scholarly journals Enhanced Data Privacy Using Vertical Fragmentation and Data Anonymization Techniques

2021 ◽  
Author(s):  
R Sudha ◽  
G Pooja ◽  
V Revathy ◽  
S Dilip Kumar
Keyword(s):  
Private Information ◽  
Credit Card ◽  
Data Privacy ◽  
Detection System ◽  
Fraud Detection ◽  
Online Banking ◽  
Data Anonymization ◽  
Vertical Fragmentation ◽  
Server Systems ◽  
Privacy Models

The use of online net banking official sites has been rapidly increased now a days. In online transaction attackers need only little information to steal the private information of bank users and can do any kind of fraudulent activities. One of the major drawbacks of commercial losses in online banking is fraud detected by credit card fraud detection system, which has a significant impact on clients. Fraudulent transactions will be discovered after the transaction is completed in the existing novel privacy models. As a result, in this paper, three level server systems are implemented to partition the intermediate gateway with better security. User details, transaction details and account details are considered as sensitive attributes and stored in separate database. And also data suppression scheme to replace the string and numerical characters into special symbols to overcome the traditional cryptography schemes is implemented. The Quasi-Identifiers are hidden by using Anonymization algorithm so that the transactions can be done efficiently.

scholarly journals Fraudulent credit card transaction detection using soft computing techniques

2021 ◽  
Vol 23 (3) ◽  
pp. 1634
Author(s):  
Aishwarya Priyadarshini ◽  
Sanhita Mishra ◽  
Debani Prasad Mishra ◽  
Surender Reddy Salkuti ◽  
Ramakanta Mohanty
Keyword(s):  
Soft Computing ◽  
Credit Card ◽  
Nearest Neighbor ◽  
Detection System ◽  
Fraud Detection ◽  
K Nearest Neighbor ◽  
Computationally Efficient ◽  
Detection Techniques ◽  
Government Organizations ◽  
Soft Computing Techniques

<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>

scholarly journals Credit Card Fraud Detection System

2020 ◽  
Vol 6 (12) ◽  
pp. 24-27
Author(s):  
Shashank Singh and Meenu Garg
Keyword(s):  
Machine Learning ◽  
Credit Card ◽  
Data Science ◽  
Detection System ◽  
Fraud Detection ◽  
Detection Problem ◽  
Credit Card Fraud ◽  
Local Outlier Factor ◽  
Local Outlier ◽  
Isolation Forest

It is essential that Visa organizations can distinguish false Mastercard exchanges so clients are not charged for things that they didn't buy. Such issues can be handled with Data Science and its significance, alongside Machine Learning, couldn't be more important. This undertaking expects to outline the demonstrating of an informational collection utilizing AI with Credit Card Fraud Detection. The Credit Card Fraud Detection Problem incorporates demonstrating past Visa exchanges with the information of the ones that ended up being extortion. This model is then used to perceive if another exchange is fake. Our target here is to identify 100% of the fake exchanges while limiting the off base misrepresentation arrangements. Charge card Fraud Detection is an average example of arrangement. In this cycle, we have zeroed in on examining and pre- preparing informational indexes just as the sending of numerous irregularity discovery calculations, for example, Local Outlier Factor and Isolation Forest calculation on the PCA changed Credit Card Transaction

Data Mining for Fraud Detection System

2011 ◽  
pp. 411-416
Author(s):  
Roberto Marmo
Keyword(s):  
Data Mining ◽  
Credit Card ◽  
User Behavior ◽  
Detection System ◽  
Fraud Detection ◽  
Modern Technology ◽  
Data Sets ◽  
Security Measures ◽  
Economic Activities ◽  
Huge Data

As a conseguence of expansion of modern technology, the number and scenario of fraud are increasing dramatically. Therefore, the reputation blemish and losses caused are primary motivations for technologies and methodologies for fraud detection that have been applied successfully in some economic activities. The detection involves monitoring the behavior of users based on huge data sets such as the logged data and user behavior. The aim of this contribution is to show some data mining techniques for fraud detection and prevention with applications in credit card and telecommunications, within a business of mining the data to achieve higher cost savings, and also in the interests of determining potential legal evidence. The problem is very difficult because fraudsters takes many different forms and are adaptive, so they will usually look for ways to avoid every security measures.

scholarly journals Credit Card Fraud Detection through Parenclitic Network Analysis

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Massimiliano Zanin ◽  
Miguel Romance ◽  
Santiago Moral ◽  
Regino Criado
Keyword(s):  
Data Mining ◽  
Complex Network ◽  
Credit Card ◽  
Reference Group ◽  
Detection System ◽  
Fraud Detection ◽  
Data Representation ◽  
Classification Algorithm ◽  
Data Set ◽  
Economic Implications

The detection of frauds in credit card transactions is a major topic in financial research, of profound economic implications. While this has hitherto been tackled through data analysis techniques, the resemblances between this and other problems, like the design of recommendation systems and of diagnostic/prognostic medical tools, suggest that a complex network approach may yield important benefits. In this paper we present a first hybrid data mining/complex network classification algorithm, able to detect illegal instances in a real card transaction data set. It is based on a recently proposed network reconstruction algorithm that allows creating representations of the deviation of one instance from a reference group. We show how the inclusion of features extracted from the network data representation improves the score obtained by a standard, neural network-based classification algorithm and additionally how this combined approach can outperform a commercial fraud detection system in specific operation niches. Beyond these specific results, this contribution represents a new example on how complex networks and data mining can be integrated as complementary tools, with the former providing a view to data beyond the capabilities of the latter.

scholarly journals AN INTELLIGENT CREDIT CARD FRAUD DETECTION SYSTEM

2021 ◽  
Vol 9 (2) ◽  
Author(s):  
Ekwealor Oluchukwu Uzoamaka ◽  
Anusiuba Overcomer Ifeanyi Alex ◽  
Ezuruka Evelyn Ogochukwu ◽  
Uchefuna Charles Ikenna
Keyword(s):  
Credit Card ◽  
Detection System ◽  
Fraud Detection ◽  
Credit Card Fraud

scholarly journals A Comparative Study on Credit Card Fraud Detection

2021 ◽  
Author(s):  
KOUSHIK DEB
Keyword(s):  
Credit Card ◽  
Financial Institution ◽  
Detection System ◽  
Dimensional Space ◽  
Fraud Detection ◽  
Modern Technology ◽  
Two Dimensional ◽  
Rating Score ◽  
Credit Card Fraud ◽  
Dependent Variables

Credit Card Fraud is increasing rapidly with the development of modern technology. This fraud detection system has been proven essential for banks and financial institution, to minimize their losses. This paper pr- oposes Credit Card Fraud Detection using clustering based on several uns- upervised Machine learning and deep learning algorithms. The method we follow to solve our problem is that we are going to plot the points into two dimensional space and some points turns out to be an outliers and some p- oints forms a valid clusters. These outliers are possible number of cheaters which is nothing but the fraudulent transactions and the bank may reject t- heir credit card application. And valid clusters are not cheaters therefore we are going to allocate them the credit card. So as a result we get the explicit list of customers i.e. the potential cheaters who have cheated. Thus, the clu- stering approach which will give better rating score can be chosen to be one of the best methods to detect fraud. In this paper, we worked with Statlog Australian Credit Card Approval Dataset in which the dependent variables have been removed to maintain the privacy of the customers.

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