Credit Card Fraud Detection using Local Outlier Factor and Isolation Forest

2019 ◽  
Vol 7 (4) ◽  
pp. 1060-1064 ◽  
Author(s):  
Hyder John ◽  
Sameena Naaz
Keyword(s):  
Credit Card ◽  
Fraud Detection ◽  
Credit Card Fraud ◽  
Local Outlier Factor ◽  
Local Outlier ◽  
Isolation Forest

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

scholarly journals Anomaly and Fraud Detection in Credit Card Transactions Using the ARIMA Model

2021 ◽  
Vol 5 (1) ◽  
pp. 56
Author(s):  
Giulia Moschini ◽  
Régis Houssou ◽  
Jérôme Bovay ◽  
Stephan Robert-Nicoud
Keyword(s):  
Anomaly Detection ◽  
Credit Card ◽  
Arima Model ◽  
Fraud Detection ◽  
Credit Card Fraud ◽  
Local Outlier Factor ◽  
Unsupervised Approach ◽  
Box Plot ◽  
Local Outlier ◽  
Isolation Forest

This paper addresses the problem of the unsupervised approach of credit card fraud detection in unbalanced datasets using the ARIMA model. The ARIMA model is fitted to the regular spending behaviour of the customer and is used to detect fraud if some deviations or discrepancies appear. Our model is applied to credit card datasets and is compared to four anomaly detection approaches, namely, the K-means, box plot, local outlier factor and isolation forest approaches. The results show that the ARIMA model presents better detecting power than that of the benchmark models.

scholarly journals Isolation Forest and Local Outlier Factor for Credit Card Fraud Detection System

2020 ◽  
Vol 9 (4) ◽  
pp. 261-265
Keyword(s):  
Credit Card ◽  
Detection System ◽  
Data Sets ◽  
Crucial Issue ◽  
New Approach ◽  
Local Outlier Factor ◽  
Card Payment ◽  
Behavior Characteristics ◽  
Local Outlier ◽  
Isolation Forest

Fraud identification is a crucial issue facing large economic institutions, which has caused due to the rise in credit card payments. This paper brings a new approach for the predictive identification of credit card payment frauds focused on Isolation Forest and Local Outlier Factor. The suggested solution comprises of the corresponding phases: pre-processing of data-sets, training and sorting, convergence of decisions and analysis of tests. In this article, the behavior characteristics of correct and incorrect transactions are to be taught by two kinds of algorithms local outlier factor and isolation forest. To date, several researchers identified different approaches for identifying and growing such frauds. In this paper we suggest analysis of Isolation Forest and Local Outlier Factor algorithms using python and their comprehensive experimental results. Upon evaluating the dataset, we received Isolation Forest with high accuracy compared to Local Outlier Factor Algorithm

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.

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