IJERT-Credit Card Fraud Detection using Novel Learning Strategy (original) (raw)

Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy

IEEE Transactions on Neural Networks and Learning Systems, 2017

Detecting frauds in credit card transactions is perhaps one of the best testbeds for computational intelligence algorithms. In fact, this problem involves a number of relevant challenges, namely: concept drift (customers habits evolve and fraudsters change their strategies over time), class imbalance (genuine transactions far outnumber frauds) and verification latency (only a small set of transactions are timely checked by investigators). However, the vast majority of learning algorithms that have been proposed for fraud detection, relies on assumptions that hardly hold in a real-world Fraud Detection System (FDS). This lack of realism concerns two main aspects: i) the way and timing with which supervised information is provided and ii) the measures used to assess fraud-detection performance. This paper has three major contributions. First we propose, with the help of our industrial partner, a formalization of the fraud-detection problem which realistically describes the operating conditions of FDSs that everyday analyze massive streams of credit card transactions. We also illustrate the most appropriate performance measures to be used for fraud-detection purposes. Second, we design and assess a novel learning strategy which effectively address class imbalance, concept drift and verification latency. Third, in our experiments we demonstrate the impact of class unbalance and concept drift in a real-world data stream containing more than 75 millions transactions, authorized over a time window of three years.

Binary Classification Model for Fraudulent Credit Card Transactions

Financial Fraud has been around for centuries since the time internet banking has taken off and is increasing substantially with the innovation of technology and the global superhighways of communication as a consequence of losing out billions of dollars worldwide each year. Unusually, large transactions or the ones that happen in atypical locations evidently deserve additional verification which could be detected by looking at on-surface and evident signals. So as to overcome these fraudulent acts we employ a fraud detection system which not only detects fraud but further makes it essential to model past credit card transactions with the ones that are atypical or turned out to be fraud. In this research paper, my objective is to identify the typical patterns found in transactions by employing a supervised learning approach and trainabinary classification model that can identify the features of these transactions and sort the data into one of two transaction classes: fraudulent or valid, based on provided, historical data. I have explained a Linear learning technique which could have two applications: Regression and Binary Classification that uses Amazon Sagemaker and an extensive review is done on Binary Classification on the basis of quantitative measurements such as accuracy, detection rate and false alarm rates.

Online Transaction Fraud Detection System Based on Machine Learning

IRJET, 2022

Transaction fraud is a major cause of concern. As online transactions become more popular, so do the types of online transaction fraud that accompany them, affecting the financial industry. This fraud detection system is capable of restricting and impeding an attacker's transaction using credit card information of a genuine user. By allowing transactions that exceed the customer's current transaction limit, this system was designed to address these issues. In order to detect fraudulent user behavior, we gather the necessary information at registration. The details of items purchased by any individual transaction are generally unknown to any Fraud Detection System (FDS) running at the bank that issues credit cards to cardholders. BLA is being used to resolve this problem (Behavior and Location Analysis). A FDS is a credit card issuing bank. Every pending transaction is sent to the FDS for approval. To determine whether or not the transaction is genuine, FDS receives the card information and transaction value. The FDS has no understanding of the technology purchased in that transaction. The bank refuses the transaction if FDS confirms it is fraudulent. If an unexpected pattern is identified, the system must be re-verified using the users' spending habits and geographic location. The system detects unusual patterns in the payment procedure based on the user's previous information. After three unsuccessful attempts, the system will ban the user. The new electronic transaction era needs the detecting of fraud in online transactions. It's extremely difficult to improve the consistency and stability of the fraud detection model because customer transaction patterns and offenders' fraud behavior are constantly changing. In this report, we'll examine about how a deep neural network's loss function affects the acquisition of deep feature representations of legitimate and fraudulent transactions. With the increasing use of technology, people all over the world were increasingly turning to online transactions rather than cash in their daily lives, opening up plenty of growth possibilities for fraudsters to use these cards in unscrupulous ways. According to the Nilsson research, global losses are estimated to exceed $35 billion by 2020. The credit card firm should create a programmer that protects these credit card users from any threats they may face in order to secure their security. As a result, we use Kegel’s IEEE-CIS Fraud Detection dataset to demonstrate our system for predicting whether transactions are authentic or fraudulent.

A Supervised Approach to Credit Card Fraud

The wide acceptability and usage of credit card-based transactions can be attributed to improved technological availability and increased demand due to ease of use. As a result of the increased adoption levels, this domain has become profitable and one of the most popular targets for fraudsters who use it to conduct regular exploitation or assaults. Merchants and financial processing providers that sell credit cards suffer substantial financial damages as a result of credit card theft. Because of the possibility of large casualties, it is one of the most serious risks to these organizations and individuals. Credit card fraudulent transaction can be viewed as a binary classification task in which a supervised machine learning technique could be used to analyze and classify a credit card transaction dataset into genuine or fraudulent cases. Therefore, this study explored the use of Artificial Neural Network (ANN) for credit card fraud detection. ULB Machine Learning Group dataset that has 284, 315 legitimate and 492 fraudulent transaction were used to validate the proposed model. Performance evaluation results revealed that model achieved a 100% and 99.95% classification accuracy during training and testing respectively. This affirmed the fact that ANN model could be efficiently used to predict credit card fraudulent transactions

A Learning Based Approach for Credit Card Fraud Detection

In the recent years, Finance fraud is a serious problem with far consequences in the financial industry. Data mining is widely used and applied to finance databases to automate analysis of huge volumes of complex data. Fraud detection in credit card is also a data mining problem. It becomes challenging due to two major reasons-first, the profiles of normal and fraudulent behavior change frequently and secondly due to reason that credit card fraud data sets are highly skewed. Here we investigate and check the performance of Naïve Bayes and KNN on highly skewed credit card fraud data. Credit card transactions data is collected from European cardholders consists of 284,786 transactions. These machine learning techniques are applied on the raw and pre-processed data. The performances of the techniques are evaluated based on accuracy, sensitivity, specificity and precision. The results indicates the optimal accuracy.

IRJET- Fraud Detection in Credit Card using Machine Learning Techniques

IRJET, 2020

Credit card fraud happens frequently and leads to massive financial losses .Online transaction have increased drastically significant no of online transaction are done by online credit cards. Therefore, banks and other financial institutions support the progress of credit card fraud detection applications. Fraudulent transactions can happen in different ways and they can be placed into various categories. Identification of fraud credit card transactions is important to credit card companies for the prevention of being charged for items transaction of items which the customer did not purchase. Data science along with machine leaving helps in tackling these issues. The fraudulent transactions are mixed up with legitimate transactions and the simple recognition techniques which include comparison of both the fraud and the legitimate data are never sufficient to detect the fraud transactions accurately. This project intends to illustrate the modelling of a knowledge set using machine learning with Credit Card Fraud Detection. The Credit Card Fraud Detection Problem includes modelling of credit card transactions which has happened earlier with the data of fraud transactions. Our model will determine whether a new transaction tends to be fraud or legitimate. We have an objective to detect 100% of the fraud transactions while reducing invalid fraud classifications.

Machine Learning on Credit Card Fraud Detection

The primary aim of this project is to make use of machine learning models for the detection and prevention of fraudulent credit card transactions, to ensure the security of customers' accounts against unauthorized use. With the increase in the rate of credit card fraud, it is necessary to implement measures to fight it effectively, to protect customers from charges for goods and services they did not authorize. To achieve this, four distinct machine learning algorithms: K-Nearest Neighbors (KNN), Logistic Regression, Decision Tree Classifier, and Support Vector Machine (SVM) were implemented, trained, and evaluated using a comprehensive dataset containing credit card transaction records, and the best of these four was selected.

A Machine Learning Approach for Credit Card Fraud Detection

Test Engineering and Management, 2020

Now a days on line transactions became a critical and essential a part of our lives. As frequency of transactions is growing, style of dishonorable transactions are growing chop-chop. On the way to reduce back dishonorable transactions, device gaining knowledge of algorithms like naïve bayes, deliver regression, j48 and adaboost etc. are noted in the course of this paper. An equal set of algorithms are enforced and examined exploitation an internet dataset. Via comparative evaluation it may be terminated that Supply regression and adaboost algorithms carry out higher in fraud detection. The rise in e-commerce commercial enterprise has reason companion degree exponential increase inside the use of credit cards for on line purchases and consequently they has been surge in the fraud related to it .in recent years, for banks has turn out to be terribly troublesome for sleuthing the fraud in grasp card machine. System learning plays a widespread position for sleuthing the grasp card fraud in the transactions. For Predicting those transactions banks build use of assorted gadget gaining knowledge of methodologies, beyond facts has been accrued and new options are been used for reinforcing the prophetical strength. The overall performance of fraud sleuthing in master card transactions is substantially suffering from the sampling method on facts-set, choice of variables and detection techniques used. This paper investigates the performance of deliver regression, call tree and random woodland for grasp card fraud detection. Dataset of master card transactions is accrued from kaggle and it consists of an entire of two, eighty four, 808 grasp card transactions of a European financial institution data set.

Machine learning Classifiers for Credit Card Fraud Detection: A Brief Survey

Utilization of credit cards encourages individuals to buy products online via the Internet. Individuals tend to do much of purchasing online or offline by utilizing the credit card facility provided by the bankers to their customers. Credit cards have turned out to be the most prominent facility available to the people around the globe to encourage paperless trades at an enormous speed. Whenever any such trade happens in exchanges or net marketing by using a paperless framework, it is subjected under high risk of fraudulent transactions due to many pitfalls in the security system of the usage of credit cards on the networks. This paper presents a brief survey of important and basic linear and non-linear machine learning algorithms that are focused to predict the fraudulent transactions by studying the patterns present in the credit card transactional datasets. The authors provide the methodology of Random Forest (RF), Support Vector machine (SVM) and Artificial Neural Network (ANN) classifiers to accurately classify whether a unseen credit card transaction is fraudulent or not.

IJERT-Credit Card Fraud Detection

International Journal of Engineering Research and Technology (IJERT), 2021

https://www.ijert.org/credit-card-fraud-detection https://www.ijert.org/research/credit-card-fraud-detection-IJERTCONV9IS04018.pdf "Fraud detection is a set of activities that are taken to prevent money or property from being obtained through false pretenses."Fraud can be committed in different ways and in many industries. Credit card frauds are easy and friendly targets. E-commerce and many other online sites have increased the online payment modes, increasing the risk for online frauds. Increase in fraud rates, researchers started using different machine learning methods to detect and analyse frauds in online transactions. Credit card fraud generally happens when the card was stolen for any of the unauthorized purposes or even when the fraudster uses the credit card information for his use. Lots of mony are lost due to credit card fraud every year. There is a lack of research studies on analyzing real-world credit card data owing to confidentiality issues. In this paper, machine learning algorithms are used to detect credit card fraud. To evaluate the model efficacy, a publicly available credit card data set is used. The System prediction level & accuracy of fraud detection is not 100 precent accurate ,So there is a chance of getting fraud. Then, a real-world credit card data set from a financial institution is analyzed. In addition, noise is added to the data samples to further assess the robustness of the algorithms. The experimental results positively indicate that the majority voting method achieves good accuracy rates in detecting fraud cases in credit cards.