Kabilesh C M, Dr. B. Raja, Dr. S. Geetha, Dr. V. Cyrilraj
As decentralized finance (DeFi) continues to scale, traditional forensic methodologies often fail due to their retrospective, "post-mortem" nature, analyzing illicit activities only after they are permanently recorded on the ledger. This project proposes coinEth, a real-time institutional blockchain surveillance and autonomous defense system designed for the Ethereum Sepolia network. The framework operates across a four-layer architecture: a Data Acquisition Layer that intercepts pending transactions via Alchemy WebSockets (WSS); a Persistence and Forensic Engine that utilizes SQLite and Python-based heuristics to detect suspicious behavioral patterns such as "structuring" and "high velocity"; a Governance Layer that executes an autonomous enforcement loop via a Solidity-based "Gatekeeper" smart contract; and a Visualization Layer built with Streamlit and PyVis. By assigning dynamic risk scores—categorized as Safe (Level 0), Warning (Level 1), and Frozen (Level 2)—the system can automatically broadcast on-chain transactions to freeze illicit accounts before fund exfiltration occurs. Furthermore, coinEth reconstructs a chronological "money trail" through sequential path mapping (T0 → T1 → T2...), ensuring a verifiable digital chain of custody for investigative reporting. This proactive approach shifts blockchain security from passive observation to active, real-time intervention, significantly enhancing the defense mechanisms available to institutional stakeholders.
Blockchain technology has emerged as one of the most transformative innovations of the digital economy, extending far beyond cryptocurrencies into sectors such as finance, healthcare, logistics, governance, and intelligent automation. This study critically examines the role of blockchain technology in enhancing global economic growth through decentralization, transparency, cybersecurity, smart contracts, and digital trust mechanisms. Drawing upon contemporary literature and emerging industrial applications, the paper explores how blockchain contributes to economic resilience, operational efficiency, supply chain optimization, decentralized finance (DeFi), central bank digital currencies (CBDCs), and AI-integrated digital ecosystems. The study adopts a conceptual and analytical approach to evaluate blockchain’s macroeconomic implications and institutional challenges in the context of Industry 4.0. Findings suggest that blockchain has the potential to reduce transaction costs, enhance cross-border economic integration, improve governance transparency, and facilitate sustainable digital transformation. However, regulatory uncertainty, scalability limitations, cybersecurity concerns, and energy consumption remain significant barriers to global adoption. The paper contributes to the literature by proposing an integrated framework linking blockchain innovation with economic sustainability, digital governance, and technological resilience. Policy implications and future research directions are also discussed.
The metaverse is emerging as a complex digital ecosystem enabled by artificial intelligence, immersive technologies, blockchain, and advanced computing infrastructures. As global scientific and technological competition intensifies, this expansion introduces critical challenges related to security, privacy, and governance. This chapter examines how scientific rivalry shapes the security architecture, regulatory frameworks, and power structures governing metaverse environments. Key risks such as biometric data exposure, avatar impersonation, decentralized finance fraud, and emerging quantum-enabled cyber threats are analyzed alongside issues of digital sovereignty and platform control. This chapter proposes a multi-layered security and governance framework integrating decentralized identity systems, AI-driven threat intelligence, privacy-preserving design principles, and global interoperability standards.
Ethics and Social Impacts of AI
Law, AI, and Intellectual Property
Legal, Health, Environmental and COVID-19 Challenges
Promoting harmonious interaction between human beings and the ecological environment has become a key issue for achieving sustainable development. Given the cross-regional mobility of resources and production activities, a single region cannot merely rely on its own efforts to balance economic expansion and carbon reduction. In this context, Digital finance can play a key role in improving information connectivity, facilitating green capital allocation, and reducing transaction costs for cross-regional low-carbon collaboration. Against this background, this study introduces and quantifies regional coordinated emission reduction potential by integrating economic ties, geographical proximity, and interregional carbon emissions from a network perspective. By using the panel fixed effect model, the study explored how digital finance shapes this potential and identified the energy-related carbon consumption structure as a transmission mechanism. Findings reveal that the carbon emission spillover effect is most powerful under the combined influence of economic similarity and geographical proximity. Digital finance significantly enhances the potential for regional coordinated emission reduction, and the effect is even stronger in provinces with lower potential for coordinated emission reduction or weaker fiscal decentralization. The level of digitalization and the depth of usage have a greater influence than the breadth of coverage. In addition, the energy-related transmission channels exhibit clear heterogeneity. The coal-related emission channel provides relatively stronger evidence, whereas the gas-related channel shows a countervailing effect.
Amid intensifying climate change and rapid urbanization, urban ecosystems face unprecedented disturbances. Enhancing urban ecological resilience (UER) is thus a key governance issue. The explosive growth of digital finance (DF) has reshaped capital flows, but its ecological effects are dynamic and stage-specific. This study uses a dynamic nonlinear model to systematically explore the DF-UER relationship. The results reveal that: (1) There exists a U-shaped relationship between DF and UER. In the early stage, DF may negatively impact UER; however, Whereas as DF matures, it positively influences UER, with robust results from various tests. (2) Heterogeneity analysis indicates that the U-shaped relationship between DF and UER is more pronounced in smart city pilot cities, high government ecological attention cities, and cities with a high degree of market integration. (3) Mechanism tests reveal that DF has a U-shaped impact on green capital allocation, renewable energy technology innovation, and industrial structure optimization, consequently leading to a U-shaped effect on UER. (4) Fiscal decentralization and financial regulation strengthen the U-shaped relationship, whereas artificial intelligence tends to weaken it and may even reverse this trend. These findings point out the possible structural friction of DF, and provide valuable insights for supporting China and other economies to balance financial expansion and ecological security.
With the rapid development of digital finance, the mode of enterprise credit risk assessment has changed, and now also requires methods that can handle large-scale, diverse data and smart computation. The old system of credit rating has been based on the results of past financial reports and is no longer suitable for evaluating the changes and risks in modern corporate finance. This paper proposes a multi-dimensional model for mining credit reports of mining enterprises and combines structured financial data, transaction information, operating indicators, and other unstructured auxiliary data such as social media presence, online communication, supply chain dynamics, etc. By building a relatively detailed credit report, the bank can gain some information on the risk of a company's credit and its repayment ability for a loan. Algorithms that use machine learning, deep learning, ensemble models and predictive analysis are also known as intelligent default risk assessment algorithms that enhance the accuracy and flexibility of credit assessment. The following are ways to discover abnormal or complex patterns in a large amount of data early on for risk early warning, online credit assessment and dynamic portfolio management. Interoperability of digital finance platforms can support lifelong learning, automation and scalable high-frequency financial data, and maintain security, privacy and regulatory compliance. Although the above have been achieved, there are still deficiencies in the quality of data, interpretability of models, adherence to regulations, and sufficient computational resources, especially for small and medium-sized enterprises and new market institutions. Future research directions include building explainable AI systems, continuous learning, integrating multiple types of data (multimodality), and decentralized finance (DeFi) based on blockchains. At this point, the above technologies are expected to help enterprises strengthen credit risk management in the age of digital finance and provide more accurate and timely credit evaluations.
Abstract The increasing use of residential distributed energy resources (DERs), including photovoltaic generation, electric vehicles, stationary batteries, and flexible electric water heaters, is changing the operation of low-voltage distribution networks. These resources can reduce household electricity costs and provide demand-side flexibility; however, uncoordinated operation can result in voltage deviations, network congestion, line overloading, and stress on distribution transformers. This paper presents a code-grounded comparison of three DER coordination architectures: decentralized household-level optimization, centralized feeder-level optimal power flow (OPF), and hierarchical aggregator-based coordination. The benchmark is conducted on a three-phase low-voltage distribution network with 157 residential households over a 24-hour horizon. The decentralized architecture optimizes each household individually, and the resulting network behavior is validated in OpenDSS. In the centralized architecture, detailed household DER models are integrated into a nonlinear AC-OPF formulation. The hierarchical architecture coordinates prosumer flexibility using local flexibility estimation, network-level activation, allocation, household-level tracking, and final OpenDSS validation. For the studied feeder, the results indicate that the hierarchical architecture provides the most favorable physically validated trade-off between customer economics and network performance among the three architectures considered. Compared with decentralized control, it decreases the mean daily household electricity cost from 6.74 to 6.14 GBP/home/day (an 8.9% reduction), reduces network peak demand by 20.4%, reduces the worst line-loading ratio by 15.3%, and reduces transformer loading by 19.0%. In this case study, all 157 households obtain lower daily electricity bills than under the decentralized baseline, indicating that the incentive-based coordination mechanism can align network-level objectives with individual customer benefits under the modeled conditions. The centralized OPF achieves the strongest nominal network performance but relies on restrictive assumptions regarding full DER observability and controllability and does not provide the same customer-level autonomy, compensation, or implementation pathway as the hierarchical framework; it is therefore treated as an idealized network benchmark rather than a directly deployable controller. It should be noted that the hierarchical architecture reduces, but does not fully eliminate, network constraint violations and that these findings are obtained for a single feeder under deterministic daily input profiles. Overall, the results suggest that DER coordination strategies should be assessed using a holistic framework that considers customer cost, network security, flexibility realization, fairness, computational effort, and physical validation, and that, under this broader evaluation, hierarchical coordination offers a practical compromise between decentralized autonomy and centralized optimality for the studied network.
Thierry Claudien Uhawenimana, Stella M. Umuhoza, Jean Bosco Ndayambaje, Yvonne Delphine Nsaba Uwera · 15 authors
BACKGROUND: Visual impairment is a major global public health concern, disproportionately affecting populations in low- and middle-income countries (LMICs), where access to eye care services remains limited. In 2019, the Rwandan Ministry of Health integrated eye care services into the Performance-Based Financing (PBF) framework to improve accessibility, service quality, and utilization. However, service users' perspectives on this initiative remain underexplored. This study aimed to examine service users' perceptions of accessibility, quality, affordability, satisfaction, and challenges in accessing eye care services at primary healthcare facilities. METHODS: An exploratory qualitative study was conducted in six purposively selected health centers in Rwanda. A total of 72 participants were recruited using maximum variation sampling, including older adults, individuals with disabilities, and participants from different socioeconomic backgrounds. Data were collected through six focus group discussions (FGDs), each comprising 8-12 participants. Discussions explored perceptions of accessibility, quality, affordability, satisfaction, and recommendations for improvement. Audio recordings were transcribed, translated into English, and analyzed inductively using Dedoose software. Trustworthiness was ensured through prolonged engagement, member checking, peer debriefing, and triangulation. RESULTS: Participants reported that the decentralization of eye care services to health centers improved geographical access and reduced travel time. However, financial barriers remained a major constraint, particularly due to the cost of eyeglasses and certain medications not fully covered by Community-Based Health Insurance (CBHI). Users also described inconsistencies in service quality, including frequent stock-outs of essential supplies, limited equipment, and variability in the availability of eye care personnel. Despite these challenges, many participants expressed satisfaction with provider attitudes and interpersonal care, highlighting respectful and supportive interactions with health workers. At the same time, dissatisfaction was reported due to systemic inefficiencies such as limited service days, understaffing, long waiting times, and referral-related burdens. CONCLUSIONS: Findings indicate that decentralization may have contributed to improved geographic accessibility and perceived service responsiveness; however, these improvements are uneven and remain constrained by financial and system-level barriers. Addressing these barriers through expanded service packages, reliable supply chains, human resource strengthening, CBHI coverage, and community education is critical to optimize equitable eye-care delivery.
K. Karthikeyen, S. Irfan Kasim, B. M. Hareez, R. Regin · 6 authors
The problem is that many current crowdfunding frameworks overly rely on centralized management and lack transparency regarding how funds flow. Filling that vacuum, researchers introduce CrowdChain as an innovative funding framework implemented solely on the Ethereum blockchain. Rather than a centralized server, the system will leverage Smart Contracts written in the Solidity programming language for transactions, voting, and status tracking, which cannot be tampered with later. Instead of loading large files, only their URLs will be stored to avoid additional transaction costs. The primary mechanism will focus on developing a new procedure for approving milestones. In this procedure, the funds will be securely held in online vaults until more than half of the participants approve their release. Researchers will ensure that CrowdChain integrates seamlessly with MetaMask by using Next.js and Ethers.js for the frontend. This will make it easier for users to participate on the platform. On-chain governance, when combined with off-chain data, has been shown to dramatically reduce operational expenses without sacrificing security or traceability, according to the tests conducted.
Blockchain oracles bridge on-chain smart contracts and off-chain data sources, but encrypted off-chain data still raises two practical challenges: how to verify retrieval integrity without exposing sensitive values, and how to keep verification information fresh when the off-chain data set changes. Existing oracle and outsourced-database retrieval mechanisms often rely on plaintext verification, heavy cryptographic proofs, or static authentication structures, which limits their applicability to latency-sensitive IoT and decentralized finance scenarios. To address these issues, this paper proposes a retrieval integrity verification mechanism based on CKKS approximate homomorphic encryption and an authenticated index named CKKS-Auth Tree. The proposed mechanism verifies encrypted query results through homomorphically aggregated metadata, while smart contracts record versioned verification commitments to detect stale or replayed results after updates. The scope of the mechanism is the integrity, completeness, privacy, and freshness of data after commitment and upload; verifying the physical authenticity of the original data source is outside the core threat model. Experimental results show that the proposed scheme reduces authentication and verification overhead compared with existing retrieval verification methods while supporting encrypted metadata updates and on-chain synchronization.
Sai Srikanth Madugula, Peplluis Esteva De La Rosa, Daya Shankar
The integration of machine learning into decentralized finance (DeFi) credit assessment is frequently undermined by opaque algorithms and severe methodological flaws regarding data leakage. This paper presents a rigorous, fully reproducible framework for explainable artificial intelligence (XAI) in invoice-backed default risk modeling. Utilizing a highly imbalanced dataset of 12,000 corporate loan originations, we engineer an XGBoost ensemble model that achieves an AUC-ROC of 0.89. We systematically eliminate the pervasive data leakage associated with the Synthetic Minority Over-sampling Technique (SMOTE) by implementing a dynamic crossvalidation pipeline, ensuring synthetic data generation is strictly isolated to training folds. To satisfy institutional accounting standards for expected loss (e.g., IFRS 9), we mathematically formulate and validate the Expected Calibration Error (ECE), achieving a highly calibrated probabilistic output of 0.08. Furthermore, we extract local explanations using SHAP (SHapley Additive exPlanations), imposing strict constraints on the background reference dataset to guarantee mathematical additivity and prevent stochastic approximation transitions. Our findings reveal that Days Payment Outstanding (DPO) and invoice age are primary default drivers, while on-chain reputation effectively mitigates perceived risk. Finally, we address critical privacy vulnerabilities, mathematically modeling Membership Inference Attacks (MIAs) on synthetic records. This work establishes a regulatory-compliant, structurally sound ML foundation for permissionless credit provision.
Cryptocurrencies have become an important variable in the global financial system. With the maturity of blockchain technology, new applications such as stablecoins, Decentralized Finance (DeFi), Non-Fungible Tokens (NFTs) and Real-World Asset (RWA) tokenization have emerged continuously, and the crypto-asset system has gradually formed a multi-layered and multi-functional complex structure. However, as the market scale expands, problems such as price volatility risks, systemic financial risks and illegal financial activities have become increasingly prominent, prompting the continuous evolution of regulatory policies in various countries. Especially after the concentrated outbreak of multiple industry risk incidents around 2022, the global regulatory attitude has been significantly tightened, and the regulatory framework has gradually evolved from fragmentation to systematization. At the same time, Central Bank Digital Currencies (CBDCs) have entered an important stage of transition from experimental research to large-scale pilots, becoming one of the core paths for the digital transformation of national monetary systems. This paper systematically sorts out the evolutionary logic of cryptocurrencies, compares the changes in regulatory policies of major countries and regions, conducts an in-depth analysis of the development trends of CBDCs and the changes in the regulatory structure of crypto-assets based on the latest global practices from 2020 to 2026, and further explores the evolutionary direction of the asymmetric regulatory framework.
Alireza Kavousi, István András Seres, Zhipeng Wang
Regulatory compliance is increasingly mandatory for decentralized finance and privacy-enhancing technologies. Current approaches rely on binary inclusion/exclusion lists or retroactive graph analysis by centralized blockchain intelligence firms. This approach strips honest users of their financial privacy, leads to false positives and negatives, and forces decentralized platforms to bear the burden of on-chain transaction monitoring. In this work, we propose a paradigm shift: moving from platform-side surveillance to user-side provenance. We introduce Proof of Source of Funds (PoSoF), a novel cryptographic framework that shifts the burden to the user. Rather than the platform tracing funds, the user locally generates a zero-knowledge proof demonstrating that their deposit originates exclusively from a set of compliant sources. The platform is thus relieved of chain-analysis duties, requiring a constant-time, O(1) verification to enforce admission control. We formulate a unified temporal Directed Acyclic Graph (DAG) abstraction that formalizes both UTXO and account-based ledger histories within a generalized value-flow model. Users extract a compliant sub-DAG of their transaction history and utilize Incrementally Verifiable Computation (IVC) to prove rigorous state-transition predicates that protect against various attack vectors. Crucially, PoSoF provides verifiable cryptographic provenance; it guarantees the legitimacy of the funds without leaking the intermediate transaction topology, intermediary addresses, or the specific origins utilized. We formally define the security properties of PoSoF and evaluate an Ethereum-compatible prototype. Our benchmarks demonstrate that fully private, proactive compliance is highly practical, requiring only ~1.8 s to incrementally update a user's PoSoF per new transaction, and a constant-time ~1.5 ms (~800k gas) for final on-chain EVM verification.
Predictive Churn Modeling and Proactive Service Using Customer Interaction Data Objectives: 1. To provide a comprehensive understanding of cloud-native architectures and middleware technologies used for designing scalable, resilient, and high-performance financial trading systems. 2. To explain the core concepts of microservices, containerization, orchestration, distributed messaging, and data management that power modern financial platforms and digital banking ecosystems. 3. To demonstrate the practical implementation of advanced technologies such as Kubernetes, Apache Kafka, Redis, gRPC, and AI-driven solutions for real-time trading and financial service delivery. 4. To equip software engineers, solution architects, researchers, and FinTech professionals with the knowledge required to build secure, fault-tolerant, low-latency, and highly observable trading infrastructures. 5. To explore emerging trends in financial technology, including serverless computing, WebAssembly, Artificial Intelligence, Machine Learning, and Decentralized Finance (DeFi), preparing readers for the next generation of cloud-native financial systems. Table of Contents CHAPTER 1 The Foundation of Customer Retention: Concepts and Definitions CHAPTER 2 The Business Value of Predicting Churn: Impact on ROI CHAPTER 3 Sources of Customer Interaction Data: CRM, Logs, and Beyond CHAPTER 4 The Architecture of a Churn Prediction System CHAPTER 5 Data Acquisition and Quality Assessment CHAPTER 6 Preprocessing High-Dimensional Interaction Data CHAPTER 7 Feature Engineering: Creating Meaningful Indicators from Raw Data CHAPTER 8 Exploratory Data Analysis for Churn Patterns CHAPTER 9 Traditional Statistical Methods in Churn Modeling CHAPTER 10 Machine Learning Approaches: From Random Forests to XGBoost CHAPTER 11 Deep Learning for Temporal Interaction Sequences CHAPTER 12 Natural Language Processing for Sentiment-Based Churn Analysis CHAPTER 13 Handling Class Imbalance in Churn Datasets CHAPTER 14 Evaluating Model Performance: Beyond Accuracy CHAPTER 15 Interpreting Black-Box Models for Stakeholder Trust CHAPTER 16 Real-Time Churn Scoring and Pipeline Automation CHAPTER 17 Designing Proactive Service Interventions CHAPTER 18 Personalized Marketing and Customer Success Strategies CHAPTER 19 Ethical Considerations and Data Privacy in Churn Modeling CHAPTER 20 Case Studies and Future Trends in Predictive Analytics
This paper assesses the role of green hydrogen and green ammonia in the low-carbon reconstruction of Ukraine’s energy sector. The country, severely affected by war, has more than 70% of its energy infrastructure damaged or destroyed, which calls for novel solutions for not only reconstructing but also rethinking Ukraine’s energy sector shaped by the Soviet-era planning. In this context, decentralized and renewable energy solutions appear to be one of the best options to achieve this goal. This study combines four novel and mutually reinforcing methods: a Scopus-based literature review of highly cited green hydrogen publications, natural language processing (NLP) and bibliometric network analysis of Ukraine-related hydrogen research, a SWOT assessment, and a geospatial hydrogen production cost model (GEOH2). The novelty of this research lies in this integrated Ukraine-specific framework, which links research trends, wartime reconstruction constraints, hub-level policy choices, and financing risk-sensitive cost modeling. Therefore, the quantitative part of GEOH2 estimates the levelized cost of green hydrogen, while ammonia is treated as a downstream screening-level conversion and export pathway rather than as a full plant-level ammonia model. Our results show that Ukrainian green hydrogen research is concentrated on renewable-energy strategy, wind and solar electrolysis, water and desalination constraints, gas grid blending, underground storage, ammonia derivatives, and decentralized energy systems. The GEOH2 results indicate that southern Ukraine has strong physical potential for competitive green hydrogen production under de-risked financing, while war risk financing can make even resource-rich areas economically unattractive. Odesa and Dnipro emerge as important export-oriented and industrial hubs, whereas Zakarpattia remains strategically relevant as a safer western corridor linked to European markets. Our findings demonstrate that Ukraine’s hydrogen and ammonia development needs to follow a phased pathway: domestic renewable build-out and grid repair, pilot electrolysis projects and screening-level ammonia conversion pathways, targeted de-risking and insurance mechanisms, and only then broader export corridor development. This pathway can support decarbonization, energy security, industrial modernization, and Ukraine’s long-term integration into European clean energy value chains.
ABSTRACT A long‐standing topic of concern in the literature on governmental auditing is whether the aims of Supreme Audit Institutions (SAIs) or other central audit institutions should include detecting fraudulent use of public money. The balance of opinion has been against this proposition, largely for reasons of infeasibility. This article takes up the same topic, but in policy contexts appropriate to this special issue, namely, where federally financed health, education, and infrastructure programs are implemented through program spending and delivery by local governments. Such policy contexts heighten the need for a capability to prevent and prosecute cases involving the fraudulent use of public money. This need, recognized within Brazil's federal executive since the early 2000s, has been addressed by its Office of the Comptroller General (Controladoria‐Geral da União—CGU) through systematic field audits of municipal implementation coupled with strategic collaboration with the Federal Police. This article examines CGU's integrated audit‐investigation approach and its policy and institutional contexts. By explaining how these strategies have functioned in attaining operational capacity and support, the article provides evidence on possibilities of how audit institution strategies aiming to detect fraudulent use of public money can be feasible within decentralized governance systems that call for innovative vertical coordination mechanisms.
S. Praveena, T. Arasulingam, M. Dineshkumar, P. Puvirajan · 7 authors
The rapid expansion of digital commerce has brought forward new challenges in payment security and transactional trust. Buyers and sellers engaging in online platforms face persistent threats such as payment fraud, unauthorized fund diversions, delayed settlements, and an overreliance on centralized financial intermediaries. Traditional mechanisms, which route payments through banks and payment gateway providers, often introduce additional costs while creating points of vulnerability that undermine consumer confidence. This paper proposes a blockchain-driven decentralized crypto escrow payment framework designed to address these shortcomings in a fundamental way. Rather than routing buyer payments directly to merchant accounts, the system temporarily secures those funds within a smart contract-governed escrow until all agreed-upon transaction conditions have been satisfied — including verified order fulfilment and successful product delivery. In the event of a dispute or transaction failure, the system enforces pre-coded refund protocols without requiring manual intervention. The proposed framework is expected to strengthen the relationship between buyers and sellers, meaningfully raise the bar for payment security, and deliver a transparent, auditable transaction environment through the principles of decentralized finance.
Pierluigi Martino, Christian Fisch, Cristiano Bellavitis
The emergence of new and powerful technologies has introduced novel players and innovative methods for financing entrepreneurial ventures. Blockchain technology is one example of a transformative technology that has significantly affected entrepreneurial finance in recent years, paving the way for a variety of alternative financial channels centered on digital technology, decentralization, and disintermediation. This chapter provides an overview of the current landscape of blockchain-based funding mechanisms by describing (1) initial coin offerings (ICOs), (2) initial exchange offerings (IEOs), (3) security token offerings (STOs), (4) non-fungible tokens (NFTs), and (5) decentralized autonomous organizations (DAOs). Initial DEX offerings (IDOs), airdrops, and cryptocurrency loans are also explored briefly. This overview aims to expand the academic understanding of the evolving blockchain-based financing landscape, helping researchers and practitioners gain insights into emerging trends, challenges, and opportunities.
Restoring forests is essential to addressing the global crisis of deforestation and biodiversity, as well as to maintaining the livelihoods of billions of forest-dependent people. Three major improvements are introduced by the suggested framework for smart forest restoration management: decentralized financial integration, community participatory governance, and the cost-effective deployment of blockchain and smart contracts for predictive and adaptive management. By coordinating ecological objectives with technological developments, these innovations seek to improve transparency, scalability, management effectiveness, and stakeholder trust in forest restoration initiatives.
Abstract: People who have digital accounts for banking, trading, and financial investment opportunities. The growing adoption of fintech apps has changed the way investors behave, especially tech-savvy users like IT professionals in Bengaluru. This review paper seeks to reconnect the dots between ABFS and investor behaviour by reviewing large sample of literature spanning the years 2002–2026. This research adopts the key theoretical frameworks: Unified Theory of Acceptance and Use of Technology (UTAUT), Theory of Planned Behaviour (TPB), behavioural finance theory and trust theory. The research method adopted was systematic literature review that was carried out by employing Scopus, Web of Science, Google Scholar, and peer-reviewed journals. According to the results, the main factors that explain the financial behaviour of adoption and investment are: financial awareness, the digital financial literacy, ease of use, Accessibility, Trust and Security, and Risk perception. The review also highlights some key gaps in the existing research, such as a lack of qualitative research, the absence of longitudinal studies, a narrow provision of emerging market studies, and poor focus on decentralized finance and AI-based investment applications. The paper proposes a conceptual and Structural Equation Model (SEM)-based framework explaining the relationship between technological, behavioural, and psychological factors influencing investor behaviour. Its finding will be valuable for the scientific community as it lays the basis for an integrated framework in understanding the adoption of fintech in emerging economies, and will also be helpful for policy makers, fintech developers and researchers Keywords: Application-based financial services, fintech adoption, investor behaviour, financial literacy, SEM model, trust and security, risk perception, digital investment platforms, TAM, TPB & UTAUT. Title: APPLICATION-BASED FINANCIAL SERVICES AND INVESTOR BEHAVIOUR IN INVESTMENT MANAGEMENT PRACTICES: A SYSTEMATIC REVIEW OF THEORETICAL INSIGHTS, TRENDS, AND FUTURE DIRECTIONS Author: Parimala.S, Dr. Annadurai International Journal of Management and Commerce Innovations ISSN 2348-7585 (Online) Vol. 14, Issue 1, April 2026 - September 2026 Page No: 502-513 Research Publish Journals Website: www.researchpublish.com Published Date: 04-June-2026 DOI: https://doi.org/10.5281/zenodo.20542559 Paper Download Link (Source) https://www.researchpublish.com/papers/application-based-financial-services-and-investor-behaviour-in-investment-management-practices-a-systematic-review-of-theoretical-insights-trends-and-future-directions
Marzanna Poniatowicz, Paweł Konopka, Agnieszka Piekutowska
The paper refers to the twelve principles of fiscal decentralization, proposed by Roy Bahl (Bahl’s Rules for Fiscal Decentralization – BRfd), with special emphasis on the first Bahl’s rule (regarding the comprehensiveness of the fiscal decentralization, CSfd). The purpose of the research was to estimate the level of CSfd in OECD countries. Thus, the objective was to construct a synthetic measure of the first Bahl’s rule (CSfd) which constitutes the value added of this research. The goal was also to rank countries as the main idea was to advance empirical assessment in the field of fiscal federalism and to provide practical guidance for shaping public finance policy including policies on the collection of public revenues and the allocation of public expenditure. This directly corresponds to the research problem of the very limited number of tools available for measuring fiscal decentralization. Based on Hellwig’s method of linear ordering two rankings were developed using the Euclidean metric and the Mahalanobis metric. The results indicate that the most comprehensive fiscal decentralisation systems in 2022 were found in the Slovak Republic, Switzerland and Canada (ranking based on the Mahalanobis metric). When the Euclidean metric is used, Switzerland emerges as the leader, followed by Spain and Austria. First published online 4 June 2026
Doan B. L. Nguyen, Pham Khanh Nam, Nguyen Thi Hong Thu
This study examines factors considered in investor decisions to invest in physical and tokenized real estate in Vietnam using discrete choice modeling on data from 413 participants in Ho Chi Minh City. Results show that legality, transparency, transaction fees, and expected returns are key determinants of investment consideration. Older and higher-income investors exhibit lower preference for tokenized assets, while female, more educated, and blockchain-familiar investors show greater adoption tendencies. The findings highlight how legality and institutional quality shape emerging digital asset markets, underscoring the need for legal clarity, transparent data, and targeted education to foster tokenized real estate development.
The quick progress of the gig economy and the appearance of digital nomadism have transformed traditional employment and financial ecosystems, increasing reliance on digital financial services. FinTech modernizations, including movable banking, digital cases, blockchain technologies, and decentralized finance, have significantly enhanced admittance to financial facilities. However, the extent to which these innovations contribute to meaningful financial inclusion remains a critical area of inquiry. This study presents a wide-ranging analysis of poetry examining the part of FinTech in enhancing financial inclusion among digital nomads. Using a narrative review approach, the study synthesizes research across themes such as ordinal finance embracing, financial literacy, gig economy dynamics, and platform-based monetary amenities. The verdicts signpost that while FinTech improves accessibility and efficiency, tests such as regulatory barriers, trust deficits, financial literacy gaps, and cross-border complexities persist. The study highlights the need for integrating technological, behavioral, and policy perspectives to achieve inclusive financial systems. The review contributes to the literature by providing a multidimensional understanding of FinTech-enabled inclusion and offers inferences for representatives, financial establishments, and gig workers.