Transaction Characteristics Research Articles

Distinguishing a fraudulent transaction from fictitious and sham transactions

The article examines the issues of distinguishing a fraudulent transaction from fictitious and sham transactions. The expediency of studying a transaction in general as one of the complex legal structures in the science of civil law is substantiated. The relevance of studying a fraudulent transaction as a new one for the practice of law enforcement of a transaction committed with the aim of causing harm to the creditor is emphasized. The author suggested that challenging a fraudulent transaction proceeds from a violation of the principle of good faith, which is difficult to disagree with. The key features of distinguishing a fraudulent transaction from fictitious and sham transactions are identified, namely: the essence of a fraudulent transaction is established; the signs of a fraudulent transaction are defined in accordance with the practice of the Supreme Court; a substantive characteristic of a fictitious transaction is provided; The essence and features of a fictitious transaction are clarified. A general characteristic of a fictitious transaction is given, where it is clarified that a fictitious transaction does not intend to create a legal consequence, unlike a fraudulent transaction. It is determined that the type of fictitious transaction is fraudulent. A fictitious transaction is characterized as one that aims to hide another transaction that was actually committed by its parties. A fictitious transaction, as indicated in the article, does not aim to create a certain legally significant result, which, on the contrary, takes place during the commission of a fraudulent transaction. It is concluded that a fictitious transaction, unlike a fraudulent transaction, is not characterized by the goal of creating a certain legally significant result. In such a case, the parties agree on other legal consequences that are important for them than those that are determined by their expression of will in the fictitious contract. It is emphasized that the separation of transactions committed to the detriment of the creditor into a separate group of fraudulent ones is an important step towards the development of Ukraine as a legal state, and creates the prerequisites for the legislative mechanism of the legal consequences of the application of the fraudster’s construct.

A Study on the Impact of Transaction Characteristics of Smart Factories in the Era of the 4th Industrial Revolution on the Quality of Relationships with Consumers and Long-term Orientation

A Study on the Impact of Transaction Characteristics of Smart Factories in the Era of the 4th Industrial Revolution on the Quality of Relationships with Consumers and Long-term Orientation

Internet Finance Non-stationary Time Series Prediction Algorithm Based on Deep Learning

Inaccurate prediction results of financial time series will lead to wrong investment decisions. Therefore, a prediction algorithm for Internet financial non-stationary time series based on deep learning is proposed. EMD (empirical mode decomposition) method is used to divide the collected historical Internet financial non-stationary time series information into high-frequency and low-frequency parts, and remove the noise in the decomposed high-frequency components to obtain the financial non-stationary time series without noise. The knowledge map method is used to mine the transaction characteristics and market characteristics of Internet finance from the financial non-stationary time series without noise, and the two are fused as the input of the improved CNN (convolutional neural network) prediction model. The prediction results of Internet financial time series are obtained through CNN. The experimental results show that after setting the CNN parameters, the predicted results are consistent with the actual market trends. The highest RSE of the predicted result is 0.551, The highest RAE is 0.443, which is relatively low, the CORR value is 0.864, which is relatively high, indicating that the relative square root error, relative absolute error, and relevant empirical coefficients of the prediction results are all good, making it a highly applicable algorithm.

Beyond traditional synergies: exploring network, relational and nonmarket gains in European M&A

Purpose This study aims to explore the sources of synergy gains in European mergers and acquisitions (M&A) from 2010 to 2021, focusing on both traditional and emerging synergies. Empirically validate novel types of synergies, including relational, network and nonmarket synergies, alongside operational, financial and market power synergies. Examine the impact of firm characteristics, governance structures and market conditions on the magnitude and success of synergy gains postacquisition. Design/methodology/approach The research uses a quantitative approach, analyzing a sample of 342 European M&A transactions from 2010 to 2021. Synergy gains are evaluated through econometric modeling, examining the effects of various firm and transaction characteristics on value creation. Findings The study reveals that operational synergies, primarily driven by cost reductions and investment savings, are the most significant contributors to value creation in European M&A. Financial synergies, while present, have a more limited impact. Additionally, relational and network synergies emerge as crucial, enhancing firms’ positions within business networks. However, nonmarket synergies did not yield significant results, highlighting the challenges of quantifying intangible benefits. Firm characteristics such as board diversity and independence are found to positively influence the realization of these synergies. Practical implications For practitioners, the findings emphasize the importance of considering both traditional and emerging sources of synergies in M&A evaluations. Decision-makers should focus not only on operational efficiency but also on enhancing relational and network positioning to maximize postacquisition value. The study also highlights the role of strong governance, particularly board diversity and independence, in ensuring successful integration and synergy realization. Originality/value This study contributes to the existing literature by empirically validating previously unexplored synergies, particularly relational, network and nonmarket synergies, in the context of European M&A. By integrating network-based perspectives, it expands the scope of traditional synergy analysis and offers a more holistic view of value creation postacquisition. The research also provides practical insights by identifying key governance factors that influence the realization of these synergies, making it a valuable resource for both academics and practitioners.

The Role of Coordination Mechanisms and Transaction Costs Promoting Sustainability Performance in Agri‐Food Supply Chains: Evidence From Ecuador

ABSTRACTSocial tensions and resource depletion pose significant challenges to the agri‐food sector, highlighting the need for coordinated strategies to ensure sustainability in supply chains. Despite its critical importance, the relationship between coordination mechanisms and sustainability performance remains underexplored. This study examines the interrelations between transactional characteristics, relational and formal coordination, and sustainability performance in agri‐food supply chains. By surveying 204 farmers and 135 downstream players across diverse supply chain arrangements, this research employs structural equation modeling and confirmatory factor analysis to analyze data suitability and test hypotheses. The empirical findings reveal nuanced relationships: relational governance has a significantly positive connection to economic and social sustainability within supply chains. There is also a positive, indirect relation between relational governance and ecological performance through the promotion of resource efficiency. Formal coordination practices facilitate strategic relationships and efficient resource allocation and as such connect to social and economic viability of supply chains. Transaction costs play a role as mediator between formal and relational coordination, and sustainability outcomes. Effective management of transaction costs can mitigate inefficiencies and foster collaborative efforts. By understanding these dynamics, policymakers and stakeholders can develop strategies to promote resilience and sustainability in global food systems.

National system of price indicators: From theory to practice

Under the conditions of sanctions against the Russian Federation, foreign price indicators, which were previously widely used by both business and regulators, have become difficult to access and generally less relevant due to changes in the direction of commodity flows. There is a need for a national system of price indicators, the design of which is currently being decided, and the concept, despite more than two years of discussion and a high degree of priority, has not yet been adopted. The purpose of the article is to outline the subject field of the problem of constructing a national system of price indicators both from the point of view of the theory of the issue and in terms of its application to the problems of antimonopoly policy. The approaches of the new institutional economic theory and Austrian economic theory serve as the methodological basis of the study, and its methods are comparison of structural alternatives. The information base of the study was made up of data from the Federal Antimonopoly Service on law enforcement practice. The results of the study emphasize that in order to fulfill their functions, national price indicators must be formed in the conditions of the market (not quasi-regulation) and competition. The volume and frequency of data collected to calculate the indicators should be determined taking into account the power of the regulator as a guarantor, the characteristics of companies and transactions as factors in demand for long-term formula-based contracts, the risks of concerted actions and tacit collusion (as a broader category). The findings enable participants in the debate both within and outside the academic community to understand the scope of what is possible in constructing a national price indicator system, both in terms of the alternatives available and the price that would have to be paid for the preferred alternative.

ТЕНДЕНЦИИ РАЗВИТИЯ РОССИЙСКОГО РЫНКА СЛИЯНИЙ И ПРИСОЕДИНЕНИЙ (ПОГЛОЩЕНИЙ)

The article discusses the issues of mergers and acquisitions in domestic practice, their forms and industry-specific features of transactions. The reasons for making transactions are highlighted, and the de-pendence of the growth of transactions due to the withdrawal of foreign investors from the Russian market. The interrelation of transactions in industries and the banking sector of the economy is determined. The strengthening of competition in the Russian market of mergers and acquisitions and the concentration of capital in economic sec-tors is justified.

Service selection and switching decisions: user behavior in high-interoperability environments

Current legislation and market dynamics require and encourage systems to become more open and interoperable. Given this shift, the challenge arises as to how service providers can navigate such environments and whether and how known platform economics are affected by that change. In this context, our work investigates behavior when users interact with services in highly interoperable environments, examining the influence of service attributes and platform economics on service selection and switching decisions, with a focus on the role of transaction cost and time, onboarding time, privacy, ownership, and community. For this purpose, we designed and conducted an extensive survey study with more than 500 respondents that combined a conjoint study with an experimental part on switching scenarios. Our findings suggest that transaction features such as cost, time, and privacy are the main factors in service selection with part-worth utilities being 2.1 to 14.7 times higher than non-transactional features. Additionally, building a strong community and offering ownership opportunities to users are effective strategies for customer retention. Further, we observe that rational choice theory does not explain switching decisions in many cases. Our study has important implications for both industry practitioners and policymakers. Practitioners can use our results to carefully manage effective customer retention strategies, while policymakers can use them to better regulate digital markets.

Automated Forensic Examination of Virtual Assets Using XGBoost

The issues of identification of virtual assets and digital currencies have become more acute in recent years because of increased numbers of transactions, their increased volumes, and complexity in forensic examination. This paper is devoted to a new method of the automated forensic investigation of virtual assets with the help of a powerful machine learning algorithm called XGBoost. XGBoost has very high speed and it is quite useful when dealing with large amounts of data and used for finding patterns that may suggest unlawful operations. The given framework is based on XGBoost by identifying the transactions using their features activities, amount, frequencies, volumes and addresses. Through training the model that is given the historical data, the model is, therefore, able to separate normal and suspicious transactions and alert the decision-making process for further review. Network analysis integration takes the system capability to a new level by enabling the identification of intricate transaction characteristics and the interconnectedness of addressing. Aside from adding automation to the forensic examinations to make it more efficient and accurate, such a procedure assists in identifying the developments of virtual asset transactions. The results show the effectiveness of the XGBoost in supporting the advance of forensic investigators helping them to meet the growing roles that cybercriminals portray in today’s digital asset markets.

The Legal Nature of Deliverable Futures

The article studies the legal nature of deliverable futures. The author aims to define a deliverable futures by comparing its legal nature with the nature of the closest financial instrument to it — a deliverable forward. Futures differs from the forward contracts by the obligatory participation of the exchange as a mandatory facilitator in the conclusion and execution of the transaction. It is proposed to consider exchange transactions as a kind of aleatorial contracts, but contrary to bets they are subject to judicial protection under Russian law. The delivery futures is a complex contract, which combines the features of aleatoric transactions and purchase contracts. One the one hand it is related to the former by the presence of stochasticity. On the other hand it is related to the latter in that the underlying assets are sold for a fee.

The impact of venture capital subscription on price deviation of private placement: evidence from China

PurposeThe purpose of this study is to examine the role of venture capital (VC) in supporting corporate growth and innovation through participation in private placements. While VC provides essential financial support to companies, it remains unclear whether this involvement serves a strategic investment role or a purely financial one. This study seeks to elucidate the role of VC by analyzing changes in the price discount of private placements following VC participation.Design/methodology/approachThe authors take the private placement events of China A share listed companies from April 2005 to January 2023 as the sample, and examine the influence of VC subscriptions on price discount rate.FindingsVC subscriptions to private placements increase information asymmetry, consequently raising the discount rate. This relationship is influenced by the transaction characteristics and information environment. Specifically, VC subscriptions further elevate the discount rate when VC are geographically dispersed from the issuers, possess industry expertise in the issuers’ sector, allocate raised funds for asset restructuring or non-digital investments and when the issuers are in their growth stages. Moreover, the positive correlation between VC subscriptions and the discount rate is more pronounced under conditions of lower internal control quality and weaker external media supervision. Higher discount rates in VC-subscribed private placements result in lower R&D investment and investment efficiency by the issuers, leading to larger-scale VC sell-offs and ultimately diminishing the market and financial performance of the issuers.Practical implicationsThe issuers should diligently assess the behaviors and motives of VC and selectively choose issuance targets and methods to manage risks associated with price deviations in private placements. Additionally, this study recommends that regulatory authorities develop a more detailed regulatory framework that considers transaction characteristics and the information environment. This strategy should help optimize external regulatory measures like media coverage and protect the interests of small and medium-sized investors.Originality/valueThis study extends research on the “name chasing” motive and certification effect of VC in private placements, enriches the literature on the mechanisms forming discount rates and provides insights for refining regulatory policies on private placements.

Cryptocurrency Transaction Fraud Detection Based on Imbalanced Classification With Interpretable Analysis

This study introduces an interpretable imbalanced data classification method for detecting cryptocurrency transaction fraud. We address data imbalance using SMOTE oversampling and data augmentation through contrastive learning. Next, we introduce a Transformer-based deep learning model that learns sample relevance. The model undergoes pre-training with a contrastive loss and fine-tuning through Bayesian optimization to effectively extract high-dimensional, higher-order, and fraud-related features. We employ a SHAP-based interpreter along with attention scores to elucidate the role of various transaction features in fraud detection. Comparative results demonstrate the model's remarkable recall performance in identifying cryptocurrency transaction fraud. Furthermore, it achieves an excellent F1 value, striking a balance between accuracy and recall. This research not only enriches financial fraud detection but also enhances cryptocurrency transaction security, promotes market development, and contributes to economic stability and social security.

DEEP LEARNING MODEL FOR DETECTING TERROR FINANCING PATTERNS IN FINANCIAL TRANSACTIONS

Terror financing remains a critical threat to global security, with illicit actors continually adapting their methods to evade detection. Traditional financial monitoring systems often struggle to identify the complex and covert patterns associated with terror-related transactions due to their reliance on predefined rules and statistical thresholds. This study introduces advanced deep learning models designed to detect terror financing patterns within vast datasets of financial transactions. We developed and evaluated several neural network architectures, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs), to capture both spatial and temporal transaction features. The models were trained and tested on a dataset comprising anonymized financial transactions labeled for suspicious activities related to terror financing. Our deep learning models demonstrated superior performance over conventional machine learning approaches, achieving higher accuracy, precision, and recall in identifying suspicious transactions. Notably, the LSTM-based model excelled in detecting sequential transaction patterns indicative of layering and integration stages commonly used in terror financing. The results underscore the potential of deep learning techniques in enhancing the capabilities of financial institutions and regulatory bodies to combat terror financing. Implementing such models can lead to more proactive and effective monitoring systems that adapt to evolving illicit financing strategies.

The Needs and Readiness of the Islamic Banks to Adopt the Comprehensive Islamic Accounting Standards: The Malaysian Experience

The Islamic finance sector in Malaysia has seen substantial growth over the past few decades, becoming one of the leading markets for Islamic financial products and services. This growth has brought about significant challenges in financial reporting, primarily due to the limitations of conventional International Financial Reporting Standards (IFRS) in addressing the unique characteristics of Islamic financial transactions. The shari’ah transactions are governed by Shariah principles, which prohibit interest (riba) and emphasize on risk-sharing, ethical investments, and social justice. The study aims to provide a comprehensive analysis of the current issues and debates surrounding the need for comprehensive Islamic accounting standards in Malaysia. It will review recent literature, regulatory developments, and the experiences of practitioners in the particular field. Furthermore, the report seeks to highlight the readiness of Bank Negara Malaysia and all Islamic Banks to adopt Accounting and Auditing Organization for Islamic Financial Institution (AAOIFI) standards and explore the feasibility of integrating these standards within the International Financial Reporting Standard (IFRS) framework. Additionally, this report aims to contribute to the ongoing discourse on Islamic accounting by offering critical insights and recommendations for policymakers, regulators, and financial institutions. The ultimate goal is to enhance the transparency, comparability, and reliability of financial reporting in the Islamic finance sector, thereby promoting its sustainable growth and integration into the global financial system.

Individual, firearm, and purchasing characteristics associated with risk of firearm-related violent crime arrest: a nested case-control study

BackgroundFirearm purchasing records offer a potentially important administrative data source to identify individuals at elevated risk of perpetrating firearm violence. In this study, we describe individual, firearm, and transaction characteristics of purchasers in California who were arrested for a firearm-related violent crime (FRV) as compared to the general population of registered purchasers in the state.MethodsRelying on a dataset of all individuals with transaction records in California (1996–2021), linked to criminal records (1980–2021), we enrolled a cohort of individuals for whom we could capture the legal firearm purchase history. We identified those arrested for FRV post purchase, and using incidence density sampling, gender-matched cases to ten purchasers (controls) who remained “at risk” at the time the case was arrested. We focused on the purchase closest in time prior to the arrest (“index” purchase). We implemented conditional logistic regression and included models with controls for individual- and community-level demographics, as well as interactions between firearm and purchasing characteristics and criminal history.ResultsThe cohort included 1,212,144 individuals, of whom 6153 were arrested for FRV (0.5%). Cases were matched to 61,530 controls to form the study sample. The largest risk factor was a prior criminal history: purchasers had 5.84 times the risk of FRV if they had a prior arrest within three years of the index purchase (CI 5.44–6.27). Several transaction and firearm characteristics were also associated with FRV. For example, risk increased if the firearm was redeemed at a pawn shop (aIRR: 1.37, CI 1.05–1.77) and decreased if the transaction was a registered private party transfer (vs. retail purchase) (aIRR: 0.83, CI 0.76–0.90) or the firearm was a bolt action firearm (vs. semi-automatic) (aIRR: 0.64, CI 0.51–0.79). In the interaction models, most of the purchase and firearm features only remained significant among those with no criminal history.ConclusionsGiven limited data on firearm transactions, there has been little research on whether the type of firearm an individual purchases or the nature of the purchase might serve as indicators of risk for FRV. We found several transaction and firearm features were associated with risk of FRV. Notably, these features provided little evidence of additional risk for those with a prior criminal record.

NEW AML TOOLS: ANALYZING ETHEREUM CRYPTOCURRENCY TRANSACTIONS USING A BAYESIAN CLASSIFIER

The emergence of cryptocurrencies as a form of digital payments has contributed to the emergence of numerous opportunities for the implementation of effective and efficient financial transactions, however, new fraud and money laundering schemes have emerged, as the anonymity and decentralization inherent in cryptocurrencies complicate the process of monitoring transactions and control by governments and law enforcement agencies. This study aims to develop a mechanism for analyzing transactions in the Ethereum cryptocurrency using a Bayesian classifier to identify potentially suspicious transactions that may be related to terrorist financing and money laundering. The Bayesian approach makes it possible to consider the probabilistic characteristics of transactions and their interrelationships to increase the accuracy of detecting anomalous and potentially illegal transactions. For the analysis, data on transactions of the Ethereum currency from June 2020 to December 2022 were taken. The developed mechanism involves determining a set of characteristics of transaction graph nodes that identify the potential for their use in illegal financial transactions and forming intervals of their permissible values. The article presents cryptocurrency transactions as an oriented graph, with the nodes being the entities conducting transactions and the arcs being the transactions between the nodes. In assessing the risks of using cryptocurrencies in money laundering, the number/amount of transactions to and from the respective node, the balance of these transactions (absolute value), and the type of node were considered. The analysis showed that among the 100 largest nodes in the network, 11 were identified as having a «critical» risk level, and the most closely connected nodes were identified. This methodology can be used not only to analyze the Ethereum cryptocurrency but also for other cryptocurrencies and similar networks.

거래특성요인, 점포이미지, 고객만족, 신뢰, 몰입, 고객충성도 간의 관계: 한국의 청년몰 중심으로

거래특성요인, 점포이미지, 고객만족, 신뢰, 몰입, 고객충성도 간의 관계: 한국의 청년몰 중심으로

The Future of Work and Human Sources: an Evaluate of Rising Tendencies and Hr's Evolving Function

In the dynamic landscape of the twenty first century, the intersection of technology, globalization, and moving societal expectancies is reshaping the future of labor. This paper delves into the multifaceted dimensions of this modification, providing a comprehensive review of emerging tendencies and the evolving function of Human Resources (HR) in navigating this new generation. The rise of automation, artificial intelligence, and remote work has caused a paradigm shift in organizational systems and worker expectancies. As businesses attempt to remain competitive, the combination of superior technologies necessitates a re-evaluation of traditional HR practices. This paper explores the impact of technological advancements on task roles, skill necessities, and the overall worker revel in. Globalization has blurred geographical obstacles, fostering numerous and multicultural places of work. HR specialists are confronted with the mission of handling a group of workers that spans across different time zones and cultural contexts. The paper examines the techniques HR departments appoint to foster inclusivity, cross-cultural collaboration, and effective communication in this globalized work environment. The evolving nature of labour additionally brings forth the importance of worker wellbeing and mental fitness. As corporations understand the link between worker delight and productivity, HR plays a pivotal position in enforcing tasks that prioritize the holistic nicely-being of the staff. Furthermore, the paper explores HR's shifting position from a transactional feature to a strategic associate in organizational decision-making. HR leaders are increasingly more called upon to make contributions to enterprise strategy, aligning human capital with average corporate goals. This paper presents a ahead-searching perspective on the future of labour and the evolving role of HR. By dissecting rising developments, technological affects, and the converting expectations of the staff, it offers treasured insights for HR specialists, organizational leaders, and researchers navigating the transformative panorama of labour within the 21st century.

K-Nearest neighbor smart contract classification with semantic feature enhancement

Abstract How to quickly and accurately retrieve relevant smart contracts from a huge amount of smart contracts has become an urgent need for users. The classification of smart contracts offers a solution by narrowing down the search space. Existing smart contract classification methods suffer from incomplete semantic feature extraction and a lack of consideration of the existence of rich semantics in existing smart contracts of the same class. To address the above problems, we propose a contrast learning and semantic feature embedding approach to enhance K-Nearest Neighbor (CL-SFE-IKNN). Our method fuses local features, global features, and account transaction features of the smart contract source code to perfect the semantics of the contract. Our method adopts KNN to retrieve multiple instances of contracts in the same class and assigns weights to the model output based on their labels. Meanwhile, we introduce contrastive learning and semantic feature embedding to enhance KNN retrieval to high-quality nearest neighbors of the same class. Experimental results show that by combining a KNN classifier with a traditional linear classifier, our model achieves the best performance compared with other baseline models.

Hawala in criminal court: the role of law and commercial culture in informal financial exchange

A historically informed understanding of ḥawāla and other varieties of informal value transfer systems (IVTS) requires consideration of the normative and cultural elements which structure and facilitate transactions in globalized financial markets. This paper argues that the Sharia-based normative and cultural framework of ḥawāla is created in the social relations of Muslim networks and that, in a criminal law context, this normativity can be used as a tool to discern between legitimate and illegitimate transactions. The new institutional economists’ (NIE) explanation of ḥawāla, which predominates in scholarly work, neglects this common frame of reference built on community, shared belief, and normative rules and expectations. The NIE views economic institutions through the lens of profitmaking and self-interest centred institutional development. Moreover, in a criminal law context, the profit-oriented perspective of law overlooks the normative characteristics that sometimes appear in the transactional characteristics of these transactions. When evidence of these characteristics is presented to the court, they offer a useful tool to differentiate legitimate from illegitimate transfers of value. Legitimate transfers will frequently be facilitated through dense networks in which the normativity and culture of ḥawāla frame the rules and expectations of parties to the transaction. Criminal transactions, on the other hand, typically involve fewer participants, sometimes from different ethnic and linguistic backgrounds, are profit-orientated, and involve means of enforcement such as the use of violence which traditional networks do not employ. The NIE perspective does not fully account for non-western legal orders or for commercial practices in which exchange remains culturally, historically, and socially embedded.