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.
Distributed ledger technologies (DLTs) are expected to disrupt the corporate finance field by offering a cheaper, quicker and simpler funding alternative for companies, thus playing a relevant role in improving access to the financial markets. This paper seeks to test this premise. Based on information collected from advisors in two transactions completed in the market, the paper compares the process of a traditional international bond issue with one on a DLT platform. The paper clarifies how a DLT-based bond issue is conducted in practice and concludes that such issues are not more affordable or time efficient and are equally complex when compared to a conventional issue. These conclusions are, however, limited in scope due to the case study methodolody adopted. The conclusions may also be impacted by the analysis of legal matters beyond the process of issuance and by the consideration of quantitative data concerning the costs incurred with the issue.
Abstract This chapter explains why policymakers should take a precautionary approach to fintech innovations. It devotes considerable time to exploring the catastrophic consequences of the financial crisis of 2008, and the financial innovations that helped cause it. It also engages in a thought experiment, imagining how distributed ledgers and smart contracts could have made the last crisis even worse had they existed in the mid-2000s. The purpose of this historical review and thought experiment is to vividly demonstrate the potential harms associated with new financial technologies: as a society, we tend to support precautionary regulation of pharmaceuticals and self-driving cars because we recognize their potential to threaten human lives, but we’re less comfortable with precautionary regulation intended to protect our financial system. To help overcome this reluctance toward precautionary regulation of new financial technologies, this chapter demonstrates that the costs of financial crises are not just economic, but also tear at the fabric of our society.
Abstract This chapter uses mobile payments and distributed ledger business models and technologies as case studies to demonstrate how operational problems in the payments system can trigger and transmit financial distress. It also examines proposals for central bank digital currencies (CBDCs) and proposals to use distributed ledger technology for post-trade processing, concluding that while there are strong use cases for these types of fintech innovations, they are not without financial stability risks. This chapter therefore stresses the need for a new type of “macro-operational” regulation that responds to the potential systemic interactions of operational problems. Finally, this chapter considers the risks associated with increased reliance on a small group of third-party technology vendors (particularly cloud computing vendors).
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
The financial industry continues to evolve, specifically in payment processing. Payment processing is at the forefront of technological advancements, significant growth, and constant change. Companies desiring to grow their consumer base must remain current with trends and developments in payment processing. Payment processing has entered a new era of technology advancements inspired by AI, ML, and cloud computing, impacting operations and transforming businesses in today’s marketplace. The banking industry faces fierce competition from agile Fintech players that leverage technology to deliver services and new offerings faster and cheaper. Significant public sector funding catalyzed the growth of open and responsive payments ecosystems. Innovative combination models like Buy now, pay later (BNPL) and crypto-currency are springing forth, shifting risks from the lenders as well as intermediaries to the consumers, end-users, and providers. In blue-ocean areas such as e-Commerce, instant, cross-border payments, and digital assets, competition is not only intense, but the playing field is constantly growing and redefining itself. Vendors in on-and-off line electronic payments across different ecosystem parameters—service provider, regulation, traffic, payment format—have the latitude to partner or compete with each other, creating a complex environment defined by misaligned incentives on legacy systems, incomplete data, and bureaucratic inertia. On the other hand, irrespective of the payment type, frauds are nearing pre-global-financial-crisis levels, and direct losses are growing exponentially. Payment vulnerabilities translating into cyber risks and data breaches loom large, and hacking the supply chain has become a top concern for enterprises. Federated Financial Institutions, Private Sector Players, Financial Data Exchanges, and RegTech will need to collaborate and invest in differentiated technology capabilities to secure their pieces of a slim pie. It would require immediate injections of cash upfront, time To Income (TTI) on legacy IT assets, acquisitions of technology providers, setting up scalable and proactive test environments, and a radical shift in the approach to assessing partners and vendors. Growing customer expectations for payments to flow instantly are forcing banks to intensify their innovation programs and invest in real-time payment systems. There are dedicated task forces at supervisors and regulators worldwide Engineering Distributed-control and Self-governed Payment Systems; Global Consensus Standardization of Payment Internet Protocols; and morphing payment systems into programmable platforms. Banks are even incentivized to capture new geographies and market segments by embracing Public Open Payment Protocols, Distributed-ledger Technology, and Edge-based Devices for Processing Payment Signals with control residing at the edge.
In the present study, the concept of smart contract is considered. The emergence of smart contracts is associated with the application of new technological solutions in various public spheres. The main goal is to analyze the individual opinions and to point out their peculiarities in comparison with classical contracts.