Credit Card Approval Research Articles

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.

Application of Business Intelligence in Decision Making for Credit Card Approval

This paper aims to show how business intelligence can be applied in the credit card approval process. More specifically, the paper investigates how information like an applicant’s age, credit score, debt, income, and prior default can be used in credit card approval prediction.The dataset used for analysis is a publicly available dataset from the UCI machine learning repository. Logistic regression is used to make a prediction model with a reasonable number of attributes for a comprehensible business model. The Chi-square test of independence is used to test the dependence of credit card approval results with attributes. Research uncovers that prior default is supposed to be the most important attribute in the approval process. Finally, the authors propose several visualizations that could help make smarter decisions with effective credit risk assessment.

LEGAL ASPECTS OF USE OF CREDIT CARD IN TRADE AT BANDA ACEH CITY

Credit cards are one of the new instruments in the trading world that have a monetary value, so they can be used as a tool for transactions. However, credit cards cannot be called securities because they cannot be transferred. As a means of payment, the development of its use is increasingly widespread so that this has legal consequences for the parties involved in the use of the credit card, considering that the legal regulations are still relatively weak. The research method used is qualitative research with a normative juridical approach with descriptive analysis research specifications. This research uses secondary data as the main research material, which is obtained through documents. From the data obtained will be analyzed qualitatively normative. The legal aspect of using credit cards in trade in Banda Aceh City is that every agreement that occurs is incidental (a certain time) agreement, which arises at the time of buying and selling transactions or services. The mechanism of each credit card issuing bank is always different in giving approval, but the terms and procedures are relatively the same. Legal protection for the parties in using credit cards occurs at the time of signing the credit card approval agreement between the issuer and the credit card user, because legally there is no law that explicitly regulates it. It is recommended that there be wider legal protection efforts from the government, not only consumers in the sense of credit card holders, but in certain cases the sellers of goods/services and card issuers are also parties that need to receive balanced legal protection, including the affirmation of the rights of consumers. each party to sue the other party.

Credit card approval prediction using machine learning

This paper presents machine learning-based credit card approval prediction and gives an overview of the machine learning models and algorithms that are used to authorize credit cards for users. In order to improve credit card acceptance predictions and increase accuracy and adaptability in financial risk assessment, this study uses XGBoost in machine learning. The study emphasizes the importance of XGBoost in addressing challenges such as handling missing data, avoiding overfitting, and efficiently managing large datasets. Comparisons between the decision tree classifier and XGBoost reveal the latter's advantages, including interpretability, ability to handle complex relationships, and efficiency in processing large datasets. Results from experiments using the XGBoost algorithm demonstrate an accuracy of 90.06%, affirming its efficacy in credit card approval prediction.

Prediction of Credit Card Approval

Credit risk as the board in banks basically centers around deciding the probability of a customer's default or credit decay and how expensive it will end up being assuming it happens. It is important to consider major factors and predict beforehand the probability of consumers defaulting given their conditions. Which is where a machine learning model comes in handy and allows the banks and major financial institutions to predict whether the customer, they are giving the loan to, will default or not. This project builds a machine learning model with the best accuracy possible using python. First we load and view the dataset. The dataset has a combination of both mathematical and non-mathematical elements, that it contains values from various reaches, in addition to that it contains a few missing passages. We preprocess the dataset to guarantee the AI model we pick can make great expectations. After the information is looking great, some exploratory information examination is done to assemble our instincts. Finally, we will build a machine learning model that can predict if an individual's application for a credit card will be accepted. Using various tools and techniques we then try to improve the accuracy of the model. This project uses Jupyter notebook for python programming to build the machine learning model. Using Data Analysis and Machine Learning, we attempted to determine the most essential parameters for obtaining credit card acceptance in this project. The machine learning model we built gave an 86 % accuracy for predicting whether the credit card will be approved or not, considering the various factors mentioned in the application of the credit card holder. Even though we achieved an accuracy of 86%, we conducted a grid search to see if we could increase the performance even further. However, using both the machine learning models: random forest and logistic regression, the best we could get from this data was 86 percent.

Ethically Responsible Machine Learning in Fintech

Rapid technological developments in the last decade have contributed to using machine learning (ML) in various economic sectors. Financial institutions have embraced technology and have applied ML algorithms in trading, portfolio management, and investment advising. Large-scale automation capabilities and cost savings make the ML algorithms attractive for personal and corporate finance applications. Using ML applications in finance raises ethical issues that need to be carefully examined. We engage a group of experts in finance and ethics to evaluate the relationship between ethical principles of finance and ML. The paper compares the experts’ findings with the results obtained using natural language processing (NLP) transformer models, given their ability to capture the semantic text similarity. The results reveal that the finance principles of integrity and fairness have the most significant relationships with ML ethics. The study includes a use case with SHapley Additive exPlanations (SHAP) and Microsoft Responsible AI Widgets explainability tools for error analysis and visualization of ML models. It analyzes credit card approval data and demonstrate that the explainability tools can address ethical issues in fintech, and improve transparency, thereby increasing the overall trustworthiness of ML models. The results show that both humans and machines could err in approving credit card requests despite using their best judgment based on the available information. Hence, human-machine collaboration could contribute to improved decision-making in finance. We propose a conceptual framework for addressing ethical challenges in fintech such as bias, discrimination, differential pricing, conflict of interest, and data protection.

Application of Deep Learning for Credit Card Approval: A Comparison with Two Machine Learning Techniques

The increased credit card defaulters have forced the companies to think carefully before the approval of credit applications. Credit card companies usually use their judgment to determine whether a credit card should be issued to the customer satisfying certain criteria. Some machine learning algorithms have also been used to support the decision. The main objective of this paper is to build a deep learning model based on the UCI (University of California, Irvine) data sets, which can support the credit card approval decision. Secondly, the performance of the built model is compared with the other two traditional machine learning algorithms: logistic regression (LR) and support vector machine (SVM). Our results show that the overall performance of our deep learning model is slightly better than that of the other two models.

Predicting Credit Card Approval of Customers Through Customer Profiling using Machine Learning

In the banking sector, every banking infrastructure contains an enormous dataset for customers’ credit card approval which requires customer profiling. The customer profiling means collection of data related to what customers need. It depends on customers’ basic information like field of work, address proof, credit score, salary details, etc. This process mainly concentrates on predicting approval of credit cards to customers using machine learning. Machine Learning is the scientific study of algorithms and statistical models that computers use to perform specific tasks without any external instructions or interference. In the current trend this process is possible using many algorithms like “K-Mean, Improved K-Mean and Fuzzy C-Means”. This helps banks to have an high profitability to satisfy their customers. However, the currently prevailing system shows an accuracy percentage of about 98.08%. The proposed system aims at improvising the accuracy ratio while using only few algorithms.

IMPLEMENTATION OF BANK INDONESIA REGULATION NO. 18/40/PBI/2016 ON PAYMENT TRANSACTIONS USING CREDIT CARDS AT SHOPPING CENTERS IN BENGKULU CITY

The objective of the research was to study the implementation of Bank Indonesia Regulation No.18/40 / PBI / 2016 onDouble Swipe of Credit Card. The empirical juridical approach method in this researchused primary and secondary data as the main research data, through interviews and literature. A credit card is a credit facility provided by the issuing bank to its customers for transaction. From the research it was found thatin prior to the enactment of Bank Indonesia Regulation No.18/40/PBI/2016 on Payment Transactions Using Credit Cards, there were merchants that still do double swipe on credit card during payment transactions, first in the EDC (Electronic Data Capture) machine and on the cash register machine. However,due to the restriction on double swipe on credit card, this had been reduced. It was also strengthened by a strict regulation in the form of sanctions, reprimands and threats of crime committed by Bank Indonesia to merchants who conduct double swipe on credit card. The mechanism of bank issuing the credit card in giving approval is always different, but the terms and procedures are relatively the same. Legal protection of credit card users occurs when the signing of credit card approval agreement between the issuing bank and users since there is not clear juridical regulation. The problem mostlyarisingregarding the credit cards usage was credit card data scam. The only way to prevent this was through the use of credit cards properly, securely store credit cards when making payment transactions using credit cards, and understand the procedure of using a credit cardinpayment transactions properly. In case of credit card violation, the holder must report to the bank issuing the credit card and Bank Indonesia.

PERBANDINGAN KINERJA ALGORITMA DATA MINING PREDIKSI PERSETUJUAN KARTU KREDIT

Credit analysis needs to identify and assess factors that can influence customers in credit returns. Accurate measurement and good management capability in dealing with credit risk is an effort to save the economic operations unit and is beneficial for a stable and healthy financial system. Failure to identify credit risk results in loss of income and extends credit risk to a bad type of threat to profitability. Data mining prediction techniques are used to determine credit risk. Using the Cross-Industry Standard Process for data mining CRISP-DM. This study has tested the model using a neural network using PCA feature selection and optimized with the PSO algorithm to predict credit card approval. Several experiments were conducted to see the best results. The results of this study prove the use of a single Neural Net method produces an accuracy of 80.33%. while the use of the hybrid PCA+NN+PSO method has been proven to increase accuracy to 82.67%. Likewise, the AUC NN value of 0.706 increased to 0.749 when the NN was optimized using PSO and using the PCA. This study implements and compares PCA-based SVM, L. Regression and NN algorithms and optimize PSO to improve accuracy in credit card approval predictions.

Performance Comparison of Data Mining Algorithm to Predict Approval of Credit Card

Credit analysis needs to identify and assess the factors that can affect customers in returning credit. Accurate measurement and good management ability in dealing with credit risk is an effort to save the economic operations unit and be beneficial for a stable and healthy financial system. Data mining prediction techniques are used to determine credit risk. Using the Cross-Industry Standard Process for Data Mining (CRISP-DM) method which consists of several stages, namely Business Understanding (dataset), Data Processing (Feature Selection PCA & Dimension Reduce), Algorithm Models (NN+PSO, SVM, LR), Evaluation (Validation and Accuracy). This study has tested the model using a neural network using the PCA selection feature and optimized with the Particle Swarm Optimize (PSO) algorithm to predict credit card approval. Several experiments were conducted to see the best results. The results of this study prove that the use of a single Neural Network method produces an accuracy of 80.33%. whereas the use of PCA + Neural Network + PSO hybrid method has been proven to increase accuracy to 82.67%. Likewise, the AUC NN value of 0.706 increased to 0.749 when the Neural Network was optimized using PSO and used feature selection. The purpose of this study is to implement and compare Support Vector Machine, Logistic Regression and Neural Network algorithms based on PCA and optimize PSO (Particle Swarm Optimization) to improve accuracy in predictions of credit card approvals.

Credit Card Applications Pending – Who are Our Best Prospect Cardholders? Improved Decisions through Business Analytics and Business Intelligence

This case is designed to illustrate how to utilize the business intelligence framework and business analytics to make proper decisions in the turbulent and competitive business environment. The case started when Jong, the junior vice president in the modeling and analyst department of ABC Bank of Thailand, and his team were drafting a proposal presentation on the new credit card approval processes. Jong believed that it was time to overhaul credit card application processes by employing more sophisticated analytical techniques to analyze customer and credit card data. ABC- Premier Card, the bank's most popular card offering, would be a serendipitous test case in which to further develop the idea about improvements in approving credit card applications. Currently, there were 5 ABC- Premier card applications pending for decision in the house file. Tightening the credit approval process was one of the important strategies for the bank to potentially avoid an increase of risky customers. However, overly strict declinations of credit card applications might alienate potential customers and causing the bank to forego profitable lending opportunities. Taking the ABC- Premier card applications as a testing scenario, Jong and his team had to come up with a new method to evaluate each credit card applicant. They could approve all of them instantly, approve some of them, or even decline all of them. Consequently, Jong hoped that this proposed credit card approval method would improve the bank's policy decision to optimally balance both risky and profitable customers in the long run.

基于机器学习分类方法的信用卡审批应用

传统的信用卡审批方法往往是依靠信贷人员的经验进行审批，确定信用卡申请者是否符合申请条件，这种审批方法有很大的随意性和不稳定性。本文利用R软件并将最新的六种机器学习分类方法——决策树分类、Adaboost分类、Bagging分类、随机森林分类、支持向量机分类、人工神经网络引入到信用卡申请管理中，建立了自动化的申请管理体系，有效地降低了审批结果的随意性和不稳定性，并通过八折交叉验证计算出每种方法的分类均方误差并进行对比，筛选出分类效果最好的方法。结果表明：随机森林分类的分类误差是最小的。 The traditional method of credit card approval is often rely on the experience of credit personnel and is to decide whether the credit card applicants meet the conditions of application. Obviously, this approval method has a lot of randomness and instability. In this paper, we take advantages of R software and introduce the six latest machine learning classification method, decision tree clas-sification, AdaBoost, Bagging classification, random forest classifier, support vector machine (SVM) classification, artificial neural network (Ann) into the credit card application management, then establish the automatic application management system, effectively reducing the randomness and instability of the examination and approval results. Finally we calculate the mean square error of all the classification method through 8-fold cross validation and chose the classification with the best effect. The result shows that the classification error of random forest classification is the smallest.