Amount Of Transactions Research Articles

A Trustworthy and Untraceable Centralised Payment Protocol for Mobile Payment

Current mobile payment schemes gather detailed information about purchases customers make. This data can then be used to infer a customer’s spending behaviour, potentially violating their privacy. To tackle this problem, we propose an untraceable mobile payment scheme that strikes a better balance, preserving user privacy while allowing the Third-Party Service Provider (TPSP) to collect necessary information such as card details and transaction amount for regulatory compliance. Our scheme offers untraceability for legitimate users from malicious adversaries and curious TPSPs using cryptographic primitives such as partially blind signatures, zero-knowledge proofs and identity-based signatures. It also guarantees that only authorised TPSPs can issue valid payment tokens, and even with limited data the TPSP can still prevent dishonest customers/merchants from double-spending a payment token. We also propose a comprehensive evaluation framework to assess the untraceable payment schemes against seven key criteria such as untraceability, exculpability - merchant double-spending, exculpability - customer double-spending, unforgeability, confidentiality, message authenticity, efficiency and regulatory compliance. We rigorously benchmark the security and privacy of our proposed payment scheme against this framework and other established schemes. Furthermore, we formally verify these properties using complexity-based analysis and Proverif modelling.

LPPAC: Lightweight privacy-preserving distributed payments with access control

The privacy-preserving distributed payment system leverages the advantages of its decentralized form while upholding data privacy. Currently, numerous solutions effectively safeguard users’ privacy, encompassing transaction amounts and identities, but in practical deployments, these solutions may impose limitations due to their indiscriminate treatments. Recognizing that real-world transactions involve not only the exchange of funds but also the transfer of assets, we introduce a lightweight privacy-preserving distributed payment scheme called LPPAC, which differentiates its treatment of different levels of private data. The transformation of amounts, which directly involves monetary values, is rigorously protected using appropriate encryption techniques. Additionally, we incorporate transaction pruning techniques to reduce storage overhead and enhance operational efficiency. In the context of asset transfers, there may be instances where parties other than the transacting entities need to be informed to make adequate preparations. However, the information related to asset transfers is inherently linked to transaction amounts. Therefore, we introduce an access control mechanism to allocate corresponding access rights. Through this approach, we theoretically establish that LPPAC can offer a precise privacy protection solution without compromising privacy. Furthermore, experimental results show the strong performance of LPPAC in practical applications.

A hybrid machine learning-based cyber-threat mitigation in energy and flexibility scheduling of interconnected local energy networks considering a negawatt demand response portfolio

The interconnection of local energy networks (LENs) enables efficient exchange of energy and flexibility among them, fostering the integration of distributed energy resources and demand-side management strategies. Thus, the interconnected local energy systems (ILEN) structure is a viable approach to electrical distribution systems’ operation and management. However, implementing distributed energy management structures such as ILEN entails a great amount of information transactions. Therefore, these structures are more vulnerable to cyber threats. Thus, the newly developed efficient and secure power systems’ operation methods should take digitalization-related security risks into account. As a result, this paper is focused on the development of a secure operation method, equipped with a hybrid algorithm to mitigate cyber threats in the context of ILEN. In this regard, this research proposes a novel hybrid XGBoost-based cyber threat mitigation (HXGBTM) method to cope with the vulnerabilities of the physical and information layers of the cyber-infrastructure. The proposed cyber threat mitigation method is built upon the classification and regression capabilities of the XGBoost ensemble of decision trees to identify and mitigate anomalies in the electrical consumption data. Therefore, in the first step, the ILEN’s multi-objective energy and flexibility scheduling problem considering demand response portfolio i.e., MOEFSDRPILENis developed that encompasses a bi-level optimization problem, in which the operator of the ILEN optimizes energy and flexibility trading in the upper level. While in the lower level, each LEN operator minimizes scheduling costs along with maximizing the local flexibility as well as providing a demand response portfolio as a negawatt resource. Here, the flexibility index, which is later maximized using the second objective function, is considered as the proportion between "the available ramping capacity" and "required ramping capacity". In this paper, direct load control, and interruptible/curtailable demand response comprehensive models are implemented as candidate programs for the suggested portfolio. Furthermore, a hybrid cyber threat is modeled considering the communication line intrinsic vulnerability, as a result of natural causes, wear, and aging of the infrastructure etc., as well as false data injection (FDI) attacks that target each LEN’s electrical consumption database. Finally, the proposed HXGBTM is employed to mitigate the above-mentioned cyber-vulnerabilities and achieve near real-world conditions.

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.

Penerapan Algoritma Clustering untuk Segmentasi Pelanggan E-commerce berdasarkan Data Pembelian dan Aktivitas

In the current digital era, e-commerce has become one of the main pillars of global trade. With the ever-increasing amount of transaction and user activity data, e-commerce companies are faced with the challenge of understanding and managing diverse customer segments more effectively. This paper discusses the application of clustering algorithms for e-commerce customer segmentation based on purchasing data and user activity. The aim of this research is to identify homogeneous customer groups to support more targeted marketing strategies and increase customer retention. The problem faced is how to process big data originating from user transactions and activities on e-commerce platforms, as well as how to identify patterns that are useful for customer segmentation. The data used in this research includes purchase history, frequency of visits, length of time spent on the site, and interactions with certain products. The solution method applied in this research is the clustering algorithm, especially K-Means and DBSCAN. K-Means is used to group data into a predetermined number of clusters based on the Euclidean distance between data points. Meanwhile, DBSCAN is used to identify clusters with high density and separate data that is considered noise or outliers. Data preprocessing is carried out to clean and normalize the data before being applied to the clustering algorithm. Validation of clustering results is carried out using metrics such as Silhouette Score and Davies-Bouldin Index. The research results show that by applying the clustering algorithm, customers can be grouped into several segments that have similar characteristics. For example, we found groups of customers with high purchase frequency but low transaction value, as well as other groups with high transaction value but low purchase frequency. This information is very useful for companies to design more effective marketing strategies, such as special offers for customers with high transaction values ​​or loyalty programs for customers with high purchasing frequency. The conclusion of this research is that clustering algorithms can be a very effective tool in e-commerce customer segmentation, allowing companies to understand customer behavior patterns and develop more targeted and effective marketing strategies. Thus, implementing this method is expected to improve business performance and overall customer satisfaction.

PTTS: Zero-knowledge proof-based private token transfer system on Ethereum blockchain and its network flow based balance range privacy attack analysis

Blockchains are decentralized and immutable databases that are shared among the nodes of the network. Although blockchains have attracted a great scale of attention in the recent years by disrupting the traditional financial systems, the transaction privacy is still a challenging issue that needs to be addressed and analyzed. We propose a Private Token Transfer System (PTTS) for the Ethereum public blockchain in the first part of this paper. For the proposed framework, zero-knowledge based protocol has been designed using Zokrates and integrated into our private token smart contract. With the help of web user interface designed, the end users can interact with the smart contract without any third-party setup. In the second part of the paper, we provide security and privacy analysis including the replay attack and the balance range privacy attack which has been modeled as a network flow problem. It is shown that in case some balance ranges are deliberately leaked out to particular organizations or adversarial entities, it is possible to extract meaningful information about the user balances by employing minimum cost flow network algorithms that have polynomial complexity. The experimental study reports the Ethereum gas consumption and proof generation times for the proposed framework. It also reports network solution times and goodness rates for a subset of addresses under the balance range privacy attack with respect to number of addresses, number of transactions and ratio of leaked transfer transaction amounts.

Liquidity risk analysis via drawdown-based measures

Trading volumes are key variables in determining the degree of an asset's liquidity. We examine the volume drawdown process and crash recovery measures in rolling-time windows to assess exposure to liquidity risk. The time-varying windows protect our financial indicators from the massive amount of volume transactions that characterize the opening and closing of the stock market. The empirical study is carried out for three Nasdaq-listed assets from April to September 2022. Firstly, we shape all of the volume time series using a weighted-indexed semi-Markov (WISMC) model, as well as the EGARCH and GJR models for comparisons. Next, we calculate drawdown-based risk measures on real and synthetic data, simulated from all the considered econometric models. Finally, we employ the Kullback-Leibler divergence to compare real and simulated risk indicators. Results reveal that the WISMC model reproduces all the drawdown-based risk measures better than the EGARCH and GJR models do for all the considered stocks.

From innovation to stability: Evaluating the ripple influence of digital payment systems and capital adequacy ratio on a bank’s Z-score

This study investigated the influence of digital payment systems on banks’ stability by exploring their effect on the Z-score of the Jordanian banking sector during the period from 2004 until 2022. It specifically focused on liquidity risks generated from e-payment transactions and how sufficient capital adequacy ratios enhance banking sector stability over both short-term and long-term periods by standing against sudden volatilities yielded from large amounts of transactions executed through digital payment systems. To achieve this objective, the study utilizes time series dual regression analyses of vector autoregression and vector error correction models on E-views 12 to cover the time variation influences of digital payment on the banking sector Z-score. The regression results indicate varied effects between the benefits and risks of digital payment systems on a bank’s Z-score that influence the immediate sector’s stability, indicating that while digital payment systems can initially hold liquidity risks, leading to short-term instability; the strategic implementation of robust capital adequacy ratio stands as a protective buffer by fostering long-term banking sector resilience. The results also suggest future predictions and insights for financial sector legislators and regulators emphasizing the need for monitoring strategies that stimulate continuous innovations in the digital payment infrastructure while constantly ensuring the stability and resilience of the banking sector. Thus, prudent liquidity management and the reinforcement of capital buffers are encouraged to pilot the dual challenges and opportunities that appeared at the stages of the digital payment process, ultimately guiding the sector toward continuous growth and sustainability. AcknowledgmentThe author is grateful to the Middle East University, Amman, Jordan for the financial support granted to cover the publication fee of this research.

PENGARUH CASHLESS PAYMENT TERHADAP PERTUMBUHAN EKONOMI: STUDI KOMPARASI NEGARA MAJU DAN BERKEMBANG ANGGOTA G20

The purpose of this study is to analyze the impact of cashless payments on economic growth in G20 countries. Additionally, a comparison of the impact of cashless payments in developed and developing countries is also analyzed. In this study, economic growth is used as the dependent variable, and transaction amounts of debit cards, credit cards, and electronic money are used as the independent variables. Data were collected from various databases for the period 2013-2021 covering a total of 19 countries divided into 10 developed countries and 9 developing countries that are members of the G20. The test results using the Generalized Method of Moments (GMM) dynamic panel found that all three variables have a significant effect in both developed and developing G20 member countries. Furthermore, the study found that the biggest influence of non-cash payments in developed countries is transactions using debit cards while in developing countries it is transactions using credit cards and electronic money. From these results, it was concluded that cashless payments have an impact on economic growth. It is hoped that these findings will provide new insights into cashless payments and their impact on economic growth.

Identification and Analysis of Credit Card Fraud Based on Machine Learning Methods

Credit card has become an indispensable payment tool in People's Daily life. However, credit card fraud has also increased, resulting in huge financial losses for banks and individuals. Based on the real world credit card transaction data, this paper uses a variety of machine learning algorithms to identify and analyze credit card fraud. Firstly, the problem of missing and unbalanced data is solved through data preprocessing and feature selection, and important features are screened out, Such as transaction amount, product code, payment card type, etc. Then, integrated logistic regression, random forest, LightGBM, XGBoost, and Stacking models were constructed, and the models were trained and evaluated through cross-validation and parameter optimization. The experimental results show that the integrated Stacking model performs best in credit card fraud detection, with an AUC(Area Under the Curve) value of 0.960, which can accurately identify fraudulent transactions and provide an effective fraud warning mechanism for financial institutions. This mechanism significantly benefits both financial institutions and credit card users by reducing fraud losses and enhancing transaction security. However, it is acknowledged that the study has limitations, including the potential impact of data imbalance on model performance, the need for further testing of model generalization ability to new fraud patterns, and the importance of optimizing real-time performance to ensure practical applicability.

What Factors Influence the Increasing Dependency on Mobile Banking in Bangladesh? A Quantitative Study in Bangladesh

Mobile Banking has expanded in Bangladesh at a rapid pace for the last two decades in Bangladesh. Mobile service providers have introduced this magical financial innovation. It’s significant to understand what factors may motivate individuals to use online banking in Bangladesh. The study aims to identify influential factors of rapid growing M-banking in Bangladesh. A quantitative study based on a survey was adopted with 200 mobile banking users was selected as participants for the study. Participants think a wide variety of m-banking features with easy access and use plays a significant role in surging the system. The covid-19 pandemic has also increased people's dependency on m-banking. Apart from that, m-banking authorities have no direct contact with its customers. Safety, large amount of money transactions, deposit and profit limit features and reduced charges are all factors that would attract more customers in the coming days. Banks can presently provide managing an account administration to the country and unbanked populaces through portable phones. Around 54% of respondents opinioned that this framework is less exorbitant and timesaving and 63% of respondents felt believe in this benefit. 83% of respondents consider it is simpler to get to, but this benefit isn't accessible for interbred transactions, as well as the nonattendance of an administrative system may lead to cash washing exercises.

Firm Default Prediction by GNN with Gravity-Model Informed Neighbor Node Sampling

Firm default prediction is important in credit risk management and understanding economic trends. Both practitioners and academic researchers have long studied it. While traditional statistical methods such as discriminant analysis and logistic regression have been used recently, machine learning and deep learning methods have been widely applied. The graph neural network (GNN) is one of the latest applications of deep-learning approaches. With the use of GNNs, it is possible to reflect the non-linear relationships of features among neighboring companies around the target company, whereas ordinary machine learning and deep learning methods focus only on the features of the target company. However, when handling large-scale graphs such as inter-firm networks, it is difficult to apply vanilla GNNs naively. Although uniform neighbor node sampling is commonly used for large-scale graphs, to the best of our knowledge, no research has focused on better sampling methods for GNN applications for default prediction. From the practical viewpoint, it means which companies should be considered with priority for firm default prediction. In this study, we propose a novel gravity model-informed neighbor sampling method based on the estimated transaction volume by utilizing knowledge from econophysics. The scope of this research is to determine whether we can improve default predictions by considering neighboring companies with larger transaction amounts compared to ordinary uniform sampling. We also verified that the proposed method improves the prediction performance and stability compared to GNNs with other sampling techniques and other machine learning methods using real large-scale inter-firm network data.

Leveraging Machine Learning for Telecom Banking Card Fraud Detection: A Comparative Analysis of Logistic Regression, Random Forest, and XGBoost Models

In recent years, telecommunication bank card fraud has become a major threat to financial security, so it is necessary to develop robust detection mechanisms for telecommunication bank card fraud. This study examines the application of machine learning techniques (specifically logistic regression, random forests, and XGBoost) in identifying fraudulent telecom bank transactions. Using a dataset consisting of one million transaction records from the 2024 National Student Data Statistics and Analytics Competition, we implemented and evaluated these models based on key performance metrics such as accuracy, precision, recall, F1 score and ROC-AUC. The results show that XGBoost outperforms the other models, achieving superior accuracy and robustness in fraud detection, while Random Forest also performs well, achieving almost perfect classification accuracy. Logistic regression, while effective, lagged behind in terms of handling the complexity of the data. The analyses in this paper further highlight the critical role of features such as transaction amount ratios and online transaction status in predicting fraud. These findings suggest that advanced machine learning models, especially ensemble methods such as XGBoost, are highly effective in combating telecom banking fraud and should be integrated into existing detection systems to enhance their predictive capabilities.

AnoPay: Anonymous Payment for Vehicle Parking With Updatable Credential

Many existing anonymous parking payment schemes lack high efficiency and flexibility. For instance, the calculation and communication costs involved in payment may linearly increase with the payment amount. In this paper, we propose an anonymous payment system (dubbed AnoPay) for vehicle parking, which leverages updatable attribute-based anonymous credentials and efficient zero-knowledge proof (ZKP) to achieve user anonymity and constant overhead for parking fee payment. To further improve the efficiency, we design a secure parking fee aggregation protocol based on linear homomorphic encryption to aggregate parking transactions, where the amount of each parking transaction is hidden and the privacy of the parking lot in terms of its revenue is guaranteed. AnoPay achieves both unlinkability and accountability, malicious payments can be efficiently traced when it is necessary. We provide a security model and rigorous proof for each security property of AnoPay. Extensive experiments and comparisons demonstrate the efficiency and practicality of the system.

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.

La Balanza de Pagos y la Posición de Inversión Internacional de España en 2023

Rationale The balance of payments and international investment position provide insight into Spain’s economic relations with the rest of the world, which have important implications for the domestic economy. Analysis of these statistics takes on added significance against a backdrop of global uncertainty. Takeaways •In 2023 the Spanish economy’s net lending as a percentage of GDP reached its highest level since 1995, driven by the improvement in the trade balance and positive developments in services, with record high credits in both travel services and other services. •The negative net international investment position declined further in 2023 to stand at 52.8% of GDP (its lowest level in 19 years), as a result of GDP growth and the positive balance of financial transactions with the rest of the world. •Spain’s outward and inward foreign direct investment positions increased in 2023 due to the positive amount of financial transactions, although these were down slightly on the previous year, and the likewise positive amount of revaluations.

Smart ATM Using QR

Nowadays, dependency on banking in the virtual world has been increased to the peak position. To make it consistent advanced technologies should be used. As OTP is currently used worldwide for security purposes, it can be overruled by QR code. Main advantage of QR code over OTP data storage.OTP can only confirm that the user is authorised user and not some third party is involved in this transaction while QR code not only confirms the authorised user but QR code itself can store information such as transaction id, transaction date, time and also amount of transaction. So, there is no need of explicitly keeping track of transaction every transaction. Aim of this paper to enhance the functionality of ATM machine using android application. Proposed system is combining the ATM and mobile banking and minimizes the time of withdrawing cash from ATM. This will increase the speed of transaction almost three times fast; could have excellent impact on customer’s satisfaction. With the help of QR code information get encrypted so it also increases security. As the population increasing ATM queues will be longer day by day. By implementing proposed system current Keywords—QRcode, Cash withdrawal,, Smart Application

Unappreciated healthcare inequality against PM2.5-related mortality risk

Understanding the inequality of PM2.5-related health is crucial for promoting health, building a just society, and advancing multiple Sustainable Development Goals goals. However, previous research has predominantly concentrated on PM2.5 exposure inequality, neglecting varied prompt responses and protective behaviors against it. Here, we established the relationship between short-term healthcare expenditure and PM2.5 concentration using the number and amount of healthcare transactions across all healthcare categories based on the Union Pay data. We also assessed daily city-specific PM2.5-related mortality and healthcare expenditures and evaluated their inequalities among cities according to the income inequality index, the Gini coefficient. The results show that short-term exposure to PM2.5 leads to severe physiological and health-related economic burdens on Chinese residents. From 2017 to 2019, 77.8 (34.5–121.1) thousand deaths were attributed to daily PM2.5, with healthcare expenditures reaching 93.7 (69.1–118.3) billion Chinese Yuan. Additionally, there were significant inequalities in PM2.5-related mortality and healthcare expenditures among cities. The inequality index for PM2.5-related healthcare expenditures was 0.53, while the inequality index for PM2.5-related mortality was 0.13. The greater inequality in healthcare expenditures than in mortality, implying inadequate healthcare resources amplify the health inequality related to PM2.5 exposure. 28.6% of Chinese cities lacked affordable healthcare resources to address the high physiological burden attributable to PM2.5. Our multidimensional exploration is essential for formulating effective policies addressing environmental health inequality. Focusing on these cities with disproportionate challenges is crucial for creating a more equitable and sustainable society.

Evaluation of subjective policy reflection using the Choquet integral and its applications

This paper proposes a methodology for assessing the subjective policy reflection in national trade policies through the use of Choquet integral. Specifically, we introduce three fuzzy measures that reflect different policies based on the trade items included in data on the annual trade amounts of selected national animal products from 2010 to 2020. The Choquet expected utility serves as a tool for evaluating subjective policy reflection, allowing for the comparison of national economic policies across selected countries. To demonstrate the practical application of the proposed methodology, we provide specific numerical values that represent the evaluation of subjective policy reflection. Furthermore, we present a decision-making strategy that pertains to the allocation of subsidies on imports and exports of trade transaction amounts, assuming that government subsidies are budgeted using the Choquet expected utility. The proposed methodology offers a valuable framework for policymakers and stakeholders, facilitating in-depth analysis and improvement of the effectiveness of national trade policies.

Research on Covert Communication Technology Based on Matrix Decomposition of Digital Currency Transaction Amount

Research on Covert Communication Technology Based on Matrix Decomposition of Digital Currency Transaction Amount