established requirements for partial least squares structural equation modeling, and the researchers assessed non-response bias and common method bias, finding no statistically significant differences between early and late respondents and full collinearity variance inflation factors below the recommended threshold of 3.3.

The analysis, conducted in SmartPLS 4, proceeded in two stages. The measurement models showed strong psychometric quality: all factor loadings exceeded 0.60, Cronbach’s alpha and composite reliability values surpassed the 0.70 benchmark, average variance extracted exceeded 0.50, and discriminant validity was confirmed using both the Fornell-Larcker criterion and the heterotrait-monotrait ratio. The structural model was then evaluated with bootstrapping based on 5,000 subsamples, testing five hypothesized direct relationships between the constructs.

The results were strikingly consistent. Strategic agility exerted the strongest influence on FinTech capability maturity, with a path coefficient of 0.406, followed by managerial digital orientation at 0.344 and innovation governance at 0.207, all statistically significant. Together, the three predictors explained 63.6 percent of the variance in FinTech capability maturity — a substantial share for organizational research. In turn, maturity showed powerful downstream effects: it explained 34.8 percent of the variance in service innovation performance, with a path coefficient of 0.590, and 46.0 percent of the variance in organizational responsiveness, with a coefficient of 0.678. Model fit statistics, including a standardized root mean square residual of 0.073, fell within acceptable limits, and all inner variance inflation values remained well below collinearity thresholds.

Each capability contributes through a distinct mechanism, the authors explain. Strategic agility enables firms to perceive technological shifts early and rapidly reallocate resources toward emerging FinTech opportunities, accelerating the adoption of artificial intelligence, blockchain and big data analytics. Managerial digital orientation shapes where money and attention flow: digitally oriented leaders champion experimentation, prioritize technology investments and cultivate cultures of continuous digital learning, which smooth the integration of FinTech solutions into existing operations. Innovation governance — the formal structures, policies and oversight committees that direct innovation activity — aligns technology investments with strategy, manages the risks of emerging technologies and helps organizations navigate the stringent regulatory demands of financial services, a factor the authors consider especially important in highly regulated markets like Jordan.

The downstream consequences are equally consequential for practice. Financial institutions with high FinTech capability maturity can fuse AI, blockchain and analytics with their service processes, producing personalized offerings, faster delivery and richer customer experiences — the hallmarks of superior service innovation performance. Maturity also underpins responsiveness: real-time data, advanced analytics and integrated digital systems allow firms to detect market shifts, react to regulatory changes, launch products quickly and coordinate responses across departments. The study situates these findings in Jordan’s rapidly digitalizing financial sector, where the Central Bank of Jordan has encouraged digital banking, mobile payments and regulatory technology, and where banks face simultaneous pressures from cybersecurity requirements, financial inclusion programs and FinTech entrants.

The authors draw pointed implications for policymakers and executives. Regulators should create innovation-friendly frameworks, including regulatory sandboxes, that let institutions experiment with AI, blockchain and open banking while safeguarding stability. Central banks are urged to embed digital leadership training and national FinTech awareness programs in digital transformation strategies. Financial institutions themselves are advised to benchmark their FinTech maturity regularly, tie digital objectives to executive performance evaluations, establish cross-functional innovation committees spanning business, technology, risk and compliance, and invest in scalable platforms and data-driven decision systems. Collaboration among banks, FinTech firms, universities and technology providers, the study adds, can accelerate knowledge diffusion and raise digital competence across the entire sector.

The researchers acknowledge limitations that temper interpretation. The cross-sectional design cannot establish causality over time, the Jordanian context may limit generalizability to other economies, and self-reported survey data carries residual bias risks despite procedural and statistical safeguards. The study tested only direct relationships and three predictors, leaving room for future work on mediators such as organizational learning and AI adoption, moderators like environmental uncertainty, and longitudinal designs tracking capability development. Even so, the core message stands out with unusual clarity: in emerging digital financial markets, sustained advantage flows not from technology purchases but from the managerial capabilities — agility, digital leadership and disciplined governance — that turn FinTech potential into organizational performance.

Subject of Research: How managerial capabilities drive FinTech capability maturity and organizational performance in financial institutions

Article Title: Strategic agility and digital orientation as drivers of FinTech capability and organizational performance

Article References: Strategic agility and digital orientation as drivers of FinTech capability and organizational performance. (n.d.). https://doi.org/10.1007/s44282-026-00581-6

Image Credits: AI Generated

DOI: 10.1007/s44282-026-00581-6

Keywords: FinTech, strategic agility, digital orientation, innovation governance, dynamic capability theory, resource-based view, digital transformation, service innovation, organizational responsiveness, Jordan, banking, PLS-SEM

Professor Hongbin Cai, Dean of HKU Business School and Chair of Economics, framed the launch as the continuation of a strategy that began nearly a decade ago. In his welcome remarks, he noted that the school has been nurturing future wealth management professionals since 2017, and that wealth management has become increasingly important in economic development as Hong Kong leverages its unique position connecting the Chinese Mainland with the rest of the world. He added that the sector will be of even greater significance in Hong Kong’s upcoming Five-Year Plan, signalling that the government’s own planning documents are expected to elevate the industry’s role in the city’s economic future.

The dean was candid about the environment in which the Academy will operate. Hong Kong, he said, is navigating an era filled with uncertainties, shaped by rapid changes in the global landscape and the fast-paced evolution of artificial intelligence. His argument was that the city must respond to these challenges by continuously strengthening its overall competitiveness, and that the Academy is designed as part of that response. By providing an international platform that brings together government, industry, academia and research, the institution aims to act as a key driver of the wealth management ecosystem, contributing through three channels that the school identifies as thought leadership, talent development and industry engagement.

The timing of the launch was a central theme of the ceremony. Christopher Hui, Secretary for Financial Services and the Treasury, delivered the keynote address and told the audience that the Academy’s establishment comes at a pivotal moment as the industry enters a new chapter. He described the idea of connecting the dots as both a fitting ambition for the Academy and a timely one for Hong Kong’s current development needs. In his telling, the city having emerged as the world’s largest cross-boundary wealth management centre immediately raises a follow-up question: what kind of leadership does that top position actually represent? It is a framing that shifts the conversation from raw market size to the quality of the professional ecosystem that sustains it.

Hui also used the address to outline the policy work under way in government. He said continuous policy enhancements include refining the preferential tax regimes for funds, single family offices and carried interest, broadening the range of qualifying investments to cover private credit, digital assets and precious metals, and strengthening connectivity with Chinese Mainland markets. Notably, he argued that the growth of the wealth management sector should reach beyond the assets it manages, extending into skilled careers, investment in enterprises and support for communities. That wider definition of success matters for a university-led academy, because it positions education not merely as a pipeline for asset managers but as a contributor to the sector’s broader social and economic footprint.

The ceremony also featured a panel discussion under the theme of what comes next for Hong Kong after reaching number one. Three speakers took part: Joseph Chan, Under Secretary for Financial Services and the Treasury; Aveline San, Chief Executive Officer and Banking Head of Citi Hong Kong, Chair of Citibank (China) and Convenor of the Financial Services Development Council’s Human Capital Committee; and Lok Yim, Regional Head of HSBC Private Bank, Asia Pacific. The discussion was moderated by Professor Anna Wong, Director of the HKU Business School Wealth Management Academy, whose own appointment to lead the new institution reflects the school’s decision to place an academic at the centre of its industry-facing agenda.

Chan laid out three priorities for consolidating Hong Kong’s position as the leading international wealth management centre. The first is to proactively reach out to more jurisdictions to showcase the city’s unique advantages. The second is to continuously enhance policy in line with market development trends such as fintech and green finance so that competitiveness is maintained as the industry evolves. The third is to strengthen connections with the global financial sector and with different markets. Taken together, the three points sketch a strategy in which regulatory agility and international outreach reinforce each other, with Hong Kong competing on both product breadth and access rather than on any single advantage.

On the question of people, Chan offered a measured view of the sector’s most pressing constraint. He acknowledged that a talent shortage is a possible bottleneck for the industry, but argued that it also reflects the sector’s rapid growth rather than decline. Alongside government training initiatives, he said, the education sector can play a key role in helping young talent upskill and gain industry exposure through internship programmes that meet the sector’s evolving demands. That is precisely the territory the new Academy intends to occupy, and its programme of activities suggests a deliberately practical orientation rather than a purely academic one.

The Academy builds on an existing educational foundation. In 2017, HKU Business School launched the Bachelor of Finance in Asset Management and Private Banking, the first undergraduate programme of its kind in Asia, followed by the Master of Family Wealth Management and the Master of Wealth Management programmes. With the Academy now in place, the school’s activities, supported by Bank of China (Hong Kong), will include directing students toward internship opportunities, organising salons, forums and a wealth management challenge, and offering scholarship programmes. The institution also plans to facilitate engagement between students and industry professionals through talks and other activities designed to foster collaboration between industry and academia and to enable knowledge exchange. An advisory committee comprising industry representatives and scholars specialising in finance, investment and wealth management will advise on the development of the Academy’s programmes and its strategic direction.

For HKU Business School itself, the launch adds a specialised institution to a portfolio that has been climbing the research rankings. Established in 2001, the school is ranked number one in Asia and number six globally in the 2025 UTD Business School Research Rankings, and number one in Asia and number eleven globally in the 2026 Financial Times Research Ranking. It holds accreditation from both EQUIS and AACSB and operates an international network extending to Beijing, Shanghai, Shenzhen and Ho Chi Minh City. The significance of the new Academy, however, may lie less in rankings than in the model it represents: a formal, funded bridge between a government that is rewriting tax and investment rules, a financial industry that is short of skilled people, and a university that has been building wealth management curricula since 2017. If Hong Kong is to translate its newly won number-one status into durable leadership, the Academy’s founders are betting that the decisive variable will be talent, and that the university is the place to produce it.

Subject of Research: University-led wealth management education and talent development in Hong Kong

Article Title: HKU Business School launches the first wealth management academy nurturing future leaders and strengthening Hong Kong’s global leadership

Article References: HKU Business School launches the first wealth management academy nurturing future leaders and strengthening Hong Kong’s global leadership. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: HKU Business School, wealth management, Hong Kong, Bank of China, financial services, talent development, private banking, asset management, family offices, fintech, green finance, financial services policy

The research arrives at a pivotal moment for Bangladesh. Mobile financial services such as bKash and Nagad now process more than USD 20 billion in transactions every month, and smartphone coverage reached roughly 70 percent in 2023, creating an enormous trove of digital transaction data. Yet the banking sector remains hampered by persistent inefficiencies: slow loan processing, weak monitoring, and a non-performing loan ratio of 8.16 percent in 2023. While digital payments have flourished, the integration of FinTech into core loan management, including origination, tracking, and recovery, has lagged far behind, leaving a critical gap between the country’s payment revolution and its lending operations.

To bridge that gap, the research team extended the classic UTAUT framework, originally developed by Venkatesh and colleagues, by adding Loan Performance as a final endogenous variable. The original model posits that Performance Expectancy, the belief that technology improves job performance; Effort Expectancy, the perceived ease of use; and Social Influence, the pressure from peers, managers, and regulators, jointly shape Behavioral Intention to adopt a technology. Behavioral Intention and Facilitating Conditions, which cover infrastructure, training, and technical support, then drive actual Use Behavior. The researchers argue this original formulation fits institutional banking better than the consumer-oriented UTAUT2, because bank adoption decisions hinge on efficiency and performance rather than hedonic motivation or price value.

The data were collected through structured questionnaires administered both physically and electronically, targeting bank officials, loan officers, IT staff, and FinTech users across all major divisions of Bangladesh, including Dhaka, Chattogram, and Khulna. Of 440 initial surveys, 354 valid responses were retained, an 80.45 percent retention rate that comfortably exceeds methodological minimums for partial least squares structural equation modeling. Respondents were experienced and well qualified: nearly half had more than fifteen years in the industry, and most held graduate degrees or professional banking credentials. All constructs were measured on seven-point Likert scales adapted from validated prior instruments and refined through a pilot test with thirty respondents.

The statistical analysis, conducted with SmartPLS and verified with STATA, revealed a clear and statistically significant causal chain. Performance Expectancy exerted a strong effect on Behavioral Intention with a path coefficient of 0.381, while Effort Expectancy was the single most powerful adoption driver at 0.438, underscoring that in a market with uneven digital literacy, user-friendly systems matter enormously. Social Influence contributed a smaller but still significant 0.193. On the next stage of the chain, Facilitating Conditions dominated with a coefficient of 0.615, and Behavioral Intention added 0.380 in predicting actual Use Behavior. Most strikingly, Use Behavior predicted Loan Performance with a coefficient of 0.898, confirming that the benefits of FinTech materialize only through consistent, effective use rather than mere intention.

The explanatory power of the model was remarkable across every stage. Behavioral Intention accounted for 82.9 percent of the variance, Use Behavior for 88.8 percent, and Loan Performance for 80.7 percent, all well above the 0.75 threshold conventionally regarded as strong. Measurement quality checks were rigorous: indicator loadings, composite reliability values between 0.880 and 0.942, and average variance extracted figures above 0.50 confirmed convergent validity, while heterotrait-monotrait ratios all fell within the acceptable 0.90 limit. Harman’s single-factor test and full collinearity assessments indicated that common method bias was not a serious concern, and robustness checks re-estimating the model without high-collinearity items produced consistent results.

The theoretical contribution is substantial. Most previous UTAUT research stopped at intention or usage, and most FinTech performance studies examined macro-level indicators such as profitability without grounding them in adoption theory. By linking individual-level acceptance constructs to institutional loan outcomes through a behavior-to-performance pathway, the study converts UTAUT into a performance-oriented institutional model. The mediation analysis is particularly instructive: Behavioral Intention affects Loan Performance indirectly, only through Use Behavior, demonstrating that strategic financial gains require the transformation of positive attitudes into daily operational practice.

The findings also carry important cautions. The authors emphasize that FinTech adoption exposes banks and borrowers to significant risks, including phishing, identity theft, ransomware, fraudulent loan applications, and algorithmic bias that could disadvantage rural borrowers, women, and small enterprises with thin credit files. Data privacy, transparency in automated lending decisions, responsible affordability assessments, and accessible grievance mechanisms are essential to sustaining trust. Bangladesh’s substantial Islamic banking sector adds a further layer, since digital lending products must comply with Shariah principles and offer clear contractual terms. Regulatory foundations such as Bangladesh Bank’s ICT Security Guidelines, mobile financial services regulations, and updated electronic know-your-customer requirements provide a starting framework, but regulatory clarity for emerging tools like peer-to-peer lending and blockchain-based credit remains uncertain.

For policymakers and bank leaders, the practical implications are direct. Because Effort Expectancy proved so influential, banks should invest in intuitive, well-tested lending platforms and pair them with digital literacy programs for staff and customers. The dominance of Facilitating Conditions argues for sustained spending on IT infrastructure, reliable connectivity, and technical support, especially in rural branches, alongside cybersecurity measures such as multi-factor authentication, encryption, real-time fraud detection, and rapid incident response. Social Influence, though weaker, can be harnessed through leadership-led digital transformation targets, industry-wide standards, and visible success stories that normalize FinTech-based lending.

The study does have limitations that temper interpretation. It relies on self-reported, perceptual measures of loan performance, since bank-level financial data are confidential, and it treats all banks as operating under comparable conditions despite real differences between public, private, and specialized institutions. Its cross-sectional design cannot capture how FinTech benefits accumulate over time, and moderating factors such as organizational culture, customer trust, and regulatory constraints were not modeled explicitly. Future research using objective indicators such as non-performing loan ratios and recovery rates, along with longitudinal and moderated designs, would strengthen the evidence. Even so, the Bangladesh experience offers a compelling template for other developing economies: locally adapted digital innovation, when paired with infrastructure, training, and prudent regulation, can convert technology acceptance into faster, safer, and more inclusive lending.

Subject of Research: FinTech adoption and bank loan performance in Bangladesh using the UTAUT model

Article Title: The role of FinTech in enhancing bank loan performance in Bangladesh through the UTAUT model

Article References: The role of FinTech in enhancing bank loan performance in Bangladesh through the UTAUT model. (n.d.). https://doi.org/10.1007/s44282-026-00573-6

Image Credits: AI Generated

DOI: 10.1007/s44282-026-00573-6

Keywords: FinTech, Bangladesh, UTAUT, bank loan performance, digital lending, structural equation modeling, behavioral intention, use behavior, financial inclusion, cybersecurity, mobile financial services, emerging economies

The core problem the study addresses is one of averages. Most previous analyses of technology and pollution rely on average-effect estimates, which assume that a variable’s influence on carbon intensity is uniform whether emissions are at their lowest or their highest. The authors argue that this assumption is untenable. FinTech, digitalization, ICT and green technology do not act homogeneously; their environmental consequences can differ significantly depending on the level of carbon intensity in a given period. An economy experiencing unusually high emissions may respond to digital investment very differently from one operating near its cleanest historical performance, and an average-effect model would blur precisely the distinctions policymakers need to understand.

To capture this heterogeneity, the team deployed an unusually rich methodological arsenal. Their central technique is Quantile on Quantile Regression (QQR), which estimates how different quantiles of each explanatory variable interact with different quantiles of carbon intensity, effectively mapping the relationship across the full joint distribution rather than at a single average point. They complement this with Multivariate Quantile on Quantile Regression (MQQR), which assesses the combined effect of the technological ecosystem as a whole, and with two time-frequency tools borrowed from signal processing: Wavelet Coherence (WC) and Wavelet Quantile Correlation (WQC). These wavelet methods allow the researchers to track how strongly the variables move together across short-, medium- and long-run periods throughout the 23-year sample, revealing dynamics that a fixed-coefficient regression would miss entirely.

The results are striking in their asymmetry. At lower quantiles of carbon intensity—periods when emissions were relatively low—FinTech exhibits weaker and statistically insignificant effects on carbon intensity. In other words, digital finance does not appear to be a reliable emissions-reduction tool when the economy is already running clean. This finding complicates the popular narrative that fintech innovation automatically greets environmental goals; its leverage appears conditional rather than constant. For regulators, the implication is that promoting FinTech as a climate instrument requires attention to the prevailing emission regime, not just to the volume of digital financial activity.

Digitalization tells a clearer story. Its effect on carbon intensity is mostly negative, and most pronounced at high emission quantiles—precisely where reductions matter most for climate outcomes. When China’s carbon intensity was at its dirtiest, the expansion of digital processes, automation and data-driven efficiency delivered some of its strongest curbing effects. This makes intuitive technical sense: digitalization substitutes information flows for material ones, optimizes logistics, reduces transaction frictions and enables real-time monitoring of energy use, and these mechanisms bite hardest when inefficiency and emissions are at their peak. Digitalization, the study suggests, is a technology whose climate dividend scales with the size of the problem it confronts.

ICT proves the wildcard. Unlike the other variables, information and communication technology exhibits a nonlinear and more complex pattern across quantiles of carbon intensity. Its effect flips and shifts in strength depending on where in the distribution the economy sits, reflecting what environmental economists have long described as a double-edged dynamic. ICT can compress carbon intensity by enabling dematerialization and smarter infrastructure, yet it also expands the electricity-hungry footprint of networks, servers and devices. The study’s quantile-based evidence shows that neither a uniformly optimistic nor a uniformly pessimistic reading of ICT is defensible; its environmental role depends on the emission context in which it operates.

The clearest winner is green technology. Across almost all quantiles of carbon intensity, Gtech shows the strongest and most stable negative effect of any explanatory variable in the model. Green technology patents and innovations—covering renewable energy, energy efficiency and pollution control—consistently reduce emissions per unit of output regardless of whether the economy is running clean or dirty. This stability is the crucial technical distinction: while FinTech’s effect fades at low emission levels and ICT’s effect oscillates, green technology behaves like a dependable structural reducer of carbon intensity. For policymakers deciding where to concentrate research funding and industrial policy, that consistency is a powerful argument.

Perhaps the most consequential finding comes from the multivariate analysis. The MQQR results reveal that the overall effect of the technological ecosystem—FinTech, digitalization, ICT and green technology acting together—is more emission-reducing than the sum of the individual impacts estimated by conventional QQR. The technologies reinforce one another: digital infrastructure amplifies the diffusion of green patents, fintech channels capital into clean innovation, and ICT enables the monitoring and optimization that make both effective. This complementarity means that treating these tools as separate policy levers understates their potential. An integrated technological strategy, in which digital and financial innovation are designed to accelerate green technology deployment, delivers a greater climate payoff than the same investments made in isolation.

The wavelet analysis adds the temporal dimension that annual averages conceal. By examining coherence across short-, medium- and long-run periods for the full sample, the study shows that the relationships among these technologies and carbon intensity are not static over the two decades examined. China’s economic transformation—from WTO accession in 2001 through its industrial supercycle to its dual-circulation digital era—means that the technology-emission nexus itself evolved, with structural breaks detected in the data confirming regime changes along the way. Time-frequency methods of this kind are increasingly seen as essential in energy economics, because policy prescriptions drawn from one era may simply not transfer to another.

The study’s implications align explicitly with the United Nations Sustainable Development Goals, particularly SDG 7 on affordable and clean energy, SDG 9 on industry, innovation and infrastructure, SDG 12 on responsible consumption and production, and SDG 13 on climate action. For a country that produces roughly a third of global carbon emissions while simultaneously leading the world in renewable energy capacity, electric vehicle manufacturing and digital payments, understanding which technologies actually bend the carbon-intensity curve—and under what conditions—is a question of global significance. The authors’ evidence underscores the significance of FinTech, digitalization, ICT and green technology in supporting the transition to energy-efficient and low-carbon economic systems in China. The broader lesson for the international community is methodological as much as substantive: in a world of nonlinear, state-dependent environmental dynamics, the era of single-coefficient answers is over, and quantile-aware, time-aware analysis is becoming the standard against which credible climate-economics research will be judged.

Subject of Research: The asymmetric effects of FinTech, digitalization, ICT and green technology on carbon intensity in China.

Article Title: Decoding carbon intensity in China through FinTech, digitalization, and green technology

Article References: Tariq, A., Khan, N., Shah, S. Z. A., & Rizwan, M. F. (2026). Decoding carbon intensity in China through FinTech, digitalization, and green technology. Discover Sustainability. https://doi.org/10.1007/s43621-026-04587-7

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04587-7

Keywords: carbon intensity, China, FinTech, digitalization, ICT, green technology, quantile on quantile regression, wavelet coherence, climate policy, SDG 13, energy transition, emissions