Credit Card Transactions Research Articles

Detection of fraud card and data breaches in credit card transactions

Data breaches and credit card fraud are now among the biggest problems affecting financial organizations and customers globally. The purpose of this study is to develop an effective fraud detection system that can detect fraudulent credit card transactions and prevent data breaches. The strategy proposed in this paper makes use of machine learning techniques like decision trees and logistic regression, to analyze large datasets of credit card transactions and identify suspicious patterns. The proposed system also includes a real-time monitoring mechanism that alerts the relevant authorities in case of any suspicious activity. The results of the experiments show that the suggested system achieves great accuracy and efficiency in detecting fraudulent transactions and data breaches. It can provide a powerful tool for financial institutions to prevent financial losses and maintain their customers' trust.

Leveraging the E-commerce footprint for the surveillance of healthcare utilization.

The utilization of healthcare services serves as a barometer for current and future health outcomes. Even in countries with modern healthcare IT infrastructure, however, fragmentation and interoperability issues hinder the (short-term) monitoring of utilization, forcing policymakers to rely on secondary data sources, such as surveys. This deficiency may be particularly problematic during public health crises, when ensuring proper and timely access to healthcare acquires special importance. We show that, in specific contexts, online pharmacies' digital footprint data may contain a strong signal of healthcare utilization. As such, online pharmacy data may enable utilization surveillance, i.e., the monitoring of short-term changes in utilization levels in the population. Our analysis takes advantage of the scenario created by the first wave of the Covid-19 pandemic in Mainland China, where the virus' spread lead to pervasive and deep reductions of healthcare service utilization. Relying on a large sample of online pharmacy transactions with full national coverage, we first detect variation that is strongly consistent with utilization reductions across geographies and over time. We then validate our claims by contrasting online pharmacy variation against credit-card transactions for medical services. Using machine learning methods, we show that incorporating online pharmacy data into the models significantly improves the accuracy of utilization surveillance estimates.

Show Me the Money: Tracking Consumer Spending with Daily Card Transaction Data During the Pandemic

Abstract The COVID-19 pandemic has been an unprecedented shock to economic activity with abrupt and unexpected changes in household consumption behavior. This paper investigates how the spread of the pandemic and government interventions have affected consumer spending using daily card transaction data in the Baltics. The analysis shows significant effects on the amount and composition of debit and credit card transactions. First, the number of new COVID-19 infections or deaths has a strongly negative effect. Second, while public health measures designed to contain the spread of the pandemic has a negative effect, economic support measures designed to assist businesses and households have a stimulative effect. Third, there is heterogeneity across spending categories, but the drop is mostly concentrated in sectors that are restricted by lockdowns and the risk of infection. Fourth, the impact of government interventions, especially in terms of stimulating consumer spending, appears to be more pronounced on goods than services.

Implementation of Gradient Boosted Tree, Support Vector Machinery and Random Forest Algorithm to Detecting Financial Fraud in Credit Card Transactions

According to Google Trends data, machine learning-based credit card identification has grown over the last five years, at the very least, across all nations. In order to detect credit card fraud in this study, the authors will use machine learning methods such random forests, support vector machines, and gradient-boosted trees. The authors used the Synthetic Minority Oversampling Technique (SMOTE) and Random Under Sampling (RUS) sampling methods in each algorithm to compare because there was a class imbalance in this investigation. The research findings demonstrate that the author's algorithm and sample technique were successfully used, as shown by the AUC values obtained for each being > 0.7. The top score in RUS was 0.7835 using the Random Forest algorithm, whereas the greatest score in SMOTE was 0.73 with the Gradient Boosted Trees approach. The Random Forest algorithm and the Random Under Sampling (RUS) technique are developed as a result of this research, and they are useful for identifying fraudulent credit card transactions.

E-commerce and local labor markets: Is the “Retail Apocalypse” near?

The rapid growth of e-commerce is widely blamed for job losses in brick-and-mortar retail. Using geographic variations in online spending, constructed from over 30 billion credit card transactions in Korea, we examine the causal effect of e-commerce on local retail employment. We find that the rise in the share of online spending from 2010 to 2015 decreased county-level retail employment by about 4.9 percent. We also find that employment shifted from offline retail to other local businesses, such as restaurants and personal services. However, the shift in employment was confined to metropolitan areas, falling far short of offsetting employment losses in non-metropolitan areas. Our finding suggests that a Retail Job Apocalypse is likely in certain local labor markets (i.e., non-metropolitan areas).

Detection of Credit Card Fraud with Artificial Neural Networks

Along with the Internet, digital technologies are frequently used in every moment of our lives. Many transactions that we carry out in monetary terms such as shopping in our daily life are now done digitally. With the developing digitalization in the world, people's lives become easier and people can access different products in a short time. In particular, people can spend and shop quickly and easily without carrying cash in their pockets with a credit card. However, with the increase in the use of credit cards, there are also some security vulnerabilities. Fraudsters can gain unfair advantage by obtaining certain credit card information such as passwords. They can shop with someone else's credit card without permission. These transactions cause substantial financial damage to individuals and institutions. With the increase in the use of credit cards with the developing technology, such credit card fraud is also increasing rapidly. Taking precautions against credit card fraud is a very important issue in order to ensure the safety of people. For this reason, in order to ensure the security of both banks and financial institutions that provide credit card services, it is necessary to prevent credit card fraud and to detect fraud that may occur in credit cards within the scope of combating fraud. In our study, Artificial Neural Networks were used to detect credit card fraud transactions. A prediction model has been developed to detect fraud in credit card transactions with ANN. Using the Credit Card data set obtained from the Kaggle database, modeling was done with the Feed Forward Artificial Neural Network method. The aim of this study is to automatically detect abnormal behaviors made with credit cards. 98.44% success was achieved with feedforward artificial neural network.

The Loss Allocation for Unauthorized Credit Card Transaction and Superior Risk Bearer

The Loss Allocation for Unauthorized Credit Card Transaction and Superior Risk Bearer

A Succinct Analysis of Deep LSTM Model-Based Credit Card Fraud Detection

More people today use credit cards to buy their essentials thanks to technological advancements, which has sparked a gradual rise in credit card theft. Today, credit cards are used almost universally by businesses, whether they are small or large. All types of businesses, including banks, the auto and appliance industries, are susceptible to credit card theft. Fraud is defined as a deceit committed with the aim of generating unauthorised financial benefit. Some of the ways that scams happen include hacking billing systems at stores or restaurants, hacking an online retailer, losing or stealing cards, and installing fraud devices in card readers at petrol stations or ATMs to acquire credit card PIN data. The 2 basic types of card fraud are behavioural fraud and application fraud. Application fraud describes scenarios in which a credit card application is false. It happens when a fraudster submits an application for a new credit card using false identification information, and the card issuer accepts it. Behaviour fraud occurs after a credit card has been approved and issued. Credit or debit card transactions that exhibit fraudulent conduct are referred to. Fraud identifying and prevention have long been big issues for card providers and key research areas for researchers due to the fact that identifying and preventing even a little amount portion of fraudulent behaviour would prevent millions of dollars in losses. Our study focuses on the challenge of recognising fraud behaviour.

Mobile Payment Adoption: An Empirical Investigation of Alipay

The rapid growth in the adoption of mobile payments has already begun to reshape bank payment practices. Utilizing a unique data set from a leading bank in Asia that records credit card transactions of its customers before and after the launch of Alipay mobile payment, the largest mobile payment platform in the world, this study aims to understand the impact of mobile payment adoption on bank customer credit card activities and the change of this impact after the mobile payment expansion. To do so, we employ the difference-in-differences method coupled with matching to estimate the effects. We find that mobile payment adoption not only increases customer credit card activities at the focal bank through both off-line and online channels, but also enhances customer loyalty to the bank by reducing churn. Specifically, the total credit card transaction amount and frequency of our focal bank increased by 9.4% and 10.7%, respectively. Moreover, we examine the change in the treatment effect after the mobile payment expansion and find an increase in adopters’ credit card activities and a reduction in their churn after the expansion. Next, we discuss the underlying mechanisms and show that mobile payment acts as a substitute for physical card payment in the off-line channel, and this supports the key underlying mechanism of the reduced transaction cost. However, a certain complementary effect exists between personal computer and mobile payments, likely driven by the coadoption of the two. Finally, we provide empirical evidence on conditions that facilitate the use of mobile payments, following the unified theory of acceptance and use of technology. History: Yong Tan, Senior Editor; Tianshu Sun, Associate Editor. Funding: This work was supported by National Natural Science Foundation of China [Grants U1811462, 72071102]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2021.0156 .

Credit Card Fraud Detector Based on Machine Learning Techniques

The massive development of technology has affected commerce and given rise to e-commerce and online shopping. Nowadays, consumers prioritize e-shopping over the brick and motor stores due to numerous benefits, including time and transport convenience. However, this progressive upsurge in online payment increases the number of credit card frauds. Therefore, defending against fraudsters’ activity is obligatory and can be achieved by securing credit card transactions. The objective of this paper is to build a model for credit card fraud detection using Machine learning techniques. An innovative approach to credit card fraud detection grounded on machine learning is proposed in this study. Machine learning (ML) is an artificial intelligence subfield comprising learning techniques from experience and completing tasks without being explicitly programmed. Three ML techniques have been used: Support vector machine, logistic regression, Random Forest, and Artificial Neural network. First, the most significant features that affect the type of transaction (fraud or not fraud) have been selected. After that, the ML model was applied. The performance of the proposed approach is tested using a confusion matrix, recall, precision, f-measure, and accuracy. The proposed method is tested using accurate data that consists of 284807 transactions. The result shows the efficiency of the proposed approach.

Fraud Detection of Credit Card Using Machine Learning

Abstract: This paper is focused on credit card fraud detection in real world scenarios. Nowadays credit card frauds are increasing in number as compared to earlier times. Criminals are using fake identity and various technologies to trap the users and get the money out of them. Therefore, it is very essential to find a solution to these types of frauds. In this proposed paper we designed a model to detect the fraud activity in credit card transactions. This system can provide most of the important features required to detect illegal and illicit transactions. As technology changes constantly, it is becoming difficult to track the behavior and pattern of criminal transactions.

Credit Card Fraud Detection using Ensemble Machine Learning Method - Gradient Boosting Framework

Abstract: The use of credit cards has become commonplace in our daily lives, transforming the way we make cashless payments and making all types of transactions more convenient for buyers. However, credit card fraud has also become increasingly prevalent as the number of users has risen. Criminals may illegally obtain an individual's credit card information and use it for fraudulent activities. This project involved collecting a sample of a publicly available dataset from Kaggle, consisting of 284,807 credit card transactions. Extreme Gradient Boost was used to detect fraudulent transactions, and it was found to be the most effective method, achieving the highest AUC value of 97.67% after thorough analysis.

Credit Card Fraud Detection Using Random Forest Classification

Abstract: In recent years credit card became one of the essential parts of the people. Sudden increase in E-commerce, customer started using credit card for online purchasing therefore risk of fraud also increases. Instead of carrying a huge amount in hand it is easier to keep credit cards. But nowadays that too becomes unsafe. Nowadays we are facing a big problem on credit card fraud which is increasing in a good percentage. The main purpose is the survey on the various methods applied to detect credit card frauds. From the abnormalities, in the transaction, the fraudulent one is identified. We address this issue in order to implement some machine learning algorithm like random forest, logistic regression in order to detect this kind of fraud. In this paper we increase the efficiency in finding the fraud. However, we discussed and evaluated employee criteria. Currently, the issues of credit card fraud detection have become a big problem for new researchers. We implement an intelligent algorithm which will detect all kind of fraud in a credit card transaction. We handled the problem by finding a pattern of each customer in between fraud and legal transaction. Isolation Forest Algorithm and Local Outlier Factor are used to predict the pattern of transaction for each customer and a decision is made according to them. In order to prevent data from mismatching, all attribute are marked equally.

Using accounting theory to develop a theoretical model for credit card rewards programme transactions

PurposeAfter the effective date of International Financial Reporting Standard (IFRS) 15, the accounting treatment of credit card rewards programmes (CCRPs) is no longer explicitly prescribed. Uncertainty regarding what constitutes faithful representation, and the inconsistent accounting practices observed, has created a need for guidance on the appropriate accounting treatment of CCRP transactions. Accounting theory has the potential to provide the foundation for this guidance. As a result, the objective of this study was to develop a theoretical model for the accounting treatment of CCRP transactions using accounting theory.Design/methodology/approachThis non-empirical qualitative conceptual study utilised document analysis, focussing specifically on accounting theory, to construct an accounting treatment model.FindingsApplying the relevant accounting theory (International Accounting Standards Board's (IASB's) Conceptual Framework), a theoretical model for the accounting treatment of CCRP transactions was developed, which emphasises the importance of understanding the economic phenomenon (the CCRP transaction) and determining how management views the transaction (in isolation as marketing or as an integral part of the credit card transaction).Originality/valueAddressing the problem of accounting for CCRP transactions with reference to accounting theory (which is the main element of scholarly activity in accounting) distinguishes this study from previous research on the topic. The CCRP accounting treatment theoretical model could assist CCRP management in faithfully accounting for a CCRP transaction and reduce uncertainty and inconsistency in practice. Moreover, this study identified the procedures to be employed when using accounting theory to determine the appropriate accounting treatment of business transactions. These procedures could be employed by accountants when faced with other transactions not covered by specific accounting standards.

The Imbalanced Classification of Fraudulent Bank Transactions Using Machine Learning

This article studies the development of a reliable AI model to detect fraudulent bank transactions, including money laundering, and illegal activities with goods and services. The proposed machine learning model uses the CreditCardFraud dataset and utilizes multiple algorithms with different parameters. The results are evaluated using Accuracy, Precision, Recall, F1 score, and IBA. We have increased the reliability of the imbalanced classification of fraudulent credit card transactions in comparison to the best known results by using the Tomek links resampling algorithm of the imbalanced CreditCardFraud dataset. The reliability of the results, using the proposed model based on the TPOT and RandomForest algorithms, has been confirmed by using 10-fold cross-validation. It is shown that on the dataset the accuracy of the proposed model detecting fraudulent bank transactions reaches 99.99%.

Predicting merchant future performance using privacy-safe network-based features

Small and Medium-sized Enterprises play a significant role in most economies by contributing to job creation and economic growth. A majority of such merchants rely on business financing, and thus, financial institutions and investors need to assess their performance before making decisions on business loans. However, current methods of predicting merchants’ future performance involve their private internal information, such as revenue and customer base, which cannot be shared without potentially exposing critical information. To address this problem, we first propose a novel approach to predicting merchants’ future performance using credit card transaction data. Specifically, we construct a merchant network, regarding customers as bridges between merchants, and extract features from the constructed network structure for prediction purposes. Our study results demonstrate that the performance of machine learning models with features extracted from our proposed network is comparable to those with conventional revenue- and customer-based features, while maintaining higher privacy levels when shared with third-party organizations. Our approach offers a practical solution to privacy concerns over data and information required for merchants’ performance prediction, enabling safe data-sharing among financial institutions and investors, helping them make more informed decisions on allocating their financial resources while ensuring that merchants’ sensitive information is kept confidential.

A Machine Learning Method with Hybrid Feature Selection for Improved Credit Card Fraud Detection

With the rapid developments in electronic commerce and digital payment technologies, credit card transactions have increased significantly. Machine learning (ML) has been vital in analyzing customer data to detect and prevent fraud. However, the presence of redundant and irrelevant features in most real-world credit card data degrades the performance of ML classifiers. This study proposes a hybrid feature-selection technique consisting of filter and wrapper feature-selection steps to ensure that only the most relevant features are used for machine learning. The proposed method uses the information gain (IG) technique to rank the features, and the top-ranked features are fed to a genetic algorithm (GA) wrapper, which uses the extreme learning machine (ELM) as the learning algorithm. Meanwhile, the proposed GA wrapper is optimized for imbalanced classification using the geometric mean (G-mean) as the fitness function instead of the conventional accuracy metric. The proposed approach achieved a sensitivity and specificity of 0.997 and 0.994, respectively, outperforming other baseline techniques and methods in the recent literature.

Comparison of machine learning algorithms for credit card fraud transaction prediction

Nowadays, credit card transaction is widely used in commercial activities, the number of fraud transaction is increasing. Especially when e-commerce and online shopping have arisen explosively and COVID-19 pandemic spread, the fraud transaction increasing surprisingly. Detecting the credit card fraud transaction is of great importance for both banks and depositors. To verify the effectiveness of machine learning algorithms for this task, several models are leveraged and examined in this work. These models include naive Gaussian bayes model, logistic regression model, random forest classifier, convolutional neural network (CNN) and support vector machine (SVM). Besides, the effectiveness of pre-processing techniques, including undersampling and oversampling, is also conducted on the training dataset for validating their effectiveness on predicting whether a transaction is a fraud or not and their joint effort with different models. In this work, these models are implemented with European dataset of credit card fraud and the best method is selected for this application.

Fraud Prediction in Movie Theater Credit Card Transactions using Machine Learning

This paper highlights how the proliferation of online transactions, especially those involving the use of credit cards, has resulted in the emergence of new security flaws that pose threats to customers and enterprises worldwide. E-commerce and other forms of online monetary transactions have become essential in the manufacturing and service sectors, propelling the global economy. The widespread and dependent connectivity of mobile payment systems using credit card transactions presents chances for fraud, risk, and security breaches. In light of the importance of accurately predicting fraud incidents through payment procedures, this study investigated the credit card payment methods used for movie tickets, using the machine learning logistic regression method to analyze and predict such incidents. This study used a dataset from cinema ticket credit card transactions made in two days of September 2013 by European cardholders, including 284,807 transactions out of which 492 were fraudulent purchases. The results of the proposed method showed a prediction accuracy of 99%, proving its high prediction performance.

The Nonlinear Impact of Payment System Innovation on Financial System Stability in the ASEAN-4 Countries

The increasing growth of financial system encourages payment system innovation that can affect financial system stability, particularly in ASEAN countries. This study explored a variety of payment system innovation, i.e. debit cards, credit cards, electronic money, and RTGS. The financial system stability index is measured by calculating the composite indexes of non-performing loans, Z-score from ROA and CAR, share price volatility, and yield bonds. The components of the indexes are structured to reflect risks from the banking, stock, and bond markets. The resulting index value indicates the level of risk in the financial system. A higher index specifies a higher risk and a more vulnerable financial system. Furthermore, it is noted that the effects of the independent variable can change according to economic conditions. The panel threshold model was applied to calculate the effects of various regimes, namely innovation, GDP, credit ratio, and stability index. The panel data were obtained from the ASEAN-4 countries (Indonesia, Malaysia, Thailand, and the Philippines) from 2012 to 2020. The panel threshold analysis shows an increase in the value of debit card, credit card, and RTGS transactions. Specifically, innovation and GDPR negatively affect the stability index. Increasing the value of payment system innovation will decrease the risk to financial system stability in ASEAN countries. The monetary authorities of each country can implement these findings by considering the rapid development of payment system innovation and the danger it may pose to financial system stability.