A Synergistic Approach for Enhancing Credit Card Fraud Detection using Random Forest and Naïve Bayes Models (original) (raw)
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Credit card fraud detection using Random Forest Algorithm
International Journal of Advance Research Ideas and Innovations in Technology
This Project is focused on credit card fraud detection in real-world scenarios. Nowadays credit card frauds are drastically increasing in number as compared to earlier times. Criminals are using fake identity and various technologies to trap the users and get the money out of them. Therefore, it is very essential to find a solution to these types of frauds. In this proposed project we designed a model to detect the fraud activity in credit card transactions. This system can provide most of the important features required to detect illegal and illicit transactions. As technology changes constantly, it is becoming difficult to track the behavior and pattern of criminal transactions. To come up with the solution one can make use of technologies with the increase of machine learning, artificial intelligence and other relevant fields of information technology, it becomes feasible to automate this process and to save some of the intensive amounts of labor that is put into detecting credit card fraud. Initially, we will collect the credit card usage data-set by users and classify it as trained and testing dataset using a random forest algorithm and decision trees. Using this feasible algorithm, we can analyze the larger data-set and user provided current data-set. Then augment the accuracy of the result data. Proceeded with the application of processing of some of the attributes provided which can find affected fraud detection in viewing the graphical model of data visualization. The performance of the techniques is gauged based on accuracy, sensitivity, and specificity, precision. The results is indicated concerning the best accuracy for Random Forest are unit 98.6% respectively.
Credit Card Fraud Detection Using Random Forest Classification
International Journal for Research in Applied Science and Engineering Technology
Credit card fraudulent happens through the account holder's card number, card details and personal information. E-commerce payment system is providing the payment for online transaction. The model is used to identify whether a new transaction is fraudulent or not. Aim is to detect 100% of the fraudulent transactions while minimizing the incorrect fraud classifications. A standard scalar model is initially trained with the normal behavior of a card holder. If an incoming credit card transaction is not accepted by the trained standard scalar model with sufficiently high probability, it is considered to be fraudulent, which defines a plot of test perception as the y coordinate versus its 1-specificity or false positive rate (FPR) as the x coordinate, is an effective method of estimate the quality or performance of diagnostic tests. The significance of the application technique reviewed in the minimization of credit card fraud. Still some issues when genuine credit card customers are misclassified as fraudulent. SMOTE is a statistical technique for increasing the number of cases in your dataset in a balanced way. Random forest builds multiple decision trees and integrate them together to get stable prediction and accuracy of about 98.6%.
Implementation of Credit Card Fraud Detection Using Random Forest Algorithm
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
Credit card fraud processing is presently the most frequently arising problem in the present world. This is due to the rise in both online transaction and ecommerce platforms. To detect these fraudulent activities the credit card fraud detection system was introduced, this project main aim is to focus on the machine learning algorithms. The voting based classification algorithm approach is applied for credit card fraud detection. We use different types of classification algorithms such as SVM, Naïve bayes and Random forest. We consider their results based on confusion matrix for the above classification algorithms. We analyze their performance based on accuracy, precision, recall and f1-score. We compare random forest algorithm with other algorithm. We considered random forest algorithm has greatest accuracy, precision, recall and F1-score, considered as the best algorithm that is used to detect the fraud.
2020
As electronic commerce has gained wide spread popularity payments made for the transactions performed by users through credit card also gained an equal amount of reputation. Whenever shopping through web is made the chance for the occurrence of fraudulent activities are escalating. In this paper we have proposed a three phase scheme to detect the fraudulent activities. A profile for the card users based on their behavior is created by employing machine learning technique. In the second phase extraction of group of precise communicative pattern for the card users depending upon the accumulated transactions and the users earlier transactions. A collection of classifiers are then trained based on all behavioral pattern. The trained collection of classifiers are then used to detect the online fraudulent activities occurred and if an emerging transaction is found to be fraudulent, a feedback is taken which resolves the quandary caused by the drift in the notion. Experiments performed ind...
IRJET- Optimized Random Forest for Credit Card Fraud Detection with User Interface
IRJET, 2020
Due to the rapid advancement in electronic commerce technology, the use of credit card has dramatically increased. With the development in information technology and improvements in communication channels, credit card fraud events are also spreading. Credit card frauds are those events in which the criminals make use of a stolen card to steal the confidential information of other peoples credit card. Due to its growing trend of transaction frauds, it has resulted in great loss of money every year. Currently in an online transaction environment the physical card is no longer required as if information can used to make a payment. This has made it much easier for criminals to conduct a fraud which has brought a negative influence on economy. Therefore ,fraud detection is very essential in order to identify fraud on time before the criminal uses stolen information.so here we are proposing an effective fraud detection model using the random forest ML algorithm in an optimized way with an user interface can capable of predicting the credit card fraud events more accurately. We are also going through the limitations of the machine learning algorithm that we selected for this prediction model and we are trying maximum to over come this limitation through the parameter tuning. We are training the system with the normal and abnormal behavior features. The prediction models classify the test transaction that we given is fraud or not. Finally we are also providing and user interface as an security enhancement, while any fraud events may occurred.
A Survey on Fraudulent Transaction Detection using Random Forest
International Journal of Scientific Research in Science, Engineering and Technology, 2022
In the evolution of the electronic money system, frequent transaction fraud has been a shadow behind the prosperity. It not only endangers the property security of users, but also hinders the development of digital finance in the world. With the development of data mining and machine learning, some mature technologies are gradually applied to the detection of transaction fraud. This paper proposes a transaction fraud detection model based on random forest. The experimental results of IEEE CIS fraud dataset show that the method of this model is better than the benchmark model, such as logistic regression, support vector machine. Finally, the accuracy of our model reached 97.4%, and the AUC ROC score was 92.7%. The random forest classifier is composed of a group of decision trees. Each tree is generated by independent sampling random vectors, and each tree votes to find the most popular category to classify the input. Random forest has both sample randomness and characteristic randomness, and its generalization performance is superior. At the same time, random forest has good processing ability for high-dimensional data sets, which is very suitable for IEEE CIS data sets. It can process a large number of inputs and determine the most important characteristics. Therefore, further feature mining is carried out on the data extracted by RFECV.
Credit Card Fraud Detection using Machine Learning Framework
Journal of emerging technologies and innovative research, 2020
Machine Learning has been successfully applied to finance databases to automate analysis of huge volumes of complex data. Machine Learninghas 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. This paper research and checks the performance of Random Forest on highly skewed credit card fraud data. Dataset of credit card transactions is sourced from European cardholders containing 1 lakh transactions. These techniques are applied on the raw and pre-processed data. The performance of the techniques is evaluated based on accuracy, sensitivity, and specificity, precision.
MACHINE LEARNING APPROACHES FOR CREDIT CARD FRAUD DETECTION: A PREDICTIVE ANALYSIS
IAEME PUBLICATION, 2024
Technology is increasing at a very rapid pace and with this growing technology ecommerce and online transactions have also grown up and it mostly contains transactions through credit cards. When people use credit cards more often, the chances of credit card fraud are rising drastically. The most susceptible system to fraud is the credit card system. Customers and financial institutions lose billions of dollars per year due to credit card fraud, and criminals are constantly searching for new ways to commit crimes. As a result, for banks and financial institutions to reduce their losses, fraud detection systems have become critical. This research investigates the output of different types of classification models, including Decision Tree, Random Forest, Support Vector Machines (SVM), Naive Bayes, and K-Nearest Neighbors (KNN). An open data set on credit card transactions from Kaggle was used in this research. Three classifiers namely random forest, KNN, and SVM were used for modelling. The performance of the three machine learning models was compared using Precision, Recall, Accuracy, and F1 measures. It was found that Random Forest and KNN performed equally better than SVM. The knowledge acquired from this research may direct future investigations targeted at enhancing the ability of financial institutions and banks to withstand such fraudulent activities
Effective Machine Learning Approaches for Credit Card Fraud Detection
Innovations in Bio-Inspired Computing and Applications: Proceedings of the 11th International Conference on Innovations in Bio-Inspired Computing and Applications (IBICA 2020) held during December 16-18, 2020, 2021
Credit card is a critical element in digital banking. As modern technology is evolving day by day, credit card fraud is increasing significantly. Many financial companies lose billions of dollars annually because of credit card fraud. Criminals constantly try to discover various rules to require illegal actions. As a result, fraud detection methods are crucial for non-banking and banking financial institutions for minimizing the losses. This paper mainly discusses the performance of random forest (RF), AdaBoost, CatBoost classifiers for classification of credit card fraudulent activities. In this paper, the most important features are also selected by applying these classifiers. Results show that among the classifiers considered, RF and CatBoost have the best performance obtaining 99.92% accuracy in detecting credit card frauds.
JUITA: Jurnal Informatika
The following credit card records were used in this study of 284.807 transactions made by credit card holders in Europe for two days from the Kaggle dataset. This is a very poor data set, having 492 transactions, an imbalance of only 0.172% of the 284.807 transactions. The purpose of this study is to obtain the best model and then simulate it by electronically detecting unauthorized financial transactions in bank payment systems. The dataset for this study is unbalanced class data with 99.80% for the major class and 0.2% for the minor class. This type of class-imbalanced data problem is solved by applying method a combination of minority oversampling techniques using Synthetic Minority Oversampling Technique (SMOTE). To determine the most appropriate and accurate classification in solving class balance problems, comparisons were made with the Random Forest Classifier (RFC), Logistic Regression (LGR), and Gradient Boosting Classifier (GBC) algorithms. The test results in this study a...