A Comparative Study of Machine Learning Classifiers for Credit Card Fraud Detection

Now a day’s credit card transactions have been gaining popularity with the growth of e-commerce and shows tremendous opportunity for the future. Therefore, due to surge of credit card transaction, it is a crying need to secure it . Though the vendors and credit card providing authorities are showing dedication to secure the details of these transactions, researchers are searching new scopes or techniques to ensure absolute security which is the demand of time. To detect credit card fraud, along with other technologies, applications of machine learning and computational intelligence can be used and plays a vital role. For detecting credit card anomaly, this paper analyzes and compares some popular classifier algorithms. Moreover, this paper focuses on the performance of the classifiers. UCSD -FICO Data Mining Contest 2009 dataset were used to measure the performance of the classifiers. The final results of the experiment suggest that (1) meta and tree classifiers perform better than other types of classifiers, (2) though classification accuracy rate is high but fraud detection success rate is low. Finally, fraud detection rate  should be taken into consideration to assess the performance of the classifiers in a credit card fraud detection system

Fraud Detection in Credit Card System Using Web Mining

Abstract: Now a day the usage of credit cards has dramatically increased. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. Various techniques like classification, clustering and apriori of web mining will be integrated to represent the sequence of operations in credit card transaction processing and show how it can be used for the detection of frauds. Initially, web mining techniques trained with the normal behaviour of a cardholder. If an incoming credit card transaction is not accepted by the web mining model with sufficiently high probability, it is considered to be fraudulent. At the same time, the system will try to ensure that genuine transactions will not be rejected. Using data from a credit card issuer, a web mining model based fraud detection system will be trained on a large sample of labelled credit card account transactions and tested on a holdout data set that consisted of all account activity. Web mining techniques can be trained on examples of fraud due to lost cards, stolen cards, application fraud, counterfeit fraud, and mail-order fraud. The proposed system will be able to detect frauds by considering a cardholder"s spending habit without its significance. Usually, the details of items purchased in individual transactions are not known to any Fraud Detection System. The proposed system will be an ideal choice for addressing this problem of current fraud detection system. Another important advantage of proposed system will be a drastic reduction in the number of False Positives transactions. FDS module of proposed system will receive the card details and the value of purchase to verify, whether the transaction is genuine or not. If the Fraud Detection System module will confirm the transaction to be of fraud, it will raise an alarm, and the transaction will be declined

An Assessment on Credit Card Fraud Detection: Survey

Credit card fraud is a costly problem for many financial institutions, costing businesses billions of dollars a year. Many adversaries still escape fraud detection systems because these systems often do not include information about the adversary's knowledge of the fraud detection mechanism. This thesis aims to include information on the motivations of "crooks" and the knowledge base in an adaptive fraud detection system. In this thesis, we use a theoretical adversarial learning approach to classification to model the best fraudster strategy. We proactively adapt the fraud detection system to classify these future fraudulent transactions better. Therefore, this document aims to provide an over-supervised bird's-eye approach with a suitable feature extraction technique that improves fraud detection rather than mistakenly classifying an actual transaction as fraud

CREDIT CARD FRAUD DETECTION USING LINEAR DISCRIMINANT ANALYSIS (LDA), RANDOM FOREST, AND BINARY LOGISTIC REGRESSION

The growth of electronic payment usage makes the monetary tension of credit-card deception is changing into major defiance for finance and technology companies. Therefore, pressuring them to continuously advance their fraud detection system is crucial. In this research, we describe fraud detection as a classification issue by comparing three methods. The method used is Linear Discriminant Analysis (LDA), Random Forest, and Binary Logistic Regression. The dataset used is a dataset containing transactions made by credit cards. The challenge in this analysis is that the dataset is highly unbalanced, so SMOTE must perform better on the data. The dataset contains only continuous features that are transformed into Principal Component Scores (PCs). The results show that the binary regression algorithm, the Random Forest algorithm, and the Linear Discriminant Analysis with variables that have SMOTE have AUC values greater than using the original variables. The largest AUC value was obtained by binary logistic regression with 90:10 separation data and Random Forest Algorithm with 60:40 separation data

Credit Card Fraud Detection Using Machine Learning Algorithms

One of the main challenges to the security of an online business is credit card fraud. For this reason, algorithms based on artificial intelligence and machine learning are being introduced to enable the most accurate and fast detection of card fraud. This paper presents an approach to the detection of card fraud based on machine learning algorithms more specifically, a multilayer perceptron (MLP) and a Decision tree. The aforementioned algorithms were trained and tested using a publicly available data set on card fraud. The data set used consists of 7 characteristics of the card transaction and information on whether there was card fraud or not. In total, the data set contains information on 1,000,000 transactions, and it is highly imbalanced. To handle the class imbalance, random undersampling, SMOTE, and SMOTE-Tomek algorithms were proposed. From the achieved results it can be seen that the highest performances are achieved if MLP (AUC = 0.99, f1 = 0.99, MCC = 0.98, and Kappa = 0.98) and Decision tree (AUC = 0.99, f1 = 0.99, MCC = 0.99, and Kappa = 0.98) are trained by using data set re-sampled by using SMOTE-Tomek algorithm. If the performance of the mentioned algorithms is examined using fewer characteristics of the transaction, it can be seen that by reducing the number of characteristics a significant decrease in classification performances can be noticed if a Decision tree in combination with SMOTE-Tomek is used. However, if an MLP in combination with SMOTE-Tomek is used, a significantly lower decrease in performance can be observed, pointing to the higher robustness to input vector dimension reduction. Such a robust system can provide information about transaction validity even in a condition where the input data is limited to a few input variables. From the achieved results, it can be concluded that MLP in combination with the SMOTE-Tomek algorithm can be used for credit card fraud detection, even in conditions with a lower number of input variables

Credit Card Fraud Detection Using Machine Learning Algorithms

One of the main challenges to the security of an online business is credit card fraud. For this reason, algorithms based on artificial intelligence and machine learning are being introduced to enable the most accurate and fast detection of card fraud. This paper presents an approach to the detection of card fraud based on machine learning algorithms more specifically, a multilayer perceptron (MLP) and a Decision tree. The aforementioned algorithms were trained and tested using a publicly available data set on card fraud. The data set used consists of 7 characteristics of the card transaction and information on whether there was card fraud or not. In total, the data set contains information on 1,000,000 transactions, and it is highly imbalanced. To handle the class imbalance, random undersampling, SMOTE, and SMOTE-Tomek algorithms were proposed. From the achieved results it can be seen that the highest performances are achieved if MLP (AUC = 0.99, f1 = 0.99, MCC = 0.98, and Kappa = 0.98) and Decision tree (AUC = 0.99, f1 = 0.99, MCC = 0.99, and Kappa = 0.98) are trained by using data set re-sampled by using SMOTE-Tomek algorithm. If the performance of the mentioned algorithms is examined using fewer characteristics of the transaction, it can be seen that by reducing the number of characteristics a significant decrease in classification performances can be noticed if a Decision tree in combination with SMOTE-Tomek is used. However, if an MLP in combination with SMOTE-Tomek is used, a significantly lower decrease in performance can be observed, pointing to the higher robustness to input vector dimension reduction. Such a robust system can provide information about transaction validity even in a condition where the input data is limited to a few input variables. From the achieved results, it can be concluded that MLP in combination with the SMOTE-Tomek algorithm can be used for credit card fraud detection, even in conditions with a lower number of input variables

Credit Card Fraud Detection using One-Class Classification Algorithms

Similar to most things in everyday life, the advent of payment cards also has good and bad sides. It undoubtedly, made life easier by bringing the whole payment system to a single card, but it also paved the way for a new set of illegal activities and frauds. The credit card fraud has been carried out since the payment cards came into existence, and since then, the trend in such frauds has been an increasing one. Therefore, a quest to attenuate the losses caused by such frauds began. For this purpose, many preventive and detective measures have been taken in the past, and new ways are sought to further improve the policies. These measures, however, reduce the losses temporarily only and have not yet succeeded in converting the uptrend in the losses by such frauds into a downtrend because fraudsters always come up with a new way of tricking the people and the system. Thus, a new way of solving this ever-existing challenge is needed, which can detect even those fraudulent instances that are executed by techniques and methods that are yet-to-be-invented by fraudsters. Moreover, the occurrence of normal (non-fraudulent) credit card transactions is much more than fraudulent ones, and therefore, the data for credit card fraud detection is highly imbalanced. Another challenge in credit card fraud detection systems is the high dimensionality of datasets. Therefore, to address the imbalance nature of the data, to cope with the curse of dimensionality with a new way of making the model to regulate and extract the discriminative features, and to detect the fraud carried out by yet-to-be-invented techniques, we implemented a set of novel and state of the art subspace learning-based One-Class Classification algorithms. We experimented with integrating a projection matrix and geometric data information in the training phase to improve credit card fraud detection. We also experimented by using a maximization-update rule in updating the projection matrix instead of the classical minimization-update rule in the subspace leaning-based data description. We found that the linear version of Graph-embedded Subspace Support Vector Data Description with kNN graph, gradient-based solution, and minimization-update rule works better than all other models

Intelligent Financial Fraud Detection Practices: An Investigation

Financial fraud is an issue with far reaching consequences in the finance industry, government, corporate sectors, and for ordinary consumers. Increasing dependence on new technologies such as cloud and mobile computing in recent years has compounded the problem. Traditional methods of detection involve extensive use of auditing, where a trained individual manually observes reports or transactions in an attempt to discover fraudulent behaviour. This method is not only time consuming, expensive and inaccurate, but in the age of big data it is also impractical. Not surprisingly, financial institutions have turned to automated processes using statistical and computational methods. This paper presents a comprehensive investigation on financial fraud detection practices using such data mining methods, with a particular focus on computational intelligence-based techniques. Classification of the practices based on key aspects such as detection algorithm used, fraud type investigated, and success rate have been covered. Issues and challenges associated with the current practices and potential future direction of research have also been identified.Comment: Proceedings of the 10th International Conference on Security and Privacy in Communication Networks (SecureComm 2014

Mining Bad Credit Card Accounts from OLAP and OLTP

Credit card companies classify accounts as a good or bad based on historical data where a bad account may default on payments in the near future. If an account is classified as a bad account, then further action can be taken to investigate the actual nature of the account and take preventive actions. In addition, marking an account as "good" when it is actually bad, could lead to loss of revenue - and marking an account as "bad" when it is actually good, could lead to loss of business. However, detecting bad credit card accounts in real time from Online Transaction Processing (OLTP) data is challenging due to the volume of data needed to be processed to compute the risk factor. We propose an approach which precomputes and maintains the risk probability of an account based on historical transactions data from offline data or data from a data warehouse. Furthermore, using the most recent OLTP transactional data, risk probability is calculated for the latest transaction and combined with the previously computed risk probability from the data warehouse. If accumulated risk probability crosses a predefined threshold, then the account is treated as a bad account and is flagged for manual verification.Comment: Conference proceedings of ICCDA, 201