Transaction Data Research Articles

Does restricting private school enrollment increase the public school housing premium? Evidence from the Chengdu real estate market

Despite a growing body of literature demonstrating the role of private schools in narrowing the housing premium associated with high-quality public schools, there is little evidence on how policy shocks that limit access to private schools affect this premium. Using 21,364 observations of house transaction data from 2017 to 2021, this paper examines the impact of one policy limiting private schools’ autonomy in enrolling students on the public school housing premium in Chengdu, China. My findings reveal that when private schools lose the right to select students, the housing premium associated with high-quality public schools increases significantly. The escalation in high-quality school housing premium is attributed to the growing uncertainty regarding the accessibility of private schools as alternative options. The policy effect on housing premium remains robust at school district boundaries. My conclusion implies that policy-makers should pay attention to the positive role of private schools in promoting educational equity.

A Three-Party Dynamic Pricing Mechanism for Customized Data Products Based on the Stackelberg Game and Bargaining Model

In the era of big data, breaking down data silos to enable efficient data transactions has become essential, with the fairness and transparency of pricing mechanisms being paramount. This study addresses these challenges by introducing a novel tripartite pricing model for customized data products that integrates the Stackelberg and bargaining game frameworks. By designing distinct utility functions for buyers, sellers, and the platform, the model effectively aligns the varying objectives of each participant. A dynamic adjustment mechanism further enhances this model by adaptively recalibrating the guidance price and pricing range based on real-time updates to buyer budgets and seller offers, thus ensuring fairness and responsiveness throughout the negotiation process. Experimental simulations comprising 100 transaction rounds across diverse buyer–seller profiles validate the model’s effectiveness, achieving a transaction success rate of 92.70% with an average of 6.86 bargaining rounds. These findings underscore the model’s capacity to optimize transaction outcomes, promote pricing equity, and enhance bargaining efficiency. The proposed model has broad applications in sectors such as finance, healthcare, and e-commerce, where precise data pricing mechanisms are essential to maximize transactional value.

Pengembangan Aplikasi Pemesanan Makanan Berbasis Web Mobile Menggunakan Metode Prototyping pada KAF Fried Chicken

The development of information and communication technology has brought significant changes in various aspects of life, including in terms of ordering food. The design of a food ordering application at the KAF Chicken outlet is very necessary to reduce the constraints of conventional systems. The problem commonly faced by various culinary businesses is the absence of an automated system to record and manage orders, customer data, and financial reports, which often results in inaccuracy and slows down the process. This study aims to design a mobile web-based food ordering application that can facilitate culinary business management in ordering, payment, and transaction data management. The application uses the spiral method that allows gradual development and uses the Express Js Framework and Mongodb as data management. Application features include menu management, online ordering, and transaction recording. Based on the test results, this system can help control transaction data and manage reports accurately and quickly. The design of this application can also be expected to contribute positively to improving the operational efficiency of the culinary business, especially in managing orders and transaction data.

Data Transactions in China: Legislation, Promotion, and Regulation

Data Transactions in China: Legislation, Promotion, and Regulation

Appropriately matching transport care units to patients in an interhospital Transport Care: an implementation report.

In interfacility transport care, a critical challenge exists in accurately matching ambulance response levels to patients' needs, often hindered by limited access to essential patient data at the time of transport requests. Existing systems cannot integrate patient data from sending hospitals' electronic health records (EHR) into the transfer request process, primarily due to privacy concerns, interoperability challenges, and the sensitive nature of EHR data. We introduce a distributed digital health platform, Interfacility Transport Care (ITC)-InfoChain, designed to solve this problem without compromising EHR security or data privacy. This report details the implementation of Interfacility Transport Care (ITC)-InfoChain, a secure, blockchain-based platform designed to enhance real-time data sharing without compromising data privacy or EHR security. The ITC-InfoChain platform prototype was implemented on AWS cloud infrastructure, employing Hyperledger Fabric as a permissioned blockchain. Key elements included participant registration, identity management, and patient data collection isolated from the sending hospital's EHR system. The client program submits encrypted patient data to a distributed ledger, accessible to the receiving facility's critical care unit at the time of transport request and EMS teams during transport through the PatienTrack web application. Performance was evaluated through KPIs such as data transaction times and scalability across transaction loads. The ITC-InfoChain demonstrated strong performance and scalability. Data transaction times averaged 3.1 seconds for smaller volumes (1-20 transactions) and 6.4 seconds for 100 transactions. Optimized configurations improved processing times to 1.8-1.9 seconds for 400 transactions. These results confirm the platform's capacity to handle high transaction volumes, supporting timely, real-time data access for decision-making during transport requests and patient transfers. The ITC-InfoChain platform addresses the challenge of matching appropriate transport units to patient needs by ensuring data privacy, integrity, and real-time data sharing, enhancing coordination of patient care. The platform's success suggests potential for regional pilots and broader adoption in secure healthcare systems. Stakeholder resistance due to blockchain unfamiliarity and data privacy concerns remains. Funding has been sought to support a pilot program to address these challenges through targeted education and engagement.

Parametric Quantile Autoregressive Conditional Duration Models With Application to Intraday Value‐at‐Risk Forecasting

ABSTRACTThe modeling of high‐frequency data that qualify financial asset transactions has been an area of relevant interest among statisticians and econometricians—above all, the analysis of time series of financial durations. Autoregressive conditional duration (ACD) models have been the main tool for modeling financial transaction data, where duration is usually defined as the time interval between two successive events. These models are usually specified in terms of a time‐varying mean (or median) conditional duration. In this paper, a new extension of ACD models is proposed which is built on the basis of log‐symmetric distributions reparametrized by their quantile. The proposed quantile log‐symmetric conditional duration autoregressive model allows us to model different percentiles instead of the traditionally used conditional mean (or median) duration. We carry out an in‐depth study of theoretical properties and practical issues, such as parameter estimation using maximum likelihood method and diagnostic analysis based on residuals. A detailed Monte Carlo simulation study is also carried out to evaluate the performance of the proposed models and estimation method in retrieving the true parameter values as well as to evaluate a form of residuals. Finally, we derive a semiparametric intraday value‐at‐risk (IVaR) model and then the proposed models are applied to two price duration data sets.

Blockchain Based Proxy Re-Encryption for Secure Data Sharing

This paper proposes a solution to secure data sharing within the Internet of Things (IoT) by combining Proxy Re-Encryption (PRE) and Blockchain. In IoT networks, data must often be shared across multiple devices and users, raising concerns over data privacy and security. Proxy Re-Encryption enables controlled data sharing without disclosing original information, while Blockchain technology offers an immutable, transparent ledger for tracking data transactions. Together, these technologies ensure data integrity, privacy, and efficient access control, making them suitable for modern IoT applications

AI-Driven Fraud Detection in Banking: Enhancing Transaction Security

An important step forward in risk management and fraud detection has been achieved with the integration of Artificial Intelligence (AI) in the banking sector. In this paper, we take a look at how AI has revolutionized various fields, shedding light on the benefits and drawbacks of this technology. The effects of AI on risk management are complex. More complex credit risk assessment models are made possible by algorithms that can see patterns in massive datasets that people might miss. When it comes to market and liquidity issues, real-time transaction monitoring is absolutely essential for quick risk mitigation. Automating compliance with regulatory norms is another critical function of AI, which helps to decrease human mistake and assures quick adaptability to changes in regulations. The automation of mundane processes and the reinforcement of cybersecurity measures further reduce operational risks. By examining client behaviour and transaction data, enhanced algorithms may adeptly spot anomalies that could indicate fraud. Artificial intelligence's capacity to foresee future events enables it to foil possible fraud attempts. The systems are designed to respond to changing fraudster strategies with its adaptive learning feature.

Approaching Multifractal Complexity in Decentralized Cryptocurrency Trading

Multifractality is a concept that helps compactly grasp the most essential features of financial dynamics. In its fully developed form, this concept applies to essentially all mature financial markets and even to more liquid cryptocurrencies traded on centralized exchanges. A new element that adds complexity to cryptocurrency markets is the possibility of decentralized trading. Based on the extracted tick-by-tick transaction data from the Universal Router contract of the Uniswap decentralized exchange, from 6 June 2023 to 30 June 2024, the present study using multifractal detrended fluctuation analysis (MFDFA) shows that even though liquidity on these new exchanges is still much lower compared to centralized exchanges, convincing traces of multifractality are already emerging in this new trading as well. The resulting multifractal spectra are, however, strongly left-side asymmetric, which indicates that this multifractality comes primarily from large fluctuations, and small ones are more of the uncorrelated noise type. What is particularly interesting here is the fact that multifractality is more developed for time series representing transaction volumes than rates of return. On the level of these larger events, a trace of multifractal cross-correlations between the two characteristics is also observed.

Integrating Blockchain Technology in Pharmaceutical Supply Chains

This paper presents a transformative approach to enhancing transparency, security, and traceability. By utilizing a decentralized ledger system, stakeholders can access real-time data about drug provenance and distribution, thereby increasing consumer confidence and improving regulatory compliance. Existing pharmaceutical supply chains face significant challenges, including counterfeit drugs, lack of visibility, and inefficiencies in tracking products through complex logistics networks. These issues lead to financial losses, compromised patient safety, and regulatory penalties. This study proposes a novel framework, the Pharmaceutical Supply Chain using Blockchain Technology (PSC-BCT), designed to address these existing challenges. The PSC-BCT framework leverages smart contracts, distributed ledgers, and IoT integration to create a secure, transparent, and efficient supply chain ecosystem. This framework enables all participants, from manufacturers to pharmacists, to share and verify transaction data, thereby enhancing accountability and reducing the risk of fraud. The proposed method is utilized to streamline operations, reduce delays, and enhance data integrity within the pharmaceutical supply chain. By ensuring that every transaction is recorded immutably, stakeholders can verify product authenticity and track the drug's journey from production to the end consumer. The findings indicate that the PSC-BCT framework significantly improves supply chain transparency and efficiency while reducing instances of counterfeit drugs. Furthermore, the implementation of blockchain technology fosters trust among stakeholders and enhances compliance with regulatory standards, ultimately leading to improved patient outcomes and safety.

FinSafeNet: securing digital transactions using optimized deep learning and multi-kernel PCA(MKPCA) with Nyström approximation.

With the swift advancement of technology and growing popularity of internet in business and communication, cybersecurity posed a global threat. This research focuses new Deep Learning (DL) model referred as FinSafeNet to secure loose cash transactions over the digital banking channels. FinSafeNet is based on a Bi-Directional Long Short-Term Memory (Bi-LSTM), a Convolutional Neural Network (CNN) and an additional dual attention mechanism to study the transaction data and influence the observation of various security threats. One such aspect is, relying these databases in most of the cases imposes a great technical challenge towards effective real time transaction security. FinSafeNet draws attention to the attack and reproductive phases of Hierarchical Particle Swarm Optimization (HPSO) feature selection technique simulating it in a battle for extreme time performance called the Improved Snow-Lion optimization Algorithm (I-SLOA). Upon that, the model then applies the Multi-Kernel Principal Component Analysis (MKPCA) accompanied by Nyström Approximation for handling the MKPCA features. MKPCA seeks to analyze and understand a non-linear structure of data whereas, Nyström Approximation reduces the burden on computational power hence allows the model to work in situations where large sizes of datasets are available but with no loss of efficiency of the model. This causes FinSafeNet to work easily and still be able to make accurate forecasts. Besides tackling feature selection and dimensionality reduction, the model presents advanced correlation measures as well as Joint Mutual Information Maximization to enhance variable correlation analysis. These improvements further help the model to detect the relevant features in transaction data that may present a threat to the security of the system. When tested on commonly used database for testing banks performance, for instance the Paysim database, FinSafeNet significantly improves upon the previous and fundamental approaches, achieving accuracy of 97.8%.

TRANSFORMING BANKING SECURITY: THE ROLE OF DEEP LEARNING IN FRAUD DETECTION SYSTEMS

In the digital banking landscape, the increasing volume of online transactions has heightened the risk of fraudulent activities, necessitating the development of more effective detection systems. This study investigates the efficacy of various machine learning and deep learning algorithms in identifying fraudulent transactions, emphasizing Long Short-Term Memory (LSTM) networks. We implemented and evaluated multiple algorithms, including Logistic Regression, Random Forest, Gradient Boosting Machines (GBM), and XGBoost, on a large-scale credit card transaction dataset. Our results demonstrate that the LSTM model outperforms traditional machine learning algorithms, achieving an accuracy of 98.5%, precision of 87.2%, recall of 85.0%, and an Area Under the Curve (AUC) score of 0.94. These findings highlight the superior capability of LSTM networks to capture complex patterns in sequential transaction data, making them an asset for real-time fraud detection in banking. This research underscores the need for financial institutions to adopt advanced deep learning techniques to enhance their fraud detection systems, thereby minimizing financial losses and improving customer trust.

Pendampingan Digitalisasi Data Transaksi Kolektor pada Koperasi Simpan Pinjam Maju Mapan Jaya Denpasar

Koperasi Simpan Pinjam (KSP) Maju Mapan Jaya is one of the cooperatives engaged in savings and loans. In an effort to provide maximum service to customers, KSP Maju Mapan Jaya has collectors who are tasked with collecting credit installments and taking customer savings deposits directly to the customer's location for the convenience and security of customer funds.Transaction recording is still done manually by cooperative collectors by recording in the savings ledger or loan ledger and then submitting it to the cooperative office. Partners expect assistance in the application of information technology, especially the digitization of collector transaction data to minimize obstacles in the field. The purpose of this community service activity is to help partners provide assistance and implement a digital-based collector transaction data information system.

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.

The Impact of Unnatural Death Stigmatization on Neighboring Property Prices: Propensity Score Matching and Difference-In-Differences Analysis.

This study primarily investigated the impacts of unnatural in-house deaths on nearby property prices. Propensity score matching was used to assess the homogeneity of experimental and control groups, and the difference-in-differences (DID) method was used for their comparison. Data for real estate transactions in Taipei City from 2012 to 2022 were retrieved from the Ministry of the Interior's real estate actual transaction price registration query system. The DID interaction variable coefficient of 0.066 was significant at the 10% level. A comparison of the experimental and control groups showed a 6.8% property price difference following an unnatural in-house death. This suggests that an unnatural in-house death has a positive impact on property prices nearby. There were four unnatural deaths at the Xining Public Housing from 2003 to 2012 and four at the Jinxing Building from 1984 to 2012. The high incidence of events and the disclosure of information may have depreciated nearby property prices. The study results show that the stigmatization of unnatural in-house deaths reduces nearby property prices. The depreciation of implicit prices reverted and followed a U-shape pattern after several years.

Insights and caveats from mining local and global temporal motifs in cryptocurrency transaction networks

Distributed ledger technologies have opened up a wealth of fine-grained transaction data from cryptocurrencies like Bitcoin and Ethereum. This allows research into problems like anomaly detection, anti-money laundering, pattern mining and activity clustering (where data from traditional currencies is rarely available). The formalism of temporal networks offers a natural way of representing this data and offers access to a wealth of metrics and models. However, the large scale of the data presents a challenge using standard graph analysis techniques. We use temporal motifs to analyse two Bitcoin datasets and one NFT dataset, using sequences of three transactions and up to three users. We show that the commonly used technique of simply counting temporal motifs over all users and all time can give misleading conclusions. Here we also study the motifs contributed by each user and discover that the motif distribution is heavy-tailed and that the key players have diverse motif signatures. We study the motifs that occur in different time periods and find events and anomalous activity that cannot be seen just by a count on the whole dataset. Studying motif completion time reveals dynamics driven by human behaviour as well as algorithmic behaviour.

Optimization of frequent item set mining parallelization algorithm based on spark platform

In this paper, we propose a new method that combines the parallelism of the Spark-based platform with fast frequent mining, called STB_Apriori. Previous research has shown that traditional frequent itemset mining algorithms have high overhead when faced with large datasets and high-dimensional data computation, and generate a large number of candidate itemsets; at the same time, when faced with diverse user requirements, they often generate very sparse and diverse data. In order to solve the problem of fast mining of massive data, our idea originates from the capability of Spark distributed computing and the common optimisation ideas in Apriori mining, by using the efficient operator BitSet to achieve transaction compression, bit storage and data manipulation by Boolean matrices, and at the same time by parallelising the processing and optimising the algorithmic logic to achieve fast and frequent mining. In experiments on real-world datasets, our model consistently outperforms five widely used methods by a significant margin on very large data and maintains its excellence in the remaining cases, proving its effectiveness on real-world tasks, while further analysis shows that increasing the number of distributed nodes also incrementally and continuously improves performance.

The effects of macroprudential policies on house prices: Evidence from non-ordinary house purchasing policy using China real transaction data

This paper contributes to the international policy debate on the effect of macroprudential policy on housing prices. Our identification relies on the fact that ordinary housing and non-ordinary housing in Beijing face different loan-to-value (LTV) restrictions and transaction tax rates. This paper uses regression discontinuity (RD) design to test whether the differentiated regulation of ordinary housing and non-ordinary housing caused a discontinuous change in housing prices with house-level transaction data. We find that housing prices have a significant discontinuous decline at the cutoff point caused by macroprudential policy, which we take 140 square meters since it is the identification standard of non-ordinary housing. Further, we carry out RD-DID analysis to investigate the influence of loan-to-value (LTV) restrictions on housing prices. We find that around 2% decline in house prices is caused by credit policy.

Texas Rural Land Market Integration: A Causal Analysis using Machine Learning Applications

Texas rural land markets have several special features that makes it unique from other rural land markets in the United States. In 2021, Texas agricultural land, including buildings, is valued at $299.88 billion, which is almost 10% of the nation's total agricultural real estate value and 83% of the state's land is categorized as rural. In addition, due to its size and geologic features, Texas’ diverse landscape creates complex and widely divergent conditions affecting ownership and marketing of the land. Despite this complexity, lack of granular level and reliable transactional data on land sales has prevented thorough investigation into Texas land markets to uncover various interdependencies. Using quarterly transactional land value data from 1966-2017, this study uses cutting-edge machine learning algorithms and probabilistic graphical models to uncover causal interaction patterns of different land markets in Texas. The results reveal that Texas rural land markets are interdependent. Current and potential landholders and lenders can use the results from this work to aid strategic decision making. Financial institutions and investment groups could be made aware of the trend of one land market relative to other markets and adjust their holdings accordingly. Landowners may better understand changes in net wealth, which affect their ability to borrow capital and operate efficiently. Moreover, lenders may also benefit from the information to manage collateral and thus maintain the stability of their operation.

An approach based on data mining and genetic algorithm to optimizing time series clustering for efficient segmentation of customer behavior

In today's highly competitive market, organizations face significant challenges in accurately understanding and segmenting customer behavior due to the inherently dynamic and evolving nature of customer interactions over time. Traditional customer segmentation methods often neglect these temporal variations, leading to ineffective business strategies and missed opportunities. This research addresses this critical gap by introducing an innovative time series-based approach for customer behavior segmentation. By modeling each customer's behavior as a time series capturing key metrics such as purchase frequency, transaction amounts, and customer lifecycle costs the proposed method dynamically adapts to behavioral changes over time. To enhance segmentation precision, a genetic algorithm is employed to optimize feature weights, ensuring that the most relevant factors are emphasized. These optimized features are then clustered using spectral clustering to identify distinct and meaningful customer segments. The effectiveness of the proposed method is validated using 30 months of transactional data from a payment services company. The results demonstrate that the proposed approach, particularly when combined with spectral clustering and optimally weighted features, significantly surpassing the performance of traditional static segmentation techniques. This research not only provides a more accurate framework for uncovering hidden patterns in customer behavior but also delivers actionable insights for targeted marketing and personalized customer strategies.