Decentralized Finance (DeFi) has revolutionized lending by replacing intermediaries with algorithm-driven liquidity pools. However, existing platforms like Aave and Compound rely on static interest rate curves and collateral requirements that struggle to adapt to rapid market changes, leading to inefficiencies in utilization and increased risks of liquidations. In this work, we propose a dynamic model of the lending market based on evolving demand and supply curves, alongside an adaptive interest rate controller that responds in real-time to shifting market conditions. Using a Recursive Least Squares algorithm, our controller tracks the external market and achieves stable utilization, while also controlling default and liquidation risk. We provide theoretical guarantees on the interest rate convergence and utilization stability of our algorithm. We establish bounds on the system's vulnerability to adversarial manipulation compared to static curves, while quantifying the trade-off between adaptivity and adversarial robustness. We propose two complementary approaches to mitigating adversarial manipulation: an algorithmic method that detects extreme demand and supply fluctuations and a market-based strategy that enhances elasticity, potentially via interest rate derivative markets. Our dynamic curve demand/supply model demonstrates a low best-fit error on Aave data, while our interest rate controller significantly outperforms static curve protocols in maintaining optimal utilization and minimizing liquidations.
"Recent years have witnessed the rise of non-fungible tokens (NFTs) as vehicles for non-investment finance, including in nonprofit and political fundraising. As with other financial sectors in which NFTs have a role, the use of NFTs in financing nonprofits and political campaigns and committees has revealed gaps and ambiguities in existing legal regulatory systems. Appetite exists to evolve legal frameworks to complete and clarify applicable bodies of law and regulation.
Minthiva Pitchaya-Auckarakhun, Kasama Kasorn, Pornchai Jedaman, Sanya Kenaphoom
Compared to traditional financial institutions, new-generation investors prefer speculative assets like cryptocurrencies, NFTs, and DeFi platforms due to their high return potential and Blockchain appeal. This shift has resulted in increased portfolio volatility and a desire for financial independence, compelling the financial industry to adapt. The study, which uses secondary data, identifies trends in investment behavior that differ from previous generations. According to the findings, new investors are reshaping the investment landscape by leveraging technology, values-based investing with ESG criteria, and decentralized finance (DeFi). Their preference for digital platforms and AI improves accessibility and personalization, whereas DeFi opens up new possibilities and challenges. Traditional financial institutions must adopt technology and sustainability trends to remain competitive as new-generation investors reshape the investment landscape through technology, ethical investing, and decentralized finance (DeFi).
While studying the scenarios of sensitive data cross-domain sharing, we found there was no security assessment of smart contracts within existing data sharing schemes, which might lead to data leakage or unauthorized data access. To address this issue, we proposed a comprehensive security assessment of the smart contracts involved in existing data sharing solutions. In which, we extended the current smart contract tools to include detection capabilities for 7 additional types of vulnerabilities and implemented automatic repair functions for smart contracts. We also designed 3 types of smart contracts for the Hyperledger Fabric platform to facilitate sensitive data cross-domain sharing, specifically for data upload, data access, and data management functions to make sure that complex vulnerabilities could be automatically repaired. In our designation, the smart contracts for data upload and data access utilize the CP-ABE method and the data management smart contract includes various functions for secure management and tracing of data on the blockchain, which collectively ensure the security and comprehensive functionality of sensitive data cross-domain sharing scheme.
Blockchain-based digital assets, particularly cryptocurrencies and non-fungible tokens (NFTs), have gained significant popularity in recent years due to their unique attributes, benefits and challenges. This book chapter aims to provide a comprehensive understanding of the financial reporting and valuation of digital assets. The chapter is divided into three sections, with the first section presenting an overview of digital assets, highlighting the attributes, benefits and challenges of cryptocurrencies and NFTs. The second section delves into the valuation and financial reporting of digital assets, including the regulatory frameworks for cryptocurrencies and NFTs, tax implications for these assets and the types of valuation methodology used. The final section explores the future prospects of cryptocurrencies and NFTs, considering their global impact and adoption on the economy. The chapter discusses the similarities and differences between cryptocurrencies and NFTs. Both types of digital assets rely on blockchain technology; but cryptocurrencies represent fungible digital assets that can be exchanged for other assets or currencies, while NFTs are unique digital assets that represent ownership of a specific asset, such as artwork or music. In terms of valuation, the chapter discusses various methodologies used to value digital assets, including the cost, market, and income approaches and how these methods may be adapted to account for the unique characteristics of digital assets. Furthermore, the chapter provides insights into the financial and corporate reporting requirements for digital assets and how they may differ from traditional financial reporting standards. The regulatory framework for digital assets is also discussed with a focus on the current regulatory environment and the need for clarity and consistency in regulations to promote investor confidence and protect consumers. The chapter also explores the impact of taxation on cryptocurrencies and NFTs, highlighting the complexities of tax regulations and compliance considerations for digital assets. It examines the current tax regulations and their potential impact on the valuation and reporting of digital assets. The chapter also discusses the future prospects of cryptocurrencies and NFTs, considering their potential impact on the global economy, including their role in disrupting traditional financial systems and promoting financial inclusion. Finally, the chapter provides recommendations and best practices for stakeholders to consider when investing, valuing or reporting digital assets.
Recent advancements in anti-money laundering (AML) strategies and the emergence of new government smart contract platforms underscore the need for more accurate and efficient detection systems. This paper proposes a robust approach to significantly reduce false positive alerts utilizing machine learning (ML) to enhance the AML framework within the Brazilian Smart Contract/Digital Currency (DREX) platform, which is built on the Hyperledger Besu technology and expected to be fully operational in 2025. By integrating data from ‘Know Your Customer’ (KYC) due diligence databases and comprehensive transactional datasets, our methodology identifies high-risk transactions while adhering to stringent regulatory standards and minimizing operational costs. A comparative analysis of various ML models, including Decision Trees, Random Forest, Logistic Regression, and Artificial Neural Networks (ANN), among others, revealed that the Decision Tree model notably decreased false positive rates to 6.1% while maintaining a high detection rate for potentially illicit transactions. This model surpasses traditional rule-based systems in performance, confirming its efficacy and suitability for broad implementation. By streamlining the smart contract AML process and reducing compliance-related expenditures, this study presents a scalable and efficient approach that enhances operational efficiency and cost-effectiveness for financial institutions in combating economic crimes.
Josip Zilic, Vincenzo De Maio, Shashikant Ilager, Ivona Brandić
Mobile devices offload latency-sensitive application tasks to edge servers to satisfy applications' Quality of Service (QoS) deadlines. Consequently, ensuring reliable offloading without QoS violations is challenging in distributed and unreliable edge environments. However, current edge offloading solutions are either centralized or do not adequately address challenges in distributed environments. We propose FRESCO, a fast and reliable edge offloading framework that utilizes a blockchain-based reputation system, which enhances the reliability of offloading in the distributed edge. The distributed reputation system tracks the historical performance of edge servers, while blockchain through a consensus mechanism ensures that sensitive reputation information is secured against tampering. However, blockchain consensus typically has high latency, and therefore we employ a Hybrid Smart Contract (HSC) that automatically computes and stores reputation securely on-chain (i.e., on the blockchain) while allowing fast offloading decisions off-chain (i.e., outside of blockchain). The offloading decision engine uses a reputation score to derive fast offloading decisions, which are based on Satisfiability Modulo Theory (SMT). The SMT models edge resource constraints, and QoS deadlines, and can formally guarantee a feasible solution that is valuable for latency-sensitive applications that require high reliability. With a combination of on-chain HSC reputation state management and an off-chain SMT decision engine, FRESCO offloads tasks to reliable servers without being hindered by blockchain consensus. We evaluate FRESCO against real availability traces and simulated applications. FRESCO reduces response time by up to 7.86 times and saves energy by up to 5.4% compared to all baselines while minimizing QoS violations to 0.4% and achieving an average decision time of 5.05 milliseconds.
Abstract In recent years, new and technologically innovative financial products and services, generally subsumed under the fintech umbrella, have permeated all areas of capital markets at an exponential rate. Primarily driven by developments in Web3 and advancements in artificial intelligence (AI), fintech solutions offer valuable benefits to all existing markets and participants and are the basis for introducing wholly new segments to classic capital market ecosystems. However, this increasing fintech adaptation does not come without challenges. Due to the technologies' nascent nature and often unregulated status, many products are susceptible to manipulation and fraud. The result can be sizable investor losses and excessive regulatory and public scrutiny. This chapter highlights the most essential and prominent fintech solutions used in capital markets today, along with their features, value additiveness, and degree of adaptation.
Abstract State-of-the-art macroeconomic agent-based models (ABMs) include an increasing level of detail in the energy sector. However, the possible financing mechanisms of renewable energy are rarely considered. In this study, an investment model for power plants is conceptualized, in which energy investors interact in an imperfect and decentralized market network for credits, deposits and project equity. Agents engage in new power plant investments either through a special purpose vehicle in a project finance (PF) structure or via standard corporate finance (CF). The model portrays the growth of new power generation capacity, taking into account technological differences and investment risks associated with the power market. Different scenarios are contrasted to investigate the influence of PF investments on the transition. Further, the effectiveness of a simple green credit easing (GCE) mechanism is discussed. The results show that varying the composition of the PF and CF strategies significantly influences the transition speed. GCE can recover the pace of the transition, even under drastic reductions in PF. The model serves as a foundational framework for more in-depth policy analysis within larger agent-based integrated assessment models.
In the modern era of digital education, ensuring the authenticity and security of academic credentials is crucial for maintaining trust and efficiency. Blockchain technology, with its secure, transparent, and immutable nature, offers promising solutions, but static smart contracts have limitations in dynamic environments. This paper proposes a novel approach utilizing dynamic smart contracts and non-fungible tokens (NFTs) to revolutionize diploma verification systems. Dynamic smart contracts provide the flexibility needed to adapt to changing educational policies and requirements without redeploying the entire contract, while NFTs ensure each diploma is uniquely identifiable and tamper-proof. Our proposed system leverages smart contract interaction to automate and streamline the complex processes between educational institutions, students, and employers. Empirical assessments demonstrate significant improvements in processing speed and accuracy, confirming the practical benefits of this approach. This study provides new insights into how blockchain technology can be effectively applied in the education sector to enhance the integrity and reliability of academic credentials. By focusing on a conceptual model and demonstrating its potential through testing and evaluation, this research lays the groundwork for future implementations that could significantly outperform traditional methods in terms of efficiency, security, and adaptability.
Hala S. Omar, Tamer O. Diab, Wageda I. El sobky, M. A. Elsisy
Abstract This paper presents how smart contracts are based on mathematics. Smart contracts rely on mathematics to guarantee their immutability, security, and enforceability. Cryptographic procedures that are used to safeguard and confirm the contract’s implementation, including hash functions and digital signatures, might be used to illustrate this. Mathematical approaches known as hash functions embrace an input of arbitrary size and generate a fixed-size digest or hash. It is impossible to go backwards the process and ascertain the input from the outcome since the outcome is specific to the input. Digital signature techniques are used for digitally signing smart contracts. The most well-known digital signature schemes—Schnorr, Elgamal, and Elliptic curve schemes—that are employed in smart contracts are described in this research.
Pierluigi Martino, Tom Vanacker, Igor Filatotchev, Cristiano Bellavitis
Abstract Drawing on institutional and demand-side perspectives, we investigate performance implications of (de)centralized governance modes in platform-based new ventures, and the conditions under which (de)centralization generates more value. Using a sample of 1,431 Initial Coin Offerings (ICOs), a new source of entrepreneurial finance, we find that centralization of decision-making is positively associated with platforms’ market value. Further, we consider how platform characteristics affect this relationship, finding that both the presence of an experienced Chief Technology Officer (CTO) and project transparency negatively moderate the positive relationship between centralization and market value. Thus, decentralized platforms need leaders with technical experience and project transparency to generate more value. Overall, this study provides a better understanding of the boundary conditions that increase the value of (de)centralized governance.
The research investigates cryptocurrency's function in enhancing Pakistani financial market portfolios while examining the digital asset popularity, surged as an investment choice. The analysis combines cryptocurrencies with conventional financial products to show how they affect both risk performance and risk spread capabilities. The main goal of this research is to understand if adding cryptocurrency investments produces superior returns than standard asset allocation strategies. This study intends to join the current discussion regarding digital asset adoption in emerging economies particularly Pakistan. A time period of six years extending from January 1, 2018 to December 31, 2023 contains daily financial data which includes both traditional assets and cryptocurrencies. The dataset receives preprocessing treatments which include normalization together with outlier removal and missing value imputation. The portfolio optimization process in Jupiter Notebook implements machine learning models under naïve equal weighting and maximum return and maximum Sharpe ratio and minimum variance constraints. Excel was used to run robustness checks for the analysis which demonstrated that cryptocurrency portfolios generate higher risk-adjusted performance than traditional investment collections. This research presents digital assets as a valid investment strategy component by improving portfolio diversity and overall performance while focusing specifically on the Pakistani financial market through combination of machine learning and standard financial modeling. The research enhances available scientific understanding of cryptocurrency integration in emerging market economies while failing to find sufficient existing literature on this subject matter. Succeeding studies should analyze digital asset regulatory measures and economic conditions alongside investor acceptance patterns towards crypto adoption in Pakistan. The research could benefit from additional analysis that incorporates alternative risk management approaches alongside sophisticated portfolio optimization algorithms.
Rob McLaughlin, Nir Chemaya, Dingyue Liu, Dahlia Malkhi
This paper introduces a trade ordering rule that aims to reduce intra-block price volatility in Automated Market Maker (AMM) powered decentralized exchanges. The ordering rule introduced here, Clever Look-ahead Volatility Reduction (CLVR), operates under the (common) framework in decentralized finance that allows some entities to observe trade requests before they are settled, assemble them into "blocks", and order them as they like. On AMM exchanges, asset prices are continuously and transparently updated as a result of each trade and therefore, transaction order has high financial value. CLVR aims to order transactions for traders' benefit. Our primary focus is intra-block price stability (minimizing volatility), which has two main benefits for traders: it reduces transaction failure rate and allows traders to receive closer prices to the reference price at which they submit their transactions accordingly. We show that CLVR constructs an ordering which approximately minimizes price volatility with a small computation cost and can be trivially verified externally.
This research paper delves into the intricate world of smart contract derivatives, aiming to unravel the technical intricacies and explore their applications. Smart contract derivatives represent a burgeoning intersection of blockchain technology and financial instruments, providing decentralized and automated solutions for derivative trading. The paper navigates through the complex landscape of smart contract derivatives, addressing both the technical aspects of their implementation and the diverse range of applications they unlock. Through a comprehensive review of existing literature, case studies, and real-world examples, this research aims to provide a holistic understanding of the challenges, opportunities, and implications associated with smart contract derivatives. By comprehensively addressing both the technical intricacies and practical applications of smart contract derivatives, this study contributes valuable insights into the rapidly evolving field of decentralized finance.
Michael Osinakachukwu Ezeh, Adindu Donatus Ogbu, Augusta Heavens Ikevuje, Emmanuel Paul-Emeka George
Effective contract management is critical for the energy sector, where complex agreements and regulatory requirements demand precision and oversight. Leveraging technology for improved contract management can transform how energy companies manage their contracts, enhancing efficiency, compliance, and strategic alignment. This paper explores the impact of technological advancements on contract management processes in the energy sector, emphasizing digital solutions and automation. The energy sector deals with multifaceted contracts involving various stakeholders, including suppliers, contractors, regulatory bodies, and customers. Traditional contract management methods, often characterized by manual processes and paper-based documentation, are prone to errors, delays, and inefficiencies. Technology, particularly contract lifecycle management (CLM) software, offers comprehensive solutions to these challenges by digitizing and automating contract management processes. CLM software facilitates the entire contract lifecycle, from drafting and negotiation to execution and renewal. These platforms provide centralized repositories for all contract documents, ensuring easy access and retrieval. Advanced features such as automated alerts and notifications for key dates and obligations help companies stay compliant with contractual and regulatory requirements, reducing the risk of penalties and legal disputes. Moreover, artificial intelligence (AI) and machine learning (ML) capabilities integrated into CLM solutions enable intelligent contract analysis and risk assessment. AI-driven tools can extract critical data from contracts, identify potential risks, and suggest mitigative actions. This predictive insight enhances decision-making, allowing energy companies to proactively address issues before they escalate. Blockchain technology also holds significant potential for contract management in the energy sector. Smart contracts, enabled by blockchain, offer a secure and transparent way to automate contractual obligations. These self-executing contracts reduce the need for intermediaries and enhance trust among parties, ensuring that terms are met efficiently and without dispute. In addition to these technologies, cloud-based platforms offer scalability and flexibility, allowing energy companies to manage contracts remotely and collaboratively. This is particularly beneficial in an industry where projects span multiple locations and jurisdictions. In conclusion, leveraging technology for contract management in the energy sector results in streamlined processes, improved compliance, and enhanced strategic alignment. By adopting digital solutions and automation, energy companies can mitigate risks, reduce costs, and drive operational efficiency, ultimately contributing to their sustainability and competitiveness in a rapidly evolving market. Keywords: Leveraging, Technology, Energy Sector, Contract Management, Improved.
In this zealous word, for a better understanding of a decentralized system, might take a revolution in the traditional banking system. This chapter examines the relationship between decentralized finance that is DeFi and technology readiness in the context of a smart global value chain. As a substitute for the centralized finance system, blockchain technology creates the groundwork for efficiency, transparency, and safety. It helps in a decentralized financial ecosystem. This chapter provides a deep introduction, working and delving into the concept of DeFi. This chapter talks about significant elements with the combination of blockchain census process, (DAPPs) that is decentralized apps, and smart contracts. Secondly, it also examines the decentralized banking system (DeFi) that is currently used in terms of technology maturity, with elements like interoperability, safety, security, and scalability. With the help of a decentralized system development of DeFi is discussed under the friction of lower transactions, democratizing access to financial services, and reducing the risk of counterparty. Various case studies and empirical research demonstrate how DeFi banking practices are posing a threat. DeFi promotes financial inclusion and is also beneficial economically and opens new doors for economic benefits. This chapter evaluates the regulatory and financial benefits of the DeFi method. Artificial intelligence and Internet of Things (IoT) and technology are also covered in it. For the decentralized banking services complex technologies and other financial ecosystems can be improved so that working on technologies, AI can increase and make work more effective, quick, and easy for making the system decentralized.
Legal scholars highlight the tensions that exist between different classes of shareholders in startups. We model a startup owned by undiversified investors with heterogeneous capital contributions and risk preferences. A social planner runs the firm on behalf of all investors. We compare investors’ expected utility with a hypothetical first-best decentralized benchmark. The startup’s optimal investment policy is procyclical and a time-varying weighted average of shareholders’ optimal investment policies. The optimal contracts issued to investors are tailor-made, interdependent, and include equity claims resembling preferred stock with heterogeneous payout caps, leading to a complex capitalization table as more investors join the startup. This paper was accepted by Will Cong, finance. Funding: This work was supported by the Cambridge Endowment for Research in Finance and Keynes Fellowship. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2022.01724 .
This paper investigates the application of Machine Learning for credit risk assessment in Multichain Decentralized Finance (DeFi). With DeFi expanding its scope, the need for effective credit risk evaluation becomes paramount. Our study utilizes a diverse dataset gathered from multiple blockchains, including Ethereum, and employs rigorous data preprocessing techniques. DeFi-specific features are extracted, capturing transaction-related statistics. Machine learning models, such as Logistic Regression, Random Forest, XGBoost, CatBoost, LightGBM and a CNN, are deployed to predict wallet liquidations. Evaluation metrics, including accuracy, ROC curve and Area Under the Curve, demonstrate the efficacy of DeFi-related features in credit risk assessment. Furthermore, we analyze feature importance and inter-feature correlations, providing insights into critical risk factors within the DeFi ecosystem. This research contributes valuable insights to the DeFi landscape, offering data-driven approaches to credit risk management and investment strategies. Our findings hold significance for DeFi stakeholders seeking to navigate the evolving financial frontier while mitigating credit risk effectively.