AN INNOVATIVE APPROACH FOR CREDIT CARD FRAUD DETECTION

Computer Engineering

Due to the growing number of daily electronic transactions, which make credit cards more susceptible to fraud, they are the most popular form of electronic payment. Card fraud has cost credit card firms a lot of money. The most frequent problem right now is the detection of credit card fraud. Companies that issue credit cards are searching for the best systems & technology to reduce & detect credit card fraud. There are numerous ways to detect credit card fraud. Financial fraud cases, such credit card fraud, have increased as a result of recent developments in e-commerce & e-payment systems. Implementing systems that can identify credit card theft is therefore essential. When using machine learning to detect credit card fraud, features of these frauds must be carefully chosen because t hey play a significant part in the process. For clients to avoid being charged for products they did not buy, credit card issuers must be able to recognize fraudulent credit card transactions. With the use of credit card fraud detection, this project aims to demonstrate how to model a data set using machine learning. Modeling previous credit card transactions using information from those that turned out to be fraudulent is part of the Credit Card Fraud Detection Problem. The validity of a new transaction is then determined using this approach. Here, finding fraudulent transactions while reducing erroneous fraud categories is our goal.

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