Debit Card Transactions Research Articles

The Digital Payment System: How Does It Impact Indonesia's Poverty?

Digital payments are currently well-developed and available through various methods, especially in Indonesia. Digital payments are essential in ensuring the country’s economy keeps running and in helping to boost the economy. However, the impact of such payments on poverty is currently unknown. This study examines the impact of digital payments on poverty rates in Indonesia. We use the ECM (Error Correction Model) to analyze the impact of digital payments on poverty in the short and long term, using data from 2009 to 2022. The results show that an increase in digital payments through the volume of ATM and debit card transactions is associated with short and long-term poverty reduction. Meanwhile, the variable volume of credit card transactions and volume of electronic money transactions does not significantly impact poverty in the short or long term. Economic growth as a control variable also plays a role in reducing poverty significantly in the long term.

Nowcasting Norwegian household consumption with debit card transaction data

SummaryWe use a novel data set covering all domestic debit card transactions in physical terminals by Norwegian households, to nowcast quarterly Norwegian household consumption. These card payments data are not subject to revisions and are available weekly without delays, providing a valuable early indicator of household spending. To account for mixed‐frequency data, we estimate various quantile mixed‐data sampling (QMIDAS) regressions using predictors sampled at monthly and weekly frequency. We evaluate both point and density forecasting performance over the sample 2011Q4–2019Q4. Our results show that MIDAS regressions with debit card transactions data improve both point and density forecast accuracy over competitive standard benchmark models that use alternative high‐frequency predictors. Finally, we illustrate the benefits of using the card payments data by obtaining a timely and relatively accurate nowcast of 2020Q1, a quarter characterized by heightened uncertainty due to the COVID‐19 pandemic. We further show how debit card data have been useful in nowcasting consumption during the four subsequent quarters.

Enhancing fraud detection in banking by integration of graph databases with machine learning

The banking sector's shift from traditional physical locations to digital channels has offered customers unprecedented convenience and increased the risk of fraud for customers and institutions alike. In this study, we discuss the pressing need for robust fraud detection & prevention systems in the context of evolving technological environments. We introduce a graph-based machine learning model that is specifically designed to detect fraudulent activity in various types of banking operations, such as credit card transactions, debit card transactions, and online banking transactions. This model uses advanced methods for anomalies, behaviors, and patterns to analyze past transactions and user behavior almost immediately. We provide an in-depth methodology for evaluating fraud detection systems based on parameters such as Accuracy Recall rate and False positive rate ROC curves. The findings can be used by financial institutions to develop and enhance fraud detection strategies as they demonstrate the effectiveness and reliability of the proposed approach. This study emphasizes the critical role that innovative technologies play in safeguarding the financial sector from the ever-changing strategies of fraudsters while also enhancing banking security.•This paper aims to implement the detection of fraudulent transactions using a state-of-the-art Graph Database approach.•The relational graph of features in the dataset used is modelled using Neo4J as a graph database.•Applying JSON features from the exported graph to various Machine Learning models, giving effective outcomes.

Navigating the Complexity of Money Laundering: Anti–money Laundering Advancements with AI/ML Insights

Abstract This study explores the fusion of artificial intelligence (AI) and machine learning (ML) methods within anti–money laundering (AML) frameworks using data from the US Treasury’s Financial Crimes Enforcement Network (FinCEN). ML and deep learning (DL) algorithms—such as random forest classifier, elastic net regressor, least absolute shrinkage and selection operator (LASSO) regression, gradient boosting regressor, linear regression, multilayer perceptron (MLP) classifier, convolutional neural network (CNN), random forest regressor, and K-nearest neighbor (KNN)—were used to forecast variables such as state, year, and transaction types (credit card and debit card). Hyperparameter tuning through grid search and randomized search was used to optimize model performance. The results demonstrated the efficacy of AI/ML algorithms in predicting temporal, spatial, and industry-specific money-laundering patterns. The random forest classifier achieved 99.99% average accuracy in state prediction, while the gradient boosting regressor and random forest classifier excelled in predicting year and state simultaneously, and credit card transactions, respectively. MLP and CNN showed promise in the context of debit card transactions. The gradient boosting regressor performed competitively with low mean squared error (MSE) (2.9) and the highest R-squared (R 2) value of 0.24, showcasing its pattern-capturing proficiency. Logistic regression and random forest classifier performed well in predicting credit card transactions, with area under the receiver operating characteristic curve (ROC_AUC) scores of 0.55 and 0.53, respectively. For debit card prediction, MLP achieved a precision of 0.55 and recall of 0.42, while CNN showed a precision of 0.6 and recall of 0.54, highlighting their effectiveness. The study recommends interpretability, hyperparameter optimization, specialized models, ensemble methods, data augmentation, and real-time monitoring for improved adaptability to evolving financial crime patterns. Future improvements could include exploring the integration of blockchain technology in AML.

The Healthcare Cost of Air Pollution: Evidence from the World's Largest Payment Network

Abstract This paper exploits the universe of credit- and debit-card transactions in China during 2013-2015 and provides the first nationwide analysis of the healthcare cost of PM2.5 for a developing country. We leverage spatial spillovers of PM2.5 from long-range transport to generate exogenous variation in local pollution and employ a flexible distributed lag model to capture semiparametrically the dynamic response of pollution exposure. We find significant impacts of PM2.5 on healthcare spending in both the short and medium terms. A 10 μg/m3 decrease in PM2.5 would reduce annual healthcare spending by over $9.2 billion, about 1.5% of China's annual healthcare expenditure.

Monthly unconditional income supplements starting at birth: Experiences among mothers of young children with low incomes in the U.S.

Recently, U.S. advocates and funders have supported direct cash transfers for individuals and families as an efficient, immediate, and non-paternalistic path to poverty alleviation. Open questions remain, however, about their implementation. We address these using data from debit card transactions, customer service call-line logs, and in-depth interviews from a randomized control study of a monthly unconditional cash gift delivered via debit card to mothers of young children living near the federal poverty line. Because much of the impact of the intervention occurs through mothers' decisions about how to allocate the Baby's First Years (BFY) money, we argue that implementation science must recognize the role of policy targets in implementing policy, not just in terms of policy outcomes but also policy implementation processes. Further, our analysis shows that mothers experience key aspects of the cash intervention's design as intended: they viewed the cash gift as unconditional and knew the money was reliable and would continue monthly, receiving the correct amount with few incidents. Delivering funds via debit card worked well, offering mothers flexibility in purchasing. We also illuminate how design features shaped mothers' experiences. First, although they knew it was unconditional, the social meaning of the BFY money to mothers-seen as "the baby's money"-shaped their engagement with and allocation of it. Second, low public visibility of mothers' receipt of this money limited the financial demands or requests from others, potentially facilitating more agency over and a greater ability to use the money as they chose, without claims from kin.

CONSUMER ADOPTION OF MOBILE PAYMENT IN THE KINGDOM OF SAUDI ARABIA AN EMPIRICAL STUDY

Objective: The rapid stride in mobile payment adoption has transformed the payment practices in the 21st Century. Admittedly, m-payment practice has become more intense due to the surge in usage of internet, mobile devices (smart phones), accelerated further by the spread of the Covid-19 Pandemic. Against such a backdrop, it is the need of the hour to investigate: Why are people starting to use mobile payments more and how will that affect their purchasing habits? The study's intention is to add to what's already been written on electronic banking and mobile banking specifically. The study provides a report on m-banking's development, assesses the potential for its adoption, and identifies potential avenues for expansion in Saudi Arabia. They aid Saudi Arabian banking decision-makers in locating and developing strategies for supplying m-banking services that will help them keep their competitive edge. Methods: While electronically distributed surveys can reach more people more quickly and for less money, this method also has the potential to introduce bias into the sample by excluding people who don't have access to the internet. In addition, the data acquired from the respondents in many mobile payment and mobile banking studies can enhance selection bias. Researchers used both paper-print and online survey methods to disseminate survey instruments, reducing selection bias and increasing response rates. Other potential sources of common method bias (CMB) include the retrospective nature of the survey, the use of a commonly used scale format for constructs, and the fact that respondents were asked to provide information about both the independent and dependent variables in the same question. As a result, SEM has emerged as the gold standard in the field of theory testing, especially as it pertains to studies of IS/IT adoption. Statistical modelling with structural equation modelling (SEM) was chosen as the advanced inferential analytic technique for this study. Aim: The present study also aims to identify the perceived risk, trust, self-efficacy, past experience, attitude, anxiety, and customer support associated with the mobile payment. Lastly, this study will be a path-breaking one because of its intended contribution to the policy-makers and to the society at large in the Kingdom of Saudi Arabia. Needless to say, outcome of the study was providing managerial inputs to various mobile payment platforms. Results: Consumers' intent to utilize mobile banking was strongly correlated with their level of trust and Application of Mobile Banking. In similar way Self-efficacy directly and positively related to the consumer intention towards mobile banking adoption. In the same ways, past experience directly and positively related to the consumer intention towards mobile banking adoption. In the same way, there is a positive and direct relationship between customer intention and the use of mobile banking. Lack of a causal relationship between attitude and consumer desire to use mobile banking. Perceived risk not directly and positively related to the consumer intention towards mobile banking adoption Anxiety not directly and positively related to the consumer intention towards mobile banking adoption. Conclusion: This study intends to identify the various drivers of mobile payment adoption by examining how consumers' spending habits have changed in response to the popularity of mobile payment systems and the relationship between various payment mechanisms. The study's primary aim is to establish precisely the point when mobile payments will become the norm can replace debit and credit card transactions. Besides transaction activities, online payment channels are enhancing mobile payment adoption and lead to increase in the online transactions, local penetration further escalating increased transaction activities.

Drivers of Credit and Debit Card Transactions

Drivers of Credit and Debit Card Transactions

Analysis of Non-Cash Transactions, Debit Cards, Credit Cards, and Electronic Money, on the Money Supply in Indonesia

This study was conducted to determine the effect between the independent variable, namely non-cash transactions (debit card transactions, credit card transactions, and electronic money transactions) on the dependent variable, namely the Money Supply (M1) in Indonesia. The research was conducted using a quantitative approach and the type of research used secondary data in the form of time series data from 2012-2021 obtained from Bank Indonesia and the Central Bureau of Statistics. The purpose of this study is to determine the effect of non-cash transactions on the money supply in Indonesia during 2012-2021. The analysis model used is multiple linear regression using SPSS. This study also uses the statistical t test, statistical F test, and the coefficient of determination R2. Based on the results of the research that has been done, it shows that debit card transactions have a positive effect on the amount of money in circulation and credit card transactions have a negative effect on the money supply in 2012-2021. Meanwhile, electronic money transactions have no effect on the money supply in 2012-2021

Non-cash transactions and the money supply

This study examines the impact of non-cash transactions on the money supply (M1) in Indonesia, with inflation as a moderating variable. Using data from 2019 to 2021, we analyze the effects of debit cards, credit cards, and electronic money (e-money) on the money supply. The findings indicate that debit card transactions have no immediate impact, credit card transactions have a slight negative effect, and e-money transactions significantly increase the money supply. These results offer valuable insights for policymakers and financial analysts in understanding and managing the relationship between non-cash payment methods and monetary policy.

Association Between a State-Level Fat Tax and Fast Food Purchases

India faces an increasing obesity problem, including in the Indian state of Kerala in which the fat tax was implemented but was nullified 11 months later. A fat tax, defined as a tax on unhealthy foods, may be associated with changes in food purchases and outcomes for multiple diet-related diseases. To investigate the association between the state-level fat tax and fast food purchases in Kerala, India. This cohort study analyzed a large-scale credit and debit card transaction data set and aggregated this sample at the account-year-month level of fast food purchases in Kerala state and 9 major cities in other Indian states (Ahmedabad, Bangalore, Bhubaneswar, Chennai, Delhi, Gurgaon, Kolkata, Mumbai, and Surat). Purchase records were obtained for January 1, 2016, to December 31, 2017. The association between the fat tax and fast food purchases was examined using the difference-in-differences method. This analysis was initiated on December 1, 2022. The exposure was the fat tax. Kerala was the exposed group, and 9 major Indian cities were the control group. The main outcome was the fast food purchase ratio, defined as the proportion of fast food purchases of the total food purchases. Changes in the fast food purchase ratio were estimated in Kerala across the sample period and then compared with 9 major cities. The sample for analysis included 238 015 credit and debit card accounts, of which 36.7% were in Kerala and 63.3% were in 9 major cities. The cardholders included 191 603 males (80.5%) with a mean (SD) age of 36.6 (12.8) years. During the fat tax implementation (August 2016-June 2017), Kerala's fast food purchase ratio decreased by 3.9 percentage points (β [SE], -0.039 [0.002]; 95% CI, -0.042 to -0.036), compared with 9 major cities. After the fat tax was nullified, the fast food purchase ratio reduced by 5.6 percentage points (γ [SE], -0.056 [0.002]; 95% CI, -0.059 to -0.052) compared with 9 major cities and using the pretax period as the benchmark. Results of this cohort study suggest that the Kerala fat tax was associated with fewer fast food purchases. Food tax policies need to have an elaborate design, and related issues, such as social inequality, nutritional deficiency, and political concerns, need to be evaluated in future studies.

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.

Cash Is King: The Role of Financial Infrastructure in Digital Adoption

AbstractThis paper examines whether a one-time, extensive, but temporary shock to cash supply can affect the adoption of digital payments. We exploit the 2016 demonetization episode in India, which overnight discontinued 86% of cash in circulation. Using novel administrative data from retail debit card transactions, we identify a 12% increase in digital payments in areas adversely affected by the cash shortage, which persisted well after the restoration of cash supply. Examining mechanisms, we find a limited role for social networks and stronger support for learning by doing. Further, information frictions hinder the immediate adoption of digital payments. (JEL E5, 023)Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

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.

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.

THE EFFECT OF NON-CASH PAYMENT TRANSACTIONS ON THE VELOCITY OF MONEY IN INDONESIA

This study aims to examine the effect of non-cash payment transactions on velocity of money in Indonesia. Specifically, it investigates the impact of electronic money transactions, debit card transactions, credit card transactions, and the COVID-19 pandemic on velocity of money. The research adopts a quantitative approach and utilizes time series data from 2018 to 2021 obtained from the Bank Indonesia website. Multiple linear regression analysis is employed as the analytical technique. The findings reveal that electronic money transactions do not significantly affect velocity of money in Indonesia. Debit card transactions show a negative and significant impact, while credit card transactions do not show a significant effect. Furthermore, the COVID-19 pandemic has a negative and significant influence on the velocity of money in Indonesia. Simultaneously, all the independent variables have a significant effect on the velocity of money in Indonesia. The R2 testing indicates that the independent variables explain 85.94% of the variability in the velocity of money variable, while the remaining 14.06% is attributed to other factors beyond this study.

Investigating the Effect of Gasoline Prices on Transit Ridership and Unobserved Heterogeneity

Based on a unique scanner panel data set on debit and credit card transactions, we examined the effect of gasoline prices on individual choices between private vehicle use and public transit ridership. The unique feature of our data allowed us to address possible heterogeneity in the effect of gasoline prices and to explicitly incorporate the link between private vehicle use and public transit ridership. A series of empirical analyses reveal that there is significant heterogeneity in the effect of gasoline prices on fuel consumption and that financial constraints and commitment to vehicle use determine individual sensitivities to the price of gasoline. The substantive empirical knowledge provided about individual decisions concerning transit modes contributes meaningful implications and effective guidance for practitioners and policymakers.

Determinants of Real Money Demand in India.

The foundation of a nation's monetary policy is determined by the components of its money supply. The advent of technological advancements in the banking industry has led to the development of digital payment methods that serve as substitutes for the currency in circulation. The proliferation of digital payment methods has an impact on real money demand, thereby disturbing the money supply-demand equilibrium of the country. The current study made an effort to evaluate the factors that affect the real money demand in India, taking into account the use of digital payment methods like NEFT, debit cards, and credit cards, as well as macroeconomic variables like GDP, the exchange rate, and price level. The study used secondary time series data from the RBI database for the period from 2005–06 to 2020–21. The linear regression model was used to estimate the real money demand function, and it was found that income, exchange rate, and NEFT transactions positively influenced real money demand. Debit cards had a negative impact on the real money demand, concluding that an increase in the value of debit card transactions caused a decline in the real money demand. The study’s findings will help the RBI to enable methods to incorporate digital payment instruments in the components of the money supply, thereby maintaining a stable money demand function.

Influence of the Non-Cash Payment System on the Money Supply in Indonesia

This study aims to determine the effect of using a non-cash payment system on the money supply in Indonesia. This research uses quantitative research. The data used in this study is secondary data with a period of 7 years. The method used in this research is Multiple Regression Analysis. The variables in this study are the money supply (M1) as the dependent variable and the number of debit card, credit card, and electronic money transactions as the independent variable. The results of this study are debit card transactions have a positive and significant effect on the money supply, credit card transactions have a negative and significant effect on the money supply, and electronic money transactions have a positive and significant effect on the money supply.

A Study on Growth and Development of E-Banking Services of Selected Private Sector Banks in India

The banking industry has always been essential to any economy, particularly developing economies. With the emergence of new advances and technical improvements, this sector has changed from its traditional form to e-banking. The objective of this paper is to e-banking services of selected private sector banks in India. The study is based on secondary data only. The secondary data is collected from RBI Website. The study has considered 05 private sector banks in India. The time period of the study is 10 years starting from 2011-12 to 2020-21. To measure the E-Banking services different services namely ATM, POS, Debit card, Credit card, NEFT inward, NEFT Outward, RTGS Inward, RTGS Outward, and Mobile transactions have been taken for the analysis. The researcher has calculated E-banking services, Growth of E-Banking services and One-Way ANOVA test has been applied to examine the significant difference with the help of Microsoft Excel and SPSS-20 version. As per One-way ANOVA test result, there is a significant difference in E-banking services of selected private sector banks found by the study. However, there is a significant difference found in the Growth of debit cards, NEFT service, and RTGS service and an insignificant difference found in Growth of ATM service, POS service, Credit card service, and Mobile service of selected private sector banks.