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"CREDIT CARDS"
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Getting a credit card
\"Readers learn how to make credit work for them instead of falling into long-term debt. This invaluable guide covers secured and unsecured credit, how to calculate interest, understanding statements, choosing the right card, fees, billing cycles, minimum payments, balance transfers, and cash advances. Readers will learn about credit scores and credit reports, whether they are a good credit risk, and how to protect their personal information.\"-- Publisher's description.
Book
REGULATING CONSUMER FINANCIAL PRODUCTS
We analyze the effectiveness of consumer financial regulation by considering the 2009 Credit Card Accountability Responsibility and Disclosure (CARD) Act. We use a panel data set covering 160 million credit card accounts and a difference-in-differences research design that compares changes in outcomes over time for consumer credit cards, which were subject to the regulations, to changes for small business credit cards, which the law did not cover. We estimate that regulatory limits on credit card fees reduced overall borrowing costs by an annualized 1.6% of average daily balances, with a decline of more than 5.3% for consumers with FICO scores below 660. We find no evidence of an offsetting increase in interest charges or a reduction in the volume of credit. Taken together, we estimate that the CARD Act saved consumers $11.9 billion a year. We also analyze a nudge that disclosed the interest savings from paying off balances in 36 months rather than making minimum payments. We detect a small increase in the share of accounts making the 36-month payment value but no evidence of a change in overall payments.
Journal Article
Financial literacy and financial resilience: Evidence from around the world
We measure financial literacy using questions assessing basic knowledge of four fundamental concepts In financial decision making: knowledge of interest rates, Interest compounding, inflation, and risk diversification. Worldwide, just one in three adults are financially literate—that is, they know at least three out of the four financial concepts. Women, poor adults, and lower educated respondents are more likely to suffer from gaps in financial knowledge. This is true not only in developing countries but also in countries with welldeveloped financial markets. Relatively low financial literacy levels exacerbate consumer and financial market risks as increasingly complex financial instruments enter the market. Credit products, many of which carry high interest rates and complex terms and conditions, are becoming more readily available. Yet only around half of adults in major emerging countries who use a credit card or borrow from a financial institution are financially literate. W e discuss policies to protect borrowers against risks and encourage account holders to save.
Journal Article
How do credit cards and loans work?
\"Credit cards and loans can be helpful tools in your financial future. Credit cards and loans can help you finance purchases and build a credit history when used correctly\"--Provided by publisher.
Book
Feature selection strategies: a comparative analysis of SHAP-value and importance-based methods
In the context of high-dimensional credit card fraud data, researchers and practitioners commonly utilize feature selection techniques to enhance the performance of fraud detection models. This study presents a comparison in model performance using the most important features selected by SHAP (SHapley Additive exPlanations) values and the model’s built-in feature importance list. Both methods rank features and choose the most significant ones for model assessment. To evaluate the effectiveness of these feature selection techniques, classification models are built using five classifiers: XGBoost, Decision Tree, CatBoost, Extremely Randomized Trees, and Random Forest. The Area under the Precision-Recall Curve (AUPRC) serves as the evaluation metric. All experiments are executed on the Kaggle Credit Card Fraud Detection Dataset. The experimental outcomes and statistical tests indicate that feature selection methods based on importance values outperform those based on SHAP values across classifiers and various feature subset sizes. For models trained on larger datasets, it is recommended to use the model’s built-in feature importance list as the primary feature selection method over SHAP. This suggestion is based on the rationale that computing SHAP feature importance is a distinct activity, while models naturally provide built-in feature importance as part of the training process, requiring no additional effort. Consequently, opting for the model’s built-in feature importance list can offer a more efficient and practical approach for larger datasets and more intricate models.
Journal Article
STATUS GOODS
This article provides field-experimental evidence on status goods. We work with an Indonesian bank that markets platinum credit cards to high-income customers. In a first experiment, we show that demand for the platinum card exceeds demand for a nondescript control product with identical benefits, suggesting demand for the pure status aspect of the card. Transaction data reveal that platinum cards are more likely to be used in social contexts, implying social image motivations. In a second experiment, we provide evidence of positional externalities from the consumption of these status goods. A final experiment provides suggestive evidence that increasing self-esteem causally reduces demand for status goods, indicating that social image might be a substitute for self-image.
Journal Article
Enhanced credit card fraud detection based on attention mechanism and LSTM deep model
As credit card becomes the most popular payment mode particularly in the online sector, the fraudulent activities using credit card payment technologies are rapidly increasing as a result. For this end, it is obligatory for financial institutions to continuously improve their fraud detection systems to reduce huge losses. The purpose of this paper is to develop a novel system for credit card fraud detection based on sequential modeling of data, using attention mechanism and LSTM deep recurrent neural networks. The proposed model, compared to previous studies, considers the sequential nature of transactional data and allows the classifier to identify the most important transactions in the input sequence that predict at higher accuracy fraudulent transactions. Precisely, the robustness of our model is built by combining the strength of three sub-methods; the uniform manifold approximation and projection (UMAP) for selecting the most useful predictive features, the Long Short Term Memory (LSTM) networks for incorporating transaction sequences and the attention mechanism to enhance LSTM performances. The experimentations of our model give strong results in terms of efficiency and effectiveness.
Journal Article
Preventing credit card fraud
Everyone is affected by credit card fraud, if they are aware of it or not. Every day there are a variety of ways that scams and fraudsters can get your card and personal information. Today so much business occurs over the Internet or via the phone where no card is present. What can start as a seemingly legitimate purchase can easily turn into fraudulent charges -- or worse, sometimes a physical confrontation, when a criminal steals a credit card from a consumer who meets to pick up a product or receive a service. In Preventing Credit Card Fraud, Jen Grondahl Lee and Gini Graham Scott provide a helpful guide to protecting yourself against the threat of credit card fraud. While it may not be possible to protect yourself against all fraudsters, who have turned scamming Internet businesses into an art, these tips and techniques will help you avoid many frauds. As a growing concern in today's world, there is a need to be better informed of what you can do to keep your personal information secure and avoid becoming a victim of credit card fraud. Preventing Credit Card Fraud is an important resource for both merchants and consumers engaged in online purchases and sales to defend themselves against fraud.
eBook
A machine learning based credit card fraud detection using the GA algorithm for feature selection
The recent advances of e-commerce and e-payment systems have sparked an increase in financial fraud cases such as credit card fraud. It is therefore crucial to implement mechanisms that can detect the credit card fraud. Features of credit card frauds play important role when machine learning is used for credit card fraud detection, and they must be chosen properly. This paper proposes a machine learning (ML) based credit card fraud detection engine using the genetic algorithm (GA) for feature selection. After the optimized features are chosen, the proposed detection engine uses the following ML classifiers: Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), Artificial Neural Network (ANN), and Naive Bayes (NB). To validate the performance, the proposed credit card fraud detection engine is evaluated using a dataset generated from European cardholders. The result demonstrated that our proposed approach outperforms existing systems.
Journal Article