Real Transactions Research Articles

Does Media Tone Influence Pre‐IPO Earnings Management? Evidence from IPO Approval Regulation in China

Unlike the reputation view on the governance role of the media, we propose that media coverage influences pre‐IPO earnings management through the regulation mechanism. Using the Chinese initial public offering (IPO) approval regulation setting, we find that negative pre‐IPO media tone is associated with lower abnormal accruals but with higher real transactions manipulation and total earnings management in IPO prospectuses. We also find that the media's effect is more pronounced when IPO firms face stronger regulatory scrutiny such as when they have no approval committee connections, no political connections, or no state ownership. Further, we find that the effect of media tone is stronger when the media outlet has high circulation or high credibility. Our findings suggest that, in a highly regulated market, the media plays a limited governance role because, although it has the effect of restraining accruals management, this effect is substituted with more real transactions manipulation to reduce the risk of being punished by regulators.

A proposed model for enhancing e-bank transactions: an experimental comparative study

In this paper, we introduce a novel approach to address the dynamic prediction of customer activity in electronic payment transactions for individual clients. Our approach is founded on customer online payment transaction records from registered UK-based online retailers between 01/12/2009 and 09/12/2011. These retailers primarily specialize in unique gift items for various occasions, catering to a wide range of clients, including wholesalers. We used classification analysis based on the correlation coefficient to measure and describe a customer's electronic payment capability based on the quality of products they purchase. Furthermore, we trained multi-layered models (linear model, deep learning, random forest, and support vector machines (SVM)) to capture the dynamics of e-bank transaction reinforcement for retail customers using machine learning. Real transaction data from a UK online retailer was employed in our study. The experimental results consistently demonstrated the effectiveness of our proposed strategy.

NUS: Noisy-Sample-Removed Undersampling Scheme for Imbalanced Classification and Application to Credit Card Fraud Detection

Since minority samples are substantially less common than majority samples, many industrial applications, such as credit card fraud detection (CCFD) and defective part identification, call for imbalanced classification. The performance of a classifier tends to suffer from the noisy samples in majority or minority classes. This work proposes a new undersampling scheme, called a clustering-based noisy-sample-removed undersampling scheme (NUS) for imbalanced classification. The majority class samples are first clustered. The distance of the majority class sample from the cluster center that is furthest away is used as the radius to build a hypersphere, with each cluster’s center assumed to be a spherical center. We determine the Euclidean distance between the center of a cluster and each minority sample to find whether they are in the hypersphere or not. Afterward, we exclude noisy samples from the minority class. The noisy samples of majority classes are removed by using the same procedure. Second, we propose an NUS, which combines noisy sample removal with undersampling techniques. Finally, to prove the effectiveness of NUS, we integrate NUS with the basic classifiers random forest (RF), decision tree (DT), and logistics regression (LR). We conduct their comparison with seven undersampling, oversampling, and noisy-sample-removed methods. This work performs experiments on 13 public and three real transaction datasets related to e-commerce. The results show that NUS plays a positive role in promoting existing classifiers’ performance.

Credit Card Fraud Detection using Machine Learning (Logistic Regression)

Abstract: Credit Card Fraud Detection (CCFD) is a crucial application in machine learning. It aims for safeguarding financial transaction. Identifying fraud transaction and real transactions and telling that is it fraud or real. Traditional methods for credit card fraud detection rely on basic statistical techniques .credit card remains a significant challenge in the financial industry .It rely on machine learning algorithms .Fraud detection very useful for financial institution and consumers worldwide .particularly logistic regression, has emerged as a potent tool in identifying fraudulent transactions. It will consider the before amount ,after amount ,time, pin numbers based on that it will detect fraud transactions .This paper explores the application of logistic regression in credit card fraud detection , focusing on its implementation ,challenges and effectiveness .This project works for finding fraud transactions. Logistic Regression ,have shown promising results in detecting fraudulent transactions.

Mobile Food Ordering Apps, Restaurant Performance, and Customer Satisfaction

Mobile food ordering apps (MFOAs) have become increasingly prevalent in the food service industry. This study investigated the links between intensity of MFOA use, restaurant performance, and customer satisfaction in dine-in restaurants using real transaction data and crowdsourced reviews. Our distinctive datasets stem from a renowned chain of restaurants in China as well as from Da Zhong Dian Ping (referred to as the Chinese equivalent of Yelp), spanning the years 2018 to 2021. Using a high-dimensional fixed effects model, we found that the intensive use of MFOA is positively correlated with an increase in restaurant dine-in revenue through higher table turnovers. However, MFOA is negatively correlated with dine-in restaurant consumer satisfaction, which was associated with lack of adequate, favorable, and high-quality comments. We also conducted several heterogeneity analyses to investigate how selected restaurant characteristics may moderate these effects. Thus, our study provides valuable theoretical insights and practical implications for academia and food service practitioners.

Pricing of Al-Urbun and a Class of Al-Istijrar Islamic Contracts under the Black–Scholes Framework

Islamic financial contracts necessarily need to abide by Shariah principles. As such, some contracts have been introduced for risk-hedging real transactions that differ from those seen in conventional financial markets. In this paper, we examine two such products, Al-Urbun and Al-Istijrar, and determine fair prices for both the Al-Urbun and a class of Al-Istijrar under the Black–Scholes framework.

Is Monetary Policy a Driver of Cryptocurrencies? Evidence from a Structural Break GARCH-MIDAS Approach

The introduction of Bitcoin as a distributed peer-to-peer digital cash in 2008 and its first recorded real transaction in 2010 served the function of a medium of exchange, transforming the financial landscape by offering a decentralized, peer-to-peer alternative to conventional monetary systems. This study investigates the intricate relationship between cryptocurrencies and monetary policy, with a particular focus on their long-term volatility dynamics. We enhance the GARCH-MIDAS (Mixed Data Sampling) through the adoption of the SB-GARCH-MIDAS (Structural Break Mixed Data Sampling) to analyze the daily returns of three prominent cryptocurrencies (Bitcoin, Binance Coin, and XRP) alongside monthly monetary policy data from the USA and South Africa with respect to potential presence of a structural break in the monetary policy, which provided us with two GARCH-MIDAS models. As of 30 June 2022, the most recent data observation for all samples are noted, although it is essential to acknowledge that the data sample time range varies due to differences in cryptocurrency data accessibility. Our research incorporates model confidence set (MCS) procedures and assesses model performance using various metrics, including AIC, BIC, MSE, and QLIKE, supplemented by comprehensive residual diagnostics. Notably, our analysis reveals that the SB-GARCH-MIDAS model outperforms others in forecasting cryptocurrency volatility. Furthermore, we uncover that, in contrast to their younger counterparts, the long-term volatility of older cryptocurrencies is sensitive to structural breaks in exogenous variables. Our study sheds light on the diversification within the cryptocurrency space, shaped by technological characteristics and temporal considerations, and provides practical insights, emphasizing the importance of incorporating monetary policy in assessing cryptocurrency volatility. The implications of our study extend to portfolio management with dynamic consideration, offering valuable insights for investors and decision-makers, which underscores the significance of considering both cryptocurrency types and the economic context of host countries.

Design and Development of Mobile Payment Platform Software Supported by Analytical Capabilities for Payment Systems

The Paycell Mobile Payment application serves as a platform where users can benefit from various payment and shopping services. Each user explores services within the application based on their own interests and needs, and this process generates a significant amount of data. The data created has become an important resource for improving user experience and enhancing the services offered. The primary goal of our project is to enrich the user experience and offer personalized recommendations based on real transactions to meet financial needs more effectively. This approach represents a significant step in the mobile payment systems industry in terms of data analytics and personalization. Simultaneously, this approach aims to position the Paycell application as a 'super app' where users can personalize their financial transactions. Our project captures user-initiated transactions with the aim of providing personalized recommendations tailored to users' interests and needs. These data are analyzed using data processing methods to obtain meaningful results. The results are then used to offer personalized recommendations to users. This approach contributes to users having more tailored experiences and meeting their financial needs more effectively.

Analysis of Housing Credit Financing Between Commercial Banks and Sharia Banks

This study tries to compare the housing loan financing system between commercial banks and Islamic banks. The purpose of this comparison is to find out the differences in the housing loan financing system by Bank Mandiri KCP Majalaya and Bank Syariah Indonesia (BSI) as samples. Many people know that conventional banks use interest rates which are used as interest and Islamic banks do not use them. This analysis compares the conditions that apply, the Housing Loan process is implemented, the Akad or Initial Requirements, Installment Calculations, and the care of customers who are late paying or paying prematurely. Based on the analysis that has been done, it can be concluded that the comparison of lending to Mandiri Conventional Banks and Murabahah Financing at Indonesian Sharia Banks has many similarities in the procedures and requirements files. It's just that the Akad/agreement aspect has a difference. Conventional Banks fully use the Interest System. Meanwhile, Islamic Banks apply a Murabahah Contract with a profit sharing system from service profits and real transactions.

Research on improvements of fraud detection system: basing on improved machine learning algorithms

Nowadays, commercial fraud behaviors commonly occur in many industries. However, due to obstacles like concept drift, imbalanced dataset and uneven distribution of fraud entries, Fraud Detection System (FDS) fails to identify such behaviors. Among the problems mentioned above, most research focus on dealing with skewed dataset. This paper first presents common application scenarios of FDS which consist of credit card fraud, insurance fraud and supply chain fraud. Then, this study introduces five representative methods in dealing with problems mentioned above, which are K Nearest Neighbors-Synthetic Minority Oversampling Technique-Long Short-term Memory Networks (kNN-SMOTE-LSTM), Generative Adversarial Nets-AdaBoost-Decision tree (GAN-AdaBoost-DT), Wasserstein GAN-Kernel Density Estimation-Gradient Boosting DT (WGAN-KDE-GBDT), Time-LSTM (TLSTM) and Adaptive Synthetic Sampling-Sequential Forward Selection-Random Forest (ADASYN-SFS-RF). KNN-SMOTE-LSTM adopts KNN as an identifying classifier so as to only retain true samples. GAN-AdaBoost-DT generates new samples without referring to real transactions. WGAN-KDE-GBDT uses Wasserstein Distance as distance measurement instead, and thus improves training speed and guarantees successful generation. TLSTM tires to consider the weights of different time intervals and measures the similarity between the simulated behavior and the genuine behavior. ADASYN-SFS-RF employs SFS algorithm, basing on RF, to only reserve optimal subsets of features. Finally, result metrics prove that those improved algorithms do improve the overall performance of FDS, even if with limitations at some indicators.

Research on Financial Fraud Detection Models Integrating Multiple Relational Graphs

The current fraud risk in digital finance is increasing year by year, and the mainstream solutions rely on the inherent characteristics of users, which makes it difficult to explain fraud behaviors and fraud behavior patterns are less researched. To address these problems, we propose an integrated multiple relational graphs fraud detection model Tri-RGCN-XGBoost, which analyzes the impact of user association patterns on fraud detection by mining the behavioral associations of users. The model builds a heterogeneous information network based on real transaction data, abstracts three types of bipartite graphs (user–device, user–merchant, and user–address), aggregates the information of the user’s neighbor nodes under the three types of behavioral patterns, and integrates the graph convolution classification results under the three behavioral patterns with the XGBoost model to achieve fraudulent user detection with integrated multiple relational graphs. The results show that the performance of this model in fraud identification is significantly improved, especially in reducing the fraudulent user underreporting rate. Further, the behavioral associations that play a key role in fraud user identification are analyzed in conjunction with shape value to provide a reference for fraud pattern mining.

Revealing the Default Risks in MSMEs Financial Leasing: Identification and Underlying Mechanism

ABSTRACT We explored the default risk of small and medium-sized financial leasing businesses in China based on real transactions of a Chinese financial leasing company from 2018 to 2021. The results show that there is a negative relationship between the financing scale and the default risk of lessee companies. The management cost of financial leasing business moderates the relationship between the business scale and the risk of overdue default. In addition, the improvement of industry competition is expected to select financially sound MSMEs for lessees. These enterprises have demonstrated higher risk resistance in obtaining large-scale financial leasing services. We provide novel insights into the risk management of Chinese financial leasing companies and offer policy recommendations for regulation and development.

Semi-supervised Credit Card Fraud Detection via Attribute-Driven Graph Representation

Credit card fraud incurs a considerable cost for both cardholders and issuing banks. Contemporary methods apply machine learning-based classifiers to detect fraudulent behavior from labeled transaction records. But labeled data are usually a small proportion of billions of real transactions due to expensive labeling costs, which implies that they do not well exploit many natural features from unlabeled data. Therefore, we propose a semi-supervised graph neural network for fraud detection. Specifically, we leverage transaction records to construct a temporal transaction graph, which is composed of temporal transactions (nodes) and interactions (edges) among them. Then we pass messages among the nodes through a Gated Temporal Attention Network (GTAN) to learn the transaction representation. We further model the fraud patterns through risk propagation among transactions. The extensive experiments are conducted on a real-world transaction dataset and two publicly available fraud detection datasets. The result shows that our proposed method, namely GTAN, outperforms other state-of-the-art baselines on three fraud detection datasets. Semi-supervised experiments demonstrate the excellent fraud detection performance of our model with only a tiny proportion of labeled data.

Achieving a Secure and Traceable High-Definition Multimedia Data Trading Scheme Based on Blockchain

The Internet has penetrated into every aspect of life. Large amounts of data are generated by multimedia collection equipment every day. As an asset, data can achieve value circulation through transactions. However, the existing centralized transaction model is not secure enough, has the risk of user privacy leakage, and the protection of data copyright is insufficient. In this paper, in order to solve the transaction security and traceability problems of multimedia data, especially high-definition data such as vector graphics, we implement a transaction scheme STTS without third-party based on blockchain. For high-definition multimedia data, we use zero watermarking combined with oblivious transfer to embed copyright information. A two-stage verification process is then implemented by using group signature and a secret sharing scheme to complete data distribution. Finally, the smart contract is used to complete copyright tracking. We test the performance of our scheme by simulating the real transaction environment in the Internet of Things (IoT) and demonstrate the feasibility of our scheme, which can be applied to large-scale multimedia data trading schemes in the IoT.

Measure cross-sectoral structural similarities from financial networks

Auditing is a multi-billion dollar market, with auditors assessing the trustworthiness of financial data, contributing to financial stability in a more interconnected and faster-changing world. We measure cross-sectoral structural similarities between firms using microscopic real-world transaction data. We derive network representations of companies from their transaction datasets, and we compute an embedding vector for each network. Our approach is based on the analysis of 300+ real transaction datasets that provide auditors with relevant insights. We detect significant changes in bookkeeping structure and the similarity between clients. For various tasks, we obtain good classification accuracy. Moreover, closely related companies are near in the embedding space while different industries are further apart suggesting that the measure captures relevant aspects. Besides the direct applications in computational audit, we expect this approach to be of use at multiple scales, from firms to countries, potentially elucidating structural risks at a broader scale.

AWI-BS: An adaptive weight incentive for blockchain sharding

The sharding technique enables blockchain to process transactions in parallel by dividing blockchain nodes into small groups, each of which handles a subset of all transactions. One of the issues with blockchain sharding is generating a large number of cross-shard transactions that need to be checked on the output shard as well as the destination shard. Our analysis suggests that the processing efficiency of cross-shard transactions is consistent with the barrel effect, i.e., that efficiency is more dependent on slower processing shard. Most of the existing studies focus on how to deal with cross-shard transactions, but neglecting the fact that the relative independence between sharding results in different incentive costs between sharding. We perform a sharding analysis on 100,000 real transactions data on Ethereum, and the results show that there is a large difference in gas prices between different shards indeed. In this paper, we propose an Adaptive Weight Incentive (AWI) for Blockchain Sharding, which uses adaptive weight in place of traditional incentive, to address the problem of differing incentive costs for each shard. Take Ethereum as an example, AWI-BS computes the weight of a transaction as a function of a combination of the underlying gas price, the latency of the transaction, and the urgency of the transaction. Then the node chooses which transaction to pack based on the AWI-BS. Lastly, we also perform an in-depth analysis of AWI-BS's security and effectiveness. The evaluation indicates that AWI-BS outperforms the other alternatives in terms of transaction confirmation latency, transaction hit rate, and system throughput.

Financial activity as the formation and materialization of a new virtual reality

Subject. The article considers financial activity as a complex, systemic concept, the final result of which is the redistribution of financial resources – the formation of a new financial reality. Objectives. The aim is to show that financial activity is a complex process. It includes, firstly, the formation of virtual reality as a model of a real transaction. Secondly, the materialization of virtual reality into a new financial reality, defined as a new financial condition of the entity resulting from a transaction, the consequence of which is the redistribution of financial resources. Methods. The study rests on principles and methods of systems analysis, the interdisciplinary approach to the subject of analysis, i.e. a transaction. The transaction is considered as an elementary component of the economy, i.e. ‘a quantum of the economy’. Results. I constructed a system model of transaction as a whole, including the elements of cognitive and material activity. The paper gives a definition of financial activity as a complex process, which includes an element of cognitive activity and an element of materialization of its results in reality. The final result of financial activity is the redistribution of resources of parties to the transaction. This is equivalent to the formation of a new financial reality, different from their original financial condition. Conclusions. The study shows that traditional representation of financial activity is only a consequence of cognitive process and materialization of virtual reality as a model of possible financial behavior. Such a representation requires a formation of a certain way of thinking that ‘fits in’ the functioning of artificial intelligence systems.

Machine learning approach to uncover customer plastic bag usage patterns in a grocery store

Plastic bags are used by many people because they are inexpensive, lightweight, durable, and waterproof. Plastic bags, on the other hand, do not break down and can pollute the environment if not handled properly. Indonesia produces a lot of plastic waste and is one of the top ten countries that has a problem with plastic waste. In this study, we used three months of data of real transactions from a grocery store. This study shows how the decision tree can identify patterns on plastic bag usage at a small grocery store by using demography and products purchase. The attribute weights showed that in the hometown, the total of several products bought were the factors that affected the use of plastic bags.

An efficient blockchain‐based privacy‐preserving scheme with attribute and homomorphic encryption

As a distributed ledger technology, blockchain has excellent openness and transparency, which can provide data security management services for distributed intelligent systems and establish effective security guarantee mechanisms. However, precisely due to the open nature of blockchain, malicious users can trace the real transaction transfer path with high probability and even obtain the real identity of users by collecting transaction information on the blockchain and performing data analysis. Besides, existing intelligent systems lack effective encryption or desensitization measures, and attackers are able to access data in intelligent systems through identity forgery and other means, posing a huge potential risk to user privacy. To alleviate the above security issues, this paper proposes an efficient blockchain-based privacy-preserving scheme with attribute and homomorphic encryption, which can not only achieve user-level fine-grained secure access control but also supports the transmission and verification of blockchain data in the form of ciphertext. The access control method based on attribute-based encryption and the data transmission method based on homomorphic encryption are proposed, and the blockchain-based access whitelist mechanism is designed to reduce the resource loss due to repeated authentication. Simulation calculations and blockchain performance experiments show that this scheme can develop flexible access policies according to the attributes of users, has good performance in computational efficiency, and the blockchain performance test results are stable with errors in milliseconds for all operations. It has certain application potential in the field of distributed intelligent systems and blockchain.

Feature generation and contribution comparison for electronic fraud detection

Modern money transfer services are convenient, attracting fraudulent actors to run scams in which victims are deceived into transferring funds to fraudulent accounts. Machine learning models are broadly applied due to the poor fraud detection performance of traditional rule-based approaches. Learning directly from raw transaction data is impractical due to its high-dimensional nature; most studies construct features instead by extracting patterns from raw transaction data. Past literature categorizes these features into recency, frequency, monetary, and anomaly detection features. We use various machine learning algorithms to examine the performance of features in these four categories with real transaction data; we compare them with the performance of our feature generation guideline based on the statistical perspectives and characteristics of (non)-fraudulent accounts. The results show that except for the monetary category, other feature categories used in the literature perform poorly regardless of which machine learning algorithm is used; anomaly detection features perform the worst. We find that even statistical features generated based on financial knowledge yield limited performance on a real transaction dataset. Our atypical detection characteristic of normal accounts improves the ability to distinguish them from fraudulent accounts and hence improves the overall detection results, outperforming other existent methods.