Credit Card Fraud Detection System Research Articles

Sustainable Financial Fraud Detection Using Garra Rufa Fish Optimization Algorithm with Ensemble Deep Learning

Sustainable financial fraud detection (FD) comprises the use of sustainable and ethical practices in the detection of fraudulent activities in the financial sector. Credit card fraud (CCF) has dramatically increased with the advances in communication technology and e-commerce systems. Recently, deep learning (DL) and machine learning (ML) algorithms have been employed in CCF detection due to their features’ capability of building a powerful tool to find fraudulent transactions. With this motivation, this article focuses on designing an intelligent credit card fraud detection and classification system using the Garra Rufa Fish optimization algorithm with an ensemble-learning (CCFDC-GRFOEL) model. The CCFDC-GRFOEL model determines the presence of fraudulent and non-fraudulent credit card transactions via feature subset selection and an ensemble-learning process. To achieve this, the presented CCFDC-GRFOEL method derives a new GRFO-based feature subset selection (GRFO-FSS) approach for selecting a set of features. An ensemble-learning process, comprising an extreme learning machine (ELM), bidirectional long short-term memory (BiLSTM), and autoencoder (AE), is used for the detection of fraud transactions. Finally, the pelican optimization algorithm (POA) is used for parameter tuning of the three classifiers. The design of the GRFO-based feature selection and POA-based hyperparameter tuning of the ensemble models demonstrates the novelty of the work. The simulation results of the CCFDC-GRFOEL technique are tested on the credit card transaction dataset from the Kaggle repository and the results demonstrate the superiority of the CCFDC-GRFOEL technique over other existing approaches.

A Novel Method of Fraud Detection of Credit Cards by Fuzzy, LSTM, and PSO Optimization

Since online shopping has become so popular, credit card theft has skyrocketed. This makes it hard to spot fake charges on accounts. In this research, credit card fraud detection is performed using a fuzzy database. It has been considered a data mining challenge to reliably identify whether or not a transaction is legitimate. This paper discusses the LSTM method and fuzzy logic. The learning process was sped up and made more precise by using a technique called particle swarm optimization (PSO). Performance values have been compared with those of the LSTM and fuzzy logic techniques, and a PSO-based neural network has been intensively trained by increasing the number of iterations and the population, or number of swarms. It has been shown that the PSO-based algorithm yields the best result for detecting fraudulent transactions. The goal of this method is to lower the mean square error (MSE) rate of the system. PSO is a popular optimization technique that can be used to locate the optimal set of features for the credit card fraud detection system. The proposed method PSO shows less mean squared error compared with Fuzzy and LSTM techniques.

CCNN: An Artificial Intelligent based Classifier to Credit Card Fraud Detection System with Optimized Cognitive Learning Model

Nowadays digital transactions play a vital role in money transaction processes. Last 5 years statistical report portrays the growth of internet money transaction especially credit card and unified payments interface. Mean time increasing numerous banking threats and digital transaction fraud rates also growing significantly. Data engineering techniques provide ultra supports to detect credit card forgery problems in online and offline mode transactions. This credit card fraud detection (CCFD) and prevention-based data processing issues raising because of two major reasons first, classification rate of legitimate and forgery uses is frequently changing, and next one is fraud detection dataset values are vastly asymmetric. Through this research work investigating performance of various existing classifier with our proposed cognitive convolutional neural network (CCNN) classifier. Existing classifiers like Logistic Regression (LR), K-nearest neighbor (KNN), Decision Tree (DT) and Support Vector Machine (SVM). These models are facing various challenges of low performance rate and high complexity because of low hit rate and accuracy. Through this research work we introduce cognitive learning-based CCNN classifier methodology with artificial intelligence for achieve maximum accuracy rate and minimal complexity issues. For experimental data analysis uses dataset of credit card transactions attained from specific region cardholders containing 284500 transactions and its various features. Also, this dataset contains unstructured and non-dimensional data are converted into structured data with the help of over sample and under sample method. Performance analysis shows proposed CCNN classifier model provide significant improvement on accuracy, specificity, sensitivity and hit rate. The results are shown in comparison. After cross-validation, the accuracy of the CCNN classification algorithm model for transaction fraudulent detection archived 99% which using the over-sampling model.

Smart credit card fraud detection system based on dilated convolutional neural network with sampling technique

Smart credit card fraud detection system based on dilated convolutional neural network with sampling technique

Credit Card Fraud Detection using Big Data Technologies

The use of credit cards is prevalent in modern day society. But it is obvious that the number of credit card fraud cases is constantly increasing in spite of the chip cards worldwide integration and existing protection systems. This is why the problem of fraud detection is very important now. Credit card fraud detection is the most frequently occurring problem in the present world. This is due to the rise in both online transactions and e-commerce platforms. 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. In the present world, we are facing a lot of credit card problems. To detect the fraudulent activities the credit card fraud detection system was introduced. This project aims to focus mainly on validating various Business Rules to Identify whether the transaction happened is Fraud/Genuine and report the same accordingly.

Credit Card Fraud Detection using Decision Tree and Random Forest

It is the time of technology advancement. Due to internet everything is available at the touch of a finger. There is a benefit of online shopping: first it saves lots of time and second it does not demand to go to market to buy anything. There exists various mode of payments and credit card payment is one of them. Today, there exists a good number of credit card users in the world. Every day so many credit cards transactions are taken place. Some of these transactions are fraudulent. Due to such fraudulent transactions banks and customers need to suffer. In order to prevent financial losses due to credit card fraud, a secure credit card fraud detection system is essential. Various machine learning algorithms like Naïve Bayes, Logistic regression, SVM, Decision trees, Random Forest, Genetic algorithm, J48 and AdaBoost, etc. are used for credit card fraud detection. The motive of this paper is to provide some insight about the credit card fraud along with the analysis of the dataset and also Decision tree and random forest algorithms are going to be discussed.

Credit card fraud detection system

Credit card fraud is a significant problem for financial institutions and cardholders and can result in significant financial losses and damage to reputation. Detecting and preventing fraud in credit card transactions is critical to minimizing losses and maintaining customer trust. The main objective of this project is to build a model (a web-based system) that can effectively identify fraudulent transactions. This involves collecting and processing large datasets of credit card transactions, including both legitimate and fraudulent transactions, and using machine learning algorithms like decision trees and random forests to train a model to recognize patterns and anomalies. In addition, the system will have strong user authentication protocols that must be in place to prevent unauthorized access to the system.

RHSOFS: Feature Selection Using the Rock Hyrax Swarm Optimization Algorithm for Credit Card Fraud Detection System

In recent years, detecting credit card fraud transactions has been a difficult task due to the high dimensions and imbalanced datasets. Selecting a subset of important features from a high-dimensional dataset has proven to be the most prominent approach for solving high-dimensional dataset issues, and the selection of features is critical for improving classification performance, such as the fraud transaction identification process. To contribute to the field, this paper proposes a novel feature selection (FS) approach based on a metaheuristic algorithm called Rock Hyrax Swarm Optimization Feature Selection (RHSOFS), inspired by the actions of rock hyrax swarms in nature, and implements supervised machine learning techniques to improve credit card fraud transaction identification approaches. This approach is used to select a subset of optimal relevant features from a high-dimensional dataset. In a comparative efficiency analysis, RHSOFS is compared with Differential Evolutionary Feature Selection (DEFS), Genetic Algorithm Feature Selection (GAFS), Particle Swarm Optimization Feature Selection (PSOFS), and Ant Colony Optimization Feature Selection (ACOFS) in a comparative efficiency analysis. The proposed RHSOFS outperforms existing approaches, such as DEFS, GAFS, PSOFS, and ACOFS, according to the experimental results. Various statistical tests have been used to validate the statistical significance of the proposed model.

Machine Learning for Credit Card Fraud Detection System

In recent years, for banks has become terribly troublesome for police investigation the fraud in credit-card system. Machine learning plays a significant role for police investigation the credit-card fraud within the transactions. For predicting these transactions banks build use of assorted machine learning methodologies, past knowledge has been collected and new options square measure been used for enhancing the prophetical power. The performance of fraud police investigation in credit-card transactions is greatly full of the sampling approach on data-set, choice of variables and detection techniques used. This paper investigates the performance of supply regression for credit-card fraud detection. Dataset of credit-card transactions is collected from Kaggle and it contains a complete of two,84,808 credit-card transactions of a ecu bank knowledge set. It considers fraud transactions because the “positive class” and real ones because the “negative class”. the info set is very unbalanced, it's concerning zero.172% of fraud transactions and also the rest square measure real transactions.

Application of the automated teller machine (ATM) card digit validation algorithm as a credit card fraud detection system

An Automated Teller Machine (ATM) card is a type of credit card that is a transaction tool, especially in trading. Payment activities can be done at various service providers because banks have multifunctional ATM cards. Payment transactions can be done through the internet today, but it can cause problems of crime in both the real world and the virtual world. To solve this problem, the ATM card number is designed to be verified first before the related bank verification number. The method is called the check method on a combination of numbers known as the check digit algorithm. This study uses the Luhn Algorithm to prove the validity of the ATM card used as a transaction. The goal is to find out the authenticity of the digit number of an ATM card and avoid fraud such as counterfeiting the digit number of an ATM card. Based on the tests conducted, it is concluded that the Luhn algorithm can be used as an effective input data validation technique. This is an initial check before users make transactions using ATM cards to avoid misuse and fraud of ATM card data by irresponsible parties. Kartu Automated Teller Machine (ATM) merupakan salah satu jenis kartu kredit yang menjadi alat transaksi khususnya dalam perdagangan. Aktivitas pembayaran dapat dilakukan pada berbagai penyedia jasa layanan karena bank telah memultifungsikan kartu ATM. Transaksi pembayaran dapat dilakukan melalui internet saat ini, namun dapat menimbulkan masalah terjadinya kejahatan baik di dunia nyata maupun dunia maya. Untuk mengatasi masalah tersebut, nomor kartu ATM dirancang untuk diverifikasi terlebih dahulu sebelum nomor verifikasi bank terkait. Metode disebut metode cek pada kombinasi angka yang dikenal dengan algoritma cek digit. Penelitian ini menggunakan Algoritma Luhn untuk membuktikan keabsahan kartu ATM yang dipakai sebagai transaksi. Tujuannya adalah untuk mengetahui keaslian nomor digit dari sebuah kartu ATM dan menghindari penipuan seperti pemalsuan nomor digit dari sebuah kartu ATM. Berdasarkan pengujian yang dilakukan menyimpulkan bahwa Algoritma Luhn dapat digunakan sebagai sebuah teknik validasi data masukan yang efektif. Ini sebagai awal pengecekan sebelum pengguna melakukan transaksi menggunakan kartu ATM untuk menghindari penyalahgunaan dan penipuan data kartu ATM oleh pihak-pihak yang tidak bertanggung jawab.

Credit Card Fraud Detection System

Since the growth in online shopping, an increasing number of people are paying online using modes of payment like credit cards to pay their bills, and like every other online interface, Credit cards are also subject to hacking. online businesses and financial companies need to detect these fraudulent activities to save their clients from getting charged for items they didn’t purchase. For this purpose, banks and payment companies can implement algorithms to detect fraudulent behavior. The Credit Card Fraud Detection System includes taking into consideration past credit card transactions with the data of the ones that were fraud. This system can be used to identify a fraudulent transaction beforehand. Our objective is to identify all of the fraudulent transactions while also minimizing the incorrect fraud classifications. In this technique we first examine and pre-process the Data set to address unbalanced data, then we train our model using a Logistic regression algorithm to detect fraud.

Survey Paper on Credit card Fraud Detection System

Abstract: Credit card fraud detection is presently the most frequently occurring problem in the present world. This is due to the rise in both online transactions and e-commerce platforms. 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. In the present world, we are facing a lot of credit card problems. To detect the fraudulent activities the credit card fraud detection system was introduced.

Implementation of Credit Card Fraud Detection Using Random Forest Algorithm

Abstract: 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. Keywords: Fraud detection, Naive Bayes, SVM, and Random Forest.

Developing an Intelligent Credit Card Fraud Detection System with Machine Learning

Credit card fraud is a significant issue in the economic services sector. Each year, billions of rupees are lost due to credit card fraud. Due to confidentiality concerns, there are an absence of studies examining actual credit card records. In this paper, machine learning algorithms are employed to detect credit card fraud. First, standard models are utilized. Then, hybrid techniques consisting of Random Forest, AdaBoost, XGBoost, and majority voting are implemented. To evaluate the effectiveness of the version, a set of publicly accessible credit card records is utilized. Then, credit card records from a real-world economic institution are analyzed. The experimental results suggest that Random Forest and majority voting accomplish precise accuracy estimates for detecting credit card fraud instances.

REVIEW on FRAUD DETECTION in CREDIT CARD TRANSACTIONS USING MACHINE LEARNING TECHNIQUES

Due to the rapid improvement in the e-commerce technology, the utilization of the credit cards has augmented to the maximum. Usage of credit card has become the trendiest style of payment for an individual online as well as habitual acquisition, luggage of credit card fraud also growing linearly. Economic fraud is increasing highly with the development of modern technology, consequential in the loss of billions of dollars worldwide each year. The fraud transactions have been happening very much lately, such that the simple pattern corresponding techniques is not sufficient to find. Implementation of efficient fraud detection systems based on machine learning techniques has thus become effective for all credit card issuing banks to minimize their loss. Many techniques based on Artificial Intelligence, Data mining, Machine learning, Genetic Programming etc., has evolved in detecting various credit card fraudulent transactions. The details of the credit card transactions undergo a scrutiny process to allow a modeled credit card fraud detection system to be tested. This method gives an accurate and fraud score and tries to classify a transaction between a genuine one and a fraud one, the probability of fraud transactions can also be determined.

Credit Card Fraud Detection Based on Random Forest Model

This paper uses a classifier named random forest to detect credit card fraud. Credit card fraud is one of the main issues in the economic industry. To construct a credit card fraud detection system, a certain amount of samples is required. In this paper, a dataset containing 284,807 credit card transactions is used. This dataset has gone through the PCA transformation and includes 492 frauds out of 284,807 transactions. Based on the huge amount of data and imbalanced samples, this paper compresses the dataset and uses the synthetic minority over-sampling technique (SMOTE) to address the problem of imbalanced samples. Also, in this paper, we use random forest as a classification model while constructing the fraud detection system/method.

Credit Card Fraud Detection System Using Machine Learning

The security of any system is a key factor toward its acceptability by the general public. We propose an intuitive approach to fraud detection in financial institutions using machine learning by designing a Hybrid Credit Card Fraud Detection (HCCFD) system which uses the technique of anomaly detection by applying genetic algorithm and multivariate normal distribution to identify fraudulent transactions on credit cards. An imbalance dataset of credit card transactions was used to the HCCFD and a target variable which indicates whether a transaction is deceitful or otherwise. Using F-score as performance metrics, the model was tested and it gave a prediction accuracy of 93.5%, as against artificial neural network, decision tree and support vector machine, which scored 84.2%, 80.0% and 68.5% respectively, when trained on the same data set. The results obtained showed a significant improvement as compared with the other widely used algorithms.

Behavioral Based Credit Card Fraud Detection

Credit card fraud is a significant threat in the BFSI sector. This credit card fraud detection system analyzes user behavioral patterns and their location to identify any unusual patterns. This consists of user characteristics, which includes user spending styles as well as standard user geographic places to verify his identity. One of the user behavior patterns includes spending habits, usage patterns, etc. This system deals with user credit card data for various characteristics, which includes user country, usual spending procedures. Based upon previous transactions information of that person, the system recognizes unusual patterns in the payment method. The fraud detection system contains the past transaction data of each user. Based on this data, it identifies the standard user behavior patterns for individual users, and any deviation from those normal user patterns becomes a trigger for the detection system. If it detects any unusual patterns, then user will be required to undergo the security verification, which identifies the original user using QR code recognition system. In case of any unusual activity, the system not only raises alerts but it will block the user after three invalid attempts.

Support Vector Machine with Information Gain Based Classification for Credit Card Fraud Detection System

In the credit card industry, fraud is one of the major issues to handle as sometimes the genuine credit card customers may get misclassified as fraudulent and vice-versa. Several detection systems have been developed but the complexity of these systems along with accuracy and precision limits its usefulness in fraud detection applications. In this paper, a new methodology Support Vector Machine with Information Gain (SVMIG) to improve the accuracy of identifying the fraudulent transactions with high true positive rate for the detection of frauds in credit card is proposed. In SVMIG, the min-max normalization is used to normalize the attributes and the feature set of the attributes are reduced by using information gain based attribute selection. Further, the Apriori algorithm is used to select the frequent attribute set and to reduce the candidate’s itemset size while detecting fraud. The experimental results suggest that the proposed algorithm achieves 94.102% higher accuracy on the standard dataset compared to the existing Bayesian and random forest based approaches for a large sample size in dealing with legal and fraudulent transactions

Credit Card Fraud Detection using PSO Optimized Neural Network

Credit card fraud is one of the most important problems that financial institutions are currently facing. Although the technology has allowed to increase the security in the credit cards with the use of PIN keys, the introduction of chips in the cards, the use of additional keys such as tokens and improvements in the regulation of its use is also a necessity for banks, to act preventively against this crime. To act preventively, it is necessary to monitor in real time the operations that are carried out and have the ability to react in a timely manner against any doubtful operation that is performed. This paper presents an implementation of automatic credit card fraud detection system using Particle Swarm Optimized Neural Network classifier on Kaggle dataset. The selection of proper attributes for reducing the training overhead and claiming higher accuracy for the fraud detection using soft computing. Performance evaluation is achieved using confusion matrix plot with accuracy, sensitivity and precision values.