Research on Credit Card Fraud Detection Model Based on Distance Sum

Yu, Wenfang; Wang, Na

doi:10.1109/jcai.2009.146

Cited by 56 publications

(24 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Yu et al [11] state that that in today's era of technology especially in the Internet commerce and banking, the transactions by the Mastercards have been increasing rapidly. The Mastercard becomes the highly useable equipment for Internet shopping.…”

Section: Literature Surveymentioning

confidence: 99%

An Enhanced Secure Deep Learning Algorithm for Fraud Detection in Wireless Communication

Sanober

Alam

Pande

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

In today’s era of technology, especially in the Internet commerce and banking, the transactions done by the Mastercards have been increasing rapidly. The card becomes the highly useable equipment for Internet shopping. Such demanding and inflation rate causes a considerable damage and enhancement in fraud cases also. It is very much necessary to stop the fraud transactions because it impacts on financial conditions over time the anomaly detection is having some important application to detect the fraud detection. A novel framework which integrates Spark with a deep learning approach is proposed in this work. This work also implements different machine learning techniques for detection of fraudulent like random forest, SVM, logistic regression, decision tree, and KNN. Comparative analysis is done by using various parameters. More than 96% accuracy was obtained for both training and testing datasets. The existing system like Cardwatch, web service-based fraud detection, needs labelled data for both genuine and fraudulent transactions. New frauds cannot be found in these existing techniques. The dataset which is used contains transaction made by credit cards in September 2013 by cardholders of Europe. The dataset contains the transactions occurred in 2 days, in which there are 492 fraud transactions out of 284,807 which is 0.172% of all transaction.

show abstract

Section: Literature Surveymentioning

confidence: 99%

An Enhanced Secure Deep Learning Algorithm for Fraud Detection in Wireless Communication

Sanober

Alam

Pande

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…Yu and Wang [10] have proposed an outlier detection method that computes the distance of each transaction from every other transaction. A transaction whose sum of distances is greater than a threshold distance is considered to be an outlier.…”

Section: Related Workmentioning

confidence: 99%

Conditional Weighted Transaction Aggregation for Credit Card Fraud Detection

Lim

Sachan

Thing

2014

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

View full text Add to dashboard Cite

Part 1: Internet Crime InvestigationsInternational audienceCredit card fraud causes substantial losses to credit card companies and consumers. Consequently, it is important to develop sophisticated and robust fraud detection techniques that can recognize the subtle differences between fraudulent and legitimate transactions. Current fraud detection techniques mainly operate at the transaction level or account level. However, neither strategy is foolproof against fraud, leaving room for alternative techniques and improvements to existing techniques. Transaction-level approaches typically involve the analysis and aggregation of previous transaction data to detect credit card fraud. However, these approaches usually consider all the transaction attributes to be equally important. The conditional weighted transaction aggregation technique described in this paper addresses this issue by leveraging supervised machine learning techniques to identify fraudulent transactions. Empirical comparisons with existing transaction level methods and other transaction aggregation based methods demonstrate the effectiveness of the proposed technique

show abstract

“…According to Jianrong Yao (2018) et al [7] ,Fraud in financial statements has become a complicated problem for both public and government auditors, so different data mining methods have been used to detect fraud in financial statements to support decisions. According to Wen-Fang YU et al [16] A credit card fraud detection model that uses a typical value detection mining based on the sum of the distance in the detection of credit card fraud and proposes this survey procedure & its experimental procedure. lastly, this resulting process is accurate to predict fraudulent transactions through an atypical emulation experiment of mining the credit card data set of a particular commercial bank.…”

Section: Related Workmentioning

confidence: 99%

The Effect of Best First Search Optimization on Credit Card Fraudulent Transaction Detection

Sahu*¹,

agarawal²,

Baraskar³

2019

IJITEE

View full text Add to dashboard Cite

In the digital world, Recently growth of online shopping site for purchasing clothes, electronic items, glossary etc and online transaction for transfer money is increasing day by day . At the same time, criminals have become able to doing fault and earning money through wrong ways .that’s why fraud grows. With the development of Machine Learning in the field of Computer Science and Engineering, its application in the different domain also in fields like Medical, Marketing, Telecommunication, finance, etc. The reason for the popularity of Machine Learning in these domains is due to its high accuracy prediction. That’s why over many years, machine learning has been used in fraud detection. With the advancement of technology in online transactions, fraud is the greatest issue for businesses and has become difficult to recognize than the traditional form of this crime. Historically, the area of Fraud Detection is interrelated to Data Mining & Text Mining. Due to the sudden growth of fraud whose outcome is loss of trillions of rupees worldwide every year, various modern techniques in detecting fraud were proposed that are progressed without interruption and applied to many business fields. Bank frauds worth ₹2.05 trillion happened in the last 11 years, among which there were overall 53,334 fraud issues in the usage of RBI data. The principle purpose behind this write up is to review different methods in identifying frauds corresponding to the unusualness in the transactions. The supervised and unsupervised machine learning algorithms will be used to identify fraud and the best first search optimization will be analyzed to compare both results, i.e., before and after optimization

show abstract

Research on Credit Card Fraud Detection Model Based on Distance Sum

Cited by 56 publications

References 3 publications

An Enhanced Secure Deep Learning Algorithm for Fraud Detection in Wireless Communication

An Enhanced Secure Deep Learning Algorithm for Fraud Detection in Wireless Communication

Conditional Weighted Transaction Aggregation for Credit Card Fraud Detection

The Effect of Best First Search Optimization on Credit Card Fraudulent Transaction Detection

Contact Info

Product

Resources

About