Proceedings Paper DOI: 10.58190/icat.2023.15 PROCEEDINGS OF INTERNATIONAL CONFERENCE ON ADVANCED TECHNOLOGIES https://proceedings.icatsconf.org/ 11 th International Conference on Advanced Technologies (ICAT'23), Istanbul-Turkiye, August 17-19, 2023. 38 Detection of Credit Card Fraud with Artificial Neural Networks Ferhat YEŞİLYURT 1 , Hasan TEMURTAŞ 2 , Çiğdem BAKIR 3 1 Graduate School of Education, Com. Department, Kutahya Dumlupinar University , Kutahya, TURKEY ferhat.yesilyurt@ogr.dpu.edu.tr, ORCID: 0000-0002-4119-3318 2 Computer Engineering Department, Kutahya Dumlupinar University , Kutahya, TURKEY hasan.temurtas @dpu.edu.tr, ORCID: 0000-0001-6738-3024 3 Software Engineering Department, Kutahya Dumlupinar University , Kutahya, TURKEY cigdem.bakir@dpu.edu.tr, ORCID: 0000-0001-8482-2412 Abstract— Along with the Internet, digital technologies are frequently used in every moment of our lives. Many transactions that we carry out in monetary terms such as shopping in our daily life are now done digitally. With the developing digitalization in the world, people's lives become easier and people can access different products in a short time. In particular, people can spend and shop quickly and easily without carrying cash in their pockets with a credit card. However, with the increase in the use of credit cards, there are also some security vulnerabilities. Fraudsters can gain unfair advantage by obtaining certain credit card information such as passwords. They can shop with someone else's credit card without permission. These transactions cause substantial financial damage to individuals and institutions. With the increase in the use of credit cards with the developing technology, such credit card fraud is also increasing rapidly. Taking precautions against credit card fraud is a very important issue in order to ensure the safety of people. For this reason, in order to ensure the security of both banks and financial institutions that provide credit card services, it is necessary to prevent credit card fraud and to detect fraud that may occur in credit cards within the scope of combating fraud. In our study, Artificial Neural Networks were used to detect credit card fraud transactions. A prediction model has been developed to detect fraud in credit card transactions with ANN. Using the Credit Card data set obtained from the Kaggle database, modeling was done with the Feed Forward Artificial Neural Network method. The aim of this study is to automatically detect abnormal behaviors made with credit cards. 98.44% success was achieved with feedforward artificial neural network. Keywords— ANN, credit card, fraud, feed forward, confusion matrix I. INTRODUCTION Today, with the rapid increase in the widespread use of the internet, many companies, institutions and organizations have started to carry out their activities online. Especially, financial companies have started to conduct most of their transactions online in order to save time and make fast transactions[1]. Especially in recent years, many institutions and individuals have been doing their online shopping (market, white goods, television purchase, fuel, etc.) and all other transactions with debit and credit cards in order to provide cash transfer. Generally, large hotels and various car rental companies may require the buyer to make transactions with a credit card. People even pay their bills via bank or credit card. However, there are some disadvantages as well as these advantages of online transactions. Especially in many institutions such as banking and finance companies, fraud and illegal activities are increasing rapidly due to online transactions[2]. Fraud detection related to the use of bank and credit cards can be handled in many ways[3]: The most common and common ones are ATM fraud and the operation of card information, account redirection, online shopping and copying of card information. It is very important for the security of individuals and institutions to detect different transactions and movements on these accounts. Because, in terms of data security and privacy, the transfer of bank and credit card information to other people and their misuse is becoming a very serious problem today. Fraudsters can capture people's information from e-commerce sites and copy this information for different purposes. Credit card fraud causes financial losses all over the world and researchers are trying to prevent credit card fraud with various data analyzes. Credit card fraud is a growing problem in many areas such as banking and online shopping. Especially credit card fraudsters develop new strategies and engage in illegal transactions. These illegal transactions cause consumers and financial institutions to lose money every day. In our study, we proposed a model that detects attacks on credit card transactions in order to prevent these illegal transactions and to transfer data securely with credit cards. The scientific contribution of our study is to detect credit card fraud and prevent the elements that will threaten data security. In our study, the dataset used for the detection of abnormal behavior in credit card transactions or fraud detection was trained with