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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
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
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
Credit Card Fraud Detection Using a New Hybrid Machine Learning Architecture
The negative effect of financial crimes on financial institutions has grown dramatically over the years. To detect crimes such as credit card fraud, several single and hybrid machine learning approaches have been used. However, these approaches have significant limitations as no further investigation on different hybrid algorithms for a given dataset were studied. This research proposes and investigates seven hybrid machine learning models to detect fraudulent activities with a real word dataset. The developed hybrid models consisted of two phases, state-of-the-art machine learning algorithms were used first to detect credit card fraud, then, hybrid methods were constructed based on the best single algorithm from the first phase. Our findings indicated that the hybrid model Adaboost + LGBM is the champion model as it displayed the highest performance. Future studies should focus on studying different types of hybridization and algorithms in the credit card domain.
Journal Article
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
Agent-based modelling of credit card promotions
PurposeInterest-free promotions are a prevalent strategy employed by credit card lenders to attract new customers, yet the research exploring their effects on both consumers and lenders remains relatively sparse. Selecting an optimal promotion strategy is intricate, involving the determination of an interest-free period duration and promotion-availability window, all within the context of market dynamics and complex consumer behaviour. The purpose of this study is to develop an agent-based model to assist with determining optimal promotion strategies.Design/methodology/approachIn this paper, we introduce a novel agent-based model that facilitates the exploration of various credit card promotions under diverse market scenarios.FindingsOur experiments reveal that, in the absence of competitor promotions, lender profit is maximised by an interest-free duration of approximately 12 months, while market share is maximised by offering the longest duration possible. In the context of concurrent interest-free promotions, we identify that the optimal lender strategy entails offering a more competitive interest-free period and a rapid response to competing promotional offers. Notably, a delay of three months in responding to a rival promotion corresponds to a 2.4% relative decline in income.Originality/valueOur model consists of multiple lender and consumer agents that interact through a novel set of mechanisms based on well-studied consumer behaviours. Distinct from previous works, our model adopts a realistic billing cycle with a focus on interest charged to revolving accounts and supports a range of lender promotion strategies. It is calibrated to historical benchmarks and validated against both stylised facts and time-series data, ensuring a realistic reflection of market behaviour, which has been neglected in prior studies.
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
A systematic review of AI-enhanced techniques in credit card fraud detection
The rapid increase of fraud attacks on banking systems, financial institutions, and even credit card holders demonstrate the high demand for enhanced fraud detection (FD) systems for these attacks. This paper provides a systematic review of enhanced techniques using Artificial Intelligence (AI), machine learning (ML), deep learning (DL), and meta-heuristic optimization (MHO) algorithms for credit card fraud detection (CCFD). Carefully selected recent research papers have been investigated to examine the effectiveness of these AI-integrated approaches in recognizing a wide range of fraud attacks. These AI techniques were evaluated and compared to discover the advantages and disadvantages of each one, leading to the exploration of existing limitations of ML or DL-enhanced models. Discovering the limitation is crucial for future work and research to increase the effectiveness and robustness of various AI models. The key finding from this study demonstrates the need for continuous development of AI models that could be alert to the latest fraudulent activities.
Journal Article
Credit Card Fraud Detection: An Improved Strategy for High Recall Using KNN, LDA, and Linear Regression
Efficiently and accurately identifying fraudulent credit card transactions has emerged as a significant global concern along with the growth of electronic commerce and the proliferation of Internet of Things (IoT) devices. In this regard, this paper proposes an improved algorithm for highly sensitive credit card fraud detection. Our approach leverages three machine learning models: K-nearest neighbor, linear discriminant analysis, and linear regression. Subsequently, we apply additional conditional statements, such as “IF” and “THEN”, and operators, such as “>“ and “<“, to the results. The features extracted using this proposed strategy achieved a recall of 1.0000, 0.9701, 1.0000, and 0.9362 across the four tested fraud datasets. Consequently, this methodology outperforms other approaches employing single machine learning models in terms of recall.
Journal Article