Given the continuing fascination with “magic computers” and “self-executing code,” it is necessary to re-examine the promises – and premises - of technology-driven improvements to transacting practices purportedly introduced by smart contracts. Contrary to the popular narrative, smart contracts do not eliminate the need for trust and are technically incapable of guaranteeing performance. The fascination with clear and unbreakable rules that are executed by code obfuscates the fact that such rules may be suboptimal and may incorrectly represent what was agreed. It also obscures the fact that it is impossible to write perfect code. Being in plain view and impossible to modify, changes nothing in this regard. Trust and certainty do not magically emerge from immutability or transparency. Regulatory efforts in this area must be based on facts, not fairy tales.
As the Fourth Industrial Revolution gains momentum and involves a plethora of disruptive technology concepts, such as blockchain, they have infiltrated economies that have only experienced a small portion of their scope, consequences, and applications in their different branches. This research aims to examine the potential uses of blockchain technology within the framework of smart contracts in the insurance sector, notably in the event of a pandemic that results in business interruption. Businesses hardly ever take business interruption insurance into account, particularly in a country similar to Lebanon, where natural disasters and pandemics are scarce. Due to the complexity of the task and the numerous requirements for trust in terms of risk consistency, traditional insurance companies are not interested in offering these kinds of insurance contracts. In this current study, a quantitative study was conducted over 213 businesses in various fields and revealed acceptance and socio-demographic differences in the activity sectors of this potentially ground-breaking solution for a developing country that is undergoing a sanitary and economic crisis. As a result, smart contracts and decentralized finance (DeFi) were proposed in the current research as potential solutions to overcome the Lebanese currency devaluation and high insurance costs.
Real-world application development through Smart Contracts on the Ethereum Blockchain platform is one of the emerging technologies. It also has much vulnerability, and reentrancy is among the most popular ones. In our work, we have reviewed the tools based on ML for vulnerability detection in Ethereum smart contracts. Based on that, we proposed a framework that can dynamically monitor threats based on the blockchain platform's transaction meta-data and balance data. It does not require any changes or updates to the existing system and does not require expertise to implement. This framework will extract features for machine learning classifier models from the transaction data and identify the transaction as agreeable or unfavorable. It will help to identify the reentrancy threat as well as the cause of it and help the developer to trace it from where the attack is generated. In the ML classifier for the framework, random forest and decision tree are used. The cumulative performance of both is 98 percent on 540 transactions.
John R. Graham, Jillian Grennan, Campbell R. Harvey, Shivaram Rajgopal
Culture is given credit for some of the greatest business successes and blamed for some of the biggest failures. Policymakers often point to dysfunctional corporate culture in banking as a first-order contributor to the recent financial crisis.1 Several books identify culture as a key driver of Google's success.2 What is corporate culture? How important is corporate culture? What mechanisms underlie the creation and effectiveness of corporate culture?4 How do other formal institutions (e.g., governance or compensation) reinforce or work against culture? Do companies think their culture is effective and if not, what deters firms from having an effective corporate culture? Are the upside benefits of an effective culture greater than the downside costs of ineffective culture? What aspects of business performance does corporate culture affect? Does culture impact firm value, productivity, corporate risk-taking, growth, M&A, financial and tax reporting, whether employees take a long-run view, and/or corporate ethics? How can corporate culture be measured? We try to answer these questions in multiple ways. First, we surveyed 1348 chief executives and financial officers (CEOs and CFOs, referred to interchangeably as executives or managers) across a wide range of North American public and private firms. The details underlying the survey evidence and an econometric investigation into the effects of culture on business outcomes are reported in an accompanying paper5 by the same four authors that supplement this paper; referred to henceforth as GGHR. Second, the survey contained several open-ended questions. We analyze the text of these questions to enhance our understanding of the survey respondents' views of the corporate culture. Third, we conducted in-depth interviews with business executives representing over 20% of the US equity market capitalization. The purpose of this paper is to discuss the interview evidence and the open-ended responses from the survey. We summarize the survey statistics to provide context for the interviews and open-ended responses. Survey evidence offers a number of insights into corporate culture. Briefly, the survey shows that managers are largely united in believing that corporate culture is one of the most important forces behind value creation and the ultimate success or failure of a firm. The majority of executives consider corporate culture to be a top three value driver at their companies. Almost every officer believes that improving their corporate culture would increase their firm's value. The current CEO is seen as the most influential person responsible for setting the firm's current culture. The interviews offer insight into how other firm policies and practices may reinforce or work against the effectiveness of the culture. Boards affect culture not via active management but primarily via CEO choice. The finance function may influence the culture, especially when it serves an internal governance role by acting as steward of integrity. Incentive compensation and hiring, firing, and promotion decisions also may modify the effectiveness of a firm's culture. 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Financial industries operate within a framework of strict regulatory requirements, making compliance a top priority. Smart contracts, integral to the operations of FinTech companies, must align with these regulations. Cloud-based platform offers security as a service (SecaaS) to the scalable and cost-effective solution for analyzing, monitoring, and predicting vulnerabilities in smart contracts. This approach allows FinTech firms to concentrate on their core services while benefiting from specialized security tools. The potential consequences of smart contract vulnerabilities, such as financial losses, fraud, or data manipulation, underscore the critical need for proactive prediction and mitigation. By addressing vulnerabilities in advance, FinTech platforms can prevent financial losses and uphold the integrity of their transactions. Given that FinTech platforms handle customer funds, sensitive financial information, and automated transactions, maintaining trust and reliability is paramount. Predicting vulnerabilities plays a pivotal role in building and sustaining trust among users and stakeholders. This study introduces a hybrid artificial intelligence and optimization technique for smart contract vulnerability prediction in FinTech. The modified barnacles mating optimization (MBMO) algorithm is employed for the extraction of complex syntactic and semantic features, enhancing the accuracy of vulnerability predictions. Additionally, the general regressive artificial neural network (GR-ANN) is utilized to predict vulnerabilities, specifically describing vulnerability types in smart contracts deployed in a cloud environment. The evaluation of this framework involves rigorous testing using the ScrawID-real Ethereum smart contract benchmark dataset, demonstrating its capability and accuracy in predicting smart contract vulnerabilities. The study introduces a novel hybrid artificial intelligence and optimization technique aimed at predicting vulnerabilities in cloud-based smart contracts, specifically in the FinTech sector. Utilizing the modified barnacles mating optimization algorithm and the general regressive artificial neural network, this approach enhances the accuracy of vulnerability detection. The paper demonstrates the methods efficacy through rigorous testing with the ScrawID-real Ethereum smart contract benchmark dataset, highlighting its potential to bolster security in FinTech applications.
Climate insurance has become a crucial issue due to the increasing number of climate-related catastrophic events and the associated losses for the economy in general and insurance companies in particular. The extremely hot and dry summers of 2018 and 2019 in some European countries highlighted existing weaknesses in European agricultural insurance mechanisms, with farmers having to wait for months before compensation payments could be made. Our paper compares features of yield-based insurance and index-based insurance (IBI) in agriculture in the light of new developments and trends in information technology (IT). The results show that applying Distributed Ledger Technologies (DLT) in combination with IBI could not only resolve existing problems but also facilitate the development of innovative risk management tools under the EU’s Common Agricultural Policy (CAP) post-2020 reform.
Chao Ni, Cong Tian, Kaiwen Yang, David Lo · 6 authors
Smart contract, a special software code running on and resided in the blockchain, enlarges the general application of blockchain and exchanges assets without dependence of external parties. With blockchain’s characteristic of immutability, they cannot be modified once deployed. Thus, the contract and the records are persisted on the blockchain forever, including failed transactions that are caused by runtime errors and result in the waste of computation, storage, and fees. In this paper, we refer to smart contracts which will cause runtime errors as crash-inducing smart contracts. However, automatic identification of crash-inducing smart contracts is limited investigated in the literature. The existing approaches to identify crash-inducing smart contracts are either limited in finding vulnerability (e.g., pattern-based static analysis) or very expensive (e.g., program analysis), which is insufficient for Ethereum.To reduce runtime errors on Ethereum, we propose an efficient, generalizable, and machine learning-based crash-inducing smart contract detector, CRASHSCDET, to automatically identify crash-inducing smart contracts. To investigate the effectiveness of CRASHSCDET, we firstly propose 34 static source code metrics from four dimensions (i.e., complexity metrics, count metrics, object-oriented metrics, and Solidity-specific metrics) to characterize smart contracts. Then, we collect a large-scale dataset of verified smart contracts (i.e., 54,739) and label these smart contracts based on their execution traces on Etherscan. We make a comprehensive comparison with three state-of-the-art approaches and the results show that CRASHSCDET can achieve good performance (i.e., 0.937 of F1-measure and 0.980 of AUC on average) and statistically significantly improve the baselines by 0.5%-60.4% in terms of F1-measure and by 41.2%-44.3% in terms of AUC, which indicates the effectiveness of static source code metrics in identifying crash-inducing smart contracts. We further investigate the importance of different types of metrics and find that metrics in different dimensions have varying abilities to depict the characteristic of smart contracts. Especially, metrics belonging to the "Count" dimension are the most discriminative ones but combining all metrics can achieve better prediction performance.
The banking sector faces significant challenges in managing operational and transactional risks, which can result in financial losses, inefficiencies, and reputational damage. With its unique attributes of decentralization, transparency, immutability, and advanced cryptographic security, blockchain technology offers a transformative solution to these challenges. This paper explores the role of blockchain in mitigating operational risks, such as human error, fraud, and system failures, through automation, enhanced auditability, and process accountability. It also examines how distributed ledger technology addresses transactional risks by improving payment security, minimizing settlement delays, and enhancing data integrity. The paper highlights the key benefits of blockchain adoption for risk management and provides recommendations for its effective implementation, including the need for regulatory adaptation, technological investment, and cross-sector collaboration. This analysis underscores the potential of blockchain to revolutionize banking operations and strengthen risk management frameworks in the financial sector.
We review the recent developments in dynamic inventory models with financial flow considerations. The focus is on the literature that introduces cash flow dynamics into the classic inventory models that do not explicitly consider the interactions between physical (or material) and financial flows. These augmented models serve two important purposes. First, they help understand the impact of financial flows on inventory dynamics and decisions. Second, with the connection to the classic inventory models, one can leverage the extant results to derive the optimal control policy or to evaluate/optimize the performance of any given type of policy and reveal insights. We summarize models for both single-stage and multi-stage inventory systems, and discuss the implications and applications to decentralized systems within a broader topic of supply chain finance.
The Legality and Effectiveness of Smart Contracts, As Well As Its Impact on Traditional Concepts of Contract Law Karwan Dhahir Saber1 and Rebin Ali Mohammed Ameen2 1Business and Management Department, Faculty of Administrative Sciences and Economics Tishk International University, Erbil, Kurdistan Region, Iraq 2Director-General of Administrative and Financial Affairs, Ministry of Natural Resources, Erbil, Iraq […]
Jan 1, 2022·Proceedings of the International Conference on Information Economy, Data Modeling and Cloud Computing, ICIDC 2022, 17-19 June 2022, Qingdao, China
As an emerging technology, smart contract has attracted more and more attention. In order to deeply analyze the research status and development trend in the field of smart contract at home and abroad, we take the core journals of CNKI and ScienceNet database in recent ten years as the research objec
Abstract Changing patterns of risk aversion may follow a non-linear counter-cyclical process. However, the evidence so far has not considered developing cryptocurrency markets. Given some unique features of cryptocurrencies, it is interesting to distinguish how these assets differ from traditional products. This paper investigates the time effects of periodicity on risk aversion for a selection of major cryptocurrencies compared to major financial assets. Significant periodic time-varying patterns are identified when analysing risk aversion. Further, bilateral and bidirectional Granger causalities are identified within cryptocurrencies, as well as between cryptocurrencies and traditional financial assets. Bitcoin is identified as a leading information transmitter of the spillover of risk aversion upon other cryptocurrencies, while estimated risk aversion of traditional financial markets plays a dominant role in the spillover processes upon the cryptocurrency cluster. The latter finding presents further evidence of developing cryptocurrency market maturity. The COVID-19 pandemic is found to have significantly influenced the connectedness of risk aversion among cryptocurrency and traditional financial markets.
This research aims to improve and integrate hospital's healthcare applications with Blockchain and smart contracts technologies to provide huge and secure storage that is immutable. This application will be able to record the patients' medical history like appointments, medical tests, etc.; As a matter of fact, these resources should be recorded to be securely retrieved, modified, and stored by an authorized party only. The utilization of these critical resources will increase the validity for participants with a high level of liability, where building a scheduling appointment system using the blockchain-based on a smart contract will enhance patients' privacy and provides a safer method to keep data away from altering through an unofficial use. COVID-19 Coronavirus is a global disaster that requires a reliable and stable network-based application with a giant and secure platform to hold a huge number of people and settings. The simulated outcomes of the developed system were significant and extremely noteworthy according to immutability and correctness.