Harsh Rudrawar, Sumitra A. Jakhete, Dhanashree Somani
Decentralized Finance (DeFi) has exposed users to sophisticated attacks (flash loans, rug pulls, phishing scams), while conventional, centralized fraud detection methods remain ineffective due to issues of privacy, data fragmentation, and lack of transparency. This paper introduces a novel hybrid framework for real-time fraud detection combining off-chain Machine Learning (ML) intelligence with on-chain smart contract enforcement via decentralized Chainlink oracles. Unlike existing ML systems that only flag behavior post-event, our design enables proactive mitigation, allowing smart contracts to automatically pause suspicious transactions or block malicious wallets based on real-time risk scores. Using an Ethereum dataset ($\approx 1.2$million transactions), the proposed model achieved an F1-score of 95.70% with XGBoost, outperforming traditional algorithms. The framework also demonstrated efficient operation, maintaining an average oracle latency of 1.25 seconds and an on-chain cost of 0.0041 ETH per action. Future work will explore privacy-preserving ML, cross-chain detection, and DAOgoverned explainable AI to improve transparency and trust in DeFi ecosystems.
Abstract Corporate treasury departments face growing challenges created by liquidity fragmentation, inefficient cash management, and delayed cross-border settlements-a perfect storm for increased financial risks for the firms and for operational difficulties. The present-day treasury systems rely on centralized banking and manual processes. A traditional one thus lacks the flexibility and the transparency needed in todayâs very uncertain global environment.Decentralized finance (DeFi) is presented in this paper as an essential infrastructure layer that has the potential to transform how businesses handle liquidity. DeFi offers programmable, real-time, and international financial execution through the use of smart contracts, algorithmic liquidity pools, decentralized exchanges, and tokenized assets. Conceptual modeling links DeFi mechanics to essential treasury functions, comparative analysis examines DeFi and traditional systems, and scenario simulations explore practical examples of corporate use cases.It is found that DeFi can enhance access to liquidity, reduce transaction costs, and automate treasury operations, especially with respect to intercompany fund flows, short-term financing, and FX execution. However, adoption needs strong governance frameworks, regulatory agreement, and technical compatibility with existing systems. This study offers a practical framework for CFOs, fintech developers, and policymakers to evaluate DeFiâs role in corporate treasury environments. It positions decentralized infrastructure as a useful tool for next-generation liquidity strategies.
Decentralized finance (DeFi) lending platforms have rapidly evolved within financial markets, with stablecoins playing a pivotal role in these ecosystems by providing price stability and enhancing capital efficiency. However, DeFi lending platforms still face challenges including market volatility, smart contract security, regulatory uncertainty, and liquidity constraints. This paper analyzes the function of stablecoins within DeFi lending platforms, explores their advantages and challenges, and forecasts future development trends. The article first introduces the fundamental concepts and classifications of stablecoins, then analyzes their advantages and challenges within lending markets, and finally looks ahead to innovations and future developments for stablecoins. Through this analysis, the paper provides in-depth insights for researching stablecoin applications in DeFi lending.
ABSTRACT Cryptocurrency, a decentralized digital asset enabled by blockchain technology, has transformed global finance by introducing novel mechanisms for value exchange, security, and governance. This comprehensive academic review synthesizes current knowledge across multiple dimensions: the technical foundations of cryptocurrencies (including distributed ledger technologies, cryptographic primitives, and consensus mechanisms), economic and financial implications (market behavior, monetary policy interactions, speculation, and investment risk), legal and regulatory frameworks (jurisdictional approaches, taxation, anti-money laundering measures, and consumer protection), as well as societal and ethical concerns (environmental impact, privacy, financial inclusion, and potential for illicit use). Drawing on recent empirical studies, case analyses, and theoretical models, the review highlights both the transformative potential of cryptocurrencies to democratize access to financial services and foster innovation, and the significant challengesâsuch as scalability, volatility, regulatory uncertainty, and energy consumptionâthat could inhibit or slow their integration. The paper concludes with a discussion of future research directions, including evolving consensus innovations (e.g. proof-of-stake, sharding), central bank digital currencies (CBDCs), and frameworks for balancing innovation with systemic risk mitigation. KEYWORDS Cryptocurrency, probabilistic forecasting, value-at-risk, expected shortfall, volatility, risk management, threat modeling, fintech, blockchain
ABSTRACT Research Question/Issue Blockchain technology promises to revolutionize governance through strong commitments, trustlessness, and transparency. This paper examines how these promises have failed to materialize in practice. Research Findings/Insights Drawing on case evidence from major blockchains, including Bitcoin and Ethereum, I argue that blockchains have evolved into technocracies where developers, foundations, and companies exercise disproportionate control. Rather than being exceptional, blockchain governance suffers from the same coordination problems, collective action failures, and centralization tendencies that plague traditional governance systems. Theoretical/Academic Implications The paper concludes that while blockchains offer valuable experiments in governance design, their alleged advantages over traditional institutions remain largely mythical. Practitioner/Policy Implications Blockchain organizations should acknowledge their reliance on offâchain coordination and informal authority. Investors must understand that blockchain governance depends on trusting technical elites, while regulators should recognize that decentralization claims often mask concentrated power structures requiring traditional oversight.
Decentralized finance (DeFi) protocols are becoming increasingly targeted by cyber threats, such as liquidity drain attacks, smart contracts flaws that leverage instant loans, and increasingly sophisticated threats that include DarkGate ransomware. We develop a hybrid framework that integrates CTI and predictive analytics to facilitate improving consensus mechanisms in a blockchain network. The proposed framework is centered on three layers , a data collection and processing layer, a security oracle layer that engages to mitigate intervention, and a dynamic adaptive mechanism to reach consensus. A 250-node testbed was built and deployed with the Hyperledger Besu and Geth deployments of Ethereum incorporating hybrid GRU-BiLSTM which utilize GNN's for predicting attacks. The results reveal improvements of transaction processing TPS of up to +236%, settlement latency improved -75%, fork rate improved to less than 3%, and downtime improved from 15% to 1.5%. Statistical tests T-Test and ANOVA also reveal these were of high statistically significance at p < 0.01. This study emphasizes that bridging functional aspects of AI with adaptive consensus mechanisms will be an effective approach at combating advanced cyber-attacks while maintaining reliability and resilience in DeFi systems.
The convergence of blockchain and Zero Trust Architecture (ZTA) offers a transformative pathway for enhancing security and resilience in financial infrastructures. Traditional network perimeter models are increasingly inadequate for safeguarding decentralized finance (DeFi), payment systems, and digital asset platforms that operate across distributed environments. This study explores how Zero Trust principles rooted in continuous verification, least privilege access, and micro-segmentation can be effectively integrated into blockchain ecosystems to mitigate identity spoofing, insider threats, and data tampering. By examining hybrid frameworks that combine permissioned blockchains with Zero Trust access controls, the research highlights a paradigm shift toward adaptive, identity-centric security postures in financial networks. The proposed model emphasizes dynamic authentication, real-time monitoring, and cryptographic assurance to ensure trustless yet verifiable interactions among nodes and participants. This integration not only fortifies compliance with emerging regulatory standards but also enhances interoperability and transparency across multi-chain financial systems. The findings suggest that embedding Zero Trust principles within blockchain-oriented infrastructures can create a self-healing, auditable, and future-ready digital finance ecosystem.
The evolution of the technical architecture of digital currencies is profoundly reshaping the global monetary system. This article starts from the core dimensions of technical architecture selection, systematically analyzes the technical characteristics and applicable scenarios of blockchain, distributed ledgers, and hybrid architectures, and combines the two-tier operation system design of central bank digital currencies (CBDC) to explore their sustainable development paths in areas such as payment efficiency, privacy protection, and regulatory compliance. Research shows that the modular reconfiguration of the technical architecture, the improvement of cross-chain interoperability, and the application of quantum-secure encryption technology are the keys to promoting the realization of "controllable anonymity" and global deployment of CBDCS. This article puts forward policy suggestions such as driving technological iteration through a regulatory sandbox mechanism and building a multilateral central bank digital currency bridge, providing theoretical support for the maintenance of monetary sovereignty and the upgrading of financial infrastructure in the digital currency era.
The third Bitcoin halving that took place in May 2020 cut down the mining reward from 12.5 to 6.25 BTC per block and thus slowed down the rate of issuance of new Bitcoins, making it more scarce. The fourth and most recent halving happened in April 2024, cutting the block reward further to 3.125 BTC. If the demand did not decrease simultaneously after these halvings, then the neoclassical economic theory posits that the price of Bitcoin should have increased due to the halving. But did it, in fact, increase for that reason, or is this a post hoc fallacy? This paper uses synthetic control to construct a weighted Bitcoin that is different from its counterpart in one aspect - it did not undergo halving. Comparing the price trajectory of the actual and the simulated Bitcoins, I find evidence of a positive effect of the 2024 Bitcoin halving on its price three months later. The magnitude of this effect is one fifth of the total percentage change in the price of Bitcoin during the study period - from April 2, 2023, to July 21, 2024 (17 months). The second part of the study fails to obtain a statistically significant and robust causal estimate of the effect of the 2020 Bitcoin halving on Bitcoin's price. This is the first paper analyzing the effect of halving causally, building on the existing body of correlational research.
Uzay IĆın Alıcı, Adem Orsdemir, Muhammad Tahir, Alptekın KĂŒpĂ§ĂŒ
Blockchain technology allows us to make trust-based transactions without third-party intermediaries. However, its rapidly developing nature brings serious security vulnerabilities. These vulnerabilities are a research priority because smart contracts (SC) maintained on the blockchain system cannot be modified or reversed after deployment. Our research indicates that Deep Learning (DL) and Machine Learning (ML) methodologies have recently become popular for detecting these vulnerabilities in SC. This systematic literature evaluation highlights its contributions compared to similar studies with the most common vulnerabilities.
Distributed Ledger Technologies (DLTs) fuse cryptographic immutability with decentralized consensus, transforming global finance, simultaneously hindering the forensic reconstruction of illicit value flows. This paper presents a systematic mapping of financial transaction tracing on DLTs through a rigorously designed Systematic Literature Review (SLR). Six research questions - covering enabling technologies, privacy primitives, tracking methods, structural limits, proposed mitigations, and future directions - guided the search. Three reviewers screened 120 publications from 2017â2025, resolving disagreements by adjudication and distilling 21 primary studies (17.5 % acceptance). The corpus converges on four technical pillars: heuristic/graph-based address clustering, machine-learning anomaly detection, cross-ledger correlation frameworks, and privacy-enhancing constructs such as ring signatures and zk-SNARKs. Our synthesis exposes a persistent tri-lemma among scalability, attribution accuracy, and privacy compliance, exacerbated by heterogeneous protocol designs and data-retention costs. To reconcile these issues, we articulate a four-layer tracing architecture that integrates high-throughput multi-chain ingestion, cache-efficient temporal graph indexing, explainable risk scoring, and privacy-preserving off-chain fusion with KYC anchors. This blueprint offers regulators, investigators, and researchers a scalable, GDPR-aligned pathway for illuminating opaque financial networks, while establishing a consolidated knowledge base and a forward research agenda for next-generation DLT traceability.
This paper investigates how the evolution of interbank payments towards central bank settlement, and thus central bank money as a settlement asset, has affected the dynamics of bank crises.We take the cluster of bank defaults in the United States in 2023 as a starting example and show how, alongside fractional reserves and fast digital communication, centralized settlement in central bank money played a critical role in triggering swift bank failures.We argue that technical centralization has amplified banks' fragility in the development of confidence crises, making bank runs easier and expanding the role of central banks to a point where conflict of interest becomes nearly inevitable.While previous literature has emphasized the effects of fast news spread and online banking, the role of settlement technology in recent bank runs has been largely overlooked.Thus we describe the stability consequences of different settlement architectures in detail, and also discuss potential improvements to the current architecture, particularly decentralized approaches built on distributed ledgers, to mitigate financial instability and reduce the negative effects of centralization without reverting to inefficient legacy systems.
Alexandru Ursu, Petru Lucian CurÈeu, Sabina Trif, Alina Maria FleĆtea
Cryptocurrencies are rapidly transforming digital finance and entrepreneurship, yet their adoption by entrepreneurs remains rather poorly understood. Drawing on the Threat-Rigidity Model (TRM) and the opportunity recognition literature, this study examines how entrepreneurial experience, financial literacy, perceived opportunities, and perceived threats influence entrepreneurial intention to use cryptocurrencies. We tested a moderated mediation model in which the association between financial literacy and experience, on the one hand, and intention to use cryptocurrencies, on the other, was mediated by perceived opportunities. In this model, perceived threats served as a moderator on the relationship between financial literacy and intention, as well as between perceived opportunities and adoption intention. Data were collected from a sample of 133 Romanian entrepreneurs across diverse industries. The results supported the mediating role of perceived opportunities in the relationship between financial literacy and intention to use cryptocurrencies in business and showed that the positive association between financial literacy and intention was attenuated by perceived threats. Entrepreneurial experience did not significantly influence perceived opportunities, while women entrepreneurs reported lower intention to adopt cryptocurrencies in business. This study is among the first to use the TRM to explore how the interplay of perceived opportunities and threats shapes cryptocurrency adoption in entrepreneurship. Other implications, limitations, and directions for future research are also discussed.
Venture capital investment and hedge fund investment are two asset classes of alternative investment fund portfolios. The purpose of this study was to determine whether the digital currency named bitcoin truly adds to diversification in an alternative investment fund portfolio. Vector auto regression was used to determine any unidirectional or bidirectional relationship between variables. The DCC-GARCH test was conducted to determine any conditional correlations that impact volatility transmission over a shorter and longer duration of time between variables. The results showed that there was no unidirectional or bidirectional relationship between bitcoin and FTSE venture capital index, as well as between bitcoin and the Barclays Hedge Fund Index. The DCC model showed no volatility transmission between bitcoin and the Barclays Hedge Fund Index, whereas volatility persists between bitcoin and the FTSE Venture Capital Index, connecting risk between the financial time series with only low correlations. These findings suggest that bitcoin could be used by investors, policy makers, and hedgers for diversification in alternative investment fund portfolios.
AI text-to-video systems, such as OpenAIâs Sora, promise substantial efficiency gains in media production but also pose risks of biased outputs, opaque optimization, and deceptive content. Using the OrientationâStimulusâOrientationâResponse (O-S-O-R) model, we conduct an empirical study with 209 Chinese new media professionals and employ structural equation modeling to examine how information elaboration relates to AI knowledge, perceptions, and adoption intentions. Our findings reveal a knowledge paradox: higher objective AI knowledge negatively moderates elaboration, suggesting that centralized information ecosystems can misguide even well-informed practitioners. Building on these behavioral insights, we propose a blockchain-based governance framework that operationalizes five mechanisms to enhance oversight and trust while maintaining efficiency: Expert Assessment DAOs, Community Validation DAOs, real-time algorithm monitoring, professional integrity protection, and cross-border coordination. While our study focuses on Chinaâs substantial new media market, the observed patterns and design principles generalize to global contexts. This work contributes empirical grounding for Web3-enabled AI governance, specifies implementable smart-contract patterns for multi-stakeholder validation and incentives, and outlines a research agenda spanning longitudinal, cross-cultural, and implementation studies.
IâFang Su, Shun-Ming Wang, Yu-Chi Chung, Yi-Hsien Tsai
Abstract In this research, we introduce an advanced approach for the detection of smart contract vulnerabilities leveraging Large Language Models (LLMs). Smart contracts are pivotal in the ecosystem of decentralized finance (DeFi), functioning as automated protocols for data management and transaction execution. The foundation of numerous blockchain-based applications lies in smart contract technology. Nevertheless, these contractsâ code vulnerabilities can become targets for malicious exploitation, leading to substantial financial damages, exemplified by the 2016 Dao smart contract incident which incurred a loss of 55 million USD. In response to such challenges, detection mechanisms for smart contract vulnerabilities have been devised, drawing upon conventional static analysis, fuzzy testing, and machine learning methodologies. Owing to the swift progression of LLMs, such as GPT, a broad spectrum of entities has adopted these models for routine operational management. By recognizing LLMsâ inherent capability to comprehend programming code, we investigate their aptitude for identifying smart contract vulnerabilities. We have integrated prompt engineering techniques, including the Chain of Thought (CoT), Plan-and-Solve, and few-shot learning, to augment the LLMsâ vulnerability detection efficacy. Furthermore, a sequence of empirical studies has been orchestrated to validate the effectiveness of our proposed prompt engineering strategies against diverse smart contract vulnerabilities.
The rapid digitization of financial services has resulted in a staggering increase in sophisticated fraud, endangering global economies and damaging public trust. The dynamic nature of current fraud is outpacing classic fraud detection systems, which frequently rely on static, rule-based methods. This study reveals a new hybrid framework that pairs distributed ledger technology for immutable transaction avoidance with Machine Learning (ML) for real-time fraud detection. The fundamental driving force is to address the inherent shortcomings of centralized systems, as well as the lack of an unchangeable audit trail in ML-only solutions. Using a range of classification algorithms, our methodology entails creating separate machine learning pathways for three important financial domains: credit card, UPI, and loan applications. A fraud verdict is subsequently produced using the top-performing model for each domain, which is determined by a thorough analysis of metrics. Through a smart contract, this decision is safely and irrevocably documented on a private blockchain. This study shows how a strong security architecture may be produced by fusing the decentralized trust and immutability of blockchain technology with the predictive performance of machine learning. The findings demonstrate that this integrated approach strengthens the integrity and dependability of digital financial transactions by achieving high performance in fraud detection as well as creating a transparent and impenetrable record.
Smart contracts have emerged as a core innovation within blockchain technology, enabling automated, trustless transactions without the need for intermediaries. While they offer a secure and transparent means of transferring assets and enforcing agreements, smart contracts are still software, and thus susceptible to bugs and vulnerabilities. Given their role in managing high-value digital assets, these flaws have been exploited to devastating effect, resulting in the loss or theft of billions of dollars. Vulnerabilities can lead to serious consequences, including unauthorized fund transfers, permanent loss or locking of assets, and the circumvention of contract logic. Critically, once deployed to the blockchain, smart contracts are immutable. Machine learning solutions in this field suffer from the lack of real accurate datasets to develop models that surpass static analyzers. In this paper we provide a methodological approach to create a curated vulnerable smart contract dataset leveraging open-source tools. A weighted ensemble mechanism is used to label contracts. Secondly, dataset preprocessing is demonstrated to create a classification friendly dataset. Finally, we train several machine learning models on a generated dataset for Solidity showing its effectiveness and strong performance in vulnerability detection.
S.Y. Wang, Laurence T. Yang, Xianjun Deng, Cannian Zou · 7 authors
As a key infrastructure for social fintech ecosystems, ethereum enables decentralized finance (DeFi) applications where security issues directly compromise ecosystem stability. Among critical security concerns, ethereum phishing scams stand as typical scams. Criminals employ distinctive fund transfer patterns (e.g., money laundering stages: placement, layering, and integration) to obscure illicit funds through long transaction paths. While graph neural networks (GNNs) dominate detection methods, they fail to model these long paths effectively. To address this, we propose the first framework to detect phishing scams through explicitly modeling fund transfer patterns. Our novel method, IMPUTATION, introduces: 1) a heuristic fund transfer path graph construction method utilizing iterative transaction pairing to capture complicated fund transfer patterns; 2) role-topology account embeddings encoding fund transfer patterns; 3) attention fusion leveraging initial transactions to suppress path noise; and 4) heterogeneous correlation graphs with weighted adjacency reconstruction modeling interpath dependencies. Extensive experiments demonstrate that IMPUTATION outperforms on all five metrics and detecting Ethereum phishing scams from fund transfer patterns is effective.
Since more users are moving to cloud computing, keeping our data safe and private now matters more. Since most cloud storage is run from just one location, it is easier for attackers and causes issues if the system fails. We come up with a new way to secure cloud storage by using blockchain technology and smart contracts. Blockchain mainly allows us to have a safe and distributed record that ensures data access can be trusted. By using smart contracts, we enable people to safely access data without any help from a middleman. It means that you can see every use of data access in the system, and these actions cannot be deleted or changed. Using blockchain technology with distributed storage, we ensure that everything happening is recorded and access is regulated correctly. We also discuss a case study to illustrate how our idea helps with transparency and trust, while at the same time mentioning concerns such as how far it can be used and problems with regulations, and our solutions for these issues.