An Efficient Techniques for Fraudulent detection in Credit Card Dataset: A Comprehensive study

Abstract

Now a day, credit card transaction is one the famous mode for financial transaction. Increasing trends of financial transactions through credit cards also invite fraud activities that involve the loss of billions of dollars globally. It is also been observed that fraudulent transactions have increased by 35% from 2018. A huge amount of transaction data is available to analyze the fraud detection activities that require analysis of behavior/abnormalities in the transaction dataset to detect and ignore the undesirable action of the suspected person. The proposed paper lists a compressive summary of various techniques for the classification of fraud transactions from the various datasets to alert the user for such transactions.

Federated learning model for credit card fraud detection with data balancing techniques

“…As per the graphical representation of these boxplots, shown in Figs. 5,6,7,8,9, for different classification techniques (machine learning classifier and CNN classifier) in combination with different resampling (individual and hybrid) techniques. For each resampling technique, the performance of all used classifiers is presented.…”

“…Fraudulent transactions may be done using either a stolen card from internal or external sources or false information about credit cards [2]. Activities of credit card fraud detection have been widely discussed by multiple researchers [3][4][5][6][7]. Most of these proposed algorithms have used supervised machine learning models to recognize whether a transaction is fraudulent or legitimate.…”