scholarly journals Credit Card Fraud Detection Using Machine Learning Techniques

2020 ◽  
pp. 436-442
Author(s):  
Aruna Kumar Joshi ◽  
Vikram Shirol ◽  
Shreekanth Jogar ◽  
Pavankumar Naik ◽  
Annapoorna Yaligar
Keyword(s):  
Credit Card ◽  
Fraud Detection ◽  
Shopping Mall ◽  
Machine Learning Techniques ◽  
Credit Card Fraud ◽  
Public And Private ◽  
Transaction Process ◽  
Illegal Activities ◽  
Cost Utilization ◽  
The Cost

Credit Card Fraud is one of the major moral issues in the public and private bans sector. The effect of this problems leads to the several ethical trouble. The important themes are to notice the distinctive kinds of credit card fraud and to locate different methods that have been used in fraud detection. The sub-point is to suppose about existing and ruin down as of late dispensed discoveries in fraud detection. Probable upon the variety of extortion appeared with the banks or different financial organizations, exceptional measures can be embraced and executed. The work carried out in this paper are usually going to have really beneficial residences as a approaches as expenditure reserve fund and time capability. The cost utilization of the strategies investigated proper right here is in the minimization of credit card fraud. Anyway, there are up to now moral troubles when appropriate credit card customers are unsorted as fraudulent. Credit Card Fraud Detection is an method which will help people for their transaction process in shopping mall and any other transaction process nowadays fraud detection is nothing but an process where the criminals are found and there are many illegal activities are taking place which causes difficulty for people. Here in this paper we are using SMOTE technique to find fraud and this technique will help to sort both the normal transaction and fraud transaction this process can make easy to find fraudulent. And Neural Network KNN are also taken place to find Credit Card Fraud.

scholarly journals Adaptive Model for Credit Card Fraud Detection

2020 ◽  
Vol 14 (03) ◽  
pp. 54
Author(s):  
Imane Sadgali ◽  
Naoual Sael ◽  
Faouzia Benabbou
Keyword(s):  
Credit Card ◽  
Fraud Detection ◽  
Machine Learning Techniques ◽  
Adaptive Model ◽  
Credit Card Fraud ◽  
New Methods ◽  
Learning Techniques ◽  
Multi Level ◽  
Set Up ◽  
High Level

<p>While the flow of banking transactions is increasing, the risk of credit card fraud is becoming greater particularly with the technological revolution that we know, fraudulent are improve and always find new methods to deal with the preventive measures that financial systems set up. Several studies have proposed predictive models for credit card fraud detection based on different machine learning techniques. In this paper, we present an adaptive approach to credit card fraud detection that exploits the performance of the techniques that have given high level of accuracy and consider the type of transaction and the client's profile. Our proposition is a multi-level framework, which encompasses the banking security aspect, the customer profile and the profile of the transaction itself.</p>

scholarly journals Machine Learning Approaches for Credit Card Fraud Detection

2018 ◽  
Vol 7 (2) ◽  
pp. 917
Author(s):  
S Venkata Suryanarayana ◽  
G N. Balaji ◽  
G Venkateswara Rao
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Language Processing ◽  
Credit Card ◽  
Fraud Detection ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Credit Card Fraud ◽  
Public Data ◽  
The Impact

With the extensive use of credit cards, fraud appears as a major issue in the credit card business. It is hard to have some figures on the impact of fraud, since companies and banks do not like to disclose the amount of losses due to frauds. At the same time, public data are scarcely available for confidentiality issues, leaving unanswered many questions about what is the best strategy. Another problem in credit-card fraud loss estimation is that we can measure the loss of only those frauds that have been detected, and it is not possible to assess the size of unreported/undetected frauds. Fraud patterns are changing rapidly where fraud detection needs to be re-evaluated from a reactive to a proactive approach. In recent years, machine learning has gained lot of popularity in image analysis, natural language processing and speech recognition. In this regard, implementation of efficient fraud detection algorithms using machine-learning techniques is key for reducing these losses, and to assist fraud investigators. In this paper logistic regression, based machine learning approach is utilized to detect credit card fraud. The results show logistic regression based approaches outperforms with the highest accuracy and it can be effectively used for fraud investigators.  

scholarly journals An Empirical Study of AML Approach for Credit Card Fraud Detection–Financial Transactions

2019 ◽  
Vol 14 (6) ◽  
pp. 670-690 ◽  
Author(s):  
Ajeet Singh ◽  
Anurag Jain
Keyword(s):  
Machine Learning ◽  
Credit Card ◽  
Evaluation Criteria ◽  
Fraud Detection ◽  
Machine Learning Techniques ◽  
Credit Card Fraud ◽  
Detection Techniques ◽  
Digital World ◽  
Learning Techniques ◽  
Sensitivity Specificity

Credit card fraud is one of the flip sides of the digital world, where transactions are made without the knowledge of the genuine user. Based on the study of various papers published between 1994 and 2018 on credit card fraud, the following objectives are achieved: the various types of credit card frauds has identified and to detect automatically these frauds, an adaptive machine learning techniques (AMLTs) has studied and also their pros and cons has summarized. The various dataset are used in the literature has studied and categorized into the real and synthesized datasets.The performance matrices and evaluation criteria have summarized which has used to evaluate the fraud detection system.This study has also covered the deep analysis and comparison of the performance (i.e sensitivity, specificity, and accuracy) of existing machine learning techniques in the credit card fraud detection area.The findings of this study clearly show that supervised learning, card-not-present fraud, skimming fraud, and website cloning method has been used more frequently.This Study helps to new researchers by discussing the limitation of existing fraud detection techniques and providing helpful directions of research in the credit card fraud detection field.

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