|
on Payment Systems and Financial Technology |
| By: | Hung Q. Tran |
| Abstract: | Amidst intense geopolitical competition, efforts to develop tokenized monetary units—tradable on programmable platforms such as blockchains—have added a new dimension to the debate about the role of a global payment and reserve currency. Tokenized monetary units are expected to greatly improve the efficiency of payment transactions in terms of their speed and cost, especially cross-border transactions. They could also meet emerging demand for technologically enabled features such as smart contracts, which can be embedded in monetary tokens. The country that can promote and develop tokenization based on its fiat money—the United States, for example—would enjoy first-mover advantages, being able to attract users to its tokenized platforms, and helping to strengthen the role of its currency in global payments and finance in the digital age. Alternatively, if several major countries could compete by developing tokenized money, the shift to a multi-currency reserve system would be accelerated. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:ocp:pbecon:p44_25 |
| By: | Christopher J. Waller |
| Date: | 2025–09–29 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgsq:101844 |
| By: | José Aurazo; Holti Banka; Guillermo Galicia; Nilima Ramteke; Vatsala Shreeti; Kiyotaka Tanaka |
| Abstract: | Fast payments are at the forefront of payments digitalisation globally. By enabling immediate availability of funds on a 24/7 basis, they offer the potential to enhance efficiency, promote financial inclusion, drive innovation and foster competition. Despite their growing adoption, key questions remain regarding the design of fast payments systems (FPS), particularly concerning pricing. Open issues around pricing of fast payments exist at three levels: between the FPS owner and participants (system level), among participants themselves (participant level) and finally between the participants and their customers (end user level). This paper provides a comprehensive overview of global practices in FPS pricing at these three levels. To relate these practices with the academic literature, particularly for the person-to-merchant (P2M) payments, we use a classical two-sided market model and analyse how different pricing schemes at the end user level might influence the volume of fast payments and overall social welfare. Our expository model shows that fast payment usage may be lower than socially optimal in many cases. Moreover, when all fees are zero, fast payments are unsustainable without external subsidies or alternative revenue streams for participants. |
| Keywords: | financial inclusion, digital payments, fast payments, interchange fee, pricing |
| JEL: | O3 E42 G28 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:bis:biswps:1295 |
| By: | Marcin W\k{a}torek; Marija Bezbradica; Martin Crane; Jaros{\l}aw Kwapie\'n; Stanis{\l}aw Dro\.zd\.z |
| Abstract: | Based on the cryptocurrency market dynamics, this study presents a general methodology for analyzing evolving correlation structures in complex systems using the $q$-dependent detrended cross-correlation coefficient \rho(q, s). By extending traditional metrics, this approach captures correlations at varying fluctuation amplitudes and time scales. The method employs $q$-dependent minimum spanning trees ($q$MSTs) to visualize evolving network structures. Using minute-by-minute exchange rate data for 140 cryptocurrencies on Binance (Jan 2021-Oct 2024), a rolling window analysis reveals significant shifts in $q$MSTs, notably around April 2022 during the Terra/Luna crash. Initially centralized around Bitcoin (BTC), the network later decentralized, with Ethereum (ETH) and others gaining prominence. Spectral analysis confirms BTC's declining dominance and increased diversification among assets. A key finding is that medium-scale fluctuations exhibit stronger correlations than large-scale ones, with $q$MSTs based on the latter being more decentralized. Properly exploiting such facts may offer the possibility of a more flexible optimal portfolio construction. Distance metrics highlight that major disruptions amplify correlation differences, leading to fully decentralized structures during crashes. These results demonstrate $q$MSTs' effectiveness in uncovering fluctuation-dependent correlations, with potential applications beyond finance, including biology, social and other complex systems. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.18820 |
| By: | Chaimaa Laamime, (École Nationale de Commerce et de Gestion (ENCG), Université Hassan II de Casablanca, Maroc); Karima Mialed (École Nationale de Commerce et de Gestion (ENCG), Université Hassan II de Casablanca, Maroc) |
| Abstract: | This study explores the impact of advanced financial technologies—artificial intelligence, predictive algorithms, intelligent trading platforms, and big data—on the decision-making processes of individual traders. Based on qualitative fieldwork with 30 Moroccan traders, the research shows that while digital tools can help reduce cognitive overload and emotional stress, they may also reinforce algorithmic overconfidence, excessive delegation of judgment, or illusions of control. The study highlights that the effects of these technologies are context-dependent, shaped by traders' techno-cognitive profiles, experience, and financial literacy. It contributes to behavioral finance by offering an integrated techno-behavioral perspective in the context of an emerging market. |
| Abstract: | L'émergence des solutions technologiques financières avancées notamment l'intelligence artificielle, les algorithmes prédictifs, les plateformes de trading intelligentes, le big data reconfigure en profondeur les pratiques décisionnelles des investisseurs et des traders en particulier dans les économies en mutation technologique. Ces dispositifs sont fréquemment présentés comme des catalyseurs de rationalité, toutefois, leur impact réel sur les biais comportementaux des traders individuels demeure controversé et ambigu. Cette étude propose une analyse critique de cette problématique à partir d'une étude qualitative conduite au Maroc, un marché émergent confronter à une adoption croissante des outils digitalisés et où les investisseurs et les traders individuels restent significativement influencés par des dimensions psychologiques, culturelles et émotionnelles. Cette étude est faite à travers des entretiens semidirectifs réalisés auprès des traders individuels marocains utilisant des plateformes mobilisant des technologies intelligentes, l'analyse met en lumière une tension permanente entre perception des gains de performance et persistance des biais. Certains outils technologiques contribuent à structurer et encadrer la prise de décision en limitant la surcharge émotionnelle, d'autres fonctionnalités technologiques tendent à engendrer une sur-confiance algorithmique, une délégation excessive du jugement ou encore une illusion de contrôle amplifiée. Ce travail s'inscrit dans une posture interprétativiste visant à comprendre les représentations des traders individuels à l'égard des technologies financières qu'ils mobilisent en s'appuyant sur des entretiens semi-directifs, analysés à partir d'un cadre ancré dans la subjectivité des auteurs, dans une logique de construction de sens. Cet article met en évidence que l'effet des technologies est contextuel dans la mesure où il dépend des représentations mentales, de l'expérience et du niveau de littératie financière des traders. L'étude apporte une contribution originale à l'approche de la finance comportementale en éclairant le rôle des technologies dans un environnement émergent comme le Maroc et en démontrant la nécessité d'une appropriation critique et réflexive des outils digitalisés dans les pratiques de trading. |
| Keywords: | Advanced technologies, Individual traders, Emerging markets, Artificial intelligence, Cognitive biases, Technologies avancées, Traders individuels, Biais cognitifs, Intelligence artificielle, Marché émergent |
| Date: | 2025–08–15 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05243730 |
| By: | Stephan Luck |
| Abstract: | Digital currencies have grown rapidly in recent years. In July 2025, Congress passed the “Guiding and Establishing National Innovation for U.S. Stablecoins Act” (GENIUS) Act, establishing the first comprehensive federal framework governing the issuance of stablecoins. In this post, we place stablecoins in a historical perspective by comparing them to national bank notes, a form of privately issued money that circulated in the United States from 1863 through 1935. |
| Keywords: | digital currencies; stablecoins; national bank notes; economic history |
| JEL: | N21 N22 |
| Date: | 2025–10–01 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fednls:101880 |
| By: | Nathan Blascak; Julia S. Cheney; Robert M. Hunt; Vyacheslav Mikhed; Dubravka Ritter; Michael Vogan |
| Abstract: | We use extended fraud alerts in anonymized credit reports to examine how identity theft, and subsequent clean-up, affects consumers’ credit outcomes. The immediate effects of fraud for these consumers are negative, relatively small, and transitory. After placing an alert, these consumers experience persistent declines in delinquencies and a 12-point increase in credit scores, and 11 percent of filers become prime consumers. Many of these consumers take advantage of their improved creditworthiness and obtain additional credit. Although alert filers have larger balances, their performance on loans is as good as better than before fraud, suggestive of a change in behavior following fraud. |
| Keywords: | identity theft; fraud alert; consumer credit; credit performance |
| JEL: | G51 D14 D18 |
| Date: | 2025–10–06 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedpwp:101886 |
| By: | Joseph E. Stiglitz; Maxim Ventura-Bolet |
| Abstract: | We develop a tractable model to study how AI and digital platforms impact the information ecosystem. News producers — who create truthful or untruthful content that becomes a public good or bad — earn revenue from consumer visits. Consumers search for information and differ in their ability to distinguish truthful from untruthful information. AI and digital platforms influence the ecosystem by: improving the efficiency of processing and transmission of information, endangering the producer business model, changing the relative cost of producing misinformation and altering the ability of consumers to screen quality. We find that in the absence of adequate regulation (accountability, content moderation, and intellectual property protection) the quality of the information ecosystem may decline, both because the equilibrium quantity of truthful information declines and the share of misinformation increases; and polarization may intensify. While some of these problems are already evident with digital platforms, AI may have different, and overall more adverse, impacts. |
| JEL: | D8 D83 O33 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34318 |
| By: | Kenichi Ueda (University of Tokyo); Chanthol Hay (National University of Battambang) |
| Abstract: | Cambodia is one of the two first countries that adopted a retail CBDC in October 2020. The design of the CBDC, called the Bakong, is a bit unique. We find a few design flaws that could potentially damage the central bank and then the Cambodian economy as a whole. We show some key statistics from our own survey in 2022 to clarify our arguments. The Bakong is offered in two currencies, the Khmer Riel (KHR) and the US dollar (USD), as Cambodia has been highly dollarized. We discuss theoretical predictions for the CBDC based on three kinds of substitutes: paper money, bank deposits, and foreign currencies. The third one is specific to the Bakong. Unlike a typical local currency CBDC, the USD Bakong may substitute for the KHR more. Moreover, it has been announced that the retail Bakong is legally not a liability of the central bank, but from the viewpoint of the underlying technology and economics, it is a central bank liability. |
| Date: | 2024–02 |
| URL: | https://d.repec.org/n?u=RePEc:cfi:fseres:cf579 |
| By: | Spyridon Boikos (University of Macedonia, Greece); Theodore Panagiotidis (Department of Economics, University of Macedonia, Greece); Georgios Voucharas (Liverpool Hope University, UK) |
| Abstract: | While significant progress has been made in exploring the importance of financial literacy, its impact on economic growth and financial development from a macroeconomic point of view, remains thinly understood. This paper provides fresh evidence on the relationship between financial literacy, financial development and economic growth. We utilise a novel dataset for 61 countries over the period 1999-2014 and employ a panel quantile regression model. We provide strong evidence that higher financial literacy levels lead to higher GDP per capita growth and the size of the impact is higher at lower quantiles of the conditional growth distribution. As financial development increases, its positive impact on economic growth diminishes, indicating an inverted U-shaped relationship. High levels of financial literacy mitigate the diminishing returns of financial development on GDP per capita growth by an average of 7.41%. Interestingly, in higher quantiles of the conditional growth distribution, the mitigating effect increases to 9.23%. |
| Keywords: | Financial Literacy, Financial Development, Economic Growth, Quantile Regression, Panel Data |
| JEL: | O16 O40 G10 G53 C21 C23 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:rim:rimwps:25-09 |
| By: | Jieun Yu; Minjung Park; Sangmi Chai |
| Abstract: | This study aims to detect pump and dump (P&D) manipulation in cryptocurrency markets, where the scarcity of such events causes severe class imbalance and hinders accurate detection. To address this issue, the Synthetic Minority Oversampling Technique (SMOTE) was applied, and advanced ensemble learning models were evaluated to distinguish manipulative trading behavior from normal market activity. The experimental results show that applying SMOTE greatly enhanced the ability of all models to detect P&D events by increasing recall and improving the overall balance between precision and recall. In particular, XGBoost and LightGBM achieved high recall rates (94.87% and 93.59%, respectively) with strong F1-scores and demonstrated fast computational performance, making them suitable for near real time surveillance. These findings indicate that integrating data balancing techniques with ensemble methods significantly improves the early detection of manipulative activities, contributing to a fairer, more transparent, and more stable cryptocurrency market. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.00836 |
| By: | Kairan Hong; Jinling Gan; Qiushi Tian; Yanglinxuan Guo; Rui Guo; Runnan Li |
| Abstract: | Cryptocurrency markets present unique prediction challenges due to their extreme volatility, 24/7 operation, and hypersensitivity to news events, with existing approaches suffering from key information extraction and poor sideways market detection critical for risk management. We introduce a theoretically-grounded multi-agent cryptocurrency trend prediction framework that advances the state-of-the-art through three key innovations: (1) an information-preserving news analysis system with formal theoretical guarantees that systematically quantifies market impact, regulatory implications, volume dynamics, risk assessment, technical correlation, and temporal effects using large language models; (2) an adaptive volatility-conditional fusion mechanism with proven optimal properties that dynamically combines news sentiment and technical indicators based on market regime detection; (3) a distributed multi-agent coordination architecture with low communication complexity enabling real-time processing of heterogeneous data streams. Comprehensive experimental evaluation on Bitcoin across three prediction horizons demonstrates statistically significant improvements over state-of-the-art natural language processing baseline, establishing a new paradigm for financial machine learning with broad implications for quantitative trading and risk management systems. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.08268 |
| By: | Aadi Singhi |
| Abstract: | This paper presents a Multi Agent Bitcoin Trading system that utilizes Large Lan- guage Models (LLMs) for alpha generation and portfolio management in the cryptocur- rencies market. Unlike equities, cryptocurrencies exhibit extreme volatility and are heavily influenced by rapidly shifting market sentiments and regulatory announcements, making them difficult to model using static regression models or neural networks trained solely on historical data [53]. The proposed framework overcomes this by structuring LLMs into specialised agents for technical analysis, sentiment evaluation, decision-making, and performance reflection. The system improves over time through a novel verbal feedback mechanism where a Reflect agent provides daily and weekly natural-language critiques of trading decisions. These textual evaluations are then injected into future prompts, al- lowing the system to adjust indicator priorities, sentiment weights, and allocation logic without parameter updates or finetuning. Back-testing on Bitcoin price data from July 2024 to April 2025 shows consistent outperformance across market regimes: the Quantita- tive agent delivered over 30% higher returns in bullish phases and 15% overall gains versus buy-and-hold, while the sentiment-driven agent turned sideways markets from a small loss into a gain of over 100%. Adding weekly feedback further improved total performance by 31% and reduced bearish losses by 10%. The results demonstrate that verbal feedback represents a new, scalable, and low-cost method of tuning LLMs for financial goals. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.08068 |
| By: | Gabriela Wojak; Ernest G\'orka; Micha{\l} \'Cwi\k{a}ka{\l}a; Dariusz Baran; Rafa{\l} \'Swiniarski; Katarzyna Olszy\'nska; Piotr Mrzyg{\l}\'od; Maciej Frasunkiewicz; Piotr R\k{e}czajski; Daniel Zawadzki; Jan Piwnik |
| Abstract: | This paper examines how Polish consumers are adapting to online insurance purchasing channels and what factors influence their preferences. Drawing on a structured survey of 100 respondents with varied demographic profiles, the study explores purchasing frequency, channel usage, price sensitivity, trust, and decision-making behaviors. Results indicate a clear shift toward digital tools, with many consumers valuing the speed, convenience, and transparency of online platforms, particularly for simple insurance products. However, barriers remain, including concerns about data security, lack of personal guidance, and difficulty understanding policy terms. A hybrid model is emerging, where online tools are used for research and comparison, while traditional agents are consulted for complex decisions. Respondents emphasized the importance of trust and personal contact, showing that emotional and psychological factors still play a role in digital adoption. Price was the dominant decision factor, but many consumers also prioritized service quality and reliability. The study concludes that insurers should invest in user-friendly digital experiences while maintaining human support options. Strategic omnichannel integration is recommended to meet diverse customer needs and reduce digital exclusion. Limitations of the study include a modest sample size and focus on the Polish market. Future research should investigate the role of AI in digital distribution, segment preferences by insurance type, and analyze trends across different regions or age groups. This paper adds empirical value to the understanding of insurance distribution and consumer behavior in digitally transforming financial markets. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.07933 |
| By: | El Mahdi Juiher (Université Ibn Zohr = Ibn Zohr University [Agadir]); Hmad Ouaddi (Université Ibn Zohr = Ibn Zohr University [Agadir]) |
| Abstract: | This study analyzes the impact of artificial intelligence-generated virtual influencers on consumer trust and purchase intention in the context of digital marketing in Morocco. By mobilizing the theoretical frameworks of authenticity, brand trust and perceived cybersecurity risk, the research examines how the type of influencer (virtual versus human) shapes consumer attitudes and behaviors. A quantitative survey was conducted among 230 Moroccan social network users, measuring perceived authenticity, brand trust, purchase intention, influencer type and perceived cybersecurity risk. The results reveal a strong positive relationship between perceived authenticity and brand trust, as well as between brand trust and purchase intention. Human influencers are perceived as significantly more authentic than their virtual counterparts. Above all, the analysis shows that the perception of cybersecurity risk moderates the effect of influencer type on purchase intention: as security concerns increase, so does the preference for human influencers. These results underline the need for marketing professionals to integrate issues of digital trust, transparency and security into the design of their campaigns with virtual influencers. The study contributes to a better understanding of the challenges and opportunities associated with AI-driven marketing in specific cultural contexts, and opens up prospects for future research into digital ethics and consumer-perceived risk management. |
| Keywords: | digital, cybersecurity risk, purchase intent, trust, authenticity, Virtual influencers, Virtual influencers authenticity trust purchase intent cybersecurity risk digital |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05250808 |
| By: | Grace Cimaszewski; Francesco Da Dalt; Thomas Moser; Adrian Perrig |
| Abstract: | Cross-border payments remain expensive, slow, and opaque due to reliance on correspondent banking. Distributed ledger technology (DLT) offers a possible alternative, enabling peer-to-peer transactions at lower cost with greater speed, transparency, and resilience. However, DLT systems largely rely on the public Internet, which exposes them to network-based outages and attacks. SCION, a secure next-generation Internet architecture, can operate side-by-side with today's Internet to mitigate network-related risks. In addition to the security benefits, SCION also provides a novel approach to enforce regulatory compliance on a permissionless DLT with a governance model well-suited for multi-jurisdictional platforms. We present the first practical blueprint for deploying a DLT-based settlement system on SCION and demonstrate, through a simulation-based analysis of the real-world DLT system Sui, that SCION can mitigate more than half of routing-based network attacks, even when only partially adopted by DLT validators. Overall, SCION can provide a robust infrastructure foundation for DLT-based cross-border payment systems by enhancing their security, reliability, and regulatory compliance. |
| Keywords: | DLT reliability, DLT availability, Network attacks, Routing attacks, DDoS |
| JEL: | E42 F33 G21 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:snb:snbwpa:2025-15 |
| By: | Paul, Revocatus Washington; Sharma, Dhiraj |
| Abstract: | This paper examines the welfare effects of Tanzania’s 2021 levy on mobile money transfers, a policy that sharply increased transaction costs in a country where mobile money is the primary channel for financial access and remittances. Using two waves of the Tanzania National Panel Survey (2014/15 and 2020/22) combined with high-frequency phone survey data, a triple-difference identification strategy was implemented to isolate the impact of the levy on rural and urban households before and after its introduction. The findings show that rural households—who rely more heavily on mobile money and have fewer financial alternatives—experienced a 10–18 percent decline in per capita food consumption and a significant rise in food insecurity following the levy. Robustness checks using variation in bank penetration, shock incidence, and remittance dependence support these results. |
| Date: | 2025–10–07 |
| URL: | https://d.repec.org/n?u=RePEc:wbk:wbrwps:11228 |
| By: | Jinho Cha; Justin Yoo; Eunchan Daniel Cha; Emily Yoo; Caedon Geoffrey; Hyoshin Song |
| Abstract: | Decentralized coordination and digital contracting are becoming critical in complex industrial ecosystems, yet existing approaches often rely on ad hoc heuristics or purely technical blockchain implementations without a rigorous economic foundation. This study develops a mechanism design framework for smart contract-based resource allocation that explicitly embeds efficiency and fairness in decentralized coordination. We establish the existence and uniqueness of contract equilibria, extending classical results in mechanism design, and introduce a decentralized price adjustment algorithm with provable convergence guarantees that can be implemented in real time. To evaluate performance, we combine extensive synthetic benchmarks with a proof-of-concept real-world dataset (MovieLens). The synthetic tests probe robustness under fee volatility, participation shocks, and dynamic demand, while the MovieLens case study illustrates how the mechanism can balance efficiency and fairness in realistic allocation environments. Results demonstrate that the proposed mechanism achieves substantial improvements in both efficiency and equity while remaining resilient to abrupt perturbations, confirming its stability beyond steady state analysis. The findings highlight broad managerial and policy relevance for supply chains, logistics, energy markets, healthcare resource allocation, and public infrastructure, where transparent and auditable coordination is increasingly critical. By combining theoretical rigor with empirical validation, the study shows how digital contracts can serve not only as technical artifacts but also as institutional instruments for transparency, accountability, and resilience in high-stakes resource allocation. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.05504 |
| By: | Falk Bräuning; Joanna Stavins |
| Abstract: | Monetary policy impacts consumer spending via the effect of interest rate changes on credit card borrowing. Using supervisory account-level spending and balance data, we estimate that a 1 percentage point increase in the interest rate reduces credit card spending by nearly 9 percent and revolving balances by close to 4 percent. Aggregate results are primarily driven by revolving accounts, while we estimate small and statistically insignificant interest-rate elasticity for transaction accounts. Consistent with financial constraints, low-credit-score accounts tend to adjust spending, while high-credit-score accounts adjust balances. |
| Keywords: | credit cards; interest rates; consumer spending |
| JEL: | D12 D14 E43 G21 |
| Date: | 2025–09–01 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedbwp:101889 |