Debit Card Fraud Research Articles

Leveraging Face Recognition Technology for Secure ATM Transaction

ATM or Automated Teller Machines are widely used by people nowadays. Performing cash withdrawal transactions with ATMs is increasing day by day. ATMs are a very important device throughout the world. The existing conventional ATM is vulnerable to crimes because of the rapid technology development. A total of 270,000 reports have been reported regarding debit card fraud and this was the most reported form of identity theft in 2021. A secure and efficient ATM is needed to increase the overall experience, usability, and convenience of the transaction at the ATM. In today's world, the area of computer vision is advancing at a breakneck pace. The recent progress in biometric identification techniques, including fingerprinting, retina scanning, and facial recognition has made a great effort to rescue the unsafe situation at the ATM. Specifically, the goal of this project is to give a computer vision method to solve the security risk associated with accessing ATM machines. This project proposes an automatic teller machine security model that uses electronic facial recognition using Deep Convolutional Neural Network. If this technology becomes widely used, faces would be protected as well as their accounts. Face Verification Clickbait Link will be generated and sent to bank account holders to verify the identity of unauthorized users through some dedicated artificial intelligent agents, for remote certification. However, it is obvious that man’s biometric features cannot be replicated, this proposal will go a long way to solve the problem of account safety making it possible for the actual account owner alone to have access to his accounts. This eliminates the possibility of fraud resulting from ATM card theft and copying. The experimental results on real-time datasets demonstrate the superior performance of the proposed approach over state-of-the-art deep learning techniques in terms of both learning efficiency and matching accuracy

Identifying Credit Card Fraudulent Transactions through Machine Learning Analysis

Identifying Credit Card Fraudulent Transactions through Machine Learning Analysis

Real-time Card Fraud Detection: A Stacked Ensemble Machine Learning Approach

As the world witnesses, Card fraud detection has become more and more crucial observes a rise in new and innovative techniques used by offenders. Machine learning and artificial intelligence are at the forefront of the various fraud detection technologies that credit card firms and financial institutions are utilizing. The Nilson Report's startling statistics demonstrate the scope of the issue. Global losses due to credit and debit card fraud increased to $40.6 billion in 2022, up from $34.7 billion the year before, with the United States being responsible for a large 36.8% of these losses. Additionally, younger people, especially those between the ages of 20 and 29, were disproportionately affected by credit card fraud. Our study is unique in a number of respects. We used a carefully controlled dataset to guarantee that the values after preprocessing were accurate and complete. We enhanced the detection of fraudulent transactions by our model by using a wide range of features. To find the most suitable machine learning algorithm for our dataset, we also investigated a range of other techniques. Our study used a dataset with the transaction histories of over 300,000 people from the UCI Machine Learning Repository. We determined the best methods through a multi-step procedure that included data pretreatment, dataset division, and model selection. Our study is unique in a number of respects. We used a carefully controlled dataset to guarantee that the values after preprocessing were accurate and complete. We enhanced the detection of fraudulent transactions by our model by using a wide range of features. To find the most suitable machine learning algorithm for our dataset, we also investigated a range of other techniques. The Stacked Ensemble model, SN Algorithm (SVM + Naive Bayes), beat other models according to the results in terms of important metrics. Impressive AUC, CA, F1, accuracy, and recall ratings demonstrated the system's effectiveness in preventing card theft.

Enhancing Credit Card Fraud Detection Using P-XGBoost: A Comparative Study Classical Machine Learning Techniques

Abstract: Ability of debit card companies to detect and prevent fraudulent transactions is crucial to safeguard customers from unauthorized charges. Data Science, particularly Machine Learning, plays a pivotal role in addressing this challenge. This project aims to demonstrate the application of machine learning in Credit debit Fraud Detection by modeling a dataset of past credit card transactions, distinguishing fraudulent ones from legitimate ones. The objective is to achieve a fraudulent transactions while minimizing false positives. Debit Card Fraud Detection is a classic classification issue. A research focuses on data analysis, preprocessing, and the utilization of XGBoost on Credit Card Transaction data. To prevent overfitting, grid search is employed to fine-tune the models' hyperparameters. The performed of XGBoost and P-XGBoost is compared with further usual machine learning techniques. Surprisingly, P-XGBoost best XGBoost in fraud detection, presenting a viewpoint for effectively identifying fraudulent behavior while ensuring the privacy of clients.

Debit Card Fraud: An Imminent Issue in Cyber Crime

Debit Card Fraud: An Imminent Issue in Cyber Crime

Using Visual Cryptography, A Safe Online Payment Method

Due to the growing usage of online shopping and online payments in recent years, the E-Payment system has experienced enormous growth. The acceptance of electronic payments for offline transfers is made easier by an electronic payment system. An electronic payment system is a technique to conduct transactions or pay for goods and services online. Systems for electronic payments are now widely employed across all industries, including banking, finance, online commerce, and many more. There were some security risks in the conventional e-payment systems, including phishing, credit card fraud, and debit card fraud. In order to solve this issue, we developed an electronic payment system that combines visual and quantum cryptography methods for safe transmission. Quantum cryptography secures the transmission of the one-time password, whereas visual cryptography conceals customer information by creating shares. We adjusted our system using the email OTP notification mechanism for OTP processing. The share is securely transmitted to the bank thanks to image steganography, which embeds a one-time password in the share. The proposed approach offers unconditional security as opposed to the conventional E-payment mechanism. Key Words: One-time passwords, online payments, image processing Quantum cryptography, visual cryptography, and image encryption.

Fraud Detection for Online Interbank Transaction Using Deep Learning

The World Banking with its various financial services is an easy target for fraudsters to carry out their actions. Various kinds of fraud that occurred such as credit card fraud, online payment fraud, debit card fraud, online transaction fraud, e-commerce fraud and other services including interbank online transactions. Fast and reliable fraud detection is important because many financial losses have occurred due to fraud. The objective of this study is obtaining a more effective deep learning model for fraud detection in the interbank online transaction system compared to similar models. This study using CNN, LSTM and hybrid model CNN-LSTM models are used to build an interbank online transaction system. The proposed model CNN consist of three convolution layer, one maxpooling layer, one dropout layer and one fully connected layer. The proposed model LSTM built by double layer LSTM with each layer consist 32 cell LSTM, dropout layer and one fully connected layer. The proposed model CNN-LSTM built by combination three convolution layer, 1 maxpooling layer, dropout layer, 1 LSTM layer with 64 LSTM cell and one fully connected layer. The Dataset taken from an interbank online transaction in March 2021 from one of the switching company in Indonesia. SMOTE is use to overcome the imbalance Dataset in training and validation Dataset. The Dataset contains 279513 transactions with 2374 transactions categorized as fraud. The results showed that the CNN model scored an F1-score value at 93,09%, followed by the LSTM model at 86,25% and the CNN-LSTM hybrid model at 69,22%. Based on these results, the proposed CNN model can be accurate for fraud detection in interbank online transaction systems compared to similar models.

Multimodal Bank Transaction System With Real-Time Secure Clickbait and Biometric ATM User Authentication

ATM or Automated Teller Machines are widely used by people nowadays. Performing cash withdrawal transaction with ATM is increasing day by day. ATM is very important device throughout the world. The existing conventional ATM is vulnerable to crimes because of the rapid technology development. A total of 270,000 reports have been reported regarding debit card fraud and this was the most reported form of identity theft in 2021. A secure and efficient ATM is needed to increase the overall experience, usability, and convenience of the transaction at the ATM. In today's world, the area of computer vision is advancing at a breakneck pace. The recent progress in biometric identification techniques, including finger printing, retina scanning, and facial recognition has made a great effort to rescue the unsafe situation at the ATM. Specifically, the goal of this project is to give a computer vision method to solve the security risk associated with accessing ATM machines. This project proposes an automatic teller machine security model that uses electronic facial recognition using Deep Convolutional Neural Network. If this technology becomes widely used, faces would be protected as well as their accounts. Face Verification Clickbait Link will be generated and sent to bank account holder to verify the identity of unauthorized user through some dedicated artificial intelligent agents, for remote certification. However, it obvious that man’s biometric features cannot be replicated, this proposal will go a long way to solve the problem of account safety making it possible for the actual account owner alone have access to his accounts. This eliminates the possibility of fraud resulting from ATM card theft and copying. The experimental results on real-time datasets demonstrate the superior performance of the proposed approach over state-of-the-art deep learning techniques in terms of both learning efficiency and matching accuracy. By using this real time dataset, the proposed system achieves the highest accuracy with 97.93%

The Best Decision for E-Commerce Funds Transfer Based on Cloud Computing Technique

Cloud computing offers an efficient, scalable, and flexible platform; through techniques and methods proven by it, many complex projects were successfully carried out. It provides consumers the opportunity to interact with previously unknown ease to measure sources and connections to IT-managed services. Online shopping has been considered as one of the commonly used channels for easy shopping in the Internet world and becomes a convenient way to search for products from various places and to make the operation quicker. The digital economy is powered by high-speed Internet connectivity launched by IT as a practical platform for interaction between online shopping and organizations. The platforms for e-commerce have led to extensive international access by local consumers to goods and services. E-commerce has improved productivity in enterprises and has added to the economies in the countries of the business industry. However, e-commerce security is the primary concern to protect details from electronic theft such as credit card or debit card fraud, retailer, or bank leakage of details while a card is not used for purchases online. In this paper, Cloud Computing-Based Multilevel Decision Support System (CCMDSS) has been proposed for secure fund transfer in the e-commerce platform. Furthermore, this study utilizes cryptography techniques and multiple networks to safeguard the security of information and decrease the systematic risks. The experimental results show that the proposed method enhances the efficiency and feasibility and secures the information while transferring the money. Compared with other models, Prediction Ratio validation has a significant improvement, and the error rate is greatly reduced, only 5.99%. Multiple data prove that the comprehensive performance of CCMDSS model ranks among the top among existing models.

ROBOTIC PROCESS AUTOMATION: CHALLENGES AND SOLUTIONS FOR THE BANKING SECTOR

In order to cater to the growing customer domain with new service conditions such as 24x7 service, fail-safe service, and mobile service, organizations are incorporating more and more technology-based solutions. Robotic Process Automation (RPA) has emerged as one of the key technology strategies for scaling services with robustness and efficiency. RPA is being increasingly used as a tool to automate, scale-up, manage, analyze, and provide superior customer service. This research paper explains the key challenges banks face in the implementation of RPA and proposes suggestions for banks to avoid these challenges in RPA implementation. The issues faced before and post-implementation of RPA have been discussed. How to deal with security issues on the implementation of RPA has also been discussed. An improved RPA has been proposed to how banks can address new challenges such as debit card fraud and showed how automation benefits the banks in terms of improved error rate, processing time, accuracy, and reliability.

Debit Card Fraud Recognition Using Enhanced Auxiliary Classifier Generative Adversarial Networks

Debit Card Fraud Recognition Using Enhanced Auxiliary Classifier Generative Adversarial Networks

Multi-Factor Authentication and Fingerprint-based Debit Card System

One thing can be said to be common to all forms of debit card fraud – authentication bypass. This implies that a secure debit card transaction system can only be guaranteed by a safe and reliable authentication system. Many approaches have been adopted to ensure a secure authentication system, but often, these approaches are either focused on the Automated Teller Machines (ATM)/Point of Sales (POS) terminals or Online/e-commerce transactions, thus not providing full security on both fronts. In this work, we address this problem by adopting a multi-factor debit card system that uses a combination of the traditional Personal Identification Number code (PIN) and the mobile-phone delivered One-Time Password (OTP) with a biometric authentication option (fingerprint). We demonstrate that this approach ensures the security of both online and terminal transactions. The fingerprint option makes it easy to use by people who find memorizing PINs difficult.

On Reducing Misclassifications and Error on CART Models using Rtosynr Dataset for Improved Debit Card Fraud Detection

On Reducing Misclassifications and Error on CART Models using Rtosynr Dataset for Improved Debit Card Fraud Detection

The evolution of payment card fraud

Payment cards have evolved considerably over the decades. But a plethora of competing standards, and their patchy take-up around the globe, have left cards vulnerable to creative fraudsters. Steve Gold looks at the problem of credit and debit card fraud, the evolution of the technology, its problem with backwards compatibility, and analyses some of the more leading-edge fraud methodologies used by increasingly technology-aware cyber-criminal gangs. Credit and debit card fraud has changed significantly over the past few years. Gone are the days when fraudsters used simple skimming techniques – with pinhole cameras to record the associated PIN – at the ATM to harvest card credentials for card cloning. Today's fraudsters harvest card credentials – and allied information – on a commodity basis, using a variety of techniques.

An Update on Trends in the Debit Card Market

On March 20, 2007, the Payment Cards Center of the Federal Reserve Bank of Philadelphia hosted a workshop led by Stan Paur, chairman of PULSE EFT Association LP, a Discover Financial Services LLC company, and Tony Hayes, vice president of Dove Consulting, a division of Hitachi Consulting. Paur and Hayes shared findings from PULSE's 2007 Debit Issuer Study, conducted by Dove Consulting with 55 debit card issuers of varying sizes. In examining developments in the debit card market, Hayes and Paur shared survey results and provided additional insights into four key areas: performance metrics, networks and interchange, debit rewards, and debit card fraud. This paper describes trends and issues affecting the debit card market in each of these four areas, in an effort to expand understanding of this dynamic and increasingly important category of consumer payments.

513 POSTER Erythropoietin in patients with malignant extradural spinal cord compression: functional and pharmacokinetic outcomes

Payment cards have evolved considerably over the decades. But a plethora of competing standards, and their patchy take-up around the globe, have left cards vulnerable to creative fraudsters.Steve Gold looks at the problem of credit and debit card fraud, the evolution of the technology, its problem with backwards compatibility, and analyses some of the more leading-edge fraud methodologies used by increasingly technology-aware cyber-criminal gangs.Credit and debit card fraud has changed significantly over the past few years. Gone are the days when fraudsters used simple skimming techniques – with pinhole cameras to record the associated PIN – at the ATM to harvest card credentials for card cloning. Today's fraudsters harvest card credentials – and allied information – on a commodity basis, using a variety of techniques.