Decentralized finance (DeFi) protocols now intermediate over USD 100 billion in value, including regulated stablecoins and tokenized assets deployed as collateral, yet no widely adopted framework operationalizes risk assessment at the rigor institutional adoption demands. Existing approaches emphasize protocol-specific parameter optimization or conceptual taxonomies without providing explainable, composability-aware, and structurally independent assessment methodologies. We propose a nine-dimension DeFi risk assessment framework extending the six-dimension taxonomy introduced by Moody's Analytics and Gauntlet with three novel dimensions: composability risk, comprehension debt, and temporal risk dynamics. We additionally introduce a transparency confidence modifier separating assessment reliability from risk severity. The framework is grounded in structural analysis of protocol dependencies conducted through an ontology-based protocol intelligence infrastructure covering more than 8,000 DeFi protocols. We retrospectively analyze 12 major DeFi-related incidents from 2024-2026 representing approximately USD 2.5 billion in direct losses. Five of the 12 incidents require at least one novel dimension for complete root-cause characterization, including the two highest-systemic-impact events in the dataset.
Three traits of decentralized finance are studied. First, the market impact function is derived for optimal-growth liquidity providers. For a standard random walk, the classic square-root impact is recovered. An extension is then derived to fit general fractional Ornstein-Uhlenbeck processes. These findings break with the linearized liquidity models used in most decentralized exchanges. Second, a Constant Product Market Maker is viewed as a multi-phase Carnot engine, where one phase matches the exchange of tokens by a liquidity taker, and another the change of pool size by a liquidity provider. Third, stablecoin de-pegging is a form of catastrophe risk. By using growth optimization, default odds are linked to the cost of catastrophe bonds. De-pegging insurance can act as a counterweight and a key marketing tool when the law forbids the payment of interest on stablecoins.
Purpose â This paper investigates whether perpetual futures can satisfy Islamic prohibitions on riba (interest), gharar (uncertainty), and maysir (speculation) â a question unaddressed by prior literature. Design/methodology/approach â The study develops a four-category taxonomy of perpetual futures by funding-formula structure, collects 39,406 unique funding-rate intervals across four platforms over 365 days (February 2025 â February 2026), and analyses a cross-platform comparison â with a placebo design â between interest-bearing and interest-free DEX protocols. Shariah analysis applies El-Gamal's riba conditions, Kamali's standardisation criterion, and Salamon's maysir test. Findings â The interest parameter in CEX funding formulas is mathematically unnecessary. dYdX v4 operates with I = 0 and produces a funding distribution distinct from CEX platforms (Cohen's d = 0.782 at native cadence, 0.703 cadence-matched; Kolmogorov-Smirnov D = 0.506). Hyperliquid â a DEX using the CEX formula â clusters with CEX. Formula structure rather than exchange architecture governs the riba dimension of the Shariah classification. Originality/value â The first empirically validated framework demonstrating that the riba component of perpetual futures funding is structurally removable, correcting an error of extrapolation in Islamic finance scholarship that treated a single CEX implementation as representative of the entire instrument class. Ownership (qabdh) and delivery-intent are scoped as open questions for cash-settled synthetic perpetuals. Research limitations/implications â The empirical sample covers a single 365-day period; extension to additional time windows would strengthen external validity. Practical implications â The taxonomy provides a screening tool for Shariah boards, fund managers, and exchange designers evaluating Shariah-compliant perpetual futures instruments.
Insurance governance fundamentally concerns the legitimate authority to price risk, allocate capital, and absorb correlated losses. Traditional models centralize this authority in credentialed institutions, while existing decentralized finance (DeFi) protocols either replicate centralized control or treat critical actuarial parameters as exogenous inputs. This paper resolves the decentralized insurance governance trilemma by proposing the first complete theoretical framework built on a three-tier architecture of capital-backed parameter markets, extending and completing the Risk Coin framework (Yu, 2025). The architecture transforms actuarial assumptions into endogenously discovered economic variables. At the Risk-Pool Tier, insurers commit capital to bid on parameters of an MBBEFD exposure curve. Policyholders signal their private risk assessments by declaring a coverage limit and a total premium payment; from these, an implied retention multiplier is derived after market pricing. At the Catastrophe-Pool Tier, reinsurers likewise commit capital to bid on parameters for pricing optional excess-of-loss reinsurance. The core governance innovation is a recursive design: a Catastrophe Pool operates identically to a Risk Pool, with Risk Pools as its policyholders, thereby unifying the governance logic of primary and reinsurance markets under the same capital-backed mechanism. The ecosystem is anchored by the Risk-Coin-Fund Tier, which centralizes all capitalâincluding the base risk premiums (corresponding to actuarial costs), policyholdersâ voluntary supra-actuarial contributions, and professional investment reinsurance capitalâand serves as the ultimate residual risk bearer. A core innovation is the consistent application of Risk Coin as a dual-purpose claim on the collective capital pool. RC tokens are issued to participants based on two principles: (1) as compensation for voluntary capital investment (the portion of declared premiums exceeding actuarial cost), and (2) as payment for risk-bearing, with the latter quantified by the market-determined exposure curve đș(â ). This creates a unified incentive system where capital commitment confers pricing authority, and better risk management preserves RC value. We formalize the mechanism and prove its core properties: incentive-compatible parameter bidding, asymptotic information aggregation, dynamic stability of parameter markets, systemic resilience via an exponential solvency guarantee for diversifiable risks, and regulatory non-intrusiveness. A pivotal theoretical extension is the introduction of a protocol-native capital coverage ratio (Ï), which emerges from the recursive markets. This ratio is governed by a target (đtarget) and serves as a real-time, transparent solvency signal. It demonstrates how decentralized systems can achieve endogenous stability while providing a direct interface for financial oversight, effectively bridging cryptographic economics with prudential regulatory frameworks. The framework demonstrates that legitimate pricing authority and financial stability can emerge organically from cryptographic economics where tokens serve not as speculative instruments but as verifiable, economically-founded claims on underwriting capacity and capital pool ownership. By solving the governance trilemma through recursive capital-backed parameter markets, a unified RC-based capital accounting system, and a native regulatory interface, this work provides more than a new insurance mechanismâit offers a blueprint for stable, transparent, incentive-aligned, and regulatorily-compatible financial ecosystems that reimagine the foundations of risk-sharing and financial governance.
This paper presents a model in which risk-averse individuals can purchase insurance via traditional indemnity contracts or Decentralized Finance (DeFi) smart contract-based instruments. The model incorporates key features of DeFi insurance, including parametric payouts, basis risk arising from imperfect loss verification and pooled collateralization involving the risk of liquidity shortfalls. We characterize optimal insurance choices as a function of pricing, payout correlation and risk preferences. Numerical results show that DeFi insurance can complement or replace traditional coverage, improving welfare when basis and default risks are moderate or pricing advantages are substantial. The analysis reveals how DeFi-specific frictions shape insurance demand and provides insight into how DeFi instruments may shift market structure and expand the set of attainable risk transfer outcomes.
Christopher De Baets, Basem Suleiman, Armin Chitizadeh, Imran Razzak
Abstract In the growing field of blockchain technology, smart contracts exist as transformative digital agreements that execute transactions autonomously in decentralised networks. However, these contracts face challenges in the form of security vulnerabilities, posing significant financial and operational risks. While traditional methods to detect and mitigate vulnerabilities in smart contracts are limited due to a lack of comprehensiveness and effectiveness, integrating advanced machine learning technologies presents an attractive approach to increasing effective vulnerability countermeasures. We endeavour to fill an important gap in the literature by conducting a rigorous systematic review, exploring the intersection between machine learning and smart contracts. Specifically, the study examines the potential of machine learning techniques to improve the detection and mitigation of vulnerabilities in smart contracts. We analysed 88 articles published between 2018 and 2023 from the following databases: Institute of Electrical and Electronics Engineers, the Association for Computing Machinery, ScienceDirect, Scopus, and Google Scholar. The findings reveal that classical machine learning techniques, including K-nearest neighbours, random forest, decision trees, extreme gradient boosting, and support vector machines, outperform static tools in vulnerability detection. Moreover, multi-model approaches integrating deep learning and classical machine learning show significant improvements in precision and recall, while hybrid models employing various techniques achieve near-perfect performance in vulnerability detection accuracy. By integrating state-of-the-art solutions, this work synthesises current methods, thoroughly investigates research gaps, and suggests future directions. The insights gathered are intended to serve as a seminal reference for academics, industry experts, and bodies interested in leveraging machine learning to enhance smart contract security.
Lana AL-Khalaileh, Tareq Al-Billeh, Abdul Salam Al-Findi, Odai Al-Hailat
This study deals with a new technology in contracting, resulting from the information technology (IT) revolution in the field of electronic transactions, which is called âsmart contractsâ. The latter has constituted a breakthrough in the field of contracting since it provides automation, which underlies many advantages for contractors, so that the software works of smart contracts provide immediate and automatic execution of the contract, which provides speed of implementation and security from manipulation after concluding the contract. So, it provides elements of technical security and trust for this type of contract. This new contractual pattern is considered one of the first in the provisions of Islamic Sharia, which urges us to know the extent of its compatibility with its contracting system. The study concluded with several recommendations, the most significant being that international accords lack comprehensive legislation governing transactions executed through smart contracts. While they contain certain restrictions about contracts formed through contemporary electronic methods, they inadequately elucidate the characteristics of such contracts and examine their specifics. The legal issues associated with smart contracts stem from their connection to digital currency, which is banned by Sharia law.
Abdul Malik, Gayatri Putri, Hesti Putri, Ahmad Badruddin
The proliferation of crypto-assets has raised critical questions about their impact on global financial stability. This study rigorously investigates the structural evolution of the cryptocurrency market's role within the global financial system, testing the hypothesis that it has transitioned from a peripheral, shock-absorbing entity into a systemically significant transmitter of financial risk. We employ a Time-Varying Parameter Vector Autoregression (TVP-VAR) model on daily data from January 1, 2017, to December 31, 2024, examining the dynamic connectedness between a bespoke, rebalanced cryptocurrency index (CRIX20) and key global financial indicators (S&P 500, MSCI World, VIX, DXY). The econometric framework utilizes a Bayesian estimation approach with standard priors, a 200-day rolling window, and a 10-day forecast horizon for Generalized Forecast Error Variance Decompositions (GFEVD). Methodological robustness is confirmed through structural break tests and sensitivity analysis of the forecast horizon. Our findings reveal a profound structural transformation. Prior to mid-2020, the cryptocurrency market was a consistent net receiver of financial spillovers. A structural break, formally identified in the third quarter of 2020, marks a definitive regime shift. Post-break, the crypto market has become a significant and persistent net transmitter of risk to the traditional financial system. The total connectedness index for the entire system shows a marked secular increase, with the crypto market's contribution to systemic risk growing substantially. Gross spillover analysis confirms this shift is driven by a dramatic increase in risk transmission from the crypto market to other assets. In conclusion, the cryptocurrency market can no longer be considered an isolated ecosystem; it is now an integral and potentially destabilizing component of the global financial architecture. The era of crypto-assets as reliable diversifiers has waned, replaced by a new reality where shocks originating within this market pose a credible threat to broader financial stability. These findings present urgent challenges for regulatory oversight, systemic risk monitoring, and portfolio management.
Cha, Jinho, Long Hoang Pham, Thi Quynh Trang Vo, Jaeyoung Cho · 5 authors
This study develops and analyzes an optimization model of smart contract adoption under bounded risk, linking structural theory with simulation and real-world validation. We examine how adoption intensity alpha is structurally pinned at a boundary solution, invariant to variance and heterogeneity, while profitability and service outcomes are variance-fragile, eroding under volatility and heavy-tailed demand. A sharp threshold in the fixed cost parameter A3 triggers discontinuous adoption collapse (H1), variance shocks reduce profits monotonically but not adoption (H2), and additional results on readiness heterogeneity (H3), profit-service co-benefits (H4), and distributional robustness (H5) confirm the duality between stable adoption and fragile payoffs. External validity checks further establish convergence of sample average approximation at the canonical O(1/sqrt(N)) rate (H6). Empirical validation using S&P 500 returns and the MovieLens100K dataset corroborates the theoretical structure: bounded and heavy-tailed distributions fit better than Gaussian models, and profits diverge across volatility regimes even as adoption remains stable. Taken together, the results demonstrate that adoption choices are robust to uncertainty, but their financial consequences are highly fragile. For operations and finance, this duality underscores the need for risk-adjusted performance evaluation, option-theoretic modeling, and distributional stress testing in strategic investment and supply chain design.
We present LISA, an agentic smart contract vulnerability detection framework that combines rule-based and logic-based methods to address a broad spectrum of vulnerabilities in smart contracts. LISA leverages data from historical audit reports to learn the detection experience (without model fine-tuning), enabling it to generalize learned patterns to unseen projects and evolving threat profiles. In our evaluation, LISA significantly outperforms both LLM-based approaches and traditional static analysis tools, achieving superior coverage of vulnerability types and higher detection accuracy. Our results suggest that LISA offers a compelling solution for industry: delivering more reliable and comprehensive vulnerability detection while reducing the dependence on manual effort.
Code reuse is a common practice in software engineering. Developers of smart contracts pervasively reuse subcontracts to improve development efficiency. Like any program language, such subcontract reuse may unexpectedly include, or introduce vulnerabilities to the end-point smart contract. Indeed, prior empirical studies have identified a number of issues caused by code reuse in smart contracts. Unfortunately, automatically detecting such issues poses several unique challenges. Particularly, in most cases, smart contracts are compiled as bytecode, whose class-level information (e.g., inheritance, virtual function table), and even semantics (e.g., control flow and data flow) are fully obscured as a single smart contract after compilation. Therefore, it is rather difficult to identify the reused parts of subcontract from a given smart contract, not to mention finding potential vulnerabilities caused by subcontract misuse.In this paper, we propose Satellite, a new bytecode-level static analysis framework for subcontract misuse vulnerability (SMV) detection in smart contracts. Satellite incorporates a series of novel designs to enhance its overall effectiveness.. Particularly, Satellite utilizes a transfer learning method to recover the inherited methods, which are critical for identifying subcontract reuse in smart contracts. Further, Satellite extracts a set of fine-grained method-level features and performs a method-level comparison, for identifying the reuse part of subcontract in smart contracts. Finally, Satellite summarizes a set of SMV indicators according to their types, and hence effectively identifies SMVs. To evaluate Satellite, we construct a dataset consisting of 58 SMVs derived from real-world attacks and collect additional 56 SMV patterns from SOTA studies. Experiment results indicate that Satellite exhibits good performance in identifying SMV, with a precision rate of 84.68% and a recall rate of 92.11%. In addition, Satellite successfully identifies 14 new/unknown SMV over 10,011 realworld smart contracts, affecting a total amount of digital assets worth 201,358 USD.
Olaolu Samuel Adesanya, Akindamola Samuel Akinola, Lawrence Damilare Oyeniyi
Smart contract technologies are revolutionizing the landscape of global finance by enabling secure, automated, and transparent cross-border financial transactions. Traditional international transactions are often hindered by delays, high costs, and reliance on multiple intermediaries such as correspondent banks, clearinghouses, and regulatory bodies. These processes not only increase operational complexity but also expose transactions to risks of fraud, errors, and regulatory inefficiencies. Smart contracts, built on blockchain platforms, address these challenges by embedding contractual terms directly into self-executing code that autonomously enforces obligations once pre-defined conditions are met. This technological innovation eliminates the need for third-party verification, reduces transaction latency, and ensures that funds or assets are exchanged only when contractual conditions are satisfied, thereby enhancing both trust and efficiency in global markets. The application of smart contracts in cross-border financial transactions streamlines settlement processes by providing real-time execution and verification, reducing the risk of human error and dispute. Automated compliance mechanisms can be integrated into the contract logic, ensuring adherence to international trade and financial regulations while minimizing manual oversight. Moreover, transparency inherent in blockchain technology allows stakeholders including regulators, financial institutions, and clients to access immutable transaction records, strengthening accountability and trust. The interoperability of smart contract platforms with emerging technologies such as digital currencies and decentralized finance ecosystems further amplifies their potential to reshape international trade and investment flows. Strategically, smart contracts promote inclusivity in global markets by lowering transaction costs, expanding access for small and medium-sized enterprises, and accelerating settlement times, particularly in regions where traditional banking infrastructure is underdeveloped. However, challenges remain, including legal recognition across jurisdictions, standardization of protocols, and ensuring resilience against cyber threats. Addressing these issues through coordinated governance and international regulatory cooperation will be critical for mainstream adoption. In summary, smart contract technologies enable secure, automated, and transparent cross-border financial transactions, offering significant advances in efficiency, cost reduction, and trust-building across global economic markets. Keywords: Smart Contracts, Blockchain, Cross-Border Transactions, Financial Automation, Global Economic Markets, Transparency, Compliance, Decentralized Finance.
This chapter explores the intersection between the deterministic execution of smart contracts and the unpredictable nature of delay, a legal phenomenon historically embedded in human discretion and normative flexibility. While smart contracts promise automated, trustless enforcement, they reveal critical vulnerabilities when confronted with unforeseen disruptions, particularly in the context of technical rigidity and legislative gaps. The discussion navigates through the architectural challenges of code literalism, the oracle dependency problem, and the doctrinal limitations of classical contract law in adjudicating delays devoid of intent or culpability. It also examines emerging hybrid legal-technical frameworks, including regulatory innovations in the EU and UK, and the conceptual development of Lex Cryptographica. Ultimately, the chapter proposes a recalibration of contract theory and practice, advocating for a pluralistic approach that integrates technical resilience with normative safeguards to manage delay in a digitally autonomous age.
In recent years, surveys on vulnerability detection tools for Solidity-based smart contracts have shown that many of them display poor capabilities. One of the causes for such deficiencies is the absence of quality benchmarking datasets, where bugs typically found in smart contracts are present in quantity and accurately labeled. VulLabâs main aim is to help tackle this issue as a framework that incorporates both, state-of-the-art vulnerability insertion and vulnerability detection tools. Such capabilities empower users to seamlessly generate benchmark capable datasets from collected contracts and employ them to validate novel analysis tool and obtain an accurate comparison with current state-of-the-art solutions. The framework was able to, from 50 smart contracts collected from the Ethereum mainnet, generate an annotated dataset more than 300 entries which included 20 unique vulnerabilities, and use them to compare 14 analysis tools in approximately 24 hours. VulLab is open-source and is available at https://github.com/lsRyan/vullab.
Rad analizira digitalnu transformaciju u industriji osiguranja s posebnim naglaskom na primjenu blockchain tehnologije i pametnih ugovora. IstraĆŸuje kako telemetrija i oracle tehnologija omoguÄuju prikupljanje i koriĆĄtenje podataka iz stvarnog svijeta za dinamiÄno oblikovanje ugovora o osiguranju, ĆĄto vodi razvoju novih modela poput mikroosiguranja, peer-to-peer osiguranja i osiguranja temeljenog na stvarnoj uporabi. Rad takoÄer razmatra pravne aspekte pametnih ugovora, njihovu pravnu valjanost, ograniÄenja u interpretaciji, te izazove u zaĆĄtiti privatnosti i regulatorne izazove koje donosi njihova primjena unutar EU i Republike Hrvatske. Poseban naglasak stavlja se na vaĆŸnost stvaranja jasnih i prilagodljivih pravnih rjeĆĄenja koja Äe omoguÄiti odgovornu i uÄinkovitu integraciju novih tehnologija u osigurateljnu praksu.
This study aims to provide a comprehensive analysis of tools and methods for ensuring smart contract security.The research employs a systematic review of static analysis, dynamic testing, and formal verification approaches.Static analysis tools, including Oyente, Mythril, and Slither, are systematically evaluated regarding their effectiveness in identifying vulnerabilities at early development stages, highlighting strengths in detecting known vulnerability patterns as well as limitations such as false positives.Dynamic analysis methodologies, such as fuzz testing (e.g., Echidna, Harvey) and symbolic execution (e.g., MAIAN, teEther), are assessed for their capability to identify complex logical vulnerabilities that are typically missed by static methods, examining their accuracy, scalability, and real-world applicability.Formal verification approaches employing K-framework, Why3, and Coq are thoroughly examined for their ability to deliver rigorous mathematical guarantees of smart contract correctness, along with their practical applicability, complexity, and integration into typical smart contract development workflows.The study reveals that an integrated security strategy, combining static analysis, dynamic testing, and formal verification methods, is essential for comprehensive and robust smart contract protection, effectively mitigating diverse vulnerabilities across the entire contract lifecycle.The research contributes to the field by offering a comparative analysis of current tools, identifying their strengths and limitations, and proposing future research directions, including automated specification generation and AI-driven vulnerability prediction.
The construction industry in developing countries continues to face significant challenges due to reliance on traditional, paper-based contract administration and management. This approach frequently results in inefficiencies, disputes, transparency issues and unethical practices. Although smart contracts enabled by blockchain technology present a promising solution to these longstanding issues, their adoption in developing countries remains limited. This study investigates the barriers to and strategies for the implementation of smart contracts within the construction industry, using Nigeria as a representative case. Adopting a qualitative research methodology, data were collected through semi-structured interviews with 14 experienced project managers selected via purposive sampling. A thematic analysis of the data identified several critical barriers, including resistance to change, low awareness, privacy concerns, legal uncertainties, technical hurdles, infrastructure deficits and economic instability. To overcome these barriers, the study proposes a strategic implementation framework informed by insights from interviewees and supported by literature. Key recommended strategies include educational and awareness initiatives, governmental support and policy reform, stakeholder collaboration, robust security measures, phased deployment and establishing supportive legal frameworks. The findings of this research offer valuable guidance for developing countries encountering similar constraints, providing a clear roadmap for successfully integrating smart contracts into construction practices.
Saad AL Azzam, Raenu Kolandaisamy, Ghassan AL Dharhani
Smart contracts (SCs) have become an essential component in the world of decentralized applications, automating transactions across blockchain networks without the need for intermediaries, and with this rise in adoption, the technology has also brought forth growing concern due to security vulnerabilities, which have led to serious financial damage, and the problem is far from being solved. Traditional auditing methods often struggle to capture the more intricate vulnerabilities hidden within smart contract logic, particularly owing to the irreversible nature of blockchain transactions. Given these challenges, researchers have been actively exploring more advanced detection techniques. Despite progress, many existing studies tend to focus narrowly on specific methods, whether static analysis, dynamic testing, or machine learning models, without offering a comprehensive comparison across all available approaches. This fragmented landscape leaves a noticeable gap for practitioners looking for a well-rounded understanding of smart contract security solutions. To address this, our study set out to systematically review the existing body of work, analysing 21 reviewed studies published between 2020 and 2024. The primary aim was to combine the diverse techniques that have been proposed for detecting vulnerabilities in smart contracts, ranging from static and dynamic analyses to more recent AI-driven models, graph-based techniques, and hybrid systems, critically evaluating their strengths, weaknesses, and practical effectiveness. The methodology followed a structured approach. We searched major research databases, IEEE Xplore, ACM Digital Library, SpringerLink, ScienceDirect, and Scopusâusing carefully crafted search queries to ensure that we captured the most relevant and up-to-date papers. Our findings revealed that AI-based methods, especially those leveraging deep neural networks and graph neural networks, have achieved impressive detection accuracy in controlled environments. For example, models such as ContractWard and SCVDIE-ENSEMBLE reported Micro-F1 scores of 98.48% and 95.46%, respectively, but these models also have a trade-offâthey demand high computational resources, which limits their real-world deployment in resource-constrained settings. On the other hand, lighter tools such as Slither and NeuCheck offer faster detection and lower resource usage but might fall short in regard to identifying more complex or new vulnerabilities. We also noticed a growing trend towards real-time monitoring tools, such as SODA and GPTScan, which aim to strike a balance by reducing false positives while providing proactive security measures. However, several challenges remain unresolved where many AI-driven models still rely heavily on labelled datasets, which may not generalize well to novel attack patterns. Scalability is another concern, especially for models that are computationally intensive.
ABSTRACT This paper proposes a solution for the issue of silent shareholders lacking influence over company decisions and not receiving adequate compensation. Thus, we adopt Palmon, Kleinman, and Medinetsâs (2022) âcapital contractâ framework and extend it by integrating smart contract functionality. This study then introduces a prototype to demonstrate how this enhanced framework can be implemented through blockchain-based smart contracts. By linking silent shareholdersâ dividends to executive compensation, these smart contracts enhance the trustworthiness and transparency of the compensation processes for executives and shareholders. What is more, blockchain-based smart contracts automate the contract terms, potentially reducing the need for intermediaries to monitor managerial actions. Also, smart contracts are flexible to meet diverse reporting requirements and adapt to the unique characteristics of a particular company. Data Availability: All data used in this study are available in the manuscript. JEL Classifications: M40; O33.
Amid the ongoing advancements associated with the Fourth Industrial Revolution and the intensification of digital transformation, the deployment of artificial intelligence (AI) within the banking sector has become an inevitable trajectory, enabling substantial innovations in financial management and operational processes. AI technologies facilitate the automation of complex workflows, reduce error rates, enhance operational efficiency, and improve customer experience through personalized services and accelerated response mechanisms. Applications span various functions, including customer onboarding, service delivery, product development, marketing, and risk management, thereby optimizing the banking value chain holistically. Moreover, AIâs capabilities in big data analytics and customer behavior prediction equip financial institutions with more robust decision-making tools that mitigate credit risk and fraud incidence. The convergence of AI and blockchain technologies further augments transaction security and transparency, thereby promoting the expansion of digital banking and decentralized finance ecosystems. This study aims to systematically examine the evolving roles and emerging applications of AI throughout the banking value chain, contributing to strategic frameworks oriented toward sustainable development within the digital era.
Zhiyuan Wei, Jing Sun, Yuqiang Sun, Ye Liu · 13 authors
Blockchainâs inherent immutability, while transformative, creates critical security risks in smart contracts, where undetected vulnerabilities can result in irreversible financial losses. Current auditing tools and approaches often address specific vulnerability types, yet there is a need for a comprehensive solution that can detect a wide range of vulnerabilities with high accuracy. We propose LLM-SmartAudit, a novel framework that leverages Large Language Models (LLMs) to automate smart contract vulnerability detection and analysis. Using a multi-agent conversational architecture with a buffer-of-thought mechanism, LLM-SmartAudit maintains a dynamic record of insights generated throughout the audit process. This enables a collaborative system of specialized agents to iteratively refine their assessments, enhancing the accuracy and depth of vulnerability detection. To evaluate its effectiveness, LLM-SmartAudit was tested on three datasets: a benchmark for common vulnerabilities, a real-world project corpus, and a CVE dataset. It outperformed existing tools with 98% accuracy on common vulnerabilities and demonstrates higher accuracy in real-world scenarios. Additionally, it successfully identifies 12 out of 13 CVEs, surpassing other LLM-based methods. These results demonstrate the effectiveness of multi-agent collaboration in automated smart contract auditing, offering a scalable, adaptive, and highly efficient solution for blockchain security analysis.
This study utilizes version 6 of the regression analysis of time series (RATS) software package to implement the estimation of the bivariate diagonal generalized autoregressive conditional heteroscedasticity (GARCH) model combined with a composite asset selection approach including two hybrid performance measures to solve âthe trade-off problem between return and riskâ and âthe inconsistent results from different performance measuresâ in the problem of asset allocation within a group of minimum variance portfolios during the pre-COVID-19 and COVID-19 periods. Empirical results show that the optimal portfolios obtained from this approach and the assets added to a portfolio to achieve better performance differ between the pre-COVID-19 and COVID-19 periods. For instance, the optimal portfolios are the Chinese yuan-Ethereum and Bitcoin-Ethereum for the pre-COVID-19 period, but the WTI-Ethereum for the COVID-19 period. To achieve better performance, we added Ethereum to our portfolio during the pre-COVID-19 period, while WTI and Bitcoin were added during the COVID-19 period. Thus, the COVID-19 pandemic had a significant impact on the performance of asset allocation in the three markets. The proposed approaches in this study can be embedded in a computer as an asset allocation algorithm of Robo-advisers.
Smart contracts enable contract terms to be automatically executed and verified on the blockchain, and recent years have witnessed numerous applications of them in areas such as financial institutions and supply chains. The execution logic of a smart contract is closely related to the contract state, and thus the correct and safe execution of the contract depends heavily on the precise control and update of the contract state. However, the contract state update process can have issues. In particular, inconsistent state update issues can arise for reasons such as unsynchronized modifications. Inconsistent state update bugs have been exploited by attackers many times, but existing detection tools still have difficulty in effectively identifying them. This paper conducts the first large-scale empirical study about inconsistent state update vulnerabilities (that is, inconsistent state update bugs that are exploitable) in smart contracts, aiming to shed light for developers, researchers, tool builders, and language or library designers in order to avoid inconsistent state update vulnerabilities. We systematically investigate 116 inconsistent state update vulnerabilities in 352 real-world smart contract projects, summarizing their root causes, fix strategies, and exploitation methods. Our study provides 11 original and important findings, and we also give the implications of our findings. To illustrate the potential benefits of our research, we also develop a proof-of-concept checker based on one of our findings. The checker effectively detects issues in 64 popular GitHub projects, and 19 project owners have confirmed the detected issues at the time of writing. The result demonstrates the usefulness and importance of our findings for avoiding inconsistent state update vulnerabilities in smart contracts.