Transaction Characteristics Research Articles

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

Purpose The 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/approach The 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. Findings VC 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 implications The 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/value This 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.

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.

Readers' advisory vs reference: a difference of stance

PurposeThe purpose of this paper is to determine how we ought to distinguish between reference and readers' advisory (RA) service, given the latter’s turn toward a whole collection approach. In other words, the paper answers this question: If both reference and RA librarians aim to meet patrons’ information needs and may theoretically do so using the same materials, then how are we to differentiate the two services conceptually?Design/methodology/approachIn this conceptual paper, we posit that we can distinguish between RA and reference using Louise Rosenblatt’s theory of the aesthetic transaction. With this theory in hand, we can redefine the service distinction in terms of the stance – aesthetic or efferent – that the patron expects to take toward the material they seek.FindingsOn our account, the reader’s desired stance becomes a kind of hermeneutical lens through which a library worker may productively evaluate plausible pathways and materials. An aesthetic lens is characteristic of RA; it makes features of potential aesthetic transactions between a particular reader and a particular text (or genre or author’s oeuvre) salient.Originality/valueThe proposed account constitutes a novel application of Rosenblattian response theory, one that grounds and refines the going view that RA’s proper focus is on supporting a particular sort of experience rather than providing particular sorts of texts. This theoretical emendation also better aligns the service distinction with contemporary conceptualizations of RA as a “whole collection” service. Important practical and philosophical implications follow from the new account.

Analysis: Married Women-Segmented Module Meet Doctor Users With Low Interest in The Klikdokter App

The clinic's process for requesting a consultation with a doctor and receiving a queue number is inefficient. To help patients make the most of their time at the clinic, it is helpful to provide them with information on when physicians will be available to see them. A new approach to health development governance is required under Regulation No. 21 of 2020 of the Republic of Indonesia's Minister of Health, which addresses the Ministry's Strategic Plan for 2020–2024. This plan encompasses health information systems, research integration, and development, among other areas. First, Health Technology Transformation, which includes developing and integrating health data; second, Health Service Application Integration and Development; and third, Health Technology Ecosystem Development, will house the primary activities involved in health technology transformation. This study's findings suggest that consumers are mostly interested in finding hospitals in close proximity to their homes, rather than using the doctor's appointment component of the KlikDokter app. Because of this, KlikDokter has to implement a change movement that can increase the number of health facilities that operate with Klik Doctor, allowing it to reach the closest health facility. It also has to link insurance and BPJS so that health facilities may provide transaction features.

Understanding the effect of contractual complexity on regulatory focus in construction projects: the moderating role of organizational culture

How the behavioral motivation of each organization is shaped or emerges during the interorganizational transaction deserves more investigation. Drawing on regulatory focus theory, this study delves into the impact of contractual complexity and organizational culture on regulatory focus as behavioral motivation in construction projects. This study dissects contractual complexity into control, coordination, and adaptation grounded in a functional perspective, decomposes regulatory focus into prevention focus and promotion focus, and splits organizational culture into group culture, developmental culture, hierarchical culture, and rational culture. Through validating the empirical data from 411 questionnaires within Chinese construction sector, this study uncovers that contractual control and contractual coordination positively affect prevention focus, while contractual adaptation positively affects promotion focus. Four different types of organizational culture play different moderating roles in the process of stimulating regulatory focus. Organizations with high group culture have a strong driving force for the formation of both regulatory focuses. In contrast, the average level of hierarchical culture, developmental culture, or rational culture is not conducive to the formation of prevention focus or promotion focus. These findings explain how behavioral motivation is impacted by transaction characteristics such as contracts from the perspective of social psychology, confirm the spillover effect of organizational culture of one party at the interorganizational level, and provide suggestions for practitioners on how to develop interorganizational relationships and allocate resources appropriately.

Identifying highly-valued bank customers with current accounts based on the frequency and amount of transactions

We hypothesize that highly-valued bank customers with current accounts can be identified by a high frequency of transactions in large amounts of money. To test our hypothesis, we employ machine learning predictive models to real data, including 407851 transactions of 4760 customers with current accounts in a local bank in Jordan. Thus, we exploit three clustering algorithms: density-based spatial clustering of applications with noise, spectral clustering, and ordering points to identify the clustering structure. The two segments of customers (generated from the clustering process) have different transactional characteristics. Our customer behavioral segmentation accuracy is, at best, 0.99 and at least 0.82. Likewise, we build three classification models using our segmented data: a neural network, a support vector machine, and a decision tree. Our predictive models have an accuracy of 0.97 at best and 0.90 at least. Our experimental results confirm that the frequency and amount of transactions of bank customers with current accounts are most likely sufficient indicators for recognizing those customers whom banks highly value. Our predictive models state that the two most critical indicators are the deposit and withdrawal transactions performed on ATMs. In contrast, the least significant indicators are the transactions of credit cards and credit cheques.

조선후기 한성부의 가옥 및 空垈 매매와 그 특징

This study aims to analyze the patterns and characteristics of house transactions in the Hanseong-bu area during the late Joseon. The main data that the investigator consulted came from the documents related to house transactions housed at Kyoto University of Japan. They are part of Korea's old documents taken out of the country to Japan during the colonial years. The focus was on the transaction records for houses and vacant lots within the five sections of Hanseong-bu between the middle 17th century and the middle 19th century. As for transaction methods, they showed a tendency of Gyeonggwan transactions(經官賣買) to have them notarized by the Hanseong-bu office until the middle 18th century. This characteristic was different from other areas where Baekmun transactions(白文賣買) completed only with a contract between the parties were common for land and houses with Gyeonggwan transactions generally implemented for servant transactions. As for the content of transactions, transaction sizes were about 50~60 rooms(間) in general. It was the most prominent that the same transaction items were traded consecutively at a short time cycle. Transactions were frequent. There were three cases in which it took less than a year to resell a house after its purchase. It took less than three years to resell in 50% of the cases. Gyeonggwan transactions and short transaction cycles were attributed to various causes including the shortage of hoses and lots and diverse ways of dwelling due to frequent population movement. Considering that the same phenomenon was found in land transactions in Hanseong-bu, there is a need to broaden the scope of finding the causes to the degree of urbanization or commercialization and differences in the perceptions of houses and land as one's property.

Blockchain Technology as a Mechanism for Protecting Citizens' Rights in Remote Investment Transactions: Higher Education Market vs Labor Market

Introduction. The article studies and analyzes legal mechanisms of protection of rights and legitimate interests of private investors in the sphere of online investment based on blockchain technologies, which allow to reduce the level of inconsistency between the requirements of the labor market and the proposals of the higher education market. Materials and methods. The methodological basis of the study is interdisciplinary and covers different approaches and concepts belonging to the sphere of investment activities, digital economy, including blockchain technologies and specialized digital platforms, legal support of investment transactions, etc. It is based on the concept of development of digital economy in the conditions of the fourth industrial revolution, which is considered in the context of integration of digital technologies into investment processes and related problems of legal support of security of investment transactions in the Internet space. The harmonization of different concepts and approaches within the work is achieved through the methods of analysis and synthesis, as well as the use of a systematic approach, methods of comparative analysis, observation, abstraction, etc.Results of the study. The paper defines the essence and features of remote investment transactions, as well as their forms, reveals the essence and impact of blockchain technology on economic development and investment, provides examples of remote investment projects based on blockchain technology, characterizes the advantages and risks of remote investment instruments, proposes several mechanisms of legal protection of the interests of private investors using online instruments, substantiates the value and features of the use of smart-contracts as a blockchain technology, and provides a description of the advantages and risks of remote investment instruments. It has been established that in addition to the use of legal instruments for the protection of investment risks, primarily aimed at the use of smart contracts based on blockchain technologies, an important direction for improving the reliability of such transactions and avoiding rash financial and reputational losses is to increase the level of education of private investors. The labor market's demand for increasing the professionalism of individuals using remote investment instruments increases the relevance of educational programs aimed at studying blockchain, smart contracts, remote investment instruments and legal mechanisms for protecting the interests of private investors. Discussion and conclusion. The scientific and practical significance of the work consists in a comprehensive analysis and characterization of legal mechanisms to protect the rights of individuals investing through remote financial instruments in the Internet space, as well as in determining their advantages and disadvantages. It is substantiated that the main legal instrument for the protection of such investment transactions based on blockchain technology is smart contracts. The potential of smart contracts as instruments of legal protection of investment transactions in remote format is used due to a set of their typical functions, including decentralization, resistance to unauthorized changes, transparency, autonomy, etc.

An online fuzzy fraud detection framework for credit card transactions

Credit card transaction fraud is one of the challenging security concerns for financial firms globally. The continuously changing environment of fraud characteristics and the class imbalance and complete separation issues in fraud data create difficulties in accurately and efficiently predicting fraudulent transactions and implementing fraud detection systems in real-time. The study aims to develop a new real-time fraud detection framework that can efficiently be implemented online and address the issues caused by non-stationary changes in transaction and fraud characteristics, class imbalance, and complete separation. We propose a new approach to handle the impact of non-stationary changes in fraud transaction patterns. It enables efficiency in model training, given the sheer size of datasets. By implementing a robust fuzzy logistic regression model against class imbalance and separation problems, we address the challenge of having a very low rate of fraudulent transactions in the dataset and having separation issues due to specific characteristics of transactions. The analysis of the performance versus efficiency nexus of the proposed methodology reveals that the proposed framework shows strong performance results with specificity and sensitivity greater than 0.90 and Matthew’s correlation coefficient greater than 0.80 even on small sample sizes and produces highly accurate results in identifying fraudulent and non-fraudulent transactions with an accuracy greater than 0.99. Benchmarking with machine learning and other fraud detection approaches reveals that the proposed framework provides better detection performance while maintaining a higher rate of identifying non-fraudulent transactions than the alternative approaches. Improved classification performance leads to detecting fraudulent transactions with higher precision while avoiding misclassifying legitimate transfers, resulting in lower financial losses and improved customer satisfaction.

Anomaly Detection in Blockchain Using Machine Learning

Blockchain technology has gained significant attention as a secure and decentralized platform for various applications. However, the immutable and distributed nature of blockchain also presents unique challenges for detecting anomalies and suspicious activities within the network. This research paper proposes a novel approach to anomaly detection in blockchain using machine learning techniques. The goal of this study is to develop an effective and scalable anomaly detection framework that can analyze the vast amount of data generated within a blockchain network and identify irregularities or potential security threats. The proposed framework leverages the power of machine learning algorithms to learn patterns, relationships, and behaviours from historical blockchain data, enabling the detection of anomalous activities in real time.The research paper first focuses on feature extraction techniques tailored specifically for blockchain data. These techniques consider key characteristics of blockchain transactions, such as transaction size, timestamp, and involved addresses, to construct meaningful features that capture the underlying patterns and trends. Various dimensionality reduction techniques are also explored to handle the high-dimensional nature of blockchain data.Subsequently, several machine learning algorithms, including clustering, classification, and anomaly detection methods, are employed to train models using the extracted features. The performance of different algorithms is evaluated using benchmark datasets and real-world blockchain data to assess their accuracy, precision, and recall in detecting anomalies. Additionally, the scalability of the proposed framework is investigated to ensure its effectiveness in large-scale blockchain networks.Furthermore, the research paper investigates the integration of domain-specific knowledge, such as known attack patterns and regulatory compliance rules, into the anomaly detection framework. This hybrid approach combines the strengths of machine learning algorithms with expert knowledge to enhance the accuracy and interpretability of anomaly detection results.The experimental results demonstrate that the proposed anomaly detection framework achieves promising performance in identifying various types of anomalies in blockchain data. It exhibits high detection rates while minimizing false positives, thereby providing valuable insights for blockchain network administrators and regulators to mitigate security risks and safeguard the integrity of blockchain systems. In conclusion, this research paper presents an innovative approach to anomaly detection in blockchain using machine learning. The proposed framework addresses the unique challenges posed by blockchain's decentralized and immutable nature, offering an effective solution for detecting suspicious activities and ensuring the security of blockchain networks. The findings of this study contribute to the growing field of blockchain analytics and have significant implications for real-world blockchain applications in domains such as finance, supply chain management, and healthcare.

Data mining in economics: Unraveling merchant transactions for strategic insights

This paper explores the application of data analysis and mining techniques in the domain of economics, with a specific focus on understanding merchant transaction characteristics. The study delves into fundamental theories, including classification tasks, regression missions, and relevance analysis, showcasing the versatility of these techniques in addressing economic challenges. Neural network models, such as Multilayer Perceptron and Auto Encoder, are introduced for handling complex economic data. The research emphasizes the importance of data mining in extracting valuable insights from real-world merchant transaction data, leading to the creation of the Merchant Transaction Feature Standard Database. A detailed data preprocessing method tailored to merchant transaction data is presented, addressing issues such as missing data, noise reduction, data integration, and transformation. The unique characteristics of real merchant transaction data, including sensitivity, concentration, sparsity, and the lack of label diversity, are outlined. The study concludes by highlighting the potential benefits of employing data mining techniques in optimizing marketing, merchant management, and risk management strategies. Overall, this paper contributes to advancing the understanding and practical applications of data analysis and mining techniques in economics.

Implementation of the First In First Out (FIFO) Algorithm in the Sandal and Shoe Product Inventory (Stock) Application

This study addresses the optimization of inventory management for sandal and shoe products, at CV Diva Karya Mandiri Warehouse, which covers of five key features: a dashboard, master data management, transaction data, reporting, and user management. The First In First Out (FIFO) algorithm is specifically applied to the transaction feature, ensuring timely disbursement in line with the order of receipt. It is implemented using Rapid Application Development (RAD) methodology, which consists of Planning Requirements, User Design, Construction, and Cutover phases. The developed inventory application offers two access levels: administrators with comprehensive access and warehouse managers with limited access for viewing, searching, and filtering item data. This study successfully implementing the FIFO algorithm, with 95% Blackbox testing result achieved through boundary value analysis approach.Top of Form