Individual Transactions Research Articles

Design of an improved model using federated learning and LSTM autoencoders for secure and transparent blockchain network transactions

With the advancement of this digital era and the emergence of DApps and Blockchain, secure, robust and transparent network transaction has become invaluable today. These traditional methods of securing the transactions and maintaining transparency have encountered many challenges. It includes some such issues as follows: data privacy, centralized vulnerability, inefficiency in fraud detection and much more. To that effect, and to address such limitations, this paper provides a blockchain technology framework that is driven by advanced machine learning techniques, which will enhance security and transparency throughout the network of transactions. We begin with a design framework based on Federated Learning for Blockchain Integration where distributed datasets across blockchain nodes contribute to a global machine learning model but do not share raw data samples. Different nodes learn their own models. After that, these local models are aggregated towards a common, global model using secure aggregation methods, which makes sure that there is nozza of data privacy and hence, in the process making sure that more accurate models can be obtained due to diversified data sets. With LSTMs Autoencoders, more excellent security protocols are created for anomaly detection and fraud. So, by training the autoencoder on normal transaction data, the system can alert transactions with high reconstruction errors, meaning real-time anomalies. This proactive detection of anomalies reduces fraudulent activities significantly as most of the threats are recognized early. To this end, this paper proposes Smart Contract-based Model Management for machine learning models in a decentralized environment. Smart contracts are responsible for the submission, validation, and execution of the locally updated models in a decentralized fashion such that the management process is transparent and tamper resistant. Integrity and authenticity requirements are fulfilled by enforcing consensus mechanisms. Privacy in Machine Learning is guaranteed through Differential Privacy and Homomorphic Encryption. Differential privacy techniques, so as to ensure individual transaction data privacy in the updates of the local model before aggregation. In homomorphic encryption, computations are made in the encrypted form so when forming privacy preserving global model, privacy is preserved. The Real-time analysis of the transactions can be done with CNNs to detect fraud. Streaming transaction data is analyzed by CNNs leveraging the privacy-preserving global model and producing immediate alerts and actions for detected fraud. This real timing makes the network even more reliable and trustworthy. Our proposed framework is effective according to the interim outcomes where the aggregation of local models occurred without data leakage, detected anomalies very efficiently, managed models very transparently, with privacy of data at a very high level, and easily detected fraudulent transactions. The work presented here provides a great boost to send secure and very easily transparent transactions across the network, and thus resulted in enhanced network trust and decentralization.

Suspicious Financial Activity in the Context of In-Game Asset Exchange Marketplace

In this study, we investigated the expanding problem of suspicious activity when using online in-game asset trading platforms. The decentralized structures and anonymity offered by these platforms provide a basis for suspicious actions, creating a threat to the virtual economy. By evaluating 18,157 rows of anonymized transaction data from 38 unique sellers with the help of the interquartile range approach and network analysis, we were able to identify suspicious activities. The results highlight suspicious online activities of individual transactions. This research contributes by identifying new, concerning trends and unraveling complex networks by analyzing in-game asset transaction data. It also assists in informing experts and lawmakers about new suspicious activities.

Signs of economic development in the Kingdom of Naples under the Aragonese Crown: the Coppola Company

Abstract The article examines the role played by Loise and Francesco Coppola’s company under the Aragonese Crown, so as to outline the socio-economic situation of Southern Italy at that time and examine the impact of foreign merchant-banking companies on the development of local entrepreneurship. In the second half of the fifteenth century, Florentine bankers Filippo and Lorenzo Strozzi established a bank in Naples. Using two surviving general journals, dating back to 1473 and 1476, their lending business can be analysed. As the bookkeeper of the Strozzi Bank used to record any transaction using double-entry bookkeeping, such journals help to identify the types of clients the Strozzi Bank dealt with and the reasons for individual transactions. The article outlines the crucial role played by the Coppola family in the development of the southern economy in the fifteenth century, showing how their company was part of international commercial networks that included the wealthiest and most powerful merchant-banking businesses of the time.

Theoretical Aspects Of The Essence Of Bank Consolidation

The article considers theoretical aspects of the essence of bank consolidation. The author emphasises the diversity and multifaceted nature of «consolidation», which has led to its use in various activities. A detailed analysis of the literature sources made it possible to outline the essential meaning of the term «consolidation» by the most common areas and directions of its use. It is noted that consolidation can be used as a tool for stabilisa-tion, systematisation, transformation, etc. in various fields of activity. The carried out scientific analysis of terminological definitions revealed a clear link between the concepts of «reorganisation», «restructuring», «integration» and «consolidation» in the banking sector. It is proposed to use a comprehensive approach to determine the essence of bank consolidation, which includes three levels: legal, accounting, and economic. It is proved that the processes of bank consolidation should be studied in two aspects: as a process of capital pooling and as a process of bank merger. It is emphasised that in the narrow sense, bank consolidation is a merger of banks, in which there is a legal succession in the form of transfer of all rights and obligations of several banks to one bank. In a broader sense, bank consolidation, which includes three levels: legal, accounting, and economic. It is proved that the processes of bank consolidation should be studied in two aspects: as a process of capital pooling and as a process of bank merger. It is emphasised that in the narrow sense, bank consolidation is a merger of banks, in which there is a legal succession in the form of transfer of all rights and obligations of several banks to one bank. In a broader sense, bank consolidation can be viewed as a process focused on achieving specific goals and is a series of sequential actions implemented through a set of measures of a legal and economic nature. It is substantiated that the term «consolidation» in relation to banks is a collective concept, which includes not only individual transactions or legally significant actions regarding mergers, acquisitions or takeovers of banks, but also the acquisition of corporate control by a newly formed bank. The forms of bank consolidation include mergers, acquisitions, accessions, and amalgamations. The economic basis of bank consolidation is formed by social relations that develop in the commercial activity of banks and are aimed at combining their economic and financial potential and corporate control over banking activities in the financial market.

Network Based Feature Extraction Method for Fraud Detection Using Label Propagation

Abstract: Nowadays, judging the current transaction based on user history transactions is an important detection method. However, different users have different transaction behaviors, when all users use the same limit to judge whether the transaction is abnormal, it will result in higher misjudgment for some users. Aiming at the above problems, this paper proposes an individual behavior transaction detection method based on hypersphere model. In this model, considering multiple dimensions of normal historical transaction records, the characteristics of user’s transaction behavior is generated with the trend of transaction. Then, the user optimal risk threshold algorithm is proposed to determine the optimal risk threshold for each user. Finally combining the transaction behavior and the optimal risk threshold, the user behavior benchmark is formed, which is used to construct the multidimensional hypersphere model. On this basis, a mapping method for transforming transaction detection into midpoint in multidimensional space is proposed. The experiment proves that the proposed method is superior to other models, and it is found that the characterization effect of user behavior is related to the frequency of users’ transactions. Applied computing → Secure online transactions; Digital cash; Computing methodologies → Instance-based learning; Rule learning

A Counterfactual Analysis of Amazon’s Acquisitions Under the 2023 Merger Guidelines

The fact that Amazon was allowed to acquire hundreds of companies as it rose to become the fourth most valuable U.S. company in terms of market capitalization and a leader in three lines of business has been viewed by some as damning evidence of underenforcement by the United States antitrust authorities. In this article we ask the obvious question: If the 2023 Guidelines had been in place instead of prior guidelines, what effects would they have had on Amazon’s development? To provide an answer, we identify relevant changes in the guidelines and then select for review a subset of Amazon’s 280 acquisitions over the period 1998 to 2022. In our counterfactual, we analyze five horizontal acquisitions, four vertical acquisitions, and two sets of serial acquisitions. We find that the 2023 Guidelines would have broadened the bases for potential challenges and thereby would have increased the likelihood that Amazon would have faced greater resistance from antitrust authorities. The lack of safe harbors, the plasticity of individual Guidelines, and the optionality to challenge mergers under alternative theories would have exposed most of Amazon’s acquisitions to challenge. The lack of meaningful guidance about which individual transactions would have been challenged suggests that going forward enforcer discretion will play a yet larger role. Regarding Amazon’s serial acquisitions of nearly one hundred technology firms, we find that the 2023 Guidelines would have provided multiple rationales for intervention. Therein lies a weakness in the Guidelines and in antitrust policy: the lack of a framework for assessing both the anticompetitive and procompetitive effects of such acquisitions in high-tech industries.

Can Property Taxation Fix China’s Housing Market?: A Regression Discontinuity Analysis

How does the housing market respond to a newly imposed value-based property tax? Based on individual property transaction data spanning seven years, we investigate the repercussions of a new property tax on the local housing market in Chongqing, China. In particular, we adopt a fuzzy regression discontinuity approach to examine the impact of the tax policy on house prices, transaction volumes, and the broader social welfare. We find that the property tax in Chongqing has not caused a reduction in house prices, but it has made a non-trivial contribution to local public finance for public housing programs. Our findings suggest that the property-tax-capitalization hypothesis is not supported in Chongqing’s context, although the tax improves wealth redistribution.

Linking hypothetical extraction, the accumulation of production factors, and the addition of value

AbstractIn industrial ecology, approaches have been developed to analyze the contribution of specific sectors to environmental impacts within supply chains. In economics, a range of methods addresses the forward linkage (use of output) and backward linkage (dependency on inputs) of sectors, and the analysis of key sectors. This article offers a formal investigation of the relationship between these. It shows that both the analysis of supply chain impacts and of intersectoral linkages can be seen as special cases of a more general hypothetical extraction method (HEM). In HEM, sectors' role is assessed as the effect of their removal on the input–output model's solution. HEM also allows for the (partial) extraction of individual transactions. HEM thus offers a flexible approach to assessing the contribution of one or several sectors, or transactions, or parts thereof, to value added or footprint of any final demand. It can be applied to study the environmental footprints of companies or intermediate products, the contribution of certain inputs to sectors, or the potential impact of disruptions of supply chains on producers and consumers. In this article, the price model for HEM is introduced to identify the contribution of the extracted (target) sectors to the price or unit footprint of a commodity.

The Dependence of Online Gambling Businesses on High-Spending Customers: Quantification and Implications.

Online gambling has grown to be a significant industry but it faces regulatory threats because of perception that it is heavily dependent on a small segment of its customers who gamble heavily and at a level carrying elevated risk of harm. Employing a large multi-operator data set from Britain, which records individual transactions by some 140,000 individuals observed over one year, we are enabled to provide more precise estimates of the degree of concentration of revenue, compared with previous studies. High dependence on a relatively small number of customers is shown though there is variation from product to product in how small the group of account-holders of potential concern is. We conclude with a discussion of prospects for the industry in light of heightened awareness of gambling harm and resulting restrictions on online gambling spending introduced or proposed by governments or regulators in several jurisdictions.

Understanding Vulnerability to the Poverty Premium: An Analysis of Factors Influencing Use of High-Cost Credit Among Low-Income Individuals

Introduction & BackgroundAccess to affordable credit is essential for individuals living on low incomes to participate in society fully. However, due to their limited credit history and volatile incomes, many cannot access mainstream sources of credit, such as credit cards and personal loans. As a result, they often must rely on more expensive sources of credit such as payday loans, doorstep loans or rent-to-own loans. This leads to them paying more for credit, also referred to as the poverty premium. Incurring the poverty premium exacerbates the financial challenges faced by already vulnerable individuals and can lead to a cycle of financial distress. Identifying the behaviours and factors that lead to a need for high-cost credit can help in identifying individuals who are most vulnerable to incurring the poverty premium. Objectives & ApproachTo achieve this, we rely on anonymized Open Banking transaction data from 100,000 individuals provided by a UK-based social lender.Given the latent nature of vulnerability, we identify indicators of vulnerability to poverty premium which include frequency of overdraft use, previous debt problems, low financial resilience, and indebtedness. We use a copula-based approach to create an index of vulnerability to poverty premium. This is based on weighting the individual indicators using their Spearman rank correlation coefficient. We use a fixed effects model to identify the factors that contribute to this vulnerability, where the index of vulnerability is the dependent variable. Relevance to Digital FootprintsWe use a rich and granular dataset on individual financial transactions to address a key social issue. The findings from this study can inform policy and industry efforts to promote greater credit affordability for these vulnerable individuals. This is particularly important due to the renewed concerns regarding the increased use of high-cost credit by individuals living on low incomes due to COVID-19 and the increased cost of living. ResultsOur findings show that variables related to the financial profile of an individual are important driving factors of the vulnerability to poverty premium. These include the number of salary sources, frequency of salary receipt, benefit receipt and savings frequency. Other variables related to spending behaviour such as gambling, volatility in fixed expenses and high transaction counts all have positive relationships with this vulnerability. Conclusions & ImplicationsThis study is a first step towards examining the determinants of vulnerability to poverty premium by analyzing an Open Banking transaction data set. The innovative feature of this work is the creation of an index of vulnerability to poverty premiums based on various indicators of financial distress and high-cost credit use. Our findings on the relationships between the individual's financial profile and the vulnerability to poverty premium suggest that policymakers should consider targeting interventions for individuals with specific profiles to alleviate the need for high-cost credit. For example, programs to promote regular savings or financial education for gamblers and people with volatile spending behaviour can help assist these individuals manage their finances better.

A Research on the Dual Value Model of Individual Transaction Decision in Ethnic Chinese Society

Abstract In ethnic Chinese society, interpersonal relationships significantly influence transaction decisions, contrasting with Western economic rationality which often neglects the role of "human relationships". This research introduces a Dual Value Model (DVM) to explore how Chinese individuals navigate these complex interpersonal dynamics in transaction decisions. It highlights the integration of Potential Social Value (PSV), stemming from the counterparty's social status (vertical relationship) and emotional closeness (horizontal relationship), with Potential Economic Value (PEV), focusing on the economic outcomes of the transaction. These two potential values are defined through expectancy theory to facilitate communal comparison. This duality necessitates a balance, demonstrating a unique economic-social tradeoff where individuals might accept lower economic benefits in favor of enhancing or maintaining favorable “Guanxi.” The concept of "guanxi mark-down (GMD)" and "Compensatory Mark-up (CMU)" are introduced as strategies employed to steer this balance, reflecting the social etiquette and economic pragmatism within Chinese transactional behavior. The research aims to provide a nuanced understanding of Chinese transaction decision-making, offering insights for guiding interpersonal relationships and economic considerations in a socio-cultural context deeply influenced by Confucian principles. Keywords: Transaction decision, Dual value model (DVM), Potential social value (PSV), Potential economic value (PEV), Guanxi; Expectancy theory.

Geopolitical risk and global green bond market growth

AbstractUsing individual transaction data, we investigate how geopolitical risk influences green bond issuance across 73 countries during 2008–2021. We consider deal characteristics, as well as economic and institutional factors. We find a positive association between geopolitical risk and green bond issuance. The effect shows nonlinearity and time delays. Our findings remain robust after conducting sensitivity and endogeneity analysis. After decomposing the geopolitical risk index, we discover that all its components have positive correlations with green bond issuance. Lastly, our study highlights the crucial role of the underwriters' network and specific geopolitical jurisdictions as drivers for global green bond market expansion.

Roots of urban equality: Are low-income neighborhoods paying more for street trees?

This study investigates the potential influence of street trees on the single-family housing market in Seattle between 2010 and 2020, specifically examining how the relationship between the number of street trees and housing prices varies across neighborhoods with different income levels. It further discusses the policy implications for promoting environmental justice in urban tree-planting endeavors. We used Seattle’s street tree planting data from the 1950s to 2020 and housing transaction records between 2010 and 2020, covering 61,055 housing transactions. Four buffer lengths were applied to associate street trees with individual housing parcels and transactions. The relationship between street trees and the single-family housing market was evaluated through linear regressions and spatial lag models using Spatial Two-stage Least Squares (S2SLS). An interaction term between the number of street trees and neighborhood income was also used to examine the potential varying associations across neighborhoods with different income levels. Subsequently, we calculated the marginal implicit price (MIP) across the city. The results indicate that street trees are valued more in the housing market of lower-income areas. The findings underscore the importance of community-based intervention in the tree-planting process, especially in lower-income areas, to promote environmental justice and enhance neighborhood greenery.

Bitcoin Money Laundering Detection via Subgraph Contrastive Learning.

The rapid development of cryptocurrencies has led to an increasing severity of money laundering activities. In recent years, leveraging graph neural networks for cryptocurrency fraud detection has yielded promising results. However, many existing methods predominantly focus on node classification, i.e., detecting individual illicit transactions, rather than uncovering behavioral pattern differences among money laundering groups. In this paper, we tackle the challenges presented by the organized, heterogeneous, and noisy nature of Bitcoin money laundering. We propose a novel subgraph-based contrastive learning algorithm for heterogeneous graphs, named Bit-CHetG, to perform money laundering group detection. Specifically, we employ predefined metapaths to construct the homogeneous subgraphs of wallet addresses and transaction records from the address-transaction heterogeneous graph, enhancing our ability to capture heterogeneity. Subsequently, we utilize graph neural networks to separately extract the topological embedding representations of transaction subgraphs and associated address representations of transaction nodes. Lastly, supervised contrastive learning is introduced to reduce the effect of noise, which pulls together the transaction subgraphs with the same class while pushing apart the subgraphs with different classes. By conducting experiments on two real-world datasets with homogeneous and heterogeneous graphs, the Micro F1 Score of our proposed Bit-CHetG is improved by at least 5% compared to others.

The Significance of Corporate Losses in Transfer Pricing – The Danish Supreme Court Ruling in the Maersk Oil/Total Energies Case

On 6 September 2023, the Danish Supreme Court delivered its ruling in the largest Danish transfer pricing case to date. In addition to its considerable adjustments, the ruling has been a spectacular addition to a decade-long development in the Danish transfer pricing regulation due to the handling of corporate losses, discretionary assessments and aggregated assessments of individual transactions.

Особливості протидії відмиванню грошей на сучасних фінансових ринках

The development of new financial technologies has led to a significant acceleration of cash flows, an expansion of the range of opportunities for settlements and the use of digital financial instruments. One of the negative consequences of this process was the emergence of new money laundering schemes. The aim of the article is to qualitatively assess the changes in the sphere of money laundering with the use of financial institutions caused by the development of new financial technologies and virtual instruments, as well as to determine the list of signs of transactions with virtual assets that may indicate the implementation of money laundering schemes. Virtual assets have significant potential to stimulate financial innovation and efficiency in financial markets, but their individual characteristics create new opportunities for money laundering and terrorism financing. In view of this, it is of great importance in modern financial markets to improve methods of counteracting this phenomenon, in particular, the formation of a list of signs of transactions with virtual assets, which may indicate the illegal origin of funds or illegal purposes of their movement. The publication proposes an approximate list of signs of money laundering, which can be used by financial service providers in the process of monitoring transactions. Our approach involves dividing such attributes into six categories according to the volume and frequency of transactions, transaction patterns, anonymity, characteristics of transaction beneficiaries, source of funds, and geographic risk. A list of signs of suspicious activity when carrying out transactions, the use of assets with a high level of anonymity, client risks, links with criminal activity or high-risk jurisdictions has been determined. In order to understand the big picture and qualitatively assess the risks of money laundering and terrorism financing, the features identified in the article should be considered in the context of all available information on individual transactions. Taking into account the features and vulnerabilities of the latest financial instruments, we consider it an important prerequisite for updating the current practices of customer due diligence and detection of suspicious financial transactions.

Inferring mobility of care travel behavior from transit smart fare card data

Existing research underscores substantial gender-based variations in travel behavior on public transit. Studies have concluded that these differences are largely attributable to household responsibilities typically falling disproportionately on women, leading to women being more likely to utilize transit for purposes referred to by the umbrella concept of “Mobility of Care”. In contrast to past studies that have quantified the impact of gender using survey and qualitative data, we examine a novel data-driven workflow utilizing a combination of previously developed origin, destination, and transfer inference (ODX) based on individual transit fare card transactions, name-based gender inference, and geospatial analysis as a framework to identify mobility of care trip making. We apply this framework to data from the Washington Metropolitan Area Transit Authority (WMATA). Analyzing data from millions of journeys conducted in the first quarter of 2019, the results of this study show that our proposed workflow can identify mobility of care travel behavior, both in terms of (1) detecting times and places of interest where the share of women travelers in an equally-sampled subset (on basis of inferred gender) of transit users is 10 %–15 % higher than that of men, and (2) finding women significantly more likely to exhibit a consistent accompaniment patterns with riders who are children, elderly, or people with disabilities. The workflow presented in this study provides a blueprint for combining transit origin-destination data, inferred customer demographics, and geospatial analyses enabling public transit agencies to assess, at the fare card level, the gendered impacts of different policy and operational decisions.

Proposal for Updating Fatwa Issuing Mechanism in Financial Matters

The rapid and unprecedented progress in the field of science and technology has ushered in innovations affecting virtually every facet of life. Prior to the era of globalization, all kinds of financial issues were perceived as having singular dimension, solvable within the confines of their respective fields. However, in contemporary times, multifaceted aspects characterize nearly every such issue. For instance, the initiation of a marriage contract may prompt concerns about the medical checkup of the contracting parties. Similarly, advancements in medical science, such as the introduction of test tube baby treatments, present challenges regarding Shariah compliance in both non-Muslim and Muslim-majority countries. Financial matters undergo a similar transformation, evolving from individual transactions to intricate series of contracts within contracts. Addressing these complexities within the framework of Shariah law poses challenges, raising questions about the preparedness of scholars from traditional madrasas to navigate contemporary financial intricacies. This study seeks to analyze these multifaceted aspects, aiming to propose a methodology for issuing Shariah-compliant fatwas in the context of the finance industry in Pakistan.

Competitive advantages and the proximity of investment: evidence from Hong Kong-listed real estate development firms in Mainland China

ABSTRACT This study examines the investment behaviour of Hong Kong-based real estate firms in Mainland China. Using a sample of individual land transactions, we find that firms with higher value of growth options, as perceived in the capital market, are more likely to invest actively and further away from their home base. However, over time, Hong Kong developers tend to decrease their level of activity in Mainland China, indicating that the ‘liability of foreignness’ becomes a significant challenge. Our findings highlight the importance of balancing strategic expansion with the inherent risks of operating in distant emerging markets.

Effectiveness of investment in business acquisition: Results of disclosures 2021–2022

Aim. To perform an assessment of the performance of investments in business acquisitions over the years to 2021 by analyzing the impairments recognized in 2021 and 2022.Objectives. To examine the possible approaches to analyze the performance of mergers and acquisitions; to elaborate on one of the approaches in the aspect of investigating the impairment of goodwill previously recognized as a result of company acquisitions; to analyze the indicators of available financial statements of companies for 2021 and 2022.Methods. The study is based on the analysis of consolidated financial statements of Russian companies and sample data on foreign companies. We considered individual transactions on foreign companies’ exit from the Russian market and assessed the effect of their influence on the financial statements of foreign companies.Results. The authors have carried out a post facto evaluation of the indicators of the largest Russian companies in the sample, assessed the possibilities of goodwill recovery as a result of M&A transactions. As a result of the analysis the conclusion is formed that the situation in the considered sector of economy remains stable. Meanwhile, foreign investors who have left the Russian market recognize significant impairment of investments in their financial statements.Conclusions. The prospect for further research is to conduct a more extensive analysis based on a larger sample of companies. In 2023, some companies that preferred not to publish their 2022 financial statements have started to disclose their financial results again. This provides an opportunity to continue the research begun in the paper on the results obtained and to make further refinements.