scholarly journals Multifactor Authentication over Credit Card Fraud Detection and Prevention

2020 ◽  
Vol 9 (9) ◽  
pp. 680-685
Keyword(s):  
Face Recognition ◽  
Credit Card ◽  
Fraud Detection ◽  
Online Banking ◽  
Scanning System ◽  
User Friendliness ◽  
Credit Card Fraud ◽  
Online Transactions ◽  
Multifactor Authentication ◽  
Online Methods

Online banking becomes most used method for banking transaction now days. Now the trend is turning towards digitization and so is the population going towards the same thing. People often go to the credit/debit card, Net Banking, etc. online methods. Confidentiality may be hacked during online transactions. To reduced, fraud online activities so, as to secure the data by a two-step authentication method. The primary step of authentication is to verifying OTP. Once the OTP is verified, face recognition will be done. The data is analyzed and the results for both the valid and invalid transactions are sent to the Bank. A new card scanning system has important factor such as most safety, user-friendliness, etc. The application's importance is to mitigate credit card fraud through Face device awareness. The customers get both most usable and highly secure online banking application.

Credit Card Fraud Detection Using K-Means and Fuzzy C-Means

2021 ◽  
pp. 216-240
Author(s):  
Arti Jain ◽  
Archana Purwar ◽  
Divakar Yadav
Keyword(s):  
Credit Card ◽  
Large Scale ◽  
Fraud Detection ◽  
Online Banking ◽  
Small Scale ◽  
Classification Rate ◽  
Credit Card Fraud ◽  
Fuzzy C Means ◽  
Fcm Clustering ◽  
F Measure

Machine learning (ML) proven to be an emerging technology from small-scale to large-scale industries. One of the important industries is banking, where ML is being adapted all over the world by employing online banking. The online banking is using ML techniques in detecting fraudulent transactions like credit card fraud detection, etc. Hence, in this chapter, a Credit card Fraud Detection (CFD) system is devised using Luhn's algorithm and k-means clustering. Moreover, CFD system is also developed using Fuzzy C-Means (FCM) clustering instead of k-means clustering. Performance of CFD using both clustering techniques is compared using precision, recall and f-measure. The FCM gives better results in comparison to k-means clustering. Further, other evaluation metrics such as fraud catching rate, false alarm rate, balanced classification rate, and Mathews correlation coefficient are also calculated to show how well the CFD system works in the presence of skewed data.

scholarly journals Analysis of Credit Card Fraud detection using Machine Learning models on balanced and imbalanced datasets

2021 ◽  
Vol 9 (7) ◽  
pp. 846-852
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Predictive Models ◽  
Credit Card ◽  
Fraud Detection ◽  
Classification Algorithms ◽  
Learning Models ◽  
Credit Card Fraud ◽  
Online Transactions ◽  
Machine Learning Models

With the advent of modern transaction technology, many are using online transactions to transfer money from one person to another. Credit Card Fraud, a rising problem in the financial department goes unnoticed most of the time. A lot of research is going on in this area.The Credit Card Fraud Detection project is developed to spot whether a new transaction is fraudulent or not with the knowledge of previousdata. We use various predictive models to ascertain how accurate they are in predicting whether a transaction is abnormalor regular. Techniques like Decision Tree, Logistic Regression, SVMand Naïve Bayes are the classification algorithms to detect non-fraud and fraud transactions.

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