A Succinct Analysis of Deep LSTM Model-Based Credit Card Fraud Detection

Abstract

More people today use credit cards to buy their essentials thanks to technological advancements, which has sparked a gradual rise in credit card theft. Today, credit cards are used almost universally by businesses, whether they are small or large. All types of businesses, including banks, the auto and appliance industries, are susceptible to credit card theft. Fraud is defined as a deceit committed with the aim of generating unauthorised financial benefit. Some of the ways that scams happen include hacking billing systems at stores or restaurants, hacking an online retailer, losing or stealing cards, and installing fraud devices in card readers at petrol stations or ATMs to acquire credit card PIN data. The 2 basic types of card fraud are behavioural fraud and application fraud. Application fraud describes scenarios in which a credit card application is false. It happens when a fraudster submits an application for a new credit card using false identification information, and the card issuer accepts it. Behaviour fraud occurs after a credit card has been approved and issued. Credit or debit card transactions that exhibit fraudulent conduct are referred to. Fraud identifying and prevention have long been big issues for card providers and key research areas for researchers due to the fact that identifying and preventing even a little amount portion of fraudulent behaviour would prevent millions of dollars in losses. Our study focuses on the challenge of recognising fraud behaviour.