scholarly journals Credit Card Fraud Detection in Data Mining using XGBoost Classifier

2020 ◽  
Vol 9 (1) ◽  
pp. 603-608
Keyword(s):  
Data Mining ◽  
Credit Card ◽  
Missing Values ◽  
Real Life ◽  
Fraud Detection ◽  
Data Sets ◽  
Global Business ◽  
Data Set ◽  
Credit Card Fraud ◽  
Fraudulent Activity

In today's economy, credit card (CC) plays a major role. It is an inevitable part of a household, business & global business. While using CCs can offer huge advantages if used cautiously and safely, significant credit & financial damage can be incurred by fraudulent activity. Several methods to deal with the rising credit card fraud (CCF) have been suggested. Both such strategies, though, are meant to prevent CCFs; each of them has its own drawbacks, benefits, and functions. CCF has become a significant global concern because of the huge growth of e-commerce and the proliferation of payment online. Machine learning (ML) algo as a data mining technology (DM) was recently very involved in the detection of CCF. There are however several challenges, including the absence of publicly available data sets, high unbalanced size, and different confusing behavior. In this paper, we discuss the state of the art in credit card fraud detection (CCFD), dataset and assessment standards after analyzing issues with the CCFD. Dataset is publicly available in the CCFD data set used in experiments. Here, we compare two ML algos of performance: Logistic Regression (LR) and XGBoost in detecting CCF Transactions Real Life Data. XGBoosthas an inherent ability to handle missing values. When XGBoost encounters node at lost value, it tries to split left & right hands & learn all ways to the highest loss. This is when the test runs on the data. The experimental results show an effective use of the XGBoost classifier. Technique of performance is widely accepted metric based on exclusion: accuracy & recall. Also, the comparison between both approaches displayed based on the ROC curve

scholarly journals Credit Card Fraud Detection and Prevention using Machine Learning

2020 ◽  
Vol 9 (4) ◽  
pp. 1199-1201
Keyword(s):  
Random Forest ◽  
Credit Card ◽  
Graphical Model ◽  
Fraud Detection ◽  
Current Data ◽  
Data Sets ◽  
Random Forest Algorithm ◽  
Data Set ◽  
Credit Card Fraud ◽  
Credit Card Data

This research focused mainly on detecting credit card fraud in real world. We must collect the credit card data sets initially for qualified data set. Then provide queries on the user's credit card to test the data set. After random forest algorithm classification method using the already evaluated data set and providing current data set[1]. Finally, the accuracy of the results data is optimised. Then the processing of a number of attributes will be implemented, so that affecting fraud detection can be found in viewing the representation of the graphical model. The techniques efficiency is measured based on accuracy, flexibility, and specificity, precision. The results obtained with the use of the Random Forest Algorithm have proved much more effective

scholarly journals Credit Card Fraud Detection using Machine Learning and Data Science

2021 ◽  
Vol 9 (VII) ◽  
pp. 3788-3792
Author(s):  
Aman .
Keyword(s):  
Machine Learning ◽  
Credit Card ◽  
Data Science ◽  
Fraud Detection ◽  
Data Sets ◽  
Data Set ◽  
Credit Card Fraud ◽  
Detection Algorithms ◽  
Processing Data ◽  
Isolation Forest

It is important that companies are able to identify fraudulent credit card transactions so that customers are not charged for items that they did not purchase. These problems can be handled with Data Science and its importance, along with Machine Learning. This project aim is to illustrate the modelling of a data set using machine learning with Credit Card. Our objective is to detect 100% of the fraudulent transactions while minimizing the incorrect fraud classifications. Credit Card Fraud Detection is a sample of classification. In this process, we have focused on analysing and pre-processing data sets as well as the deployment of multiple anomaly detection algorithms such as Local Outlier Factor and Isolation Forest algorithm on the PCA transformed Credit Card Transaction data.

scholarly journals Machine Learning Techniques for Credit Card Fraud Detection

2020 ◽  
Vol 4 (2) ◽  
pp. 98-112
Author(s):  
Hossam Eldin M. Abd Elhamid ◽  
◽  
Wael Khalif ◽  
Mohamed Roushdy ◽  
Abdel-Badeeh M. Salem ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Credit Card ◽  
Fraud Detection ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Credit Card Fraud ◽  
Data Mining Techniques ◽  
Learning Techniques ◽  
Over Time

The term “fraud”, it always concerned about credit card fraud in our minds. And after the significant increase in the transactions of credit card, the fraud of credit card increased extremely in last years. So the fraud detection should include surveillance of the spending attitude for the person/customer to the determination, avoidance, and detection of unwanted behavior. Because the credit card is the most payment predominant way for the online and regular purchasing, the credit card fraud raises highly. The Fraud detection is not only concerned with capturing of the fraudulent practices, but also, discover it as fast as they can, because the fraud costs millions of dollar business loss and it is rising over time, and that affects greatly the worldwide economy. . In this paper we introduce 14 different techniques of how data mining techniques can be successfully combined to obtain a high fraud coverage with a high or low false rate, the Advantage and The Disadvantages of every technique, and The Data Sets used in the researches by researcher

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.

Data Quality Analysis based Machine Learning models for Credit Card Fraud Detection

2021 ◽  
Vol 23 (06) ◽  
pp. 318-344
Author(s):  
Amit Pundir ◽  
◽  
Rajesh Pandey ◽  
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Quality Management ◽  
Data Quality ◽  
Decision Trees ◽  
Credit Card ◽  
Fraud Detection ◽  
Data Quality Management ◽  
Credit Card Fraud ◽  
Logistics Regression

Misrepresentation of money is a developing issue in monetary business with far-reaching consequences and keeping in mind that many processes have been found. Data quality management with data mining has been effectively applied to data sets to mechanize the investigation of massive amounts of complex information. Data mining has likewise played a notable role in identifying credit card fraud in online exchanges. Fraud detection in credit cards is a data quality management issue that considered under data mining, tested for two important reasons — first, the profiles of ordinary and false practices habitually change, and also because of the explanation that charge card fraud information is exceptionally slow. This research paper examines the performance of Decision Trees, Logistics Regression, and Random Forest rely strategically on profoundly skewed credit card fraud data. The dataset of credit card transactions is sourced from Kaggle (a publically accessible dataset repository) with 284,807 transactions. These methods are applied to raw data values and data preprocessing techniques. Assessment of the performance of techniques depends on accuracy, sensitivity, specificity, precision, and recall. Results indicate the optimal accuracy for the decision trees, logistics regression, and random forest classifiers with 90.8%, 98.5%, and 99.1% respectively.

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