Credit Card Clients Research Articles

Credit Card Client’s Payment Prediction for Next Month Using Machine Learning Algorithms

With the quick growth of the credit card system, there is a rising number of misconduct rates on credit card loans, which creates a financial risk for commercial banks. Thus, successful resolutions of the risks are significant for the sound advancement of the industry in the long term. Numerous financial banks and organizations become more and more attentive to the issue of credit card default because it brings about a high probability of financial risks. Credit risk plays a significant part in the financial business. One of the main functions of a bank is to issue loans, credit cards, investment mortgages, and other credit. One of the most popular financial services offered by banks in recent years has been the credit card. With its constant rise in risk factors, the banking industry is perhaps the most fragile and volatile in the world. Credit risk remains a crucial element for financial institutions that have experienced losses amounting to hundreds of millions of dollars as a result of their incapacity to retrieve the funds disbursed to clients. In the banking industry, it is now vital to forecast whether a borrower will be able to repay the loan. In this paper, we applied different machine learning classifiers, including Random Forest, K Nearest Neighbor, Logistic Regression, Decision Tree, Decision Tree with AdaBoosting, and Random Forest with AdaBoosting, to build a credit default prediction model. The results show that the AdaBoosting model achieved better accuracy than the other machine learning algorithms. Our proposed technique can support financial organizations in controlling, identifying, and monitoring credit risk, and it can identify credit card clients who pay the loan in the next month.

An FTwNB Shield: A Credit Risk Assessment Model for Data Uncertainty and Privacy Protection

Credit risk assessment is an important process in bank financial risk management. Traditional machine-learning methods cannot solve the problem of data islands and the high error rate of two-way decisions, which is not conducive to banks’ accurate credit risk assessment of users. To this end, this paper establishes a federated three-way decision incremental naive Bayes bank user credit risk assessment model (FTwNB) that supports asymmetric encryption, uses federated learning to break down data barriers between banks, and uses asymmetric encryption to protect data security for federated processes. At the same time, the model combines the three-way decision methods to realize the three-way classification of user credit (good, bad and delayed judgment), so as to avoid the loss of bank interests caused by the forced division of uncertain users. In addition, the model also incorporates incremental learning steps to eliminate training samples with poor data quality to further improve the model performance. This paper takes German Credit data and Default of Credit Card Clients data as examples to conduct simulation experiments. The result shows that the performance of the FTwNB model has been greatly improved, which verifies that it has good credit risk assessment capabilities.

Machine Learning model based on the Light Gradient Boosting Machine to predict the probability of default in customers of the Credit Card portfolio

Bank loans are a widely used means of payment in recent times, more and more people are accessing products such as credit cards, loans, etc. Banks have implemented classic prediction models, the vast majority based on logistic regression since it allows great interpretability for the business and the effect of the model variables. The purpose of this research is to perform a predictive analysis on the probability of customer default in the credit card portfolio using a risk score. The dataset used is the so-called default of credit card clients Data Set from the UCI Machine Learning DB, the approach is quantitative and the methodology is descriptive analytics, techniques based on gradient boosters will be used to make the prediction, among the trained algorithms We have Logistic Regression with WOE, CatBoost, As a result, the light gradient enhancement machine (LightGBM) tuned with a Bayesian search was obtained, obtaining a GINI of 57.4, which improves by +6 points to the Logistic Regression with Woe and by +3p to XgBoost and CatBoost. Finally, obtaining the Gain and Shapley values made up for the lack of interpretability of the variables, allowing better decision making when evaluating clients. Likewise, as future work, it is intended to add unstructured variables that allow the Model's indicators to be improved.

Predicting default risk bancassurance using GMDH and dce-GMDH neural network models

Purpose This study aims to develop a novel approach for predicting default risk in bancassurance, which plays a crucial role in the relationship between interest rates in banks and premium rates in insurance companies. The proposed method aims to improve default risk predictions and assist with client segmentation in the banking system. Design/methodology/approach This research introduces the group method of data handling (GMDH) technique and a diversified classifier ensemble based on GMDH (dce-GMDH) for predicting default risk. The data set comprises information from 30,000 credit card clients of a large bank in Taiwan, with the output variable being a dummy variable distinguishing between default risk (0) and non-default risk (1), whereas the input variables comprise 23 distinct features characterizing each customer. Findings The results of this study show promising outcomes, highlighting the usefulness of the proposed technique for bancassurance and client segmentation. Remarkably, the dce-GMDH model consistently outperforms the conventional GMDH model, demonstrating its superiority in predicting default risk based on various error criteria. Originality/value This study presents a unique approach to predicting default risk in bancassurance by using the GMDH and dce-GMDH neural network models. The proposed method offers a valuable contribution to the field by showcasing improved accuracy and enhanced applicability within the banking sector, offering valuable insights and potential avenues for further exploration.

The Predictive Power of Credit Scores: Examining Default Probability in Taiwanese Credit Card Clients

The concept of a scorecard originated from the need to establish a standardized and objective approach to evaluate credit applicants. Various techniques have been utilized to build scoring model. This research chooses Logistic regression to construct a scorecard using SPSS modeler. In this way, the study enhances the understanding of the relationship between credit scores and default behavior. By using a scorecard constructed through logistic regression, the study provides a comprehensive and interpretable model for evaluating creditworthiness. The study also employs linear probability models (LPM), logit, and probit models to assess the predictive power of credit scores on default probability. By utilizing these statistical techniques, the research presents robust empirical evidence on the significance of credit scores in predicting default behavior. Moreover, the research paper systematically analyzes prediction effects with and without control variables. By incorporating control variables such as demographic characteristics, the study adds depth to the understanding of scoring models. Overall, the findings provide valuable guidance for credit risk assessment practices and contribute to the ongoing development of effective credit evaluation frameworks.

A novel piecewise-linear method for detecting associations between variables.

Detecting the association between two variables is necessary and meaningful in the era of big data. There are many measures to detect the association between them, some detect linear association, e.g., simple and fast Pearson correlation coefficient, and others detect nonlinear association, e.g., computationally expensive and imprecise maximal information coefficient (MIC). In our study, we proposed a novel maximal association coefficient (MAC) based on the idea that any nonlinear association can be considered to be composed of some piecewise-linear ones, which detects linear or nonlinear association between two variables through Pearson coefficient. We conduct experiments on some simulation data, with the results show that the MAC has both generality and equitability. In addition, we also apply MAC method to two real datasets, the major-league baseball dataset from Baseball Prospectus and dataset of credit card clients' default, to detect the association strength of pairs of variables in these two datasets respectively. The experimental results show that the MAC can be used to detect the association between two variables, and it is computationally inexpensive and precise than MIC, which may be potentially important for follow-up data analysis and the conclusion of data analysis in the future.

Credit Card Fraud Detection Using LSTM Algorithm

With the rapid growth of consumer credit and the huge amount of financial data developing effective credit scoring models is very crucial. Researchers have developed complex credit scoring models using statistical and artificial intelligence (AI) techniques to help banks and financial institutions to support their financial decisions. Neural networks are considered as a mostly wide used technique in finance and business applications. Thus, the main aim of this search is to help bank management in scoring credit card clients using machine learning by modelling and predicting the consumer behavior with respect to two aspects: the probability of single and consecutive missed payments for credit card customers. The proposed model is based on the bidirectional Long-Short Term Memory (LSTM) model to give the probability of a missed payment during the next month for each customer. The model was trained on a real credit card dataset and the customer behavioral scores are analyzed using classical measures such as accuracy, Area Under the Curve, Brier score, Kolmogorov–Smirnov test, and H-measure. Calibration analysis of the LSTM model scores showed that they can be considered as probabilities of missed payments. The LSTM model was compared to four traditional machine learning algorithms: support vector machine, random forest, multi-layer perceptron neural network, and logistic regression. Experimental results show that, compared with traditional methods, the consumer credit scoring method based on the LSTM neural network has significantly improved consumer credit scoring.

Credit Card Clients Classification Using Hybrid Guided wheel with Particle Swarm Optimized for Voting Ensemble

Credit card use is rapidly increasing as a result of the widespread availability of these cards, the ease of making electronic transfers, and the ubiquity of online shopping. But credit card debt poses a serious risk to businesses and governments alike, not to mention individual savers and investors. Consequently, the need for efficient, timely, and reliable ways to anticipate credit card risk has grown. In this study, we offer a framework that combines three classifiers, namely, support vector machines, multilayer perceptron and decision trees, to improve the network's accuracy. The proposed strategy is shown to be very competitive with others through simulation.

A Neural Network Ensemble With Feature Engineering for Improved Credit Card Fraud Detection

Recent advancements in electronic commerce and communication systems have significantly increased the use of credit cards for both online and regular transactions. However, there has been a steady rise in fraudulent credit card transactions, costing financial companies huge losses every year. The development of effective fraud detection algorithms is vital in minimizing these losses, but it is challenging because most credit card datasets are highly imbalanced. Also, using conventional machine learning algorithms for credit card fraud detection is inefficient due to their design, which involves a static mapping of the input vector to output vectors. Therefore, they cannot adapt to the dynamic shopping behavior of credit card clients. This paper proposes an efficient approach to detect credit card fraud using a neural network ensemble classifier and a hybrid data resampling method. The ensemble classifier is obtained using a long short-term memory (LSTM) neural network as the base learner in the adaptive boosting (AdaBoost) technique. Meanwhile, the hybrid resampling is achieved using the synthetic minority oversampling technique and edited nearest neighbor (SMOTE-ENN) method. The effectiveness of the proposed method is demonstrated using publicly available real-world credit card transaction datasets. The performance of the proposed approach is benchmarked against the following algorithms: support vector machine (SVM), multilayer perceptron (MLP), decision tree, traditional AdaBoost, and LSTM. The experimental results show that the classifiers performed better when trained with the resampled data, and the proposed LSTM ensemble outperformed the other algorithms by obtaining a sensitivity and specificity of 0.996 and 0.998, respectively.

Default on a credit prediction using decision tree and ensemble learning techniques

Due to the high importance of credit risk management and the prediction of the default on a credit loan in banks in recent years in most developed countries. This research experimented with three machine learning algorithms which are decision tree and ensemble techniques based on a decision tree-like random forest which is bootstrap aggregation machine learning model and adaptive boosting is a boosting machine learning model. All models were used to perform a binary classification to classify the clients into trusted clients who would pay back the debts who are creditable and a none trusted clients who won’t pay the debts In the time which are who are not creditable. All the models gave nearly the same results based on the selected performance measures which are precision, recall, and f1 measure. There was no significant change when they performed on a real class imbalanced dataset of the default credit card clients of Taiwan.

Modelling customers credit card behaviour using bidirectional LSTM neural networks

With the rapid growth of consumer credit and the huge amount of financial data developing effective credit scoring models is very crucial. Researchers have developed complex credit scoring models using statistical and artificial intelligence (AI) techniques to help banks and financial institutions to support their financial decisions. Neural networks are considered as a mostly wide used technique in finance and business applications. Thus, the main aim of this paper is to help bank management in scoring credit card clients using machine learning by modelling and predicting the consumer behaviour with respect to two aspects: the probability of single and consecutive missed payments for credit card customers. The proposed model is based on the bidirectional Long-Short Term Memory (LSTM) model to give the probability of a missed payment during the next month for each customer. The model was trained on a real credit card dataset and the customer behavioural scores are analysed using classical measures such as accuracy, Area Under the Curve, Brier score, Kolmogorov–Smirnov test, and H-measure. Calibration analysis of the LSTM model scores showed that they can be considered as probabilities of missed payments. The LSTM model was compared to four traditional machine learning algorithms: support vector machine, random forest, multi-layer perceptron neural network, and logistic regression. Experimental results show that, compared with traditional methods, the consumer credit scoring method based on the LSTM neural network has significantly improved consumer credit scoring.

Identification of Default Payments of Credit Card Clients using Boosting Techniques

Understanding the history of clients will act as a valuable screening method for banks by providing information that can categorize clients as defaulters on a loan. Customer credit rating is a grade process where the consumer is categorized by the grade. Credit scoring model used to ascertain credit risk from new and existing customer. Credit rating is an assessment used to measure the creditworthiness of the customer. For the huge customers related dataset we can use various classification techniques used in the field of data mining. The main idea is by analyzing the customer data and by combining machine-learning algorithm to identify the default credit card user. Default is a keyword, used for predicting the customer who cant repay the amount on time. Predicting future credit default accounts in advance is highly tedious task. Modern statistical techniques are usually unable to manage huge data. The proposed work focus mainly on ensemble learning and other artificial intelligence technique.

Weighted consensus clustering and its application to Big data

The aim of this study is the development of a weighted consensus clustering that assigns weights to single clustering methods using the purity utility function. In the case of Big data that does not contain labels, the utility function based on the Davies-Bouldin index is proposed in this paper. The Banknote authentication, Phishing, Diabetic, Magic04, Credit card clients, Covertype, Phone accelerometer, and NSL-KDD datasets are used to assess the efficiency of the proposed consensus approach. The proposed approach is evaluated using the Euclidean, Minkowski, squared Euclidean, cosine, and Chebychev distance metrics. It is compared with single clustering algorithms (DBSCAN, OPTICS, CLARANS, k-means, and shared nearby neighbor clustering). The experimental results show the effectiveness of the proposed approach to the Big data clustering in comparison to single clustering methods. The proposed weighted consensus clustering using the squared Euclidean distance metric achieves the highest accuracy, which is a very promising result for Big data clustering. It can be applied to expert systems to help experts make group decisions based on several alternatives. The paper also provides directions for future research on consensus clustering in this area.

Estimating Treatment Effects with Big Data When Take-up is Low: An Application to Financial Education

Abstract Low take-up of interventions is a common problem faced by evaluations of development programs. A leading case is financial education programs, which are increasingly offered by governments, nonprofits, and financial institutions, but which often have very low voluntary participation rates. This poses a severe challenge for randomized experiments attempting to measure their impact. This study uses a large experiment on more than 100,000 credit card clients in Mexico. The study shows how the richness of financial data allows combining matching and difference-in-difference methods with the experiment to yield credible measures of impact, even with take-up rates below 1 percent. The findings show that a financial education workshop and personalized coaching result in a higher likelihood of paying credit cards on time, and of making more than the minimum payment, but do not reduce spending, resulting in higher profitability for the bank.

Machine Learning Approaches to Predict Default of Credit Card Clients

This paper compares traditional machine learning models, i.e. Support Vector Machine, k-Nearest Neighbors, Decision Tree and Random Forest, with Feedforward Neural Network and Long Short-Term Memory. We observe that the two neural networks achieve higher accuracies than traditional models. This paper also tries to figure out whether dropout can improve accuracy of neural networks. We observe that for Feedforward Neural Network, applying dropout can lead to better performances in certain cases but worse performances in others. The influence of dropout on LSTM models is small. Therefore, using dropout does not guarantee higher accuracy.

Naïve Bayesian Classifier and Classification Trees for the Predictive Accuracy of Probability of Default Credit Card Clients

Decision Trees use a decision support tool that utilizes tree like graph model and make decisions. Naive Bayesian classifier is a binary classifier to get yes/no from the data and it is a very primitive method of finding true or false classification from a dataset. Both algorithms can be used as a predictive model in machine learning and data-mining. Here, a comparative analysis between these two machine learning algorithms is done. The data we have is used to classify if the client is the default credit card holder or not. In the perspective of risk management, the result can be used to accurately get the result of classifying credible or non-credible clients.

Estimation of Credit Card Customers Payment Status by Using kNN and MLP

The Default of Credit Card Clients dataset in the UCI machine learning repository was used in this study. The credit card customers were classified if they would do payment or not (yes=1 no=0) for next month by using 23 information about them. Totally 30000 data in the dataset’s 66% was used for training and rest of them as 33% was used for tests. The Weka (Waikato Environment for Knowledge Analysis) software was used for estimation. In estimation Multilayer Perceptron (MLP) and k Nearest Neighbors (kNN) machine learning algorithms was used and success rates and error rates were calculated. With kNN estimation success rates for various number of neighborhood value was calculated one by one. The highest success rate was achieved as 80.6569% when the number of neighbor is 10. With MLP neural network model the estimation success rates was calculated when there are different number of neurons in the hidden layer of MLP. The best estimation success rate was achieved as 81.049% when there was only one neuron in the hidden layer. MAE and RMSE values were obtained for this estimation success rate as 0.3237 and 0.388 respectively.

The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients

This research aimed at the case of customers’ default payments in Taiwan and compares the predictive accuracy of probability of default among six data mining methods. From the perspective of risk management, the result of predictive accuracy of the estimated probability of default will be more valuable than the binary result of classification - credible or not credible clients. Because the real probability of default is unknown, this study presented the novel “Sorting Smoothing Method” to estimate the real probability of default. With the real probability of default as the response variable (Y), and the predictive probability of default as the independent variable (X), the simple linear regression result (Y=A+BX) shows that the forecasting model produced by artificial neural network has the highest coefficient of determination; its regression intercept (A) is close to zero, and regression coefficient (B) to one. Therefore, among the six data mining techniques, artificial neural network is the only one that can accurately estimate the real probability of default.