scholarly journals Detecting Credit Card Fraud using Machine Learning

2021 ◽  
Vol 15 (24) ◽  
pp. 108-122
Author(s):  
Arjwan H. Almuteer ◽  
Asma A. Aloufi ◽  
Wurud O. Alrashidi ◽  
Jowharah F. Alshobaili ◽  
Dina M. Ibrahim
Keyword(s):  
Credit Card ◽  
Short Term Memory ◽  
Detection System ◽  
Fraud Detection ◽  
Short Term ◽  
Credit Card Fraud ◽  
Rule Based ◽  
Neural Organization ◽  
Long Short Term Memory ◽  
Parameter Values

Credit card is getting increasingly more famous in budgetary exchanges, simultaneously frauds are likewise expanding. Customary techniques use rule-based master frameworks to identify fraud practices, ignoring assorted circumstances, the outrageous lopsidedness of positive and negative examples. In this paper, we propose a CNN-based fraud detection system, to catch the natural examples of fraud practices gained from named information. Bountiful exchange information is spoken to by an element lattice, on which a convolutional neural organization is applied to recognize a bunch of idle examples for each example. Trials on true monstrous exchanges of a significant business bank show its boss presentation contrasted and some best-in-class strategies. The aim of this paper is to merge between Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM), and Auto-encoder (AE) to increase credit card fraud detection and enhance the performance of the previous models. By using these four models; CNN, AE, LSTM, and AE&LSTM. each of these models is trained by different parameter values highest accuracy has been achieved where the AE model has accuracy =0.99, the CNN model has accuracy =0.85, the accuracy of the LSTM model is 0.85, and finally, the AE&LSTM model obtained an accuracy of 0.32 by 400 epoch. It is concluded that the AE classifies the best result between these models.

A Rule-Based and Game-Theoretic Approach to On-Line Credit Card Fraud Detection

2011 ◽  
pp. 1-19 ◽  
Author(s):  
Vishal Vatsa ◽  
Shamik Sural ◽  
A.K. Majumdar
Keyword(s):  
Game Theory ◽  
Intrusion Detection ◽  
Credit Card ◽  
Detection System ◽  
Fraud Detection ◽  
Credit Card Fraud ◽  
Rule Based ◽  
Multi Stage ◽  
Game Theoretic ◽  
Second Tier

Traditional security mechanisms are often found to be inadequate for protection against attacks by authorized users or intruders posing as authorized users. This has drawn interest of the research community towards intrusion detection techniques. The authors model the conflicting motives between an intruder and an intrusion detection system as a multi-stage game between two players, each trying to maximize its payoff. They consider the specific application of credit card fraud detection and propose a two-tiered architecture having a rule-based component in the first tier and a Game-theoretic component in the second tier. Classical Game theory is considered useful in many situations because it permits the formulation of strategies that are optimal regardless of what the adversary does, negating the need for prediction of his behavior. However, the authors use it in a predictive application in the sense that we consider intruders as rational adversaries who would try to behave optimally, and the expected optimal behavior can be determined through Game theory.

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 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.

scholarly journals Enhanced credit card fraud detection based on attention mechanism and LSTM deep model

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ibtissam Benchaji ◽  
Samira Douzi ◽  
Bouabid El Ouahidi ◽  
Jaafar Jaafari
Keyword(s):  
Credit Card ◽  
Short Term Memory ◽  
Fraud Detection ◽  
Input Sequence ◽  
Attention Mechanism ◽  
Credit Card Fraud ◽  
Detection Systems ◽  
Deep Model ◽  
Efficiency And Effectiveness ◽  
Card Payment

AbstractAs credit card becomes the most popular payment mode particularly in the online sector, the fraudulent activities using credit card payment technologies are rapidly increasing as a result. For this end, it is obligatory for financial institutions to continuously improve their fraud detection systems to reduce huge losses. The purpose of this paper is to develop a novel system for credit card fraud detection based on sequential modeling of data, using attention mechanism and LSTM deep recurrent neural networks. The proposed model, compared to previous studies, considers the sequential nature of transactional data and allows the classifier to identify the most important transactions in the input sequence that predict at higher accuracy fraudulent transactions. Precisely, the robustness of our model is built by combining the strength of three sub-methods; the uniform manifold approximation and projection (UMAP) for selecting the most useful predictive features, the Long Short Term Memory (LSTM) networks for incorporating transaction sequences and the attention mechanism to enhance LSTM performances. The experimentations of our model give strong results in terms of efficiency and effectiveness.

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