Credit Card Fraud Detection Using Machine Learning Techniques

2020

Now a day’s online payment gaining popularity because of easy and convenience use of ecommerce. It became very easy mode of payment. People choose online payment and e-shopping; because of time convenience, transport convenience, etc. As the result of huge amount of e-commerce use, there is a vast increment in credit card fraud also. Machine Learning has been successfully applied to finance databases to automate analysis of huge volumes of complex data. Machine Learning has also played a salient role in the detection of credit card fraud in online transactions. Fraud detection in credit card is a big problem, it becomes challenging due to two major reasons–first, the profiles of normal and fraudulent behaviors change frequently and secondly due to reason that credit card fraud data sets are highly skewed. KeywordsData Analysis, Fraud in credit card, naïve bayes, Machine Learning, Security.

2021

Using new spam technologies to carry out internet banking fraud refers to shifting and withdrawing money from the user’s balance account without it’s authorization. Credit card fraud pops into the mind so far in the current scenario when the concept of fraud bursts into some conversation. Credit card fraud has escalated tremendously in recent times due to the incredible growth in credit card purchases. In order to assess, identify or prevent undesirable conduct, fraud detection requires tracking the purchase behavior of users/customers. The purpose of this project is to predict the genuine and fraud transactions with respect to the amount of the transaction utilizing various machine learning approaches like Logistic Regression, Decision Trees, Support Vector Machine, Naive Bayes, Random Forest and K-Nearest Neighbor. The model built who has greater accuracy and precision is considered to be best fit for this system.

International Journal of Engineering Applied Sciences and Technology, 2020

Credit card fraud is the term used to specify the transactions which are done without the knowledge of the account holder in order to cause monetary loss. Banks used various techniques to catch these fraud transactions but every year it happens and causes huge loss to the people and the bank. So there need to be a techniques which could use all the available data of the transaction and uses it to classify whether a transaction is fraud or not based on previous activity of the user. In this paper we've introduced some ideas of frauds associated with the credit cards or the other on-line card. We have tried to find a solution so that fraud get caught even before transaction takes place. In this paper we have applied various supervised algorithms on the dataset and the result founded are shown.

International journal of computer applications, 2022

As the world is becoming more digitalized with every sector using the internet to flourish their businesses, online transactions have become an inevitable part of life. There has been a steady rise in the number of online transactions and this will continue to increase in the future as well. One of the major modes of online transactions is credit cards and along with its extensive use comes its major drawback, that is, credit card fraud. Machine learning plays a vital role in detecting credit card frauds as it is not possible for banks to monitor every transaction. This paper explores different machine learning algorithms used to detect credit card frauds.

Engineering Journal IJOER, 2021

We focused on the study of using math modeling and machine learning to do big data analysis, therefore to detect Credit card fraud, which is one of the serious issues in real life. In order to detect credit card fraud, after reviewed many recent research, we chose the most popular models among credit card fraud detection, which are Random Forest (RF), and ANN with multi-layers (DNN). We evaluated the accuracy and recall of these models in detecting credit card fraud with or without SMOTE, and found out that there is no significant improvement in the accuracy of these models with or without SMOTE training, but RF with SOMTE has a little bit vantage than others. There is a significant improvement in recall of these three models with SMOTE training. Especially, with SMOTE training, ANN or DNN is of better performance in the recall than RF. Therefore, we combine RF and DNN to generate a hybrid model so that it produces better stability in accuracy and recall. The study discovered that neural network models have greater potential for finding abnormal data in the big data stream. This has important guiding significance for what mathematical model that credit card companies use to monitor the cash flow and remind customers of the possible risk of credit card fraud.

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2022

Development of communication technologies and ecommerce has made the credit card as the most common technique of payment for both online and regular purchases. So, security in this system is highly expected to prevent fraud transactions. Fraud transactions in credit card data transaction are increasing each year. In this direction, researchers are also trying the novel techniques to detect and prevent such frauds. However, there is always a need of some techniques that should precisely and efficiently detect these frauds. This paper proposes a scheme for detecting frauds in credit card data which uses a Neural Network (NN) based unsupervised learning technique. Proposed method outperforms the existing approaches of Auto Encoder (AE), Local Outlier Factor (LOF), Isolation Forest (IF) and K-Means clustering. Proposed NN based fraud detection method performs with 99.87% accuracy whereas existing methods AE, IF, LOF and K Means gives 97?, 98?, 98? and 99.75? accuracy respectively.

Proceedings of the International Conference on Advanced Technologies

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 fra...

IRJET, 2022

Due to the rise and rapid growth of E-commerce, use of credit card for online purchases has dramatically increased and it caused an explosion in the credit card fraud. In our project we mainly focused on finding out whether a transaction is fraud or genuine for that we were using a feature totally based only on time i.e.) a particular period of time the occurred transaction data. And extracted feature is given as input for further implementation using Artificial Neural Network (Deep Learning) and Support Vector Machine (Supervised Learning), accuracy of the process is individually collected and compared with one and another. while comparing ANN provides the better accuracy of above 95% while SVM provides only 93% and at last using confusion matrix total number of transactions, number of genuine transactions and number of fraud transactions will be displayed as output based on the data given as input.

2021

Online Transaction is a popular mode of payment and most of the payments done by using credit card but credit card fraud happens continuously and it leads to huge lose. So all the banks and other financial industries supports to the progress of Fraud identification of credit card. The fraudulent transactions can takes place in different ways, identification of credit card fraud is very important to the credit card companies so that customers will not be charged for the items those are not purchased. This paper explained the concepts related to credit card and used different machine learning algorithms like logistic regression and random forest method .2,84,808 credit card transactions of dataset is collected from kaggle and these transactions are from European bank. Identification of credit card is an example of classification and in this process includes analyzing and preprocessing the data set along with the deployment of Machine learning algorithms. Index Terms Credit card fraud,...

There are many Credit Card Fraud Detection System in two days world. This paper is going to discuss about the various techniques used in Credit Card Fraud Detection and also how the datasets are chosen or pre-processed to build the Machine Learning, Deep Learning, Neural Network models. We will be going to discuss various models like neural networks, decision tree, logistic regression, Random Forest,