Card Customers Research Articles

Forecasting of Credit Card Default Based on Incremental Random Forest

In this paper, the incremental random forest algorithm is proposed for the classification and prediction problem of dynamically increasing data. Traditional batch machine learning algorithms perform modeling at one time and cannot allow newly generated samples to participate in learning, which leads to too much model deviation. This paper combines incremental learning with random forest and proposes incremental random forest. Applying this algorithm to the problem of predicting credit card customer default behavior can help banks control risks and reduce losses. It is important to conduct card issuance audits on card issuers and early warning of risks to cardholders. The algorithm performed better in the experiment of predicting the default behavior of credit card customers based on a batch of credit card holder data of a bank in Taiwan. Compared with random forest, decision tree, logistic regression, naive bayes, BP neural network, and support vector machine, it has relatively better performance in our experiment.

The Influence Of Price, Product Quality and Service Quality on Telkomsel Card Customer Satisfaction

This study aims to determine whether there is an influence between Price, Product Quality, and Service Quality on Telkomsel Card Customer Satisfaction. The data obtained were analyzed using SPSS Statistics 26, the data were obtained using the attached questionnaire distributed at the State University of Jakarta. The results obtained by the data are based on the results of the calculation of multiple regression research obtained Y = 7,224 - 0,241 X1 - 0,235 X2 + 0,339 X3. Competition in the cellular operator business is currently increasing along with the development of communication technology. This requires companies to continue to innovate in improving the quality of their products and services and to be more careful in determining prices so that the company can compete with its competitors. The aim of this research is to see the effect of product, price and service quality influence on Telkomsel card customer satisfaction at the Jakarta State University. This study uses an associative research method with multiple regression analysis techniques. The sample used was 100 respondents. The results showed that product quality, price and service quality simultaneously and partially had a significant effect on Telkomsel card customer satisfaction. Management of PT. Telekomunikasi Indonesia, should not be quickly satisfied with the decisions obtained, but continue to innovate in order to be able to improve the quality of products and services so that the company can survive considering the increasingly tight competition in the telecommunications sector, as well as increasingly sought after consumer tastes as a result of the increasingly modern times. Keywords: price, product quality, service quality, and customer satisfaction

Machine Learning Mini Batch K-means and Business Intelligence Utilization for Credit Card Customer Segmentation

An effective marketing strategy is a method to identify the customers well. One of the methods is by performing a customer segmentation. This study provided an illustration of customer segmentation based on the RFM (Recency, Frequency, Monetary) analysis using a machine learning clustering that can be combined with customer segmentation based on demography, geography, and customer habit through data warehouse-based business intelligence. The purpose of classifying the customers based on the RFM and machine learning clustering analyses was to make a customer level. Meanwhile, customer segmentation based on demography, geography, and behavior was to classify the customers with the same characteristics. The combination of both provided a better analysis result in understanding customers. This study also showed a result that minibatch k-means was the machine learning model with the rapid performance in clustering 3-dimension data, namely recency, frequency, and monetary.

Support Vector Machine with Information Gain Based Classification for Credit Card Fraud Detection System

In the credit card industry, fraud is one of the major issues to handle as sometimes the genuine credit card customers may get misclassified as fraudulent and vice-versa. Several detection systems have been developed but the complexity of these systems along with accuracy and precision limits its usefulness in fraud detection applications. In this paper, a new methodology Support Vector Machine with Information Gain (SVMIG) to improve the accuracy of identifying the fraudulent transactions with high true positive rate for the detection of frauds in credit card is proposed. In SVMIG, the min-max normalization is used to normalize the attributes and the feature set of the attributes are reduced by using information gain based attribute selection. Further, the Apriori algorithm is used to select the frequent attribute set and to reduce the candidate’s itemset size while detecting fraud. The experimental results suggest that the proposed algorithm achieves 94.102% higher accuracy on the standard dataset compared to the existing Bayesian and random forest based approaches for a large sample size in dealing with legal and fraudulent transactions

A sequence analysis approach to segmenting credit card customers

This research examines the longitudinal relationships between patterns of customer loyalty behavior and firm financial outcomes in the context of credit card use. By examining credit card users' transactions with a focal bank and its competitors, this study creates a typology of loyalty behavior and employs a sequence analysis to segment customers on the basis of their behavioral patterns exhibited over time. The analysis results in six distinct customer segments: Loyalist, Switching Loyalist, Switching Defector, Defector, Dormant Loyalist, and Dormant Defector. We subsequently estimate the revenue, servicing costs, and profitability associated with each segment, and assess differences across segments. We discuss the implications for managing distinct credit card customer segments and optimizing the bank's marketing resource allocation.

“Repayment-by-Purchase” Helps Consumers to Reduce Credit Card Debt

Many consumers struggle to repay their credit card debt, in part because paying small portions of large bills often feels fruitless. We introduce a novel credit card payment option – repayment-by-purchase – and examine its influence on both the amount consumers’ repay and their perception of progress toward reducing their debt. With typical repayment, consumers simply enter the amount they wish to pay toward their total balance – often the minimum required payment. With repayment-by-purchase, in contrast, consumers can select specific purchases (e.g., a coffee at Starbucks, a utility bill) that they wish to repay, and make payments specifically directed toward “eliminating” these purchases. Five studies reveal that repayment-by-purchase increases awareness of what is being repaid, which increases perceptions of progress toward reducing debt, which in turn encourages higher repayment. In a large field experiment, credit card customers who were given the opportunity to allocate their payment toward specific purchase categories paid 12.18% more toward their debt balance than a control group. These findings advance our practical understanding of how consumers can be encouraged to pay more toward credit card debt and offer conceptual insight into how both increased awareness and perceived goal progress enhance consumer motivation to get out of debt.

Analisis Penerapan Prinsip Syari’ah Terhadap Aplikasi Hasanah Card di BNI Syari’ah Kantor Cabang Surabaya

The purpose of this study was to find out whether the Hasanah Card had actually implemented the Sharia Card Principles described in Fatwa DSN-MUI No. 54.The research method used is a descriptive approach by combining types of qualitative research. Data collection was conducted by interviewing the Hasanah Card Marketing Division, employees and Hasanah Card customers, direct participatory observation, and documentation. Data validity techniques use credibility techniques (internal validity), transferability (external validity), dependability (reliability), and confirmability (objectivity). Data analysis techniques use inductive, deductive, and comparative methods. The results of this study are the application of sharia principles on hasanah cards conducted by BNI Syariah KC Surabaya still not in accordance with Fatwa DSN-MUI No. 54. This can be seen from the DSN-MUI fatwa No.54 Based on the fatwa stipulated by the Sharia card, it cannot be used for transactions that are not in accordance with sharia. However, there is a difference between the DSN-MUI fatwa on sharia cards and the practices that occur in Islamic banks where credit card customers do not apply the use of Islamic credit cards in accordance with the provisions

Using Unsupervised Machine Learning Techniques for Behavioral-based Credit Card Users Segmentation in Africa

Given the fierce competition that has come up because of evolving FinTech and e-payment industries in the global market, the credit card industry has become extremely competitive. To survive, financial institutions need to offer their credit card customers with more innovative financial services that provide a personalized customer experience beyond their banking needs. While we are witnessing this high competition that aims to provide better services to credit card holders, Africa risks remaining behind once again: in 2017, the World Bank reported that only 4.47% of Africans aged 15 and above hold a credit card. In this paper, we define and describe the steps that can be taken to build a behavioral-based segmentation model that differentiates African credit cardholders based on their purchases data. We focus on African customers and African financial institutions as (i) little has been done so far when it comes to understanding the spending behavior of African credit card holders; and (ii) because we believe that this segmentation will allow boosting credit card usage in Africa, thus allowing Africans to fully benefit from credit cards as other parts of the world do. The results of this research can help tailor the market campaign to make them customer- centric and reduce the associated marketing costs. We show the proposed approach at work using anonymized credit card data of one the leading banks in Egypt, the Commercial International Bank of Egypt.

내·외부적 충격 요인이 신용카드 서비스품질에 미치는 영향: 2014년 개인정보 유출 사건과 2015년 MERS를 중심으로

The subject of this study is the effect of internal and external impact on card service quality. In particular, it analyzed what affects service quality, focusing on the leakage of personal information from credit card companies in 2014 and the MERS outbreak in 2015. To that end, KS-SQI credit card data were analyzed between 2012 and 2019. There are four main analysis results. First, the quality of credit card service affected customer loyalty, and the impact was great through unexpected additional services. Second, internal and external shocks also affected the decline in credit card customer loyalty. Third, internal shocks also led to a decline in customer loyalty, which was higher in the customer group that rated service quality high. Fourth, external shocks also showed a decline in customer loyalty among customers who rated service quality low. Therefore, we could see the importance of preparing companies for such shocks.

Mengkaji Perputaran Uang Bank Melalui Model Kaldor-kalecki: Tinjauan Numerik Untuk Sistem Kartu Kredit

Credit Card is a card payment instrument, where the cardholder's payment obligation is firstly fulfilled by the acquirer or publisher which use third party funds in the form of investments to pay the obligation payment of cardholder. These investment funds are managed as the initial fund of credit card customers. Some of the generated profits could be saved as bank deposits, while others are used for joint capital investment funds. The preview description shows the circulation of bank deposit of the credit card system which mathematically corresponds to the concept represented by Kaldor-Kalecki model. The aims of this study is that money circulation process is represented numerically by such model solution using the Runge-Kutta method. The interpretation of the numerical solution of the Kaldor-Kalecki model of the credit card system is simulated for Bank Mega's financial report data in 2017, the results shows that Bank Mega was found a decline of the number of credit card production. It could be said that the numerical solutions well represented the condition of the credit card system issued by Bank Mega. Negative values of numerical solution also reviews as period of the Bank investment

Feasibility of Using Food Assortment Scoring Tools (FAST) to Assess Supermarket Purchases

Abstract Objectives Real-world pricing interventions may increase healthy food purchases and decrease unhealthy food purchases, but simple tools for assessing the quality of food purchases are lacking. This study's primary aim is to assess the feasibility of using Food Assortment Scoring Tool (FAST) to calculate the healthfulness of individual food purchases at a community grocery store. Methods As part of a pricing intervention trial, purchasing data are being collected in a community grocery store in Baltimore, MD, using the store's point of sale system. To pilot the feasibility of utilizing FAST to assess the healthfulness of customer purchases, we analyzed 100 transactions of 7 participating loyalty card holders from September 7th, 2018 to December 3rd, 2019. FAST consists of 13 food categories. The total weight of foods in each category is calculated, divided by total weight of the entire food purchased, then multiplied by its healthfulness parameter and all are summed together. The overall FAST score ranges from 0–100 with 100 being the healthiest. Results The study team had challenges categorizing certain products sold at the supermarket, such as those with temporary barcodes to implement temporary reduced prices for quick sales. However, the process was preferable to assigning Healthy Eating Index scores to transactions. The average FAST score for individual purchases was 56.51 +/− 15.29. Beverages made up the largest average gross weight share at 40.35%. The food category with the smallest gross weight share was whole grains (0.10%). Conclusions FAST is an effective, simple tool to calculate the healthfulness of food purchases at a supermarket. We will extend our analysis of FAST scores to all 100 loyalty card customers participating in the study and report on the impact of the pricing intervention on purchasing patterns. Grocery stores and researchers implementing food retailer-based interventions should consider using FAST scores to evaluate the healthiness of consumer purchases. Funding Sources This research is supported by the Johns Hopkins Department of International Health Small Grant and a Healthy Eating Research (HER) Commissioned Analysis.

Credit Card Fraud Detection Using Machine Learning Techniques

Credit Card Fraud is one of the major moral issues in the public and private bans sector. The effect of this problems leads to the several ethical trouble. The important themes are to notice the distinctive kinds of credit card fraud and to locate different methods that have been used in fraud detection. The sub-point is to suppose about existing and ruin down as of late dispensed discoveries in fraud detection. Probable upon the variety of extortion appeared with the banks or different financial organizations, exceptional measures can be embraced and executed. The work carried out in this paper are usually going to have really beneficial residences as a approaches as expenditure reserve fund and time capability. The cost utilization of the strategies investigated proper right here is in the minimization of credit card fraud. Anyway, there are up to now moral troubles when appropriate credit card customers are unsorted as fraudulent. Credit Card Fraud Detection is an method which will help people for their transaction process in shopping mall and any other transaction process nowadays fraud detection is nothing but an process where the criminals are found and there are many illegal activities are taking place which causes difficulty for people. Here in this paper we are using SMOTE technique to find fraud and this technique will help to sort both the normal transaction and fraud transaction this process can make easy to find fraudulent. And Neural Network KNN are also taken place to find Credit Card Fraud.

Fuzzy Particle Swarm Optimization Feature Selection and Aggrandized Classifier for Uncovering Frauds in Credit Card Deals

In today's economy, credit card plays a very important role. The rise of credit card customers improved, credit card scam cases were also on the rise. Numerous procedures are anticipated to challenge the evolution of the frauds in credit cards. In this research work, proposed an innovative fraud detection method which utilizes the similar cardholder’s behavioral patterns to construct a current cardholder’s interactive profile in order to stay away from the credit card scams. However, the selection of optimal features from the samples and the decision cost for accuracy becomes main important problem. To illuminate these issues this proposed research work presents an innovative fraud detection technique that makes out of four phases: 1. To augment a cardholder’s behavioral styles, first we divide all cardholders into distinctive groups making use of the cardholder’s historical transaction data such that the members of each group have the similar transaction behavior by K-means. 2. Introduces a new Fuzzy Particle Swarm Optimization (FPSO) feature selection for the amplification of fraud detection in credit cards. 3. By means of a prolonged wrapper method, an ensemble classification are performed by Aggrandized Kernel based Support Vector Machine (AKSVM).4.Refreshing the cardholder’s social profile with an input system. This Proposed work adopts the external quality metrics as Accuracy, Recall, Concept drift rate and Fraud feature rate. The UCI dataset is used and is done in MATLAB framework. The analytical measures were used to estimate the routine of the mentioned fraud detection technique. The simulation results show that this proposed innovative fraud detection method provides better accuracy results than other fraud detection techniques. The low concept drift rate results the gain of the innovative method to classify the transactions accurately.

Implementation of the Logistic Regression Model and its Applications

The purpose of an analysis using this method is the same as that of any technique in constructing models in statistics, namely to find the best and most reasonable model to describe the relationship between a result variable and a set of variables independent. We are interested in how the costs affect them and if a customer has a travel card.
 Credit card customers are shown to be 6 times more likely to use it regardless of the cost they make.It is also shown that a customer is more likely to use a travel card when costs increase Through logistic regression, which gives the probability that a result is an exponential function of the independent variables, we will see how through our data we will come to very important conclusions.

Macroeconomic Expectations and Credit Card Spending

How do macroeconomic expectations affect consumer decisions? We examine this question using a natural field experiment with 2,872 credit card customers from a large commercial bank. We conduct a survey to measure consumer expectations about future inflation and the nominal exchange rate and combine this with an information-provision experiment that generates exogenous variation in these expectations. We merge the survey and experimental data with detailed administrative data on the subjects' credit card transactions and balances. The experiment is designed to test three standard predictions from models of intertemporal consumption choice: inflation expectations should affect spending on durables; exchange rate expectations should affect spending on tradables; and, holding constant the nominal interest rate, inflation expectations should affect borrowing. We find that the information provided to participants strongly affects subjective expectations. However, we do not find any significant effects on actual consumer behavior (as measured in administrative data) or self-reported consumption plans (as measured in survey data). Our preferred interpretation is that consumers are not sophisticated enough to factor inflation and exchange rate expectations into their consumption decisions. The absence of a link between consumer expectations and behavior has potentially important implications for macroeconomic policies such as forward guidance.

Using machine learning techniques to develop prediction models for detecting unpaid credit card customers

Customer behavior prediction is gaining more importance in the banking sector like in any other sector recently. This study aims to propose a model to predict whether credit card users will pay their debts or not. Using the proposed model, potential unpaid risks can be predicted and necessary actions can be taken in time. For the prediction of customers’ payment status of next months, we use Artificial Neural Network (ANN), Support Vector Machine (SVM), Classification and Regression Tree (CART) and C4.5, which are widely used artificial intelligence and decision tree algorithms. Our dataset includes 10713 customer’s records obtained from a well-known bank in Taiwan. These records consist of customer information such as the amount of credit, gender, education level, marital status, age, past payment records, invoice amount and amount of credit card payments. We apply cross validation and hold-out methods to divide our dataset into two parts as training and test sets. Then we evaluate the algorithms with the proposed performance metrics. We also optimize the parameters of the algorithms to improve the performance of prediction. The results show that the model built with the CART algorithm, one of the decision tree algorithm, provides high accuracy (about 86%) to predict the customers’ payment status for next month. When the algorithm parameters are optimized, classification accuracy and performance are increased.

Macroeconomic Expectations and Credit Card Spending

How do macroeconomic expectations affect consumer decisions? This paper reports results from a natural field experiment with 2,872 credit card customers from a large commercial bank to answer this question. Participants of the experiment first completed as survey that measured consumer expectations about future inflation and the nominal exchange rate. This survey was combined with an information-provision experiment that generated exogenous variation in respondents’ macroeconomic expectations. The survey and experimental data were then merged with detailed administrative data on participants’ credit card transactions and balances. The experiment was designed to test three standard predictions from models of intertemporal consumption choice: inflation expectations should affect spending on durables; exchange rate expectations should affect spending on tradables; and, holding constant the nominal interest rate, inflation expectations should affect borrowing. The analysis finds that the information provided to participants strongly affects subjective expectations. However, no significant effects are found on actual consumer behavior (as measured in administrative data) or self-reported consumption plans (as measured in survey data). The preferred interpretation is that consumers are not sophisticated enough to factor inflation and exchange rate expectations into their consumption decisions. The absence of a link between consumer expectations and behavior has potentially important implications for macroeconomic policies such as forward guidance.

Credit Card Business in Malaysia: A Data Analytics Approach

The revolution of big data has made resonance in the banking sector especially in dealing with the massive amount of data. The banks have the opportunity to know about the customer's opinions and satisfaction regarding their products by analyzing the data gathered every day. So, the banks can transform these data into high-quality information that allow banks to improve their business especially in credit cards which is becoming a short-term business for the banks nowadays. Further, the sentiment analysis has become immense in the field of data analytics especially the customers’ opinion makes a huge impact in making profitable business decisions. The outcome of the sentiment analysis does assist the banks to know the deficiencies of their product and allow them to improve their products to satisfy the customers. From the sentiment analysis, 45% of the customers were negative, 30% were positive and 25% were neutral towards the credit card facility offered by the commercial banks. Also, the prediction of credit card customer satisfaction will contribute in a significant way to create new opportunities for the banks to enhance their promotion aspects as well as the credit card business in future. Random Forest algorithm was applied with three various experiments utilizing the normal data, balanced data and the optimized model with the normal data. The optimized model with the normal data obtained the highest accuracy of 87.38% followed by the normal dataset by 85.82% and the least accuracy was for the balanced dataset by 82.83%.

A Performance Analysis of Detecting Credit Card Fraud by using CT18 Method

Credit cards are a significant component of everyday life. Whether purchasing gas and supermarket stores or reserving a hotel and lease a car for the next holiday. Credit cards are a pleasant and safe type of client payment. Advantages that differ from harm security on payments to the convenience of disputing suspect fees or suspicious activity make credit cards such an appealing form of transaction. It takes an hour for any time activities, online shopping, and paperless system. As the amount of credit card customers rises day by day, significant illegal activities eventually enhance. CT18 technique is the procedure for categorizing information directed at reformatting observations into CT18, whereby each observation belongs to the closest mean cluster. This is one of the simplest unsupervised learning algorithms that solve the well-known grouping problem

PERLINDUNGAN HUKUM BAGI NASABAH PERBANKAN TERHADAP KEJAHATAN KARTU KREDIT

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>Banking in facing the pace of technology in financial transactions in the use of credit cards as a means of payment runs safely. Protection of bank customers against misuse of credit cards so that customers are in a disadvantaged position. This study uses secondary data / literature as primary data supported by primary data, descriptive qualitative data presentation. Credit card crime has not been formulated normatively in Indonesia's positive law. The National Legal Development Agency (BPHN) tries to identify forms of cyberspace (credit card) activities with the Criminal Code, Banking Law, ITE Law and Consumer Protection Law. The policy regarding the regulation of credit card crimes is unclear, Article 263 and Article 378 of the Criminal Code are not relevant to be used to deal with criminal acts of credit card misuse, because there are some weaknesses that credit cards cannot be interpreted as letters. Therefore, legal protection for credit card customers can be surprised if there is participation from various parties regarding their rights and obligations, while the bank must be more open with credit card customers. Efforts to tackle credit card crime are carried out by means of a penal and non-penal policy that is to ensnare carders (credit card criminals) with the Criminal Code Articles by maximizing criminal threats and preventive measures by improving the credit card operational standard system as a form of security . </span></p></div></div></div>