scholarly journals Fraud prediction for credit card using classification method

2018 ◽  
Vol 7 (3) ◽  
pp. 1083 ◽  
Author(s):  
Er Monika ◽  
Er Amarpreet Kaur
Keyword(s):  
Data Mining ◽  
Credit Card ◽  
Credit Cards ◽  
Insurance Premium ◽  
Mobile Banking ◽  
Classification Methods ◽  
Bayes Method ◽  
Online Exchange ◽  
Fraud Prediction ◽  
Day By Day

With the improvement of innovation like credit cards, debit cards, mobile banking, Internet managing an account is the mainstream medium to exchange the cash starting with one record then onto the next. Credit card is picking up fame day by day which expands the online exchange with the expansion in online shopping, online charge payment, insurance premium and different charges so the extortion cases identified with this are likewise expanding and it puts an extraordinary anxiety on the economy, affecting the two clients and budgetary bodies. It costs cash as well as an awesome measure of time to reestablish the damage done. In this paper, we look whether data mining procedures are valuable to estimate and categorize the client's credit risk score (normal/fraud) to beat the future dangers. The reason for existing is to keep the clients from online exchange by utilizing particular Data mining classification methods. The fakes are ascertained by Naïve Bayes method way to deal with break down the exchange is actual or fake. The exploratory outcome demonstrates that our model has great classification accuracy, recall and precision. 

scholarly journals A Performance Analysis of Detecting Credit Card Fraud by using CT18 Method

2019 ◽  
Vol 8 (4) ◽  
pp. 7257-7260
Keyword(s):  
Performance Analysis ◽  
Everyday Life ◽  
Unsupervised Learning ◽  
Credit Card ◽  
Online Shopping ◽  
Credit Cards ◽  
Significant Component ◽  
Grouping Problem ◽  
Illegal Activities ◽  
Day By Day

Credit cards are a significant component of everyday life. Whether purchasing gas and supermarket stores or reserving a hotel and lease a car for the next holiday. Credit cards are a pleasant and safe type of client payment. Advantages that differ from harm security on payments to the convenience of disputing suspect fees or suspicious activity make credit cards such an appealing form of transaction. It takes an hour for any time activities, online shopping, and paperless system. As the amount of credit card customers rises day by day, significant illegal activities eventually enhance. CT18 technique is the procedure for categorizing information directed at reformatting observations into CT18, whereby each observation belongs to the closest mean cluster. This is one of the simplest unsupervised learning algorithms that solve the well-known grouping problem

Analysis of Various Credit Card Fraud Detection Techniques

2021 ◽  
pp. PP. 13-20
Author(s):  
admin admin ◽  
Keyword(s):  
Data Mining ◽  
Performance Analysis ◽  
Credit Card ◽  
Credit Cards ◽  
Fraud Detection ◽  
Classification Algorithms ◽  
Credit Card Fraud ◽  
Prediction Analysis ◽  
Detection Techniques

Data mining is a technique that is applied to mine valuable information from the rough data. A prediction analysis is an approach that has the potential for forecasting future possibilities based on the recent data. The CCFD is the challenge of prediction in which fraudulent transactions are predicted based on certain rules. There are several stages included in the detection of fraud in credit cards. Various classification algorithms are reviewed with respect to the performance analysis in order to detect fraud in the credit card. The performance is measured with regard to precision.

Literature Study –Data Mining Techniques on Detecting Fradulent Activities in Credit Card

2017 ◽  
Vol 6 (10) ◽  
pp. 60
Author(s):  
S. K. Saravanan ◽  
G. N. K. Suresh Babu
Keyword(s):  
Data Mining ◽  
Credit Card ◽  
Data Transfer ◽  
Study Data ◽  
Time Requirement ◽  
Literature Study ◽  
Data Mining Techniques ◽  
Online Transactions ◽  
Secured Data ◽  
Credit Card Usage

In contemporary days the more secured data transfer occurs almost through internet. At same duration the risk also augments in secure data transfer. Having the rise and also light progressiveness in e – commerce, the usage of credit card (CC) online transactions has been also dramatically augmenting. The CC (credit card) usage for a safety balance transfer has been a time requirement. Credit-card fraud finding is the most significant thing like fraudsters that are augmenting every day. The intention of this survey has been assaying regarding the issues associated with credit card deception behavior utilizing data-mining methodologies. Data mining has been a clear procedure which takes data like input and also proffers throughput in the models forms or patterns forms. This investigation is very beneficial for any credit card supplier for choosing a suitable solution for their issue and for the researchers for having a comprehensive assessment of the literature in this field.

Consumers‟ Acceptance of „Halal‟ Credit Card Services: An Empirical Analysis

2015 ◽  
Vol 3 (1) ◽  
pp. 51 ◽  
Author(s):  
Zaimy Johana Johan ◽  
Lennora Putit
Keyword(s):  
Perceived Control ◽  
Credit Card ◽  
Credit Cards ◽  
Product Knowledge ◽  
Key Factors ◽  
Developing Nation ◽  
Business Transactions ◽  
Factors Influencing ◽  
Financial Credit ◽  
Survey Questionnaires

Many past researches have been carried out in an attempt to continuously understand individuals‟ consumption behaviour. This study was conducted to investigate key factors influencing consumers‟ potential acceptance of halal (or permissible) financial credit card services. Specifically, it anticipated the influence of attitude, social influences and perceived control on consumers‟ behavioural intention to accept such services. In addition, factors such as religiosity and product knowledge were also postulated to affect consumers‟ attitude towards the act of using halal credit cards for any retail or business transactions. Using non-probability sampling approach, a total of 500 survey questionnaires was distributed to targeted respondents in a developing nation but only 220 usable feedbacks were received for subsequent data analysis. Regression results revealed that religiosity and product knowledge significantly influence consumers‟ attitude toward using halal credit card services.  Attitude in turn, subsequently has a significant impact on consumers‟ intention to accept halal financial credit card services. Several theoretical and managerial contributions were observed in this study.   

A Resamping Approach for Customer Gender Prediction Based on E-Commerce Data

2017 ◽  
Vol 3 (1) ◽  
pp. 76 ◽  
Author(s):  
Duong Tran Duc ◽  
Pham Bao Son ◽  
Tan Hanh ◽  
Le Truong Thien
Keyword(s):  
Data Mining ◽  
Bayesian Network ◽  
Web Applications ◽  
Service Providers ◽  
Main Idea ◽  
Classification Methods ◽  
Cost Sensitive Learning ◽  
Network Method ◽  
Demographic Attributes ◽  
Privacy Issues

Demographic attributes of customers such as gender, age, etc. provide the important information for e-commerce service providers in marketing, personalization of web applications. However, the online customers often do not provide this kind of information due to the privacy issues and other reasons. In this paper, we proposed a method for predicting the gender of customers based on their catalog viewing data on e-commerce systems, such as the date and time of access, the products viewed, etc. The main idea is that we extract the features from catalog viewing information and employ the classification methods to predict the gender of the viewers. The experiments were conducted on the datasets provided by the PAKDD’15 Data Mining Competition and obtained the promising results with a simple feature design, especially with the Bayesian Network method along with other supporting techniques such as resampling, cost-sensitive learning, boosting etc.

scholarly journals Neural mechanisms of credit card spending

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sachin Banker ◽  
Derek Dunfield ◽  
Alex Huang ◽  
Drazen Prelec
Keyword(s):  
Credit Card ◽  
Credit Cards ◽  
Purchase Behavior ◽  
Facilitation Effect ◽  
Product Price ◽  
Personal Credit ◽  
Psychological Mechanisms ◽  
Network Activation ◽  
Payment Instruments ◽  
Reward Network

AbstractCredit cards have often been blamed for consumer overspending and for the growth in household debt. Indeed, laboratory studies of purchase behavior have shown that credit cards can facilitate spending in ways that are difficult to justify on purely financial grounds. However, the psychological mechanisms behind this spending facilitation effect remain conjectural. A leading hypothesis is that credit cards reduce the pain of payment and so ‘release the brakes’ that hold expenditures in check. Alternatively, credit cards could provide a ‘step on the gas,’ increasing motivation to spend. Here we present the first evidence of differences in brain activation in the presence of real credit and cash purchase opportunities. In an fMRI shopping task, participants purchased items tailored to their interests, either by using a personal credit card or their own cash. Credit card purchases were associated with strong activation in the striatum, which coincided with onset of the credit card cue and was not related to product price. In contrast, reward network activation weakly predicted cash purchases, and only among relatively cheaper items. The presence of reward network activation differences highlights the potential neural impact of novel payment instruments in stimulating spending—these fundamental reward mechanisms could be exploited by new payment methods as we transition to a purely cashless society.

scholarly journals Does the visual salience of credit card features affect choice?

2021 ◽  
pp. 1-18
Author(s):  
Matthew D. Hilchey ◽  
Matthew Osborne ◽  
Dilip Soman
Keyword(s):  
Interest Rates ◽  
Random Sample ◽  
Credit Card ◽  
Credit Cards ◽  
Salient Feature ◽  
The Other ◽  
Visual Salience ◽  
Product Features ◽  
Hypothetical Scenario

Abstract Regulators require lenders to display a subset of credit card features in summary tables before customers finalize a credit card choice. Some jurisdictions require some features to be displayed more prominently than others to help ensure that consumers are made aware of them. This approach could lead to untoward effects on choice, such that relevant but nonprominent product features do not factor in as significantly. To test this possibility, we instructed a random sample of 1615 adults to choose between two hypothetical credit cards whose features were shown side by side in tables. The sample was instructed to select the card that would result in the lowest financial charges, given a hypothetical scenario. Critically, we randomly varied whether the annual interest rates and fees were made visually salient by making one, both, or neither brighter than other features. The findings show that even among credit-savvy individuals, choice tends strongly toward the product that outperforms the other on a salient feature. As a result, we encourage regulators to consider not only whether a key feature should be made more salient, but also the guidelines regarding when a key feature should be displayed prominently during credit card acquisition.

Money growth variability and output: evidence with credit card-augmented Divisia monetary aggregates

2020 ◽  
Vol 24 (5) ◽  
Author(s):  
Jinan Liu ◽  
Apostolos Serletis
Keyword(s):  
Financial Stability ◽  
Credit Card ◽  
Negative Impact ◽  
Balance Sheet ◽  
Credit Cards ◽  
Money Growth ◽  
Monetary Policies ◽  
Growth Variability ◽  
Significant Negative Impact ◽  
Growth Volatility

Abstract We reexamine the effects of the variability of money growth on output, raised by Mascaro and Meltzer (1983), in the era of the increasing use of alternative payments, such as credit cards. Using a bivariate VARMA, GARCH-in-Mean, asymmetric BEKK model, we find that the volatility of the credit card-augmented Divisia M4 monetary aggregate has a statistically significant negative impact on output from 2006:7 to 2019:3. However, there is no effect of the traditional Divisia M4 growth volatility on real economic activity. We conclude that the balance sheet targeting monetary policies after the financial crisis in 2007–2009 should pay more attention on the broad credit card-augmented Divisia M4 aggregate to address economic and financial stability.

scholarly journals COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification

Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 314 ◽  
Author(s):  
Jim Samuel ◽  
G. G. Md. Nawaz Ali ◽  
Md. Mokhlesur Rahman ◽  
Ek Esawi ◽  
Yana Samuel
Keyword(s):  
Machine Learning ◽  
The United States ◽  
Classification Methods ◽  
Reasonable Accuracy ◽  
Bayes Method ◽  
Inaccurate Information ◽  
Public Sentiment ◽  
Research Article ◽  
Textual Data ◽  
Data Visualizations

Along with the Coronavirus pandemic, another crisis has manifested itself in the form of mass fear and panic phenomena, fueled by incomplete and often inaccurate information. There is therefore a tremendous need to address and better understand COVID-19’s informational crisis and gauge public sentiment, so that appropriate messaging and policy decisions can be implemented. In this research article, we identify public sentiment associated with the pandemic using Coronavirus specific Tweets and R statistical software, along with its sentiment analysis packages. We demonstrate insights into the progress of fear-sentiment over time as COVID-19 approached peak levels in the United States, using descriptive textual analytics supported by necessary textual data visualizations. Furthermore, we provide a methodological overview of two essential machine learning (ML) classification methods, in the context of textual analytics, and compare their effectiveness in classifying Coronavirus Tweets of varying lengths. We observe a strong classification accuracy of 91% for short Tweets, with the Naïve Bayes method. We also observe that the logistic regression classification method provides a reasonable accuracy of 74% with shorter Tweets, and both methods showed relatively weaker performance for longer Tweets. This research provides insights into Coronavirus fear sentiment progression, and outlines associated methods, implications, limitations and opportunities.

scholarly journals Credit card cue effect

2019 ◽  
Vol 38 (2) ◽  
pp. 368-383
Author(s):  
King Yin Wong ◽  
Michael Lynn
Keyword(s):  
Credit Card ◽  
Design Methodology ◽  
Credit Cards ◽  
Well Being ◽  
Financial Security ◽  
Consumer Spending ◽  
Content Type ◽  
Moderation Effect ◽  
Show Evidence

Purpose The extant literature has mixed results regarding the credit card cue effect. Some showed that credit card cues stimulate spending, whereas others were unable to replicate the findings or found that cues discourage consumer spending. The purpose of this paper is to investigate how consumers’ sensitivity to the pain of payment affects their mental associations about credit cards and how the differences in credit card associations moderate the credit card cue effect on spending, providing a possible explanation for the mixed results in the literature. Furthermore, this paper examines the role of consumers’ perceived financial well-being, measured by their perceptions of current and future wealth and their sense of financial security, in mediating this moderation effect. Design/methodology/approach An experimental study was conducted with a sample of 337 participants to test the hypothesized model. Findings After being shown credit card cues, spendthrift participants had more spending-related thoughts and less debt-related thoughts, perceived themselves as having better financial well-being and consequently spent more than tightwad participants. Originality/value To the authors’ knowledge, this is the first study to investigate the direct link between an exposure to credit card cues and perceived financial well-being, and one of the few to show evidence of the moderating effect of consumers’ sensitivity to the pain of payment on spending when credit card cues are present. This study suggests that marketers may use credit card cues to promote consumer spending, whereas consumers, especially spendthrifts, should be aware of how credit card cues may inflate their perceived financial well-being and stimulate them to spend more.

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