Adina Litriwani, Shafwatul Hilwa, Muhammad Alvin, Dini Vientiany
This study aims to analyze the differences, roles, and contributions of central and regional taxes within the Indonesian taxation system. Taxes serve as the primary source of state revenue and play a crucial role in financing development and improving public welfare. Along with the implementation of fiscal decentralization, local governments are granted authority to manage regional taxes in order to enhance fiscal independence. This research employs a qualitative method with a descriptive approach, utilizing library research from various sources such as books, academic journals, and legal regulations. The results indicate that central taxes still dominate state revenue compared to regional taxes, reflecting disparities in regional fiscal capacity. Central taxes function to finance national programs and maintain economic stability, while regional taxes support local development and public services. To optimize tax revenue, strategies such as tax intensification, digitalization of the tax system, regulatory simplification, and improvement of taxpayer compliance are necessary. Therefore, an effective, transparent, and fair taxation system is expected to promote economic growth and equitable development in a sustainable manner.
The advent of decentralized cryptocurrencies has reignited fundamental debates in monetary economics about the nature and future of money. Proponents of digital currencies argue that decentralized, algorithmically governed assets can supplant central banks in managing monetary conditions and stabilizing economic outcomes. This chapter critically examines this proposition by evaluating cryptocurrencies against the classical functions of money and the core instruments of monetary policy. Grounded in monetary theory â from Friedmanâs monetarism and Misesâ Austrian framework to Modern Monetary Theory â and extended through a behavioral finance lens, the analysis reveals that widespread belief in cryptocurrency as a viable monetary policy alternative is driven not merely by technological innovation but by deeply embedded cognitive biases, including overconfidence, narrative-driven speculation, and institutional distrust. The chapter also treats money as an economic asset subject to market competition. Drawing on Austrian economic theory and classical competition principles, the analysis evaluates whether decentralized currencies can realistically compete with sovereign money in an open monetary market. By integrating monetary economics with strategic competition frameworks, the chapter explores whether cryptocurrencies can achieve monetary dominance through efficiency, cost advantages, or differentiated value propositions. Based on principles from strategic business theories such as differentiation and cost-leadership, the chapter treats money as a competitive good subject to market dynamics, ultimately concluding that while cryptocurrencies represent a significant financial innovation, they fundamentally lack the institutional architecture and behavioral predictability required to replace central bank monetary policy.
Deniz Erer, Tuna Can GĂźleç, Ăzge Korkmaz, Elif Erer
Rapid developments in blockchain, decentralized finance, and tokenization have raised the question of whether Sukuk can complement technology-based financial assets. This study compares the time-varying efficiency and multifractal dynamics of Sukuk indices, DeFi tokens, lending and borrowing tokens, and a FinTech index from May 25, 2020, to November 29, 2023. Using TGARCH, nonlinearity and long-memory tests, MF-DFA, and MF-DCCA, the study examines shock persistence, asymmetric volatility, market efficiency, and cross-market dependence. The findings show that negative shocks increase volatility more strongly than positive shocks and that all markets display nonlinear and multifractal behavior. Sukuk indices, particularly RMENA and RDJSUKUK, show lower market deficiency values than most technology-based assets. However, persistent cross-correlations indicate that Sukuk is not a direct substitute for these assets. Rather, Sukuk may serve as a relatively stable and efficient complementary asset in technology-exposed portfolios.Key Words: Sukuk, DeFi assets, Tokenization, Financial Economics, MF-DFA, MF-DCCAJEL Classification: F65, E44, G15, C58
In the digital revolution driven by blockchain technology, smart contracts emerge as a paradigm-shifting tool, poised to redefine traditional business practices across multiple domains. smart contracts stand as a cornerstone of innovation, promising to revolutionize the way we engage in business trustlessly. Driven by the pioneering spirit of exploration, this research delves into the expansive realm of smart contract use cases and applications, seeking to unveil the transformative potential they hold. Through meticulous analysis and case studies, this research illuminates the diverse array of scenarios where smart contracts can revolutionize processes, enhance accountability, and streamline operations in sectors such as finance, supply chain management, healthcare, and government services. By fostering collaboration and innovation, we seek to unlock the full potential of smart contracts, ushering in a new era of efficiency, integrity, and trust in the digital age while illuminating the path towards unlocking the untapped opportunities presented by smart contracts, reshaping the future of digital economies and organizational paradigms. Areas of application like decentralized Finance (DeFi), Non-Fungible Token (NFT), Regenerative Finance (ReFi) and many more where all discussed extensively.
Decentralized finance exposes supervisors to fast-moving, networked credit risks. General-purpose LLM agents fit this setting poorly: they over-read weak evidence and recommend high-stakes interventions, while existing evaluations offer no regulator-aligned way to measure the resulting false alarms. We introduce DeXposure-Claw, a forecast-grounded agentic supervision system that routes LLM decisions through structured evidence: (1) DeXposure-FM, a graph time-series foundation model, forecasts future exposure networks; (2) deterministic monitors and stress scenarios then turn those forecasts into typed alerts, attribution signals, and scenario evidence; and (3) data-health and confidence gates constrain escalation before DeXposure-Claw emits auditable supervisory tickets with rationales. We further develop DeXposure-Bench, a six-axis evaluation harness, whose decision axis scores tickets against a regulator-aligned absolute-loss ground truth and an explicit false-intervention rate. Experiments on five years of weekly real data fully support our system. Code is at https://github.com/EVIEHub/DeXposure-Claw.
Hasni Dyah Kurniawati, Saefudin Saefudin, fernando julio parera, Nurlyana Puspitasari ¡ 7 authors
<ns3:p> Research background In recent decades, venture capital (VC) has increasingly incorporated sustainability principles, reflecting the global shift toward environmentally and socially responsible investment. The alignment of VC with sustainability goals responds to the climate crisis, technological transformation, and social expectations for ethical finance. However, research on the VCâsustainability nexus remains fragmented across disciplines, requiring systematic mapping to clarify key trends and research gaps. This study aims to map the global evolution of VC research within the context of sustainability. It identifies publication trends, collaboration patterns, main thematic clusters, and emerging research areas to provide an integrated understanding of this growing field. Methods A mixed-methods bibliometric analysis was conducted using data retrieved from the Scopus database for the period 2002â2025. Analytical tools including <ns3:italic>RStudio and VOSviewer</ns3:italic> were applied to examine publication dynamics, co-authorship networks, and conceptual structures. The SPAR-4-SLR protocol was adopted to ensure methodological transparency and rigor. Discussion Results show that international collaborationâparticularly among China, the United States, and the United Kingdomâdrives sustainable innovation in the VC ecosystem. Three main clusters were identified: the theoretical evolution of VC, long-term policy and economic frameworks, and VCâs role in green entrepreneurship and sustainable technology. Research on emerging themes such as decentralized finance (DeFi), machine learning, and risk modeling remains limited. This study adds value by offering a systematic overview of the intellectual landscape and highlighting future research directions to strengthen VCâs contribution to global sustainability. </ns3:p>
The division between traditional finance (TradFi) and decentralized finance (DeFi) continues tohinder seamless capital mobility across ecosystems. RealâTime Payment Systems (RTPS) achievenearâinstant fiat settlements, yet bridging these assets into blockchain environments remainsdependent on fragmented, highâlatency, and centralized gateways. This gap limits the naturalstrengths of both worlds, especially speed and efficiency. Based on the publish/subscribe model, theEDSP protocol operates as a decentralized oracle system that allows for synchronization of stateupdates between two separate ledgers. This protocol will allow smart contracts to initiate fiatpayments and bank payment systems to trigger corresponding blockchain settlement actions. Themodel stresses cryptographic protections against oracle tampering through multi-party authenticationand zero-trust routing methodologies. A simulation of latency shows that an event-driven architectureis capable of resolving the deterministic nature of TradFi operations and the probabilistic aspect ofblockchain networks. Incorporating compliance events into the settlement process enables institutionsto meet their demands for regulatory and transparency obligations. This article sets a roadmap forfuture liquidity bridging models based on a secure, scalable, and compliant approach to bridgingecosystems. This article proves the value that event-driven models bring to the table in terms ofinterconnectivity and liquidity, which will allow institutional-level interactions to take place withinfiat and decentralized networks.
This study examines how technological and institutional factors interact to shape blockchain adoption in the finance sector. Combining a systematic literature review (SLR) with fuzzy-set qualitative comparative analysis (fsQCA), the study identifies configurations that link technological capabilities such as security, transparency and automation with institutional mechanisms of regulation, efficiency and legitimacy. The findings show that blockchain evolves from a decentralized concept into a regulated infrastructure where governance is embedded in design through technological and institutional hybridization. Guided by theories of hybridization and institutional layering, the study explains how transparency and regulation co-evolve, producing regulated autonomy and programmable compliance as outcomes of digital transformation in finance. The paper contributes to research on blockchain adoption, digital governance and institutional theory by clarifying how innovation becomes institutionalized within existing regulatory frameworks while sustaining efficiency and legitimacy.
Digital financial services are organized in alliance-intensive ecosystems, yet we know little about whether and how firmsâ alliance portfolios are associated with market value. Drawing on 130 publicly listed FinTechs (2019â2025), we identify 760 unique strategic alliances and represent them as a reciprocally encoded network, where each alliance is recorded as two mutual ties. We compute degree, betweenness, and closeness centrality and relate alliance portfolios and network positions primarily to firm-level market valuation (market capitalization and total enterprise value), while considering revenue, net income, and adjusted 5-year beta as supporting financial indicators. Spearman rank correlations indicate significant associations between alliances and market valuation and revenue, but not net income or founding year. In log-linear OLS with controls and fixed effects, one additional alliance is associated with â3.6% higher market capitalization. Framed by Social Capital Theory, the findings provide confirmatory evidence that alliance-based embeddedness is associated with capital market valuations.
We extend the Lindblad Cryptography Protocol (LCP) â previously applied to consensus and decentralized finance â to the problem of verifying real-world data on-chain. Existing oracle protocols solve the immutability of records on-chain but inherit a structural weakness at the data ingestion layer: the data still originates in software, run by a trusted operator, and can be fabricated at the source before being recorded. We show that hardware with silicon-derived unforgeable identity (SRAM PUF + BCH fuzzy extractor) can sign measurements directly, producing attestations that are cryptographically verifiable by any third party without trust in the operator. We demonstrate end-to-end validation on mainnet using a live commodity price (West Texas Intermediate crude oil) sourced from the U.S. Energy Information Administration, signed by a physical node, and verified by a publicly accessible mathematical check. We further describe the generalization of this primitive across five application verticals: agriculture, energy, mining and resource extraction, Real-World Asset (RWA) tokenization, and verified ad delivery. The Lindblad Oracle complements existing oracle protocols (Chainlink, API3, UMA) by providing a hardware-anchored root of trust at the data-origination layer, beneath their data-distribution layer.
The rapid proliferation of blockchain technology has fundamentally transformed global finance through the introduction of decentralized digital assets. However, the intrinsic characteristics that define cryptocurrencies namely decentralization, pseudonymity, and transactional irreversibility have simultaneously rendered the ecosystem a primary target for sophisticated cyber-attacks. This study investigates the critical dichotomy between the "code is law" philosophy and the imperative need for robust cybersecurity frameworks within a rapidly expanding market capitalization. This paper provides a multi-layered architectural analysis of vulnerabilities across the network, infrastructure, and application layers of the cryptocurrency ecosystem. Specifically, it examines systemic threats such as 51% attacks, smart contract exploits (including reentrancy and logic bugs), decentralized finance (DeFi) rug pulls, and sophisticated social engineering schemes. To address these vulnerabilities, the study evaluates the efficacy of current defense-in-depth mechanisms, including air-gapped cold storage solutions, multi-signature protocols, third-party smart contract auditing, and privacy-enhancing Zero-Knowledge Proofs (ZKPs). Furthermore, the research explores the integration of regulatory frameworks (AML/KYC standards) and proactive technological defenses like real-time on-chain analytics. Ultimately, this study proposes an enhanced, holistic threat prevention strategy designed to mitigate systemic risks, eliminate single points of failure, and safeguard the future integrity of digital asset platforms.
Mario Mihetec, Goran Stunjek, Goran KrajaÄiÄ, Gordana MikulÄiÄ Krnjaja
ABSTRACT Suburban areas with dispersed buildings and low heat flux densities present distinct challenges for the decarbonization of heating systems. While district heating is often promoted in dense urban cores, its economic viability in suburban zones remains questionable due to high network costs and thermal losses. This study investigates whether decentralized, household level solutions combining high-temperature air source heat pumps with photovoltaics can outperform centralized district heating in such contexts. Using a case study of four peripheral settlements in Croatia, the research employs a dual-scale techno-economic optimization framework: a mixed-integer linear programming model for district heating and a prosumer-level model for individual heat pump-photovoltaicâbattery systems. Three building renovation scenarios (no, partial, and full renovation) are evaluated alongside a mixed-financing scheme involving grants, household equity, and energy service company participation. Results show that decentralized heat pump-photovoltaicâbattery systems under full renovation deliver the highest energy savings (75% reduction in household energy costs), the greatest carbon dioxide reduction (2,207 tonnes annually), with a net present value of 1.49 million EUR and an internal rate of return of 7.21%. When external costs of air pollution and carbon are internalized, the economic net present value rises to 68.39 million EUR. The results suggest that, under the assumptions and boundary conditions defined in this study, decentralized renewable heating systems are both technically viable and economically favorable compared to district heating in low-heat-density suburban contexts. This work provides a replicable decision-support framework for policymakers and planners seeking to accelerate the clean heating transition in dispersed residential areas.
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 scoresâcategorized as Safe (Level 0), Warning (Level 1), and Frozen (Level 2)âthe 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 â T1 â 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 blockchainâs 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.
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.
ć˛ć¸ 揢, L Chen, Jimin Chen, Tao Li ¡ 6 authors
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, IstvĂĄn AndrĂĄs 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