Abstract: From the early stages of cards’ existence, a certain class of humans tried and unfortunately succeeded to commit frauds with this electronic payment instrument. The fraud with cards is a “disease” that affects the banking systems internationally, shaking the trust in this payment instrument and in the issuing banks. The paper starts with a short revision of the card typology and of the associated services that arise in time as an effect of the banks’ desire to acquire more and more clients. The recent pandemic offered a perfect motivation for banks to diversify their online platforms and services that can be used only if you have a card to perform online payments. Internet payments, so common today, increased the number of methods to do frauds with card payments and transactions, growing up the level of this type of financial criminality. The aim of this paper consists of reviewing the economic literature related to the methods old and new through which frauds with cards are committed in our days. Also, we highlight the methods that are used to prevent this type of frauds, and we reveal which are efficient in the fight against this scourge. We also analyze the evolution of frauds with cards at the international level. We find that the increasing card usage during the last years has led to a rise of the card scams. Finally, we discuss the measures required to prevent and mitigate the card fraud magnitude and policy implications.

Abstract: 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.

Abstract: Abstract: Credit card fraud has existed ever since credit cards were introduced, resulting in financial losses, identity theft, severe security threatas, and misuse of personal information. Such a situation already dire at an individual level only worsens when an organization gets involved. With the COVID-19 pandemic taking over the world and the introduction of quarantine, online transactions have surged exponentially. Naturally, credit cards have become one of the main means to process these transactions. With an extraordinary number of people making transactions every second, it becomes difficult to keep track of fraudulent ones. An increase in online transactions also increases the risk of cybercrimes, in this case, fraudulent transactions. In this paper, we proposed a fast yet light system to detect fraudulent transactions in the IEEE-CIS dataset. We used feature engineering and data mining techniques to analyze the dataset and make it usable for the model. Then, we fit the dataset to LightGBM and XGBoost to classify the transactions as fraud or non-fraud. Finally, we compared the performance of the two models. Since we were dealing with a heavily skewed dataset, we gauged the performance of our model with the F1 score and ROC AUC score. The obtained ROC AUC score was 95% for our proposed model.

Abstract: 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.

Abstract: Credit card is a sign of credit that is given to customers with good credit by a commercial bank or credit card firm. It takes the shape of a card with signature blank space on the back and the name of the dissipated bank, expiration date, CVS number, and cardholder name on the front. A credit card is a payment card that can give the cardholders’ abilities to enable the cardholder to exchange for goods and services based on their credibility and debt score. In this paper, it will explore the credit card fraud detection predictive model to avoid fraudulent activity. Through different algorithms, the study could easily show the potentially fraudulent activities in the given dataset. In order to effectively combat credit card fraud, a number of techniques have been developed and put into practice, including different supervised and unsupervised machine learning algorithms to predict fraudulent activities. These techniques will be used to compare between the actual dataset and estimated models to illustrate the full picture. The credit card fraud is a challenging problem, especially it is prevalent during college students.

Abstract: Fingerprinting for payment cards was developed by MasterCard in the 1990s to prevent stolen cards from being used fraudulently. To initiate a transaction, a customer places his or her card on a card reader, also known as a biometric payment device, attached to a point-of-sale terminal and then enters a Personal Identification Number (PIN) to authorize the transaction. The PIN and the fingerprint data on the card interact to provide the customer with sufficient security to be sure that the card will not be used fraudulently. Authentication occurs instantaneously and without the knowledge or use of the card number or PIN. Payment card companies offer privacy and security by taking various steps to secure and protect their networks. They work closely with retailers to build a network of secure networks that provide privacy. Retailers can use these secure networks, also known as the “Visa Net,” to process transactions securely. When a customer uses a payment card, the retailer stores their payment data on a back end, often called the vendor solution, which includes the transaction amounts and merchant numbers. Traditional payment cards need to be more securely authenticated. Even though companies have introduced the Wi-Fi enabled payment cards to increase transaction speed, it has many limitations, and can be easily used for fraudulent activities. Hence the objective of this work is to install a biometric sensor on the payment card to enhance security and reduce the risk of fraud.

Abstract: The most common issue in the modern world is the identification of credit card fraud. This is a result of the expansion of both online commerce platforms and online transactions. In utmost cases, credit card fraud occurs when the card is stolen and used for any unauthorised exertion, or indeed when the fraudster utilises the card's information for their own gain. The credit card scam detection system was introduced with machine learning algorithms to catch these actions. Financial fraud is a growing problem in the financial industry with long-term consequences. It becomes difficult for two main reasons: first, the profiles of legitimate and fraudulent behaviour are always changing, and second, the data sets for credit card fraud are quite biased. The main objectives of this study are to identify the various types of fraudulent credit cards and to investigate alternate fraud detection techniques. On severely skewed credit card fraud data, it evaluates the performance of Decision tree, Random Forest, Logistic Regression and Extreme Gradient Boosting (XG Boost).

Abstract: The report was undertaken on the comparative study between debit card and credit card with the objective of to identify which is been used more by the people in their day to day usage The report was undertaken to make a comparison of debit card and credit card because as the Ancient India is turning towards Digital India and also now people are preferring to pay via debit or credit cards 1. Brief introduction about topic and reason for selection of topic : I have selected my topic as Comparative study between debit card and credit card they are commonly also known as payment cards or plastic money as it enables an individual to carry plastic money with him/her self and can make payment at a single 2. Objective of the study : The primary objective is to know the comparison between debit and credit card users in Greater Noida and it also indicates that is what people prefer the most to pay debit card or credit card & why they prefer such. 3. Research methodology : The method used in this project is NON – PROBABLISTIC CONVENIENCY SAMPLING technique. 4. Data analysis and presentation : The data collected from the respondents are been analyzed with keeping view point of comparison between debit card and credit card. Thus from this project it could be concluded that people use more of debit cards rather then credit cards but as the environment is changing the buying and spending habit of consumers is also changing and we can say that in near future people will use more of credit cards and will buy the products of credit basis.

Abstract: The Payment Card Industry Data Security Standard (PCI DSS) was developed to encourage and enhance payment card account data security and facilitate the broad adoption of consistent data security measures globally. But it is important to understand that what is good for payment card account data security is good for any and every aspect of all data security.

Abstract: A credit card is a convenient and widely recognized method of making cashless transactions both online and offline. One of the most significant benefits of using a credit card rather than a debit card is that it allows you to borrow money to pay for your transactions. As well as the majority of online fraud occurs during a card or online transaction when a user attempts to buy something or move money. Nowadays lots of technology introduced for secured money transactions, that's blockchain technology. The blockchain has the potential to evolve into a distributed ledger, offering a revolutionary new form of trustworthy third-party authentication. Because of the long history of credit card systems, it is easier to understand and security has always been triggered by a process of delegating risk to third parties. Blockchain technology has the potential to avoid these types of losses from occurring in the first place. This study examines how Blockchain technology may be applied, how it might br made safe, and how it might be used to reduce the danger of credit card data being compromised. Additionally, this article identifies and discusses a mechanism that may be created utilizing current technologies, such as multiple identification, SR4S randomized OTP (One Time Password), and biometric tools, to avoid the loss of credit cards.

Abstract: 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

Abstract: The article is devoted to the consideration of the procedure for using payment (bank) cards when transferring funds. It is indicated that at present scientific and technological progress has led to the acceleration and improvement of settlement mechanisms, the introduction of new and optimization of already provided banking services in general. Payment (bank) cards are very popular. In the course of the study, the features characteristic of bank transfer by means of payment (bank) cards were identified. The study of the practice of using payment (bank) cards has shown that the gaps and inaccuracies existing in Russian legislation lead to an increase in the number of disputes involving citizens — cardholders. In order to avoid business risks, banks fix in their local acts a ban on the holder to transfer a payment (bank) card, as well as details, codes and passwords to it to a third party. In this regard, the article emphasizes the importance of familiarizing citizens — holders of payment (bank) cards with local acts of banks in order to ensure security when using cards and prevent unauthorized transfers.

Abstract: In the digital world, the utilization of online transactions using credit and debit cards is growing every day. However, credit or debit (CD) card fraud is one of the prime challenges confronted by customers and bankers. The financial databases are stored at various cloud servers and these types of fraud make a huge loss in the financial systems as well as security concerns in the cloud. The cloud platform provides efficient and uninterrupted access to banking databases. There are distinct security initiatives that were taken to prevent fraudulent transactions, but still, hackers try different techniques to compromise the customer's account. Currently, for online Card payments, an OTP has been sent to the customer's phone number and email-id of a customer for security during the transaction. In this article, we introduce a new approach by sending an alert message before the OTP to the card owner whenever the credit or debit card number is entered by the customer or hacker for an online transaction.

Abstract: Data breaches and credit card fraud are now among the biggest problems affecting financial organizations and customers globally. The purpose of this study is to develop an effective fraud detection system that can detect fraudulent credit card transactions and prevent data breaches. The strategy proposed in this paper makes use of machine learning techniques like decision trees and logistic regression, to analyze large datasets of credit card transactions and identify suspicious patterns. The proposed system also includes a real-time monitoring mechanism that alerts the relevant authorities in case of any suspicious activity. The results of the experiments show that the suggested system achieves great accuracy and efficiency in detecting fraudulent transactions and data breaches. It can provide a powerful tool for financial institutions to prevent financial losses and maintain their customers' trust.

Abstract: We have witnessed an enormous evolution in credit card processing over last few years, issuing chip-based credit cards, starting mobile device-based wallets like Apple Pay are some of the significant changes done to secure credit card transactions. Despite financial institutions (banks) working hard to eliminate fraud in credit card transactions, credit card fraud has been continuously rising over the last few years. Fraudsters are getting smarter and using latest technologies to steal cardholder’s information, either through hacking or through social engineering. Increasing fraud in the industry makes fraud prediction very critical to be able to identify and stop fraud in real time, and data science plays a significant role in analyzing and being able to predict fraud based on transactional and cardholder information. The scope of this project is to research and identify different types of predictive analysis algorithms available that can be applied to determine and stop fraudulent transactions.

Abstract: 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.

Abstract: In today’s rapidly evolving financial ecosystem, artificial intelligence (AI) has emerged as a cornerstone of payment gateway innovation. Leveraging AI-driven fraud detection, payment systems now possess the ability to identify and mitigate fraudulent activities in real time, often before they can cause harm. These advanced algorithms analyze vast amounts of data to detect anomalies, identify emerging threats, and protect consumers without disrupting their experience. This paper explores the transformative role of AI in enhancing payment security, focusing on predictive analytics, behavioral modeling, and anomaly detection. It also delves into implementation challenges, ethical considerations, and future trends. By examining real-world applications and the convergence of AI with other emerging technologies, we shed light on how AI is redefining the trustworthiness and resilience of payment gateways.