Card Customers Research Articles

Credit Risk Prediction Using Machine Learning and Deep Learning: A Study on Credit Card Customers

The increasing population and emerging business opportunities have led to a rise in consumer spending. Consequently, global credit card companies, including banks and financial institutions, face the challenge of managing the associated credit risks. It is crucial for these institutions to accurately classify credit card customers as “good” or “bad” to minimize capital loss. This research investigates the approaches for predicting the default status of credit card customer via the application of various machine-learning models, including neural networks, logistic regression, AdaBoost, XGBoost, and LightGBM. Performance metrics such as accuracy, precision, recall, F1 score, ROC, and MCC for all these models are employed to compare the efficiency of the algorithms. The results indicate that XGBoost outperforms other models, achieving an accuracy of 99.4%. The outcomes from this study suggest that effective credit risk analysis would aid in informed lending decisions, and the application of machine-learning and deep-learning algorithms has significantly improved predictive accuracy in this domain.

Dampak Kualitas Produk dan Harga Terhadap Keputusan Pembelian pada Generasi Milenial: Studi Kasus pada Pelanggan Kartu Xl di Kota Gorontalo

This study aimed to discover the extent to which Product Quality and Price Influence Purchase Decisions among millennials, focusing on XL Card customers in Gorontalo City. The sampling employed in this study was purposive sampling, with a sample size of 100 respondents, which was determined using the Lemeshow formula. Data collection was conducted using questionnaires distributed to the population of XL Card customers in Gorontalo City. The analytical tool used to test this study was multiple linear regression, with data analysis performed using SPSS version 27. The study findings indicated that, partially, Product Quality did not significantly influence Purchase Decisions, with a t-value of 1.023 compared to the critical value of 1.984. Conversely, Price significantly influenced Purchase Decisions, with a t- value of 11.451 exceeding the critical value of 1.984. Furthermore, the simultaneous test results indicated that both Product Quality and Price significantly influenced Purchase Decisions among millennials purchasing XL Cards. The Product Quality and Price variables collectively explained 58.8% of the variance in Purchase Decisions, while the remaining 41.2% can be attributed to other variables not included in this study

MACHINE LEARNING-BASED APPROACHES FOR CREDIT CARD DEBT PREDICTION

The primary concern in the stock market and banks that offer credit cards has been a problem over time. Regardless of their capacity to pay, most card users abuse their credit cards and accrue debt from cash cards. The most significant issue facing cardholders and banks alike is this calamity. Predicting credit card customers' default payments became vital to lowering this risk. Data mining approaches, including decision tree, logistic regression, and Naïve Bayes with feature selection methods, were applied to secondary credit card debt data to identify the significant factors that impact credit card default and to enhance the prediction of credit card default. As a result, the decision tree with Gini index splitting criteria forward selection wrapper method was identified as the best model with the highest percentages of accuracy, precision, sensitivity, and area under ROC of 76.39%, 72.02%, 85.08%, and 0.891 respectively. Additionally, the significant factors that impact credit card default are gender, education level, repayment status in July 2005, repayment status in August 2005, status of repayment in September 2005, and the amount paid in June 2005 and May 2005. This study may help financial institutions assess creditworthiness and give consumers insights into their financial behaviors.

STRATEGIES TO INCREASE MUSLIM CONSUMER LOYALTY IN THE TIME OF THE COVID-19 PANDEMIC

This study was conducted to determine the extent of the influence of promotional strategies, product quality, and customer satisfaction on customer loyalty to Indosat Ooredoo card customers. The type of research used is explanatory research. The population is all Indosat Ooredoo card customers in Demak Regency. The technique of determining the sample is by purposive sampling. In this study, the number of samples used was 131 respondents. Data analysis was performed using multiple linear regression analysis techniques. Data collection techniques through online questionnaires using a Likert scale. Sources of data are primary data and secondary data. The results of this study based on partial testing in this study are each variable namely promotion strategy, product quality, and customer satisfaction has proven to have a positive and significant effect on customer loyalty. Based on the results of simultaneous testing, it is in a strong category because promotion strategies, product quality, and customer satisfaction are proven to have a positive and significant effect on customer loyalty.

Credit card customers churn prediction by nine classifiers

Recently, losing credit card customers has been particularly serious. Using the found data set from kaggle website, this paper wants to help the bank manager by predicting for them to identify the customers who are likely to leave, so they can approach them in advance to offer them better services and sway their decisions. Nine classifiers are used to carry out model training and evaluation and finally develop credit card customers churn prediction. AdaBoost, XGBoost, Naive Bayes, Decision Tree, Random Forest, K-Nearest Neighbors, Support Vector Classifier, and Logistic Regression are the nine classifiers. The result shows that the credit card customer churn model can be predicted by all machine learning models. Among them, the XGBoost model performs exceptionally well, with a training accuracy of 100%, a test accuracy of 97%, and the highest F1 score of 92%. So it can be concluded that this model can be applied to relevant datasets for prediction in order to assist banks in better retaining their existing customers.

Imbalanced customer churn classification using a new multi-strategy collaborative processing method

The rapid advancement of big data and artificial intelligence heralds a dual-edged era of opportunities and challenges for the banking sector. Indeed, enhancing a model's capability to accurately classify imbalanced datasets represents a critical challenge within the field of customer churn prediction (CCP). In this paper, to address the challenges presented by the problem of imbalanced customer classification, a new multi-strategy collaborative processing method named IADASYN-FLCatBoost is proposed from dual perspectives: data and algorithm. At the data level, the traditional Adaptive Synthetic (ADASYN) sampling is improved, that is, the LOF (Local Outlier Factor) algorithm is introduced to eliminate outliers, and the classification features are specially processed to synthesize new minority class samples, thus an improved ADASYN (IADASYN) algorithm is obtained. At the algorithm level, the Focal Loss is embedded into the CatBoost ensemble learning framework to form a new Focal Loss-CatBoost (FLCatBoost) to make a focal-aware, cost-sensitive version of imbalanced customer churn prediction. Moreover, the empirical analysis is conducted in conjunction with the credit card customer dataset obtained from the Kaggle platform. The results of the staged comparison experiments show that the proposed method IADASYN-FLCatBoost in this paper shows the best prediction performance. Comparing the proposed method with 5 other imbalanced classification algorithms and 20 classifiers composed of classical sampling methods and ensemble learning algorithms, it is verified that the classification effect of the proposed method performs best, and the values of Recall, F1 score, G-mean and Area under Precision-Recall curve (AUPRC) have been significantly improved. In addition, further verification of the model also proves that the proposed method has certain generalizability and is still valid for other banks and customer churn datasets of other industries.

The Credit Card Anti-fraud Detection Model in the Context of Dynamic Integration Selection Algorithm

Because of its close relationship with information technology, every change in the field of information technology will have an important impact on the means, time and space distribution of the final fraud of the credit card network. The rapid development and popularization of artificial intelligence (AI) software based on Deepfake technology has greatly promoted the intelligent transformation of credit card network fraud. Through the use of advanced hardware and software equipment, criminals have iterated on their own fraud tools, from the use of traditional phone calls and text messages to the use of the latest AI software. Therefore, for the problem of credit card fraud detection with missing data set labels and highly unbalanced category distribution, A credit card approval anomaly detection model DES-HBOS (Dynamic Ensemble Selection based on Histogram-based Outlier Score) is proposed. Firstly, the unsupervised anomaly detection algorithm is used to construct the false label of the training set customer. Then, the customer capability area to be measured is determined, and the classifier performance is evaluated according to Pearson correlation coefficient. Finally, a set of optimal classifiers is selected to integrate the test customers. Experiments on real credit card customer data sets show that DES-HBOS has a higher Recall and can identify more fraudulent customers than other 6 classical anomaly detection models. Comparative experiments were carried out on 4 unbalanced data sets, and the experimental results showed that DES-HBOS had stronger anomaly detection ability than HBOS.

Comparison of Hierarchical, K-Means and DBSCAN Clustering Methods for Credit Card Customer Segmentation Analysis Based on Expenditure Level

The amount of data from credit card users is increasing from year to year. Credit cards are an important need for people to make payments. The increasing number of credit card users is because it is considered more effective and efficient. The third method used today has a function to determine the effective outcome of credit card user scenarios. In this study, a comparison was made using the Hierarchical Clustering, K-Means and DBSCAN methods to determine the results of credit card customer segmentation analysis to be used as a market strategy. The results obtained based on the best silhouette coefficient score method is two cluster hierarchical clustering with 0.82322 score. Based on the best mean value customers are divided into two segments, and it is suggested to develop strategies for both segments.

Classification Interest Customer XL Internet Card Using Naive Bayes Algorithm At PT XML Tronik

In the world of trade, competition within a company has become commonplace and makes business people always think about breakthroughs that can ensure the continuity of their business . Basically companies have valuable assets but are rarely used as a reference, these assets are in the form of databases with large numbers. PT.XML Tronik is a company that has been in the technology sector for a long time, one of which is selling GSM Card Providers in Indonesia. The classification method used is the Naïve Bayes Algorithm, where Naïve Bayes is used to classify statistical and probability methods . Method This research uses several stages starting from data collection , analysis sales , data processing using naïve Bayes method and proceed with measurements level accuracy use WEKA application . From the results of data mining using the Naïve Bayes Algorithm, this researcher found patterns of interest in buying XL Card customers at PT.XML Tronik . Based on the results of the analysis carried out, using 20% of the test data with negative and positive labels. So that it can produce a sign ending with an accuracy level of 84.6667% with a precision of 97% and a recall of 83%.

Supermarket Transaction Records In Dietary Evaluation: the STRIDE study: validation against self-reported dietary intake.

Scalable methods are required for population dietary monitoring. The Supermarket Transaction Records In Dietary Evaluation (STRIDE) study compares dietary estimates from supermarket transactions with an online FFQ. Participants were recruited in four waves, accounting for seasonal dietary variation. Purchases were collected for 1 year during and 1 year prior to the study. Bland-Altman agreement and limits of agreement (LoA) were calculated for energy, sugar, fat, saturated fat, protein and sodium (absolute and relative). This study was partnered with a large UK retailer. Totally, 1788 participants from four UK regions were recruited from the retailer's loyalty card customer database, according to breadth and frequency of purchases. Six hundred and eighty-six participants were included for analysis. The analysis sample were mostly female (72 %), with a mean age of 56 years (sd 13). The ratio of purchases to intakes varied depending on amounts purchased and consumed; purchases under-estimated intakes for smaller amounts on average, but over-estimated for larger amounts. For absolute measures, the LoA across households were wide, for example, for energy intake of 2000 kcal, purchases could under- or over-estimate intake by a factor of 5; values could be between 400 kcal and 10000 kcal. LoA for relative (energy-adjusted) estimates were smaller, for example, for 14 % of total energy from saturated fat, purchase estimates may be between 7 % and 27 %. Agreement between purchases and intake was highly variable, strongest for smaller loyal households and for relative values. For some customers, relative nutrient purchases are a reasonable proxy for dietary composition indicating utility in population-level dietary research.

Credit Card Customers' Data Visualization

Nowadays, there are many people who have credit cards, but the frequency of using them varies according to some personal factors, so many bank managers are worried because of the loss of some customers. Therefore, in this report, I have used a data set of 10,000 credit card holders' personal information and then selected and visualised this data. This will give a better understanding of the current cardholders and perhaps some banks or credit card companies can use the data to come up with effective solutions to recover some of their lost customers and find some potential credit card customers. According to the data analysis, it is not difficult to find that the current credit card users are mainly middle-age, with undergraduate degrees, and have an income below 40k dollars, with a total transfer amount between 2k-3k dollars or between 4k-5k dollars.

Understanding the difference between opportunistic and needy customers’ borrowing behavior: a new approach to segment current credit card customers

PurposeThe authors offer a new approach to segment credit card customers by classifying customers into two unobserved (latent) segments: opportunistic and needy.Design/methodology/approachThe authors develop a finite mixture model to estimate customers’ tendency to borrow using the three alternatives available to them—promotional cash advances, regular cash advances and retail balances.FindingsThe results support the presence of at least two segments among credit card customers. The authors find that relative to opportunistic individuals, needy customers are typically more sensitive to interest rates. Additionally, the results indicate that offering promotional cash advances to current credit card customers increases their sensitivity to regular interest rates. Furthermore, the findings indicate that needy customers tend to have a higher stickiness in their debt. In the post-estimation analyses, the authors observe that needy customers generate more revenue than opportunistic customers. Interestingly, the bank does not perform well in targeting needy individuals and targets both groups with the same probability.Originality/valueThe authors argue that teaser rates attract at least two segments of borrowers—the “needy” segment, which is more likely to be cash-strapped, and the “opportunistic” segment, which looks at these teaser rates as an opportunity. However, banks do not observe segment membership. Hence, the authors offer a new approach to identifying these segments and show that understanding the behavior of these latent segments could help a bank target profitable customers more effectively.

Prediction of the Reasons for Losing Credit Card Customers based on Multiple Models

The data in this article is taken from Kaggle, and the data is publicly available. The data mainly focuses on the nearly 20 different possible reasons that the bank is losing credit card customers, such as their monthly and yearly income or gender. However, some of the data in the original dataset is embodied in words. For this kind of data, the data needs to be converted into numbers and then put into the model for testing. It is also important to check the data whether it has the outliers. Putting the data set into the models to test the accuracy. By sorting the results of different models, the model with the best matching degree is finally obtained. Then, the highly correlated factors in the model are sorted to get the main reasons why banks lose credit card customers. In the end, providing some business advice for banks could be used to identify the same group of credit card customers, and providing some advice to banks could be used to save credit card customers.

Integrating deep neural network with logic rules for credit scoring

Credit scoring is an important topic in financial activities and bankruptcy prediction that has been extensively explored using deep neural network (DNN) methods. DNN-based credit scoring models rely heavily on a large amount of labeled data. The accuracy of DNN-based credit assessment models relies heavily on large amounts of labeled data. However, purely data-driven learning makes it difficult to encode human intent to guide the model to capture the desired patterns and leads to low transparency of the model. Therefore, the Probabilistic Soft Logic Posterior Regularization (PSLPR) framework is proposed for integrating prior knowledge of logic rule with neural network. First, the PSLPR framework calculates the rule satisfaction distance for each instance using a probabilistic soft logic formula. Second, the logic rules are integrated into the posterior distribution of the DNN output to form a logic output. Finally, a novel discrepancy loss which measures the difference between the real label and the logic output is used to incorporate logic rules into the parameters of the neural network. Extensive experiments were conducted on two datasets, the Australian credit dataset and the credit card customer default dataset. To evaluate the obtained systems, several performance metrics were used, including PCC, Recall, F1 and AUC. The results show that compared to the standard DNN model, the four evaluation metrics are increased by 7.14%, 14.29%, 8.15%, and 5.43% respectively on the Australian credit dataset.

Transaksi Online Nasabah Kartu Kredit Pada Consumer Loan Center PT. Bank Negara Indonesia (Persero) Tbk Cabang Denpasar

This study aims to determine how the bank's responsibility for credit card customers who experience losses in online transactions at the Consumer Loan Center of PT Bank Negara Indonesia (Persero) Tbk Denpasar Branch and efforts to resolve credit card customer losses in online transactions at the Consumer Loan Center of PT Bank Negara Indonesia (Persero) Tbk Denpasar Branch. This research uses empirical research methods that examine data through primary and secondary data analysis. The results of this study indicate that the responsibility of the Consumer Loan Center of PT Bank Negara Indonesia (Persero) Tbk Denpasar Branch is to provide compensation if the results of the investigation prove that the loss was caused by the fault of Bank Negara Indonesia. Efforts to resolve credit card customer losses in online transactions at the Consumer Loan Center PT. Bank Negara Indonesia (Persero) Tbk Denpasar Branch were resolved through internal settlement by Bank Negara Indonesia, namely by submitting an apology statement and providing compensation to credit card customers.

Implementation of Business intelligence in Know Your Customer (KYC) for credit card customers’ loan repayment status

Nowadays, the increasing technological improvements followed by data demand made businesses and organizations follow these improvements to continue operating. The Know Your Customer (KYC) assessment is still manually done, followed by the growing amount of data, resulting in data accumulation and affecting the time organizations spend analyzing the prospective applicant data. The unavailability of tools that can help review documents and data leads to more data accumulation and timeconsuming KYC assessments. This paper aims to create a Business Intelligence (BI) system to help financial organizations analyze, process applicant data, and determine applicants’ eligibility who are willing to get credit card services. This paper may be utilized by an enterprise operating in the financial sector that follows the KYC procedure to identify applicants who require credit card services. The development of BI systems is predicted to help reduce the time spent to validate application data, particularly in the banking or financial industry. This study has designed three dimension tables and a fact table using Microsoft SQL Server 18 for the data warehouse. Pentaho Data Integration is used for the ETL process, and Tableau creates the dashboard. The dashboard contains general information and the loan repayment status of an applicant. Two pivot tables were created using Microsoft Excel to summarize the loan repayment status of an applicant.

Time-Weighted Cumulative LSTM Method Using Log Data for Predicting Credit Card Customer Turnover

One of the most important challenges for credit card companies is to retain customers. To achieve this, they predict whether customers will use credit cards and respond preemptively through marketing. Improper prediction leads to an increase in marketing costs; therefore, improving the performance of the prediction model is an important challenge for credit card companies. Therefore, credit card companies are working toward increasing the predictive power of the model, but the data used for learning are limited to formal data, such as card payment data; the log history stored on the application is not used. Prediction models using sequence data, such as log history, are being studied. However, no studies so far have appropriately utilized card payment data, log history, and information on the timing of the elements constituting the sequence. In this paper, we propose a method for predicting credit card turnover using application log and credit card payment data. The method embeds log data and weights each log using a term-weighting function. In addition, the time difference between the day customers visit the app and the last day of the month in which they leave is calculated. The weight value is higher when the timespan is shorter. The weighted log data were trained with a 2-layer LSTM model and its output layer was combined with the output layer trained with the card payment data for customer turnover prediction. It was verified that the model presented in this paper had a higher performance than the model using only card payment data and the model using only a time-weighted log sequence.

Implementation Graph Database Framework for Credit Card Fraud Detection

Credit card use is becoming more and more commonplace every day. Financial organizations and credit card customers lose a lot of money because of complicated illegal transactions. Fraudsters constantly stay on top of new technology to quickly perpetrate fraud against customer transaction patterns. We analyze credit card transaction networks and identify suspicious patterns, such as transactions connected to multiple accounts or unusual transaction patterns, transactions made at unusual times, and to monitor credit card transactions in real-time and quickly identify suspicious transactions. TigerGraph is used to analyze data, display results on a dashboard, and send notifications via email. One meth’\\Vc 1``13-od commonly used in anomaly detection is to compare data values against the standard deviation. In this research, we explain the use of TigerGraph as a platform for anomaly detection above the standard deviation, as well as the use of the Louvain algorithm in finding merchant communities used by fraudsters. The data used in this study comes from Sparkov simulation data obtained from Kaggle. Our results show that by using TigerGraph, we managed to achieve a very high accuracy rate of 99.77%, precision 82.84%, recall 72.38%, and f1-score 77,26% in predicting transaction fraud on Sparkov simulation data. This is much better than the results reported in a paper that uses the supervised machine learning method with the AdaBoost algorithm which achieves the highest accuracy of 77%.

Automation of Credit Card Customer Churn Analysis using Hybrid Machine Learning Models

Credit Card Customer Churn Analysis (C4A) is a phenomenon where customers stop using a specific business credit card service. Predicting customer churn is crucial for Credit Card (CC) companies because it enables them to spot at-risk customers and take precautions to retain them. The aim of the paper named C4A is to create a model that accurately predicts customers who are most likely to stop using CC. The paper involves gathering and analyzing customer information from Kaggle, including transaction history, demographics and credit card usage patterns for prediction. Machine learning algorithms namely, Logistic Regression, KNN, XGBoost Classifier, Decision Tree and Hybrid Models integrating Logistic Regression and KNN, Logistic Regression and Decision Tree are used to train to find patterns and correlations that point to customer churn. The accuracy of the proposed method is 0.846 with LR, 0.849 with KNN, 0.90 with a hybrid model integrating LR and KNN, 0.928 by integrating LR and DT, 0.91 with DT, and 0.93 with XGBoost.

Investigation on credit card customer cancellations: A case study of AMEX credit cards

American Express (AMEX) is one of the most popular credit card services in the U.S. Lately, the company is facing loss of some valued customers for specific reasons. This paper investigates the customers’ cancellation behavior based on XGBoost in terms of the AMEX data. Besides, the factors contributing to customers’ cancellations are identified, e.g., customer age, total transaction counts, total transaction amount. According to the analysis, the proposed XGBoost model can reach 96.5% in accuracy. By implementation of such tool, it is feasible for the bank to predict their customer behavior and take measures proactively. These results shed light on guiding further exploration of credit card services.