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2021 ◽  
pp. 002224292110472
Samuel D. Hirshman ◽  
Abigail B. Sussman

US Households currently hold $770 billion in credit card debt, often managing repayments across multiple accounts. We investigate how minimum payments (i.e., the requirement to allocate at least some money to each account with a balance) alter consumers’ allocation strategies across multiple accounts. Across four experiments, we find that minimum payment requirements cause consumers to increase dispersion (i.e., spread their repayments more evenly) across accounts. We term this change in strategy the dispersion effect of minimum payments and provide evidence that it can be costly for consumers. We find that the effect is partially driven by the tendency for consumers to interpret minimum payment requirements as recommendations to pay more than the minimum amount. While the presence of the minimum payment requirement is unlikely to change, we propose that marketers and policymakers can influence the effects of minimum payments on dispersion by altering the way that information is displayed to consumers. Specifically, we investigate five distinct information displays and find that choice of display can either exaggerate or minimize dispersion and corresponding costs. We discuss implications for consumers, policy makers, and firms, with a particular focus on ways to improve consumer financial well-being.

Aishwarya Priyadarshini ◽  
Sanhita Mishra ◽  
Debani Prasad Mishra ◽  
Surender Reddy Salkuti ◽  
Ramakanta Mohanty

<p>Nowadays, fraudulent or deceitful activities associated with financial transactions, predominantly using credit cards have been increasing at an alarming rate and are one of the most prevalent activities in finance industries, corporate companies, and other government organizations. It is therefore essential to incorporate a fraud detection system that mainly consists of intelligent fraud detection techniques to keep in view the consumer and clients’ welfare alike. Numerous fraud detection procedures, techniques, and systems in literature have been implemented by employing a myriad of intelligent techniques including algorithms and frameworks to detect fraudulent and deceitful transactions. This paper initially analyses the data through exploratory data analysis and then proposes various classification models that are implemented using intelligent soft computing techniques to predictively classify fraudulent credit card transactions. Classification algorithms such as K-Nearest neighbor (K-NN), decision tree, random forest (RF), and logistic regression (LR) have been implemented to critically evaluate their performances. The proposed model is computationally efficient, light-weight and can be used for credit card fraudulent transaction detection with better accuracy.</p>

2021 ◽  
Vol 27 (2) ◽  
pp. 319-362
Soonsang Yoon ◽  
Hun Park

2021 ◽  
Vol 7 (2) ◽  
pp. 146
L. G. R. V. De Silva ◽  
S. S. J. Patabendige

Upasana Mukherjee ◽  
Vandana Thakkar ◽  
Shawni Dutta ◽  
Utsab Mukherjee ◽  
Samir Kumar Bandyopadhyay

The growth of regularly generated data from many financial activities has significant implications for every corner of financial modelling. This study has investigated the utilization of these continuous growing data by a means of an automated process. The automated process can be developed by using Machine learning based techniques that analyze the data and gain experience from the underlying data. Different important domains of financial fields such as Credit card fraud detection, bankruptcy detection, loan default prediction, investment prediction, marketing and many more can be modelled by implementing machine learning methods. Among several machine learning based techniques, the use of parametric and non-parametric based methods are approached by this research. Two parametric models namely Logistic Regression, Gaussian Naive Bayes models and two non-parametric methods such as Random Forest, Decision Tree are implemented in this paper. All the mentioned models are developed and implemented in the field of Credit card fraud detection, bankruptcy detection, loan default prediction. In each of the aforementioned cases, the comparative study among the classification techniques is drawn and the best model is identified. The performance of each classifier on each considered domain is evaluated by various performance metrics such as accuracy, F1-score and mean squared error. In the credit card fraud detection model the decision tree classifier performs the best with an accuracy of 99.1% and, in the loan default prediction and bankruptcy detection model, the random forest classifier gives the best accuracy of  97% and 96.84% respectively.

2021 ◽  
pp. 1-34
Jinan Liu ◽  
Apostolos Serletis

Abstract We use nonparametric and parametric demand analysis to empirically estimate a credit card-augmented monetary asset demand system, based on the Minflex Laurent flexible functional form, and a sample period that includes the 2007–2009 global financial crisis and the COVID-19 pandemic. We also use multivariate copulae in an attempt to capture various patterns of dependence structures. In doing so, we relax the joint normality assumption of the errors of the demand system and estimate the model without having to delete one equation as is usually the practice. We show that the Minflex Laurent copula-based demand system produces a higher income elasticity for credit card transaction services and higher Morishima elasticities between credit card transaction services and monetary assets compared to the traditional estimation of the Minflex Laurent demand system. We also show that credit cards are substitutes for monetary assets and that there is lower tail dependence between the demand for credit card transaction services and transaction balances.

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