scholarly journals EXPRESS: Default Effects of Credit Card Minimum Payments

2021 ◽  
pp. 002224372110705
Author(s):  
Hiroaki Sakaguchi ◽  
Neil Stewart ◽  
John Gathergood ◽  
Paul Adams ◽  
Benedict Guttman-Kenney ◽  
...  
Keyword(s):  
Credit Card ◽  
Unintended Effects ◽  
Transaction Data ◽  
Directing Attention ◽  
Over Time

Credit card minimum payments are designed to ensure that individuals pay down their debt over time, and scheduling minimum automatic repayments helps to avoid forgetting to repay. Yet minimum payments have additional, unintended psychological default effects by drawing attention away from the card balance due. First, once individuals set the minimum automatic repayment as the default, they then neglect to make the occasional larger repayments they made previously. As a result, individuals incur considerably more credit card interest than late payment fees avoided. Using detailed transaction data, the authors show that approximately 8% of all of the interest ever paid is due to this effect. Second, manual credit card payments are lower when individuals are prompted with minimum payment information. Two new interventions to mitigate this effect are tested in an experiment, prompting full repayment and prompting those repaying little to pay more, with large counter effects. Hence, shrouding the minimum payment option for automatic and manual payments and directing attention to the full balance may remedy these unintended effects.

MNB One Credit-Card Portfolio

2017 ◽  
pp. 1-8
Author(s):  
Samuel E. Bodily ◽  
Jason Hull ◽  
William Scherer
Keyword(s):  
Markov Process ◽  
Credit Card ◽  
Future Earnings ◽  
Over Time

A credit-card company must value portfolios of customers based on their future earnings. The payment characteristics of customers serve to classify them into states. This case can be the basis for discussing state dynamics over time in a Markov process.

scholarly journals Supervised Machine Learning Algorithms for Credit Card Fraudulent Transaction Detection

2019 ◽  
pp. 394-401
Author(s):  
Karthik R ◽  
Navinkumar R ◽  
Rammkumar U ◽  
Mothilal K. C.
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Credit Card ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Transaction Data ◽  
Online Transactions ◽  
Spending Behavior ◽  
Financial Transactions

Cashless transactions such as online transactions, credit card transactions, and mobile wallet are becoming more popular in financial transactions nowadays. With increased number of such cashless transaction, number of fraudulent transactions is also increasing. Fraud can be distinguished by analyzing spending behavior of customers (users) from previous transaction data. Credit card fraud has highly imbalanced publicly available datasets. In this paper, we apply many supervised machine learning algorithms to detect credit card fraudulent transactions using a real-world dataset. Furthermore, we employ these algorithms to implement a super classifier using ensemble learning methods. We identify the most important variables that may lead to higher accuracy in credit card fraudulent transaction detection. Additionally, we compare and discuss the performance of various supervised machine learning algorithms that exist in literature against the super classifier that we implemented in this paper.

scholarly journals Fraud Detection of Credit Card using Data Mining Techniques

2019 ◽  
Vol 8 (12) ◽  
pp. 4410-4413
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Genetic Algorithm ◽  
Credit Card ◽  
Hidden Markov ◽  
Fraud Detection ◽  
Network Clustering ◽  
Transaction Data ◽  
Using Data ◽  
The Look

The handling of credit card for online and systematic purchase is booming and scam associated with it. An industry of fraud detection where cumulative rise can have huge perk for banks and client. Numerous stylish techniques like data mining, genetic programming, neural network etc. are used in identify fraudulent transaction. In online transaction, Data mining acquire indispensable aspect in discovery of credit card counterfeit. This paper uses gradient boosted trees, neural network, clustering technique and genetic algorithm and hidden markov model for achieving upshot of the fraudulent transaction. These all model are emerging in identifying various credit card fraudulent detection. The indispensable aims to expose the fraudulent transaction and to corroborate test data for further use. This paper presents the look over techniques and pinpoint the top fraud cases.

Dynamic Data Mining

2011 ◽  
pp. 722-728 ◽  
Author(s):  
Richard Weber
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Credit Card ◽  
Knowledge Discovery In Databases ◽  
Stream Data ◽  
Dynamic Data ◽  
Change Over Time ◽  
Professional Activities ◽  
Feature Values ◽  
Over Time

Since the First KDD Workshop back in 1989 when “Knowledge Mining” was recognized as one of the top 5 topics in future database research (Piatetsky-Shapiro 1991), many scientists as well as users in industry and public organizations have considered data mining as highly relevant for their respective professional activities. We have witnessed the development of advanced data mining techniques as well as the successful implementation of knowledge discovery systems in many companies and organizations worldwide. Most of these implementations are static in the sense that they do not contemplate explicitly a changing environment. However, since most analyzed phenomena change over time, the respective systems should be adapted to the new environment in order to provide useful and reliable analyses. If we consider for example a system for credit card fraud detection, we may want to segment our customers, process stream data generated by their transactions, and finally classify them according to their fraud probability where fraud pattern change over time. If our segmentation should group together homogeneous customers using not only their current feature values but also their trajectories, things get even more difficult since we have to cluster vectors of functions instead of vectors of real values. An example for such a trajectory could be the development of our customers’ number of transactions over the past six months or so if such a development tells us more about their behavior than just a single value; e.g., the most recent number of transactions. It is in this kind of applications is where dynamic data mining comes into play! Since data mining is just one step of the iterative KDD (Knowledge Discovery in Databases) process (Han & Kamber, 2001), dynamic elements should be considered also during the other steps. The entire process consists basically of activities that are performed before doing data mining (such as: selection, pre-processing, transformation of data (Famili et al., 1997)), the actual data mining part, and subsequent steps (such as: interpretation, evaluation of results). In subsequent sections we will present the background regarding dynamic data mining by studying existing methodological approaches as well as already performed applications and even patents and tools. Then we will provide the main focus of this chapter by presenting dynamic approaches for each step of the KDD process. Some methodological aspects regarding dynamic data mining will be presented in more detail. After envisioning future trends regarding dynamic data mining we will conclude this chapter.

Potential Jurisprudential Adaptation for the Tripartite Credit Card Transaction from an Islamic Perspective

2014 ◽  
Vol 28 (4) ◽  
pp. 317-365
Author(s):  
Mohammed Jassem Mohammed ◽  
Rahmah Ismail ◽  
Ruzian Markom
Keyword(s):  
Credit Card ◽  
Credit Cards ◽  
Great Effort ◽  
Financial Instruments ◽  
Financial Contracts ◽  
The West ◽  
Islamic Perspective ◽  
Financial Contract ◽  
Over Time

The credit card represents one of the most important financial instruments at present. The credit card concept originated and was developed in the West under the rules of conventional law. Over time the credit card has invaded the Islamic markets. A credit card transaction does not fall under any of the known financial contract categories in Islamic principles. Therefore, determining the Islamic rulings and finding a jurisprudential adaptation for such credit card transactions is essential for clarifying relevant jurisprudential rulings, as one cannot specify whether a specific transaction is permitted or prohibited without a jurisprudential adaptation on the matter. Islamic researchers have taken great effort to clarify the jurisprudential adaptation of a credit card transaction. This article will examine the potential legitimate nature of the credit card transaction in order to determine Islamic rulings. The concept ‘credit card’ originated in the West and developed under the rules of conventional law. Although credit cards have since invaded the Islamic markets, their transactions do not fall under any of the known categories for Islamic financial contracts; therefore, one must determine the Islamic rulings that relate to such transactions. Islamic scholars have exerted much effort to find a jurisprudential adaptation for the credit card transaction, which is essential in order to clarify jurisprudential rulings on transactions and to specify whether a transaction is permitted or prohibited. This article examines the potential legitimate nature of the credit card transaction in order to determine the Islamic rulings.

Credit card possesion and use: Changes over time

1989 ◽  
Vol 41 (2) ◽  
pp. 127-142 ◽  
Author(s):  
James T. Lindley ◽  
Patricia Rudolph ◽  
Edward B. Selby
Keyword(s):  
Credit Card ◽  
Changes Over Time ◽  
Over Time

scholarly journals An Unique Methodology for Credit Card Fraud Detection based on Convolutional Neural Network

2021 ◽  
Vol 36 (1) ◽  
pp. 277-280
Author(s):  
S. Ravi ◽  
J. Thanga Kumar ◽  
Dr. Linda Joseph ◽  
Sumanth Raju Kunjeti ◽  
Nandu Vardhan Saniboina ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Learning Strategies ◽  
Credit Card ◽  
Fraud Detection ◽  
Web Based ◽  
Transaction Data ◽  
Itemset Mining ◽  
Identification Strategy

Internet based business, e-Services and numerous other web-based application have expanded the online payment modes, expanding the danger for online frauds. Expansion in fraud rates, analysts began utilizing distinctive machine learning strategies to identify and dissect frauds in online exchanges. The principle point of the paper is to plan and build up a novel fraud identification strategy for Streaming Transaction Data, with a target, to dissect the previous exchange subtleties of the clients and concentrate the personal conduct standards. This paper proposes a canny model for detecting fraud in credit card exchange datasets that are unusually imbalanced and enigmatic. The class irregularity issue is dealt with by finding lawful just as fraud exchange designs for every client by utilizing continuous itemset mining.

How Will State-Run Auto-IRAs Affect Workers?

2018 ◽  
Keyword(s):  
Survey Data ◽  
Credit Card ◽  
Retirement Savings ◽  
Retirement Plans ◽  
National Scale ◽  
Individual Retirement Accounts ◽  
Financial Difficulties ◽  
Retirement Accounts ◽  
Opt Out ◽  
Over Time

In order to encourage savings among workers without access to employer-sponsored retirement plans, several states have proposed defaulting workers into state-run individual retirement accounts known as Auto-IRAs. Plans such as OregonSaves automatically enroll workers and, by default, increase their contributions over time. Given low opt-out rates, these policies have the potential to increase retirement savings for workers without access to employer-sponsored plans. Using survey data, we find that over 24 million workers could automatically be enrolled in an Auto-IRA, if enacted on a national scale. Nonetheless, these policies have the potential to adversely affect individuals with debt and current financial difficulties who do not actively opt-out. One-third of potentially affected workers hold credit card debt with an average balance exceeding $5,000. Furthermore, approximately 15% of potentially affected workers have difficulty meeting basic needs.

scholarly journals Regulating Consumer Financial Products: Evidence from Credit Cards *

2014 ◽  
Vol 130 (1) ◽  
pp. 111-164 ◽  
Author(s):  
Sumit Agarwal ◽  
Souphala Chomsisengphet ◽  
Neale Mahoney ◽  
Johannes Stroebel
Keyword(s):  
Credit Card ◽  
Financial Regulation ◽  
Consumer Credit ◽  
Credit Cards ◽  
Set Covering ◽  
Difference In Differences ◽  
Data Set ◽  
Financial Products ◽  
Regulatory Limits ◽  
Over Time

Abstract 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.

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