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M.S, Prateeksha, B. Naga Swetha, and Manjula Patil. "CREDIT CARD FRAUD DETECTION USING MACHINE-LEARNING." International Journal of Advanced Research 11, no. 04 (April 30, 2023): 1559–63. http://dx.doi.org/10.21474/ijar01/16824.
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The recent advances of e-commerce and e-payment systems have sparked an increase in financial fraud cases such as credit card fraud. It is therefore crucial to implement mechanisms that can detect the credit card fraud. Features of credit card frauds play important role when machine learning is used for credit card fraud detection, and they must be chosen properly. This paper proposes a machine learning (ML) based credit card fraud detection engine using ML classifiers: Decision Tree (DT), Logistic Regression (LR), Artificial Neural Network (ANN). To validate the performance, the proposed credit card fraud detection engine is evaluated using a dataset generated from European cardholders. The result demonstrated that our proposed approach outperforms existing systems.
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Banu, Muskan, and Prof Kavitha G. "Credit Card Fraud Detection Using Machine Learning Algorithms." International Journal for Research in Applied Science and Engineering Technology 10, no. 10 (October 31, 2022): 1018–23. http://dx.doi.org/10.22214/ijraset.2022.47127.
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Abstract: Credit Card Fraud can be defined as a case where a person uses someone else’s credit card for personal reasons while the owner and the card-issuing authorities are unawareof the fact that the card is being used. 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. In the era of digitalization, the need to identify credit card frauds is necessary. Fraud detection involves monitoring and analyzing the behaviour of various users to estimate, detect or avoid undesirable behaviour. To identify credit card fraud detection effectively, we need to understand the various technologies, algorithms and types involved in detecting credit card frauds. The algorithm can differentiate transactions which are fraudulent or not. To find fraud, we need to pass dataset and knowledge of the fraudulenttransaction. Algorithms analyze the dataset and classify all transactions. Fraud detection involves monitoring the activitiesof populations of users to estimate, perceive or avoidobjectionable behaviour, which consist of fraud, intrusion, and defaulting. Machine learning algorithms are employed to analyse all the authorized transactions and report the suspicious ones. We have taken an imbalanced dataset of transactions to detect the frauds
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Bala, Bala Santhosh, Pasupula Praveen Yadav, and Mogathala Raghavendra Reddy. "An intelligent approach to detect and predict online fraud transaction using XGBoost algorithm." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 3 (September 1, 2024): 1491. http://dx.doi.org/10.11591/ijeecs.v35.i3.pp1491-1498.
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The most popular payment method in recent years is the credit card. Due to the E-commerce industry’s explosive growth, the usage of credit cards for online purchases have been greatly increased as a result frauds has increased. Banks have been facing challenges to detect the credit card system fraud in recent years. Credit card fraud happens when the card was stolen for any unauthorized purposes or if the fraudster utilizes the credit card information for his own use. In order to prevent credit card fraud, it is essential to build detection measures. While detecting credit card theft with machine learning (ML), the features of credit card frauds play an important and they must be carefully selected. A fraud detection algorithm must be created in order to correctly locate and stop fraudulent activity as technology advances along with the amount of fraud cases. ML methods are essential for identifying fraudulent transactions. The implementation of fraud detection models is particularly difficult because of the sensitive nature of the data, the unbalanced class distributions, and the lack of data. In this work, an intelligent approach to detect and predict online fraud transaction using extreme gradient boosting (XGBoost) algorithm is described. The XGBoost model predicts whether a transaction is fraud or not. This model will achieve better performance interarm of recall, precision, accuracy and F1-score for credit card fraud detection.
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Salian, Prof S. R. "Credit Card Fraudulent Transaction Detection and Prevention." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (April 30, 2023): 3255–60. http://dx.doi.org/10.22214/ijraset.2023.50849.
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Abstract: In recent years, online payment methods have been used widely as an outcome of the rapid increase in non-cash and digital electronic transactions. Credit cards represent one of the electronic payment methods. With the advancement of online payments in various products and services, the likelihood of credit card fraud has risen compared to the decades-long history of credit cards. The credit card frauds can be detected by evaluating the credit card purchasing patterns using the historical data in order to detect the frauds. This data evaluation can help the banks or other organizations offering credit cards to minimize their losses due to the credit card frauds. The historical data evaluation with the current purchasing patterns requires statistical modeling, which can automatically evaluate the fraudulent patterns and alarm the banks about the transactions. This helps the banks for early detection of the frauds, where they can easily eliminate the credit card frauds by declining the suspected transactions. And also blockchain technology is applied to prevent the hacker to view customers details so that fraudsters can't use stolen credit card information to open new accounts, obtain loans, and engage in other illegal activities. Credit card fraud detection and prevention have become essential for banks and other financial institutions to safeguard their customers' financial transactions. This paper presents an overview of credit card fraud detection and prevention techniques.
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Singh, Ajeet, and Anurag Jain. "An Empirical Study of AML Approach for Credit Card Fraud Detection–Financial Transactions." International Journal of Computers Communications & Control 14, no. 6 (November 27, 2019): 670–90. http://dx.doi.org/10.15837/ijccc.2019.6.3498.
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Credit card fraud is one of the flip sides of the digital world, where transactions are made without the knowledge of the genuine user. Based on the study of various papers published between 1994 and 2018 on credit card fraud, the following objectives are achieved: the various types of credit card frauds has identified and to detect automatically these frauds, an adaptive machine learning techniques (AMLTs) has studied and also their pros and cons has summarized. The various dataset are used in the literature has studied and categorized into the real and synthesized datasets.The performance matrices and evaluation criteria have summarized which has used to evaluate the fraud detection system.This study has also covered the deep analysis and comparison of the performance (i.e sensitivity, specificity, and accuracy) of existing machine learning techniques in the credit card fraud detection area.The findings of this study clearly show that supervised learning, card-not-present fraud, skimming fraud, and website cloning method has been used more frequently.This Study helps to new researchers by discussing the limitation of existing fraud detection techniques and providing helpful directions of research in the credit card fraud detection field.
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Venkata Suryanarayana, S., G. N. Balaji, and G. Venkateswara Rao. "Machine Learning Approaches for Credit Card Fraud Detection." International Journal of Engineering & Technology 7, no. 2 (June 5, 2018): 917. http://dx.doi.org/10.14419/ijet.v7i2.9356.
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With the extensive use of credit cards, fraud appears as a major issue in the credit card business. It is hard to have some figures on the impact of fraud, since companies and banks do not like to disclose the amount of losses due to frauds. At the same time, public data are scarcely available for confidentiality issues, leaving unanswered many questions about what is the best strategy. Another problem in credit-card fraud loss estimation is that we can measure the loss of only those frauds that have been detected, and it is not possible to assess the size of unreported/undetected frauds. Fraud patterns are changing rapidly where fraud detection needs to be re-evaluated from a reactive to a proactive approach. In recent years, machine learning has gained lot of popularity in image analysis, natural language processing and speech recognition. In this regard, implementation of efficient fraud detection algorithms using machine-learning techniques is key for reducing these losses, and to assist fraud investigators. In this paper logistic regression, based machine learning approach is utilized to detect credit card fraud. The results show logistic regression based approaches outperforms with the highest accuracy and it can be effectively used for fraud investigators.
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Ishak, Nur Amirah, Keng-Hoong Ng, Gee-Kok Tong, Suraya Nurain Kalid, and Kok-Chin Khor. "Mitigating unbalanced and overlapped classes in credit card fraud data with enhanced stacking classifiers system." F1000Research 11 (January 21, 2022): 71. http://dx.doi.org/10.12688/f1000research.73359.1.
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Background: Credit cards remain the preferred payment method by many people nowadays. If not handled carefully, people may face severe consequences such as credit card frauds. Credit card frauds involve the illegal use of credit cards without the owner’s knowledge. Credit card fraud was estimated to exceed a $35.5 billion loss globally in 2020, and results in direct or indirect financial loss to the owners. Hence, a detection system capable of analysing and identifying fraudulent behaviour in credit card activities is highly desirable. Credit card data are not easy to handle due to their inherited problems: (i) unbalanced class distributions and (ii) overlapping classes. General learning algorithms may not be able to address and handle the problems well. Methods: This study addresses these problems using an Enhanced Stacking Classifiers System (ESCS) that comprises two sequential levels. The first level is an excellent classifier for detecting normal credit card transactions (the majority class), while the second level contains stacking classifiers that distinguish credit card frauds (the minority class). The ESCS can improve the fraud detection via the second level, which contains sensitive classifiers to identify the misclassified fraud transactions as normal transactions from the first classifier. The meta-classifier then combines the decisions of the base classifiers from the levels to produce the final detections. Results: We evaluated the ESCS using the benchmark credit card fraud dataset (CCFD) that exhibits the two problems. The highest true positive rate (TPR) for detecting credit card frauds was 0.8841, which outperformed the single classifiers, bagging, boosting, and other researchers’ works. Conclusions: This study proves that the ESCS, with an additional level added to the stacking classifiers, can improve fraud detection on credit card data.
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Atchaya, S. "Credit Card Fraud Detection using ANN." International Journal for Research in Applied Science and Engineering Technology 12, no. 2 (February 29, 2024): 170–75. http://dx.doi.org/10.22214/ijraset.2024.58284.
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Abstract: Frauds in credit card transactions are common today as most of us are using the credit card payment methods more frequently. This is due to the advancement of Technology and increase in online transaction resulting in frauds causing huge financial loss. Therefore, there is need for effective methods to reduce the loss. In addition, fraudsters find ways to steal the credit card information of the user by sending fake SMS and calls, also through masquerading attack, phishing attack and so on. This paper aims in using the multiple algorithms of Machine learning such as support vector machine (SVM), k-nearest neighbor (Knn) and artificial neural network (ANN) in predicting the occurrence of the fraud. Further, we conduct a differentiation of the accomplished supervised machine learning and deep learning techniques to differentiate between fraud and non-fraud transactions.
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More, Anushka, Nidhi Musale, Himani Ranpariya, Sarthak Salunke, and Prof Sujit Tilak. "Credit Card Fraud Detection System." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (March 31, 2022): 776–80. http://dx.doi.org/10.22214/ijraset.2022.40744.
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Abstract: Credit Cards are quite useful for day to day life. The main aim of this project is to detect fraud accurately. With the increase in fraud rates, researchers have started using different machine learning methods to detect and analyze frauds in online transactions. 'Fraud' in credit card transactions is unauthorized and unwanted usage of an account by someone other than the owner of that account. Fraud detection involves monitoring the activities of users in whole in order to estimate, perceive or avoid objectionable behavior, which consist of fraud, intrusion, and defaulting. The problem itself is more challenging with respect to data science since the number of valid transactions far outnumber fraudulent ones. Also, the transaction patterns often change their statistical properties over the course of time. However, the massive stream of payment requests is quickly scanned by automatic tools that determine which transactions to authorize. Also, Messages are generated to confirm with the owner about the transactions. Machine learning algorithms are employed to analyze all the authorized transactions and report the suspicious ones. These reports are investigated by professionals who contact the cardholders to confirm if the transaction was genuine or fraudulent. This project also designs and develops a novel fraud detection method for Streaming Transaction Data, with an objective, to analyze the past transaction details of the customers and extract the behavioral patterns. To name a few techniques which we are going to implement are Isolation Forest Algorithm, Random Forest Algorithm, Logistic Regression, Confusion Matrix and Sliding-Window method. Keywords: Credit card fraud, isolation forest, local outlier factor, random forest, confusion matrix
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Patil, Pravin. "Card Defender - Credit Card Fraud Detection System." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 4775–80. http://dx.doi.org/10.22214/ijraset.2023.52748.
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Abstract: As the world becomes increasingly digitized, online transactions have become an indispensable part of our daily lives. The increased use of credit cards for online purchases has resulted in a growing concern about credit card fraud, both for businesses and consumers. To combat this issue, we propose a two-factor authentication system that integrates credit card verification with webcam-based face recognition technology to prevent online transaction fraud. Our system provides a reliable and user-friendly solution for credit card fraud detection using face recognition. By implementing a two-factor authentication process, our system reduces the risk of fraud during online transactions and enhances the overall security of online payments
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Journal, IJSREM. "Secure Scan -Analysis of Credit Card Fraud Detection Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 11 (November 1, 2023): 1–11. http://dx.doi.org/10.55041/ijsrem27368.
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Credit Card usage has been drastically increased across the world , now people believe in going cashless and are completely dependent on online transactions .The credit card has made the digital transaction easier and nowadays credit card frauds are drastically increasing in numbers compared to earlier times . A powerful fraud detection system is required to stop these frauds . Fraud detection is the process of monitoring the transaction behaviour of a cardholder to detect whether an incoming transaction is authentic and authorised or not otherwise it will be detected as illicit .Machine learning -based fraud detection systems rely on machine learning algorithms that can be trained with historical data and can perform classification , regression or finding the patterns among the data . Keywords: Credit card machine learning , fraud detection ,historical data, algorithms.
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Nandi, Soumyajit. "Credit Card Fraud Detection Using Random Forest Classification." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (June 30, 2023): 2383–90. http://dx.doi.org/10.22214/ijraset.2023.53990.
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Abstract: In recent years credit card became one of the essential parts of the people. Sudden increase in E-commerce, customer started using credit card for online purchasing therefore risk of fraud also increases. Instead of carrying a huge amount in hand it is easier to keep credit cards. But nowadays that too becomes unsafe. Nowadays we are facing a big problem on credit card fraud which is increasing in a good percentage. The main purpose is the survey on the various methods applied to detect credit card frauds. From the abnormalities, in the transaction, the fraudulent one is identified. We address this issue in order to implement some machine learning algorithm like random forest, logistic regression in order to detect this kind of fraud. In this paper we increase the efficiency in finding the fraud. However, we discussed and evaluated employee criteria. Currently, the issues of credit card fraud detection have become a big problem for new researchers. We implement an intelligent algorithm which will detect all kind of fraud in a credit card transaction. We handled the problem by finding a pattern of each customer in between fraud and legal transaction. Isolation Forest Algorithm and Local Outlier Factor are used to predict the pattern of transaction for each customer and a decision is made according to them. In order to prevent data from mismatching, all attribute are marked equally.
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Trivedi, Ishu, Monik M, and Mrigya Mridushi. "Credit Card Fraud Detection." IJARCCE 5, no. 1 (January 30, 2016): 39–42. http://dx.doi.org/10.17148/ijarcce.2016.5109.
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Gurav, Prof R. B., Mrs Shraavani Mandar Badhe, Mrs Sakshi Nagtilak, Mr Sarthak Pandit Sonawane, and Mr Siddhant Agarwal. "Credit Card Fraud Detection." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 2009–12. http://dx.doi.org/10.22214/ijraset.2022.41594.
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Abstract: Now a day’s online transactions have become an important and necessary part of our lives. It is vital that credit card companies are able to identify fraudulent credit card transactions so that customers are not charged for items that they did not purchase. As frequency of transactions is increasing, number of fraudulent transactions are also increasing rapidly. Such problems can be tackled with Machine Learning with its algorithms. This project intends to illustrate the modelling of a data set using machine learning with Credit Card Fraud Detection. The Credit Card Fraud Detection Problem includes modelling past credit card transactions with the data of the ones that turned out to be fraud. This model is then used to recognize whether a new transaction is fraudulent or not. Our objective here is to detect 100% of the fraudulent transactions while minimizing the incorrect fraud classifications. Credit Card Fraud Detection is a typical sample of classification. In this process, we have focused on analyzing and preprocessing data sets as well as the deployment of multiple anomaly detection algorithms such as Local Outlier Factor and Isolation Forest algorithm on the PCA transformed Credit Card Transaction data. Keywords: Credit Card, Python, Detection
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Gurav, Prof R. B., Mrs Shraavani Mandar Badhe, Mrs Sakshi Nagtilak, Mr Sarthak Pandit Sonawane, and Mr Siddhant Agarwal. "Credit Card Fraud Detection." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 2009–12. http://dx.doi.org/10.22214/ijraset.2022.41594.
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Abstract: Now a day’s online transactions have become an important and necessary part of our lives. It is vital that credit card companies are able to identify fraudulent credit card transactions so that customers are not charged for items that they did not purchase. As frequency of transactions is increasing, number of fraudulent transactions are also increasing rapidly. Such problems can be tackled with Machine Learning with its algorithms. This project intends to illustrate the modelling of a data set using machine learning with Credit Card Fraud Detection. The Credit Card Fraud Detection Problem includes modelling past credit card transactions with the data of the ones that turned out to be fraud. This model is then used to recognize whether a new transaction is fraudulent or not. Our objective here is to detect 100% of the fraudulent transactions while minimizing the incorrect fraud classifications. Credit Card Fraud Detection is a typical sample of classification. In this process, we have focused on analyzing and preprocessing data sets as well as the deployment of multiple anomaly detection algorithms such as Local Outlier Factor and Isolation Forest algorithm on the PCA transformed Credit Card Transaction data. Keywords: Credit Card, Python, Detection
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Gurav, Prof R. B., Shraavani Mandar Badhe, Sarthak Pandit Sonawane, Siddhant Agarwal, and Sakshi Nagtilak. "Credit Card Fraud Detection." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 3499–509. http://dx.doi.org/10.22214/ijraset.2022.42920.
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Abstract: Now a day’s online transactions have become an important and necessary part of our lives. It is vital that credit card companies are able to identify fraudulent credit card transactions so that customers are not charged for items that they did not purchase. As frequency of transactions is increasing, number of fraudulent transactions are also increasing rapidly. Such problems can be tackled with Machine Learning with its algorithms. This project intends to illustrate the modelling of a data set using machine learning with Credit Card Fraud Detection. The Credit Card Fraud Detection Problem includes modelling past credit card transactions with the data of the ones that turned out to be fraud. This model is then used to recognize whether a new transaction is fraudulent or not. Our objective here is to detect 100% of the fraudulent transactions while minimizing the incorrect fraud classifications. Credit Card Fraud Detection is a typical sample of classification. In this process, we have focused on analyzing and preprocessing data sets as well as the deployment of multiple anomaly detection algorithms such as Local Outlier Factor and Isolation Forest algorithm on the PCA transformed Credit Card Transaction data. Keywords: Credit Card, Python, Detection
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Shevgaonkar, Sanskriti, Priyanka Khadse, Omkar Shinde, Tanmay Kulkarni, and Prof Avinash Gondal. "Credit Card Fraud Detection." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (November 30, 2022): 988–93. http://dx.doi.org/10.22214/ijraset.2022.47456.
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Swami, Mrs M. M., Rushikesh Ghuge, Gurshan Singh, Harsh Tiwari, and Rohan Kalaskar. "Credit Card Fraud Detection." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (December 31, 2022): 2368–71. http://dx.doi.org/10.22214/ijraset.2022.47769.
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Abstract: Due to exponential growth in the field of online transactions, credit cards are widely used in most financial aspects and hence there are more risks of fraudulent transactions. These fraudulent transactions can be shown by analysing several behaviours of credit card users from earlier transaction history datasets. If any abnormality is noticed in the behaviour from the existing patterns, there is the possibility of fraudulent transaction. In this project the proposed will use Ensemble Learning Algorithms (XGBoost). By using these models, the proposed system will predict if the transaction is fraudulent or genuine. Therefore, by the implementation of this methodology in fraud detection systems, monetary losses which are caused due to fraudulent transactions can be decreased.
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Singh, HarshVardhan. "Credit Card Fraud Detection." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (May 31, 2024): 2238–44. http://dx.doi.org/10.22214/ijraset.2024.62049.
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Abstract: The primary objective of this project is to implement a front-end system for credit card fraud detection, leveraging machine learning algorithms to identify and flag potentially fraudulent transactions in real-time. The system will provide users with a seamless and intuitive interface to interact with the underlying fraud detection models, enabling proactive mitigation of fraudulent activities. Design and develop an intuitive front-end interface that allows users to interact with the fraud detection system. The front-end will provide functionalities such as inputting transaction details, triggering fraud detection, and displaying the model's predictions and confidence scores.
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Yadav, Yuktam. "CREDIT CARD FRAUD DETECTION." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 06 (June 12, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem35776.
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It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. In an era where digital transactions have become ubiquitous, the security of financial transactions is of paramount importance. The advent of machine learning and data science techniques offers promising avenues for enhancing fraud detection mechanisms. Analysing a dataset is a critical step in obtaining relevant insights from massive amounts of data. This research paper delves into the use case of Data science & Machine Learning and its applications for our major project. It goes into the use of Python as a powerful tool for data analysis, emphasizing its importance in dealing with complex datasets. Furthermore, the project investigates the many Python libraries used in data science and machine learning applications, the significance of data visualization with Libraries such as Seaborn and Matplotlib, etc. After which it goes on with the main work. This project of ours delves into the realm of credit card fraud detection, leveraging Python and its powerful libraries such as NumPy, Pandas, Seaborn, TensorFlow, and Matplotlib for comprehensive data analysis and visualization. Employing a host of machine learning algorithms including K-Nearest Neighbors, Logistic Regression, Random Forest, Decision Tree, and LDA, the project aims to tackle the challenge posed by highly unbalanced datasets, specifically transactions made by European cardholders in September 2013. By discerning fraudulent transactions accurately, this endeavour seeks to bolster the integrity of financial systems, safeguarding both customers and institutions from potential losses. Keywords: Python, pandas, seaborn, data science, machine learning.
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Dewi, Yuli, Harry Suharman, Poppy Sofia Koeswayo, and Nanny Dewi Tanzil. "What is the key determinant of the credit card fraud risk assessment in Indonesia? An idea for brainstorming." Banks and Bank Systems 18, no. 1 (January 10, 2023): 26–37. http://dx.doi.org/10.21511/bbs.18(1).2023.03.
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This study examined the direct effect of brainstorming on fraud risk assessment at credit card issuing banks in Indonesia. Therefore, it was expected to help improve their performance in dealing with various credit card frauds. This study involved 80 participants from the credit card fraud risk management team from four major credit card issuing banks in Indonesia, consisting of the risk management team (anti-fraud specialist) and the internal auditor team. The research was analyzed using the experimental method with a 2X1 factorial design. Analysis of Variance (ANOVA) would test the experimental data. The individuals’ performance (without brainstorming) or the brainstorming group was analyzed using the statistical ANOVA technique. ANOVA analysis produced a sig value of less than 1% and an F-count of 50.556 > 0.143443, which was higher than the F-table. The ANOVA test results concluded that there were differences in assessing the fraud between the respondents with brainstorming and those without it. Through the brainstorming method, it turned out that the respondents in the fraud risk management team provided a more accurate credit card fraud risk assessment from the point of view of the fraud causes and the credit card fraud impacts.Hence, it is crucial for credit card issuing banks in Indonesia to consistently implement anti-fraud governance by adopting brainstorming to produce a better fraud risk assessment. AcknowledgmentsThis research was conducted as part of the process of study completed at the University of Padjadjaran, Bandung, Indonesia. Very special thanks to the participants from major credit card issuer banks who have participated in this research.
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G, Kavyasri, Keerthana D, Keerthi Reddy B, Keerthi K, KesavaAditya J, and Prof S. Ramesh Kumar. "Deep Learning based Credit Card Fraudulency Detection System." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (May 31, 2024): 477–81. http://dx.doi.org/10.22214/ijraset.2024.61553.
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Abstract: Huge increase in the internet usage has been observed since last decade. It led to the emergence of services like ecommerce, tap and pay systems, online bill payment systems, etc. have proliferated and become more widely used. Due to various online payment options introduced by e- commerce and other numerous websites, the possibility of online fraud has risen drastically. Thus, due to an increase in fraud rates, research on analyzing and detecting fraud in online transactions has begun utilizing various machine learning techniques. The Deep Learning techniques viz., Convolutional Neural Network Architecture is used to detect credit card frauds in the proposed model. The Principal Component Analysis transformation gives a set of numerical input variables as output which are taken as the features to be considered. Due to confidentiality concerns, some of the original characteristics and background information about the data are not entirely disclosed. Features of credit card frauds must be chosen carefully as they play important role when deep learning techniques are used for credit card fraud detection. The TensorFlow and Keras are used for the development of the current proposed model. The proposed model aims to predict credit card frauds with 91.9% accuracy
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Ahmed, Wazir. "Fraud Detection of Credit Card Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (June 30, 2023): 2785–89. http://dx.doi.org/10.22214/ijraset.2023.54097.
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Abstract: This paper is focused on credit card fraud detection in real world scenarios. Nowadays credit card frauds are 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 paper 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.
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Motseki, Morero. "Prospects and challenges in the investigation of credit card fraud in Vall Region of the Gauteng Province, South Africa." Technium Social Sciences Journal 27 (January 8, 2022): 959–67. http://dx.doi.org/10.47577/tssj.v27i1.5136.
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Today use of Credit Card even in developing countries has become a common scenario. People use it to shop, pay bills and for online transactions. But with increase in number of Credit Card users, the cases of fraud in Credit Card have also been on rise. Credit Card related frauds cause globally a loss of billions of Rands. Credit Card fraud can be done in numerous ways. The article begins with an examination of the extent of the challenge and response by the relevant stakeholders, especially the Criminal Justice System (CJS). This study was carried out utilising a qualitative research approach with a convenience, purposive and snowball sampling techniques. Thirtynine (39) interviews were conducted to solicit the views of the participants and police investigators from Vanderbijlpark, Sebokeng, Sharpeville and Vereeniging police stations, members of the community, and victims of credit card fraud were interviewed. These interviews were analysed according to the phenomenological approach, aided with the inductive Thematic Content Analysis (TCA) to identify the participants’ responses and themes.
The findings indicated that the extent of credit card fraud in Vaal region is reaching alarming rates. Based on the findings, the authors provided recommendations such as: police investigators being taken for regular workshops and training on how to investigate sophisticated methods used by perpetrators such as technology, awareness in the society about credit card fraud should be prioritised and enhanced. This study recommends that the CCTV cameras should be installed in the ATM, where cases of credit card are taking place. In addition, the police be visible in the areas which are most prevalent to credit card fraud.
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., Aakash, Akash Kuma, and Ravi Pratap Singh. "Credit Card Fraud Detection System." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 3850–52. http://dx.doi.org/10.22214/ijraset.2022.43240.
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Abstract: In today’s world everything is online which also increases the chances of fraud. There are approximately 36.4 percent fraud related to commercial cards which include credit card, debit card, etc. In 2022 there are 64 million people who uses credit card to initiate the transaction, therefore they are also prone to the card fraud. So, in this document we talk about the various machine learning algorithms to predict the credit card fraud based on the previous transaction pattern. Keywords: K nearest neighbour, Machine learning, Credit card, decision tree, random forest
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Bhaskar, T., Ketki Wagh, Vaishnavi Wakchaure, Vaishnavi Mohadkar, Pranjal Jagtap, and Savari Jawale. "Detecting Credit Card Fraud through Data Mining." Journal of Information Security System and Cyber Criminology Research 1, no. 1 (April 23, 2024): 9–17. http://dx.doi.org/10.46610/joissccr.2024.v01i01.002.
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The credit card industry grapples with a significant challenge in the form of credit card fraud, necessitating a comprehensive understanding of the various types of fraudulent activities and the evaluation of techniques deployed to detect such activities. Diverse strategies are employed by credit card firms and financial institutions, including banks, to prevent fraud, tailored to address the range of illicit behaviours encountered. The overarching goal of these efforts is to reduce instances of credit card fraud. However, persistent issues with current methodologies can lead to the erroneous classification of legitimate credit card users as fraudulent. Detecting credit card fraud through data mining entails analysing extensive datasets to identify patterns, anomalies, and trends indicative of fraudulent activities. Consequently, when employing data mining in credit card fraud detection systems, the outcome typically involves the recognition and flagging of transactions as potentially fraudulent or valid, contributing to enhanced fraud prevention measures.
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Samy, Nermin, and Shimaa mohamed mohamed. "Credit Card Fraud Detection Using Machine Learning Techniques." Future Computing and Informatics Journal 7, no. 1 (June 1, 2022): 13–31. http://dx.doi.org/10.54623/fue.fcij.7.1.2.
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This is a systematic literature review to reflect the previous studies that dealt with credit card fraud detection and highlight the different machine learning techniques to deal with this problem. Credit cards are now widely utilized daily. The globe has just begun to shift toward financial inclusion, with marginalized people being introduced to the financial sector. As a result of the high volume of e-commerce, there has been a significant increase in credit card fraud. One of the most important parts of today's banking sector is fraud detection. Fraud is one of the most serious concerns in terms of monetary losses, not just for financial institutions but also for individuals. as technology and usage patterns evolve, making credit card fraud detection a particularly difficult task. Traditional statistical approaches for identifying credit card fraud take much more time, and the result accuracy cannot be guaranteed. Machine learning algorithms have been widely employed in the detection of credit card fraud. The main goal of this review intends to present the previous research studies accomplished on Credit Card Fraud Detection (CCFD), and how they dealt with this problem by using different machine learning techniques.
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Pundkar, Sumedh N., and Mohd Zubei. "Credit Card Fraud Detection Methods: A Review." E3S Web of Conferences 453 (2023): 01015. http://dx.doi.org/10.1051/e3sconf/202345301015.
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In today’s context, the term “fraud” has become closely intertwined with credit card-related deceit. Recent years have witnessed a notable surge in both credit card utilization and fraudulent activities. Detecting and thwarting fraud necessitates a meticulous analysis of customers’ spending patterns. The ubiquity of credit card use for both online and in-store transactions has un-fortunately led to a parallel rise in recognition valentine scam occurrences. While the primary area of deception discovery is the documentation of sham incidents, the urgency of promptly flagging such events cannot be overstated. The con-temporary landscape heavily favors credit card usage, a trend that inadvert-ently contributes to the annual expansion of fraudulent gains. This unlawful practice exerts a pernicious influence on the global economy at large, exac-erbating its impact year after year. Numerous cutting-edge methods, including as data mining, machine learning, fuzzy logic, genetic programming, sequence alignment, artificial intelligence, and fuzzy logic, have become indispensable in the fight against this threat when it comes to identifying credit card fraud. This study delves into the intricate integration of data mining methodologies, showcas-ing their robust potential to provide comprehensive coverage against fraudu-lent activities while maintaining a controlled balance between false alarms and detection accuracy. Within the financial sector, the challenge of credit card fraud detection remains both persistent and pressing. This paper intro-duces an innovative paradigm aimed at fortifying credit card fraud detection. This is achieved by synergistically harnessing the capabilities of the Artificial Underground Over-sampling Practice (SMOTE), the potency of Adaptive Increasing (ADABoost), and the privacy-enhancing attributes of Federated Learning. The incorporation of federated learning serves a dual purpose: not only does it address prevailing data privacy concerns, but it also significantly augments the precision of fraud detection across a diverse array of geograph-ically distributed data sources
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T, Achal. "Credit Card Fraud Detection Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 4124–28. http://dx.doi.org/10.22214/ijraset.2023.51848.
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Abstract: Credit Card Fraud Detection is presently the most frequently occurring problem in the present. This is due to the rise in both online transactions and E-Commerce platforms. Credit Card Fraud Detection is mainly required by credit card users. The major goal of our project is to detect the given credit card transaction is real or fraud. This project aims to focus mainly on machine learning algorithm. We design the application to help the credit card users to be able to notify the transaction is real or fraud.
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Saini, Rashi, and Prof Bipin Pandey. "Credit Card Fraud Detection project." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 2113–18. http://dx.doi.org/10.22214/ijraset.2022.41704.
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Abstract: For some time, there has been a strong interest in the ethics of banking (Molyneaux, 2007; George, 1992), as well as the moral complexity of fraudulent behavior (Clarke, 1994). Fraud means obtaining services/goods and/or money by unethical means, and is a growing problem all over the world nowadays. Fraud deals with cases involving criminal purposes that, mostly, are difficult to identify. Credit cards are one of the most famous targets of fraud but not the only one; fraud can occur with any type of credit products, such as personal loans, home loans, and retail. Furthermore, the face of fraud has changed dramatically during the last few decades as technologies have changed and developed. A critical task to help businesses and financial institutions including banks is to take steps to prevent fraud and to deal with it efficiently and effectively, when it does happen (Anderson, 2007). Anderson (2007) has identified and explained the different types of fraud, which are as many and varied as the financial institution’s products and technologies, such as Transaction products: credit and debit cards and checks, Relationship to accounts first, second and third parties, Business processes: application and transaction, Manner and timing short versus long term, Identify misrepresentation: embellishment, theft and fabrication, Handling of transaction: lost or stolen, not received, skimming and at hand, Utilization counterfeit, not present, altered or unaltered, Technologies ATM and Internet. Solutions for integrating sequential information in the feature set exist in the literature. The predominant one consists in creating a set of features which are descriptive statistics obtained by aggregating the sequences of transactions of the cardholders (sum of amount, count of transactions, location from where the payment is being made etc..). We used this method as a benchmark feature engineering method for credit card fraud detection. However, this feature engineering strategy raised several research questions. First of all, we assumed that these descriptive statistics cannot fully describe the sequential properties of fraud and genuine patterns and that modelling the sequences of transactions could be beneficial for fraud detection. Moreover, the creation of these aggregated features is guided by expert knowledge whereas sequence modelling could be automated thanks to the class labels available for past transactions. Finally, the aggregated features are point estimates that may be complemented by a multi-perspective univariate description of the transaction context. We proposed a multi-perspective HMM-based automated feature engineering strategy in order to incorporate a broad spectrum of sequential information in the transactions feature sets. In fact, we model the genuine and fraudulent behaviors of the merchants and the card-holders according to two univariate features: the country from where the payment is being made and the amount of each of the transactions being made. Moreover, the HMMbased features are created in a supervised way and therefore lower the need of expert knowledge for the creation of the fraud detection system. In the end, our multiple perspectives HMM-based approach offers automated feature engineering to model temporal correlations so as to complement and possibly supplement the use of transaction aggregation strategies in order to improve the effectiveness of the classification task. Experiments conducted on a large real world credit card transaction dataset (46 million transactions from belgium card-holders between March and May 2015) have shown that the proposed HMMbased feature engineering allows for an increase in the detection of fraudulent transactions when combined with the state-ofthe-art expert-based feature engineering strategy for credit card fraud detection. To conclude, this work leads to a better understanding of what can be considered contextual knowledge for a credit card fraud detection task and how to include it in the classification task in order to get an increase in fraud detection. The method proposed can be extended to any supervised task with sequential datasets. The main aims are, firstly, to identify the different types of credit card fraud, and, secondly, to review alternative techniques that have been used in fraud detection. Indeed, transaction products, including credit cards, are the most vulnerable to fraud. On the other hand, other products such as personal loans and retail are also at risk, and have serious ethical conflicts. Keywords: Behavior and Location Analysis (BLA); Fraud Detection System (FDS); Automated Teller Machine (ATM); Credit Card Fraud Detection; DB: Database.
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Saini, Rashi, and Prof Bipin Pandey. "Credit Card Fraud Detection project." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 2113–18. http://dx.doi.org/10.22214/ijraset.2022.41704.
Full textAbstract:
Abstract: For some time, there has been a strong interest in the ethics of banking (Molyneaux, 2007; George, 1992), as well as the moral complexity of fraudulent behavior (Clarke, 1994). Fraud means obtaining services/goods and/or money by unethical means, and is a growing problem all over the world nowadays. Fraud deals with cases involving criminal purposes that, mostly, are difficult to identify. Credit cards are one of the most famous targets of fraud but not the only one; fraud can occur with any type of credit products, such as personal loans, home loans, and retail. Furthermore, the face of fraud has changed dramatically during the last few decades as technologies have changed and developed. A critical task to help businesses and financial institutions including banks is to take steps to prevent fraud and to deal with it efficiently and effectively, when it does happen (Anderson, 2007). Anderson (2007) has identified and explained the different types of fraud, which are as many and varied as the financial institution’s products and technologies, such as Transaction products: credit and debit cards and checks, Relationship to accounts first, second and third parties, Business processes: application and transaction, Manner and timing short versus long term, Identify misrepresentation: embellishment, theft and fabrication, Handling of transaction: lost or stolen, not received, skimming and at hand, Utilization counterfeit, not present, altered or unaltered, Technologies ATM and Internet. Solutions for integrating sequential information in the feature set exist in the literature. The predominant one consists in creating a set of features which are descriptive statistics obtained by aggregating the sequences of transactions of the cardholders (sum of amount, count of transactions, location from where the payment is being made etc..). We used this method as a benchmark feature engineering method for credit card fraud detection. However, this feature engineering strategy raised several research questions. First of all, we assumed that these descriptive statistics cannot fully describe the sequential properties of fraud and genuine patterns and that modelling the sequences of transactions could be beneficial for fraud detection. Moreover, the creation of these aggregated features is guided by expert knowledge whereas sequence modelling could be automated thanks to the class labels available for past transactions. Finally, the aggregated features are point estimates that may be complemented by a multi-perspective univariate description of the transaction context. We proposed a multi-perspective HMM-based automated feature engineering strategy in order to incorporate a broad spectrum of sequential information in the transactions feature sets. In fact, we model the genuine and fraudulent behaviors of the merchants and the card-holders according to two univariate features: the country from where the payment is being made and the amount of each of the transactions being made. Moreover, the HMMbased features are created in a supervised way and therefore lower the need of expert knowledge for the creation of the fraud detection system. In the end, our multiple perspectives HMM-based approach offers automated feature engineering to model temporal correlations so as to complement and possibly supplement the use of transaction aggregation strategies in order to improve the effectiveness of the classification task. Experiments conducted on a large real world credit card transaction dataset (46 million transactions from belgium card-holders between March and May 2015) have shown that the proposed HMMbased feature engineering allows for an increase in the detection of fraudulent transactions when combined with the state-ofthe-art expert-based feature engineering strategy for credit card fraud detection. To conclude, this work leads to a better understanding of what can be considered contextual knowledge for a credit card fraud detection task and how to include it in the classification task in order to get an increase in fraud detection. The method proposed can be extended to any supervised task with sequential datasets. The main aims are, firstly, to identify the different types of credit card fraud, and, secondly, to review alternative techniques that have been used in fraud detection. Indeed, transaction products, including credit cards, are the most vulnerable to fraud. On the other hand, other products such as personal loans and retail are also at risk, and have serious ethical conflicts. Keywords: Behavior and Location Analysis (BLA); Fraud Detection System (FDS); Automated Teller Machine (ATM); Credit Card Fraud Detection; DB: Database.
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Kodimenu1,, Murali Krishna, Dr Satyanarayana S2,, and Dr Thayabba Katoon3. "Credit Card Fraud Detection Using ML & DL." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 07 (July 20, 2024): 1–11. http://dx.doi.org/10.55041/ijsrem36686.
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The ascent of the digital payments industry is accelerating as the global economy increasingly adopts online and card-based payment systems. This transition, however, brings with it an elevated risk of cyber threats and fraud, now more prevalent than ever before. For banks and financial institutions, bolstering the detection of credit card fraud is of utmost importance. Machine learning (ML) is revolutionizing this domain, making the identification of fraudulent activities both simpler and more effective. ML- powered fraud detection systems are adept at identifying patterns and halting irregular transactions. The hurdles faced in this area are significant: vast quantities of data are processed daily, with the vast majority of transactions (99.8%) being legitimate; the data, largely confidential, is not readily accessible; not all fraudulent activities are detected and reported; and fraudsters continually develop new strategies to outsmart the detection models. Machine learning algorithms are capable of pinpointing atypical credit card transactions and instances of fraud, ensuring that cardholders are not billed for purchases they did not make. These ML algorithms outperform traditional fraud detection systems, capable of discerning thousands of patterns within extensive datasets. Moreover, ML provides valuable insights into consumer behavior through the analysis of app usage, payment, and transaction patterns. The advantages of deploying machine learning in the fight against credit card fraud are manifold, including swifter detection, enhanced precision, and increased efficiency when dealing with large volumes of data. Key Words: Credit Card Frauds, Fraud Detection, Correlation matrix, principal components, Random Forest.
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Shanmugapriya, P., R. Shupraja, and V. Madhumitha. "Credit Card Fraud Detection System Using CNN." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (March 31, 2022): 1056–60. http://dx.doi.org/10.22214/ijraset.2022.40799.
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Abstract: This Project is focused on credit card fraud detection in real-world scenarios. Fraud is one of the major ethical issues in the credit card industry. In this era we can see many innovative financial services like ATMs, online banking, etc. Besides, along with the rapid advances of e-commerce, the use of credit cards has become a convenient and necessary part of financial life. A credit card is a payment card supplied to customers as a system of payment. Using a third-person credit card or its information without the knowledge of that person is referred to as credit card fraud. Application and Behavioral fraud are major types of fraud where people are easily tricked and lose their money. The same user may submit multiple applications which may lead to identical fraud. Application fraud takes place when fraudsters apply for new cards from a bank or issuing companies using false or others' information. In this project, we will be applying some supervised and unsupervised algorithms and will classify the credit card dataset. We will use CNN and correlate the data train and get the model's accuracy. Keywords: CCFD, CNN, Logistic Regression, Feature extraction.
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Vikash Chander Maheshwari, Nurul Aida Osman, and Norshakirah Aziz. "A Hybrid Approach Adopted for Credit Card Fraud Detection Based on Deep Neural Networks and Attention Mechanism." Journal of Advanced Research in Applied Sciences and Engineering Technology 32, no. 1 (August 19, 2023): 315–31. http://dx.doi.org/10.37934/araset.32.1.315331.
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Over the past few years, credit card fraud has become a serious problem as more individuals rely on credit cards for purchases. The significant increase in fraudulent activities can be attributed to advancements in technology and the prevalence of online transactions, leading to significant financial losses. To address this issue, an effective fraud detection system needs to be developed and put into practice. Machine learning techniques are commonly used to automatically detect credit card fraud, but they do not consider deceptive behaviour or behavioural issues that could lead to false alarms. The objective of this research is to determine how to identify instances of credit card fraud. This paper aimed to create a model using deep learning and SMOTE oversampling technique to anticipate credit card fraud. A Recurrent Neural Network with Long Short-Term Memory (RNN-LSTM) and an attention mechanism is suggested for detecting fraud. This model is known to be effective for processing sequential data with complex relationships between vectors. The performance of RNN-LSTM is compared to XGBoost, Random Forest, Naive Bayes, SVM, and ANN classifiers, and the experiments indicate that our proposed model achieves high accuracy of 99.4% and produces strong results. The suggested model has the potential to decrease financial losses worldwide by identifying instances of credit card scams or frauds.
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Bhardwaj, Surbhi, and Sonika Gupta. "Effects of Feature Selection with Machine Learning Algorithms in Detection of Credit Card Fraud." International Journal of Engineering Research in Computer Science and Engineering 9, no. 7 (July 21, 2022): 46–51. http://dx.doi.org/10.36647/ijercse/09.07.art011.
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As an effect of developments in e-commerce systems and communication technologies, Credit cards have become the most common mode of payment for purchases. The payments through the credit cards also involve the risk of credit card fraud such as application fraud, identity theft, lost/stolen card misuse, and phishing. These frauds lead to huge losses and require automatic and real-time fraud detection. Many studies have used Machine Learning (ML) techniques to detect fraudulent transactions. This study focuses on proposing a framework for the detection of credit card frauds by applying machine learning techniques like Random Forest (RF) and Naïve Bayes and testing the results on balanced and unbalanced datasets with and without performing the feature selection on the dataset. After comparing the results, it was discovered that Random Forest outperformed the Naïve Bayes on a balanced dataset with feature selection performed using Recursive Feature Elimination and Information Gain.
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Kumar, Dharminder, and Suman Arora. "A Hybrid Approach Using Maximum Entropy and Bayesian Learning for Detecting Delinquency in Financial Industry." International Journal of Knowledge-Based Organizations 6, no. 1 (January 2016): 60–73. http://dx.doi.org/10.4018/ijkbo.2016010105.
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The use of credit card has increased tremendously in the past few years because of the boom in the economy which has also resulted in the increase in the credit card fraud cases. Various leading banks and software development companies worldwide are taking serious measures to deal with the gravity of this situation. This paper proposes a framework for credit card fraud detection that will detect frauds using maximum entropy according to the irregular behavior of the customers in various transactions of credit card. The comparative study of above approach with existing approaches is also addressed. Results show the feasibility and validity of each approach.
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Ore-Areche, Franklin, Kelyn Nataly Muñoz-Alejo, and Cledi Puma-Condori. "Analysis and Detection of Fraud in Credit Card Using SILOF." INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING & APPLIED SCIENCES 10, no. 3 (August 6, 2022): 34–41. http://dx.doi.org/10.55083/irjeas.2022.v10i03006.
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Nowadays e-commerce and online transaction is growing rapidly. For online and offline transaction most of the customer uses credit card. Credit card used globally for online transaction, buy goods, product, and payment. The rising use of credit card can increase the chances of fraud in credit card. Credit card system is at risk now. The effect of this fraudulent transaction is on the bank and institute causing a financial loss to them. For the detection of distinguish frauds, several machine learning models are utilized for better prediction. The major objective of this article is to identify the fraudulent transaction and outlier in credit card transaction. The dataset of credit card is unbalanced. There are various techniques by which fraudulent transaction can be detected and we have used these techniques such as isolation forest method, local outlier factor and support vector machine to determine fraud in credit card. We have used different matrices for enhancing the performance and accuracy. At last comparison analysis is done by using isolation forest, support vector machine, and local outlier which give the better result.
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Madkaikar, Kartik, Manthan Nagvekar, Preity Parab, Riya Raika, and Supriya Patil. "Credit Card Fraud Detection System." International Journal of Recent Technology and Engineering (IJRTE) 10, no. 2 (July 30, 2021): 158–62. http://dx.doi.org/10.35940/ijrte.b6258.0710221.
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Credit card fraud is a serious criminal offense. It costs individuals and financial institutions billions of dollars annually. According to the reports of the Federal Trade Commission (FTC), a consumer protection agency, the number of theft reports doubled in the last two years. It makes the detection and prevention of fraudulent activities critically important to financial institutions. Machine learning algorithms provide a proactive mechanism to prevent credit card fraud with acceptable accuracy. In this paper Machine Learning algorithms such as Logistic Regression, Naïve Bayes, Random Forest, K- Nearest Neighbor, Gradient Boosting, Support Vector Machine, and Neural Network algorithms are implemented for detection of fraudulent transactions. A comparative analysis of these algorithms is performed to identify an optimal solution.
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Mathew, Alexander, Gayatri Moindi, Ketan Bende, and Neha Singh. "Credit Card Fraud Prediction System." Journal of Advance Research in Computer Science & Engineering (ISSN: 2456-3552) 2, no. 3 (March 31, 2015): 30–35. http://dx.doi.org/10.53555/nncse.v2i3.481.
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Identity crime is common, and pricey, and credit card fraud is a specific case of identity crime. The existing systems of known fraud matching and business rules have restrictions. To remove these negative aspects in real world, this paper proposes a data mining approach: Communal Detection (CD) and Spike Detection (SD). CD finds real social relationships to reduce the suspicion score, and is impervious to fake social relationships. This approach on a fixed set of attributes is whitelist-oriented. SD increases the suspicion score by finding discrepancies in duplicates. These data mining approaches can detect more types of attacks and removes the unnecessary attributes.
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Everett, Catherine. "Credit Card Fraud Funds Terrorism." Computer Fraud & Security 2003, no. 5 (May 2003): 1. http://dx.doi.org/10.1016/s1361-3723(03)05001-2.
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Menkus, Belden. "US credit card fraud escalates." Computer Fraud & Security Bulletin 1992, no. 9 (September 1992): 3–4. http://dx.doi.org/10.1016/s0142-0496(09)90152-7.
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Shashank Singh and Meenu Garg. "Credit Card Fraud Detection System." International Journal for Modern Trends in Science and Technology 6, no. 12 (December 3, 2020): 24–27. http://dx.doi.org/10.46501/ijmtst061205.
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It is essential that Visa organizations can distinguish false Mastercard exchanges so clients are not charged for things that they didn't buy. Such issues can be handled with Data Science and its significance, alongside Machine Learning, couldn't be more important. This undertaking expects to outline the demonstrating of an informational collection utilizing AI with Credit Card Fraud Detection. The Credit Card Fraud Detection Problem incorporates demonstrating past Visa exchanges with the information of the ones that ended up being extortion. This model is then used to perceive if another exchange is fake. Our target here is to identify 100% of the fake exchanges while limiting the off base misrepresentation arrangements. Charge card Fraud Detection is an average example of arrangement. In this cycle, we have zeroed in on examining and pre- preparing informational indexes just as the sending of numerous irregularity discovery calculations, for example, Local Outlier Factor and Isolation Forest calculation on the PCA changed Credit Card Transaction
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Ernest, Makombe, Chatola Fanny, J. Pradeep, and G. Glorindal. "Credit card fraud detection system." i-manager's Journal on Digital Forensics & Cyber Security 1, no. 2 (2023): 12. http://dx.doi.org/10.26634/jdf.1.2.20064.
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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.
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Petle, Dnyaneshwar Vinodrao. "Credit Card Fraud Protection Application." INTERNATIONAL JOURNAL OF ADVANCE RESEARCH IN COMPUTER SCIENCE AND MANAGEMENT STUDIES 12, no. 7 (July 2024): 330–38. http://dx.doi.org/10.61161/ijarcsms.v12i7.41.
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Jadav, Swati, and Manoj Patil. "A Succinct Analysis of Deep LSTM Model-Based Credit Card Fraud Detection." International Journal for Research in Applied Science and Engineering Technology 11, no. 7 (July 31, 2023): 1827–30. http://dx.doi.org/10.22214/ijraset.2023.54891.
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More people today use credit cards to buy their essentials thanks to technological advancements, which has sparked a gradual rise in credit card theft. Today, credit cards are used almost universally by businesses, whether they are small or large. All types of businesses, including banks, the auto and appliance industries, are susceptible to credit card theft. Fraud is defined as a deceit committed with the aim of generating unauthorised financial benefit. Some of the ways that scams happen include hacking billing systems at stores or restaurants, hacking an online retailer, losing or stealing cards, and installing fraud devices in card readers at petrol stations or ATMs to acquire credit card PIN data. The 2 basic types of card fraud are behavioural fraud and application fraud. Application fraud describes scenarios in which a credit card application is false. It happens when a fraudster submits an application for a new credit card using false identification information, and the card issuer accepts it. Behaviour fraud occurs after a credit card has been approved and issued. Credit or debit card transactions that exhibit fraudulent conduct are referred to. Fraud identifying and prevention have long been big issues for card providers and key research areas for researchers due to the fact that identifying and preventing even a little amount portion of fraudulent behaviour would prevent millions of dollars in losses. Our study focuses on the challenge of recognising fraud behaviour.
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Rao, K. Aditya, and Dr Ganesh D. "Survey Paper on Credit card Fraud Detection System." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 1361–67. http://dx.doi.org/10.22214/ijraset.2022.41524.
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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.
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Wang, Yiying. "Fraud detection based on FS-SMOTE model for credit card." Highlights in Science, Engineering and Technology 70 (November 15, 2023): 316–23. http://dx.doi.org/10.54097/hset.v70i.12479.
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In the financial security technology, credit card fraud detection technology is an important technical means, which collects and analyzes the credit card transaction data in a certain period of time, detects fraud in many credit card transactions, and takes the corresponding alarm response. At the same time, in view of the extremely imbalanced characteristics of credit card fraud customer data set, the number of minority samples is increased by Smote, which is a representative algorithm of oversampling technology. Logistic regression, KNN, Decision tree, Bagging and Stochastic gradient descent are used to construct fraud detection models. The credit card fraud data released on Kaggle platform were selected for verification. The experimental results show that the fusion model based on Feature selection and Smote greatly improves the accuracy and detection rate of credit card fraud detection as a whole.
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Patel, Karnaram, Rutul Waradkar, Amaan Shaikh, Nirmal Nemade, and Ms Smita Bansod. "Counterfeit Diagnosis Using Artificial Neural Network." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (April 30, 2023): 4345–48. http://dx.doi.org/10.22214/ijraset.2023.51243.
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Abstract: One of the best payment methods that clients may use to conduct transactions effortlessly and boost their purchasing power is a credit card. In addition, it is incredibly simple to use and provides a number of benefits, like cashback and reward points. However, the primary issue is credit card fraud, which is fast rising every day. The most common type of identity theft worldwide is still credit card fraud. The use of credit cards has grown too dangerous in recent years. In reality, there are many other kinds of credit card fraud, including phishing and vishing, keystroke logging, POS fraud, application fraud, loss of card or theft, and POS fraud and vishing. However, the person should take safety steps and safeguards to protect his or her money. The vast bulk of this fraud is being committed by organized crime rings, whose operations have been industrialized and computerized. So how may these frauds be found? Utilizing machine learning algorithms is the solution. Conventional fraud detection is another method, however, machine learning algorithms are much more accurate and exact. The basic objective is to create a model that can foretell whether a Transaction will be fraudulent or not. In this project, predictive models utilized in this research include Artificial Neural Networks, Random Forests, Support Vector Machines, KNN, Decision Trees, Gaussian Naive Bayes, and Logistic Regression. The results from each of these models are evaluated for accuracy, and the best model is selected.
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Mitra, Anurag, Mukul Siddhant, and Gururama Senthilvel P. "Credit Card Fraud Detection using Autoencoders." YMER Digital 21, no. 06 (June 12, 2022): 337–42. http://dx.doi.org/10.37896/ymer21.06/32.
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In today’s life or economy credit card plays a veryimportant role. Credit card becomes a necessary part of business, household and bank transactions. Using of credit card carefully and responsibly gives a enormous benefit to the user, fraudulent activities happen and give financial damage to the user or card holder. The growth of E-commerce industry led to use of credit card or many platform for online purchase or different transaction because of this the fraudulent activities was also increased. Bank facing many issues for detecting the credit card fraudulent transactions. For finding the fraud of credit card machine learning plays a vital role. For predicting these fraud detection of credit card we use many machine learning methods or algorithms, past data we collect and analyze it and make a machine learning model to detect the fraudulent activities which going to happen. The performance of fraud detecting in credit card transaction is greatly affected by sampling approach on data-set, selection of variable and detection technique used. This project objective to use of efficient approach to detect automatic fraud related to banks or insurance company using deep learning algorithm called autoencoder. We are using the European card holder real-time dataset of September 2013. The data was unbalanced in the dataset so for this autoencoder is perfect to provide the accurate results. We can reconstruct the normal data through the autoencoder and anomalies was detected at the time of reconstruction error threshold and consider the anomalies. Keyword: fraud detection, unsupervised learning, autoencoder, credit card
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Sreekanth, Kavuri, Ratnababu Mamidi, Thumu Srinivas Reddy, Kuruva Maddileti, and Darivemula Deepthi. "Prevention of credit card fraud transaction using GA feature selection for web-based application." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 3 (September 1, 2024): 1645. http://dx.doi.org/10.11591/ijeecs.v35.i3.pp1645-1652.
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Credit card fraud (CCF) is a regular event that generates financial losses. A considerable share of the significantly increased volume of internet transactions is made with credit cards. CCF detection programmes are consequently highly prioritised by banks and other financial organisations. These fraudulent transactions can come in a wide variety of formats and categories. To maintain data integrity, financial institutions support digital transactions. One of the most popular ways to pay the products and services can be done by both online and offline by using a credit card. Thus, there is a higher possibility of fraud during these financial transactions. This informs programmers to the requirement for a reliable technique for identifying successful fraud. Credit card users and businesses that accept credit cards have recently had to contend with the serious issue of CCF. Application-level frauds and transaction level frauds are the two categories into which CCF controlled frauds are divided. Therefore, utilizing genetic algorithm (GA) feature selection for web-based applications, it is advised to use this strategy as a method for the prevention of CCF transaction. This method's performance is evaluated based on a number of factors, including accuracy, recall, and specificity.