Analysis of Credit Card Fraud Detection Model Using Convolutional Neural Network

P. Deekshith Chary; Balusupati Anil kumar

P. Deekshith Chary Computer science Engineering (Data Science) Assistant professor, CMR Institute of Technology, Hyderabad, Telangana
Balusupati Anil kumar Computer science Engineering (Artificial Intelligence & Machine Learning) Assistant professor, CMR Institute of Technology, Hyderabad, Telangana

Keywords: Convolutional Neural Network, Logistic Regression

Abstract

The usage of credit cards has increased significantly in everyday circumstances as a result of the Internet's rapid expansion and the simplicity of electronic the purpose of transmission. A credit card allows the individual using it to pay for goods or services purchased from a merchant, but because it operates on a cash-in-advance basis, the cardholders must return the funds that they have already spent after a brief amount of period. Credit cards are vulnerable to an array of threats, such as identity theft, phishing, skimming, and card absence before transaction. We can use systems that detect fraud to predict scams well in advance to enable to use credit cards securely and fraud free. Researchers have been using a number of credit card fraud detection techniques, include rule-based systems, internal fraud detection systems, and neural networks. Credit card fraud is the fraudulent use of an individual's credit card or other data without the permission of the owner. People frequently get scammed and ripped off through significant fraud techniques such as application and behavioral fraud. Multiple applications produced by the same someone might result in identical fraud. Application fraud happens when scammers use fraudulent to apply for new cards from the bank.

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