This chapter introduces the foundational blocks of open banking and why PSD2 was instrumental in creating the phenomenon, with a worldwide overview of adoption and practical strategic advice. It covers alternative payment methods (e.g. digital wallets, money remittance, delayed payments, virtual assets) and provide real-life examples. It also explains the concepts of money, eMoney, and Central Bank Digital Currency (CBDC), and introduces the difference between centralised and decentralised payment infrastructures, covering the concepts of Distributed Ledger Technology (DLT) and blockchain, and how decentralised authorisation, clearing, and settlement function. It will also introduce the drivers for change, worldwide market statistics and projections, the role of Big Tech and technology, how the Metaverse applies to payments, and the differences between Web 2.0 and Web 3.0.
Aaron M. Green, Michael P. Giannattasio, John Erickson, Oshani Seneviratne · 5 authors
We propose a survival analysis approach for discovering and characterizing user behavior and risks for lending protocols in decentralized finance (DeFi). We demonstrate how to gather and prepare DeFi transaction data for survival analysis. We illustrate our approach using transactions in Aave, one of the largest lending protocols. We develop a DeFi survival analysis pipeline that first prepares transaction data for survival analysis through the selection of different index events (or transactions) and associated outcome events. Then we apply survival analysis statistical and visualization methods modified for competing risks when appropriate, such as Kaplan–Meier survival curves, cumulative incidence functions, Cox hazard regression, and Fine-Gray models for sub-distribution hazards to gain insights into usage patterns and risks within the protocol. We show how, by varying the index and outcome events as well as covariates, we can use DeFi survival analysis to answer questions like “How does loan size affect the repayment schedule of the loan?”; “How does loan size affect the likelihood that an account gets liquidated?”; “How does user behavior vary between Aave markets?”; “How has user behavior in Aave varied from quarter to quarter?” The proposed DeFi survival analysis can easily be generalized to other DeFi lending protocols. By defining appropriate index and outcome events, DeFi survival analysis can be applied to any cryptocurrency protocol with transactions.
ABSTRACT ‘Decentralized finance’ (DeFi) refers to a range of applications in the crypto-asset space that seek to disintermediate the provision of financial services through reliance on self-executing computer code (‘smart contracts’). DeFi has so far been mainly self-referential in that it has largely facilitated the financing and trading of crypto-assets rather than providing intermediation services to support real economic activity. Yet this may change in the future, should asset tokenization or the use of DeFi applications by existing financial institutions lead to greater interconnections with traditional finance (TradFi). We argue that many of the functions that DeFi tries to mimic are similar to those in TradFi, and so are many of the risks that this intermediation entails. The same economic rationale that has guided financial regulation for decades can hence be applied to the crypto and DeFi world as well. Risks in DeFi are often exacerbated by the severity of market failures (externalities and information asymmetries). Having compared the functions performed in TradFi and DeFi, we show how regulation to protect consumers, maintain market integrity, and ensure financial stability applies to DeFi. Finally, we sketch a possible approach to the regulation of DeFi that takes into account its specificities and functions.
Blockchain interoperability refers to the ability of blockchains to share information with each other. Decentralized Exchanges (DEXs) are peer-to-peer marketplaces where traders can exchange cryptocurrencies. Several studies have focused on arbitrage analysis within a single blockchain, typically in Ethereum. Recently, we have seen a growing interest in cross-chain technologies to create a more interconnected blockchain network. We present a framework to study cross-chain arbitrage in DEXs. We use this framework to analyze cross-chain arbitrages between two popular DEXs, PancakeSwap and QuickSwap, within a time frame of a month. While PancakeSwap is implemented on a blockchain named BNB Chain, QuickSwap is implemented on a different blockchain named Polygon. The approach of this work is to study the cross-chain arbitrage through an empirical study. We refer to the number of arbitrages, their revenue as well as to their duration. This work lays the basis for understanding cross-chain arbitrage and its potential impact on the blockchain technology.
The prevalence of maximal extractable value (MEV) in the Ethereum ecosystem has led to a characterization of the latter as a dark forest. Studies of MEV have thus far largely been restricted to purely on-chain MEV, i.e., sandwich attacks, cyclic arbitrage, and liquidations. In this work, we shed light on the prevalence of non-atomic arbitrage on decentralized exchanges (DEXes) on the Ethereum blockchain. Importantly, non-atomic arbitrage exploits price differences between DEXes on the Ethereum blockchain as well as exchanges outside the Ethereum blockchain (i.e., centralized exchanges or DEXes on other blockchains). Thus, non-atomic arbitrage is a type of MEV that involves actions on and off the Ethereum blockchain. In our study of non-atomic arbitrage, we uncover that more than a fourth of the volume on Ethereum's biggest five DEXes from the merge until 31 October 2023 can likely be attributed to this type of MEV. We further highlight that only eleven searchers are responsible for more than 80% of the identified non-atomic arbitrage volume sitting at a staggering $132 billion and draw a connection between the centralization of the block construction market and non-atomic arbitrage. Finally, we discuss the security implications of these high-value transactions that account for more than 10% of Ethereum's total block value and outline possible mitigations.
Chit fund is a peer-to-peer saving and borrowing scheme operated among trusted groups of people. It is a reliable source of funds in emergencies with no guarantor and low-interest rate. Despite their enduring benefits, chit funds face challenges related to trust, transparency, and security. At present, unscrupulous subscribers might join chit-funds, borrow money, and make payment defaults. This leads to disruptions in contribution cycle and affects overall functioning. Additionally, non-transparent record-keeping and transaction processes, hinder participants from verifying fund activities, creating a susceptible environment for fraud and malpractice. These challenges are an obstacle to the sustainability and trustworthiness of the chit-fund system. Therefore, to overcome aforementioned challenges, this paper proposes TruChit, a blockchain-based chit fund system with creditworthiness evaluation framework. It leverages the Adaptive Neuro-Fuzzy Inference System (ANFIS) to assess the creditworthiness of subscribers, which ensures the credibility and reliability of individual participants. Further, proposes permissioned blockchain-assisted chit-fund framework with role-based access control to instill trust, security, and transparency within chit-fund operations. Moreover, the efficacy of TruChit is evaluated by analyzing credit score dataset and achieves 93.5% accuracy, which is better than other approaches.
The implementation of the Know Your Customer (KYC) strategy by banks within the financial sector enhances the operational efficiency of such establishments. The data gathered from the client during the KYC procedure may be applied to deter possible fraudulent activities, money laundering, and other criminal undertakings. The majority of financial institutions implement their own KYC procedures. Furthermore, a centralized system permits collaboration and operation execution by multiple financial institutions. Aside from these two scenarios, KYC processes can also be executed via a blockchain-based system. The blockchain’s decentralized network would be highly transparent, facilitating the validation and verification of customer data in real-time for all relevant stakeholders. In addition, the immutability and cryptography of the blockchain ensure that client information is secure and immutable, thereby eradicating the risk of data breaches. Blockchain-based KYC can further improve the client experience by eliminating the requirement for redundant paperwork and document submissions. After banks grant consumers loans, a blockchain-based KYC system is proposed in this study to collect limit, risk, and collateral information from them. The approach built upon Ethereum grants financial institutions the ability to read and write financial data on the blockchain network. This KYC method establishes a transparent, dynamic, and expeditious framework among financial institutions. In addition, solutions are discussed for the Sybil attack, one of the most severe problems in such networks.
Malawi's financial sector is embracing digital transformation to achieve greater financial inclusion and efficiency. Traditional credit scoring methods struggle with incomplete data, hindering access to credit. This paper proposes a unified banking interface that leverages blockchain technology, machine learning, and digital wallets. The interface will streamline credit assessment, facilitate secure lending, and promote financial inclusion. By linking bank accounts, creating virtual credit cards, and enabling smart contracts, this paper aims to build a more robust, efficient, and inclusive financial ecosystem in Malawi.
The original motivation for the concept paper introducing the Bitcoin blockchain and distributed ledger technology was to enable peer-to-peer transfers of currency and thereby eliminate the role of fiat money, banks and central banks in payments systems worldwide. Although subsequent generations of blockchains have been designed to enable additional applications such as the transfer of artifacts other than their native currencies (eg, tokenized bonds, nonfungible token digital artwork and items or objects purchased in online video games), the most prominent networks continue to stress their decentralized payments or “currency†applications. In this paper we examine the feasibility of the widespread adoption of cryptocurrencies in payments by comparing the output and cost statistics of several centralized payments systems with those of Bitcoin, Ethereum and Solana.
<div xmlns="http://www.tei-c.org/ns/1.0"> The financial industry is undergoing a transformative evolution driven by technological advancements and shifting consumer expectations. "Future Trends and Opportunities: Opportunities for innovation and disruption in the financial industry" provides a comprehensive exploration of key trends shaping the industry's future. From the rise of digital transformation and blockchain to the integration of artificial intelligence and sustainable finance, the abstract highlights the multifaceted opportunities for innovation. Emphasis is placed on enhancing customer experiences, improving operational efficiency, promoting financial inclusion, and addressing the challenges of regulation, cybersecurity, and privacy. The abstract concludes by emphasizing the critical importance of adaptation, continuous innovation, and collaborative efforts between traditional institutions and fintech disruptors to navigate the dynamic landscape and seize the opportunities that lie ahead in the evolving financial ecosystem I. Introduction A. Brief overview of the financial industry B. Importance of innovation and disruption in driving growth C. Purpose of exploring future trends and opportunities II. Current Landscape of the Financial Industry A. Traditional banking and financial services B. Rise of fintech companies C. Emerging technologies (blockchain, artificial intelligence, etc.) D. Regulatory environment and its impact III. Future Trends in the Financial Industry A. Digital transformation and the shift to online platforms 1. Mobile banking 2. Digital wallets 3. Contactless payments B. Blockchain and cryptocurrencies 1. Decentralized finance (DeFi) 2. Central bank digital currencies (CBDCs) 3. Smart contracts C. Artificial Intelligence (AI) and Machine Learning (ML) 1. Robo-advisors 2. Predictive analytics for risk management 3. Chatbots and virtual assistants D. Open banking and API integration 1. Collaboration between traditional banks and fintechs 2. Enhanced customer experience 3. Data sharing and security concerns IV. Opportunities for Innovation and Disruption A. Enhanced customer experience 1. Personalized services 2. Real-time financial insights 3. Seamless onboarding processes B. Improved efficiency and cost savings 1. Automation of repetitive tasks 2. Streamlined back-office operations 3. Enhanced fraud detection and prevention C. Financial inclusion 1. Serving the unbanked and underbanked populations 2. Microfinance and alternative lending solutions D. Sustainable finance 1. ESG (Environmental, Social, Governance) investments 2. Green financing options 3. Social impact investing V. Challenges and Considerations A. Regulatory hurdles B. Cybersecurity concerns C. Privacy and data protection D. Resistance to change in traditional institutions VI. Conclusion A. Summary of key future trends and opportunities B. Importance of adaptation and continuous innovation C. Encouraging collaboration between traditional and new players in the financial industry </div>
Depositing and borrowing in a currency other than the local currency is a well- documented phenomenon. The associated exchange rate and monetary risks, pivotal during the Asian Financial Crisis of 1997, prompted an academic discourse. Financial dollarization literature explains why individuals and corporations domiciled in emerg- ing markets deposit and borrow in hard currencies, mainly in U.S. dollars. This thesis proposes a model for cryptocurrency lending and assesses model predictions with a data set containing more than one million Ethereum transactions. The computational modelling and statistical analysis show that the main theoretical explanations provided by financial dollarization literature may not be directly transferable to cryptocurrencies. The results suggest that the popularity of depositing and borrowing in cryptocurrencies must be largely motivated by other factors.
Charles Bertucci, Louis Bertucci, Mathis Gontier Delaunay, Olivier Guéant · 5 authors
ABSTRACT Contrasting sharply with traditional money, bond, and bond futures markets, where interest rates emerge organically from participant interactions, DeFi lending platforms employ rule‐based interest rates that are algorithmically set. Thus, the selection of an effective interest rate model (IRM) is paramount for the success of a lending protocol. This paper investigates the modeling of agents' behaviors on lending platforms and proposes a theoretical framework for formulating optimal IRMs. We show that, under perfect information, an optimal control model with a state constraint generates an optimal interest rate policy that has a shape similar to that of popular markets. Furthermore, we formally analyze interest rate policies based on PID controllers, which work efficiently based on fewer assumptions. Using public data of popular markets on the Ethereum blockchain, we analyze agents' behavior, build a realistic simulation environment, and highlight the main tradeoffs in the design of interest rates for decentralized lending platforms.