Secure Payment Research Articles

EB: Eye Biometrics Based a Novel Human Recognition System for Cardless Online Payment Security Improvement in ATMs

Here we are introducing a novel approach for enhancing the security of traditional transactions of ATM .Individual confirmation is quite possibly the main ways to deal with work on the security. Be that as it may, the conventional individual validation techniques or advance hybrid. Biometrics, which naturally utilizes the physiological or social quality of individuals to perceive their characters, is one of the powerful procedures to defeat these issues. Biometrics is a field of programmed individual ID dependent on physiological and social attributes of people. A conduct trademark is progressively an impression of an individual’s physiological cosmetics Validations. We used Eye Biometric Recognition System using OpenCV for cardless transaction & Identity Verification after ATM Transactions. We proposed EB verification model in the research for final compilation of transaction. Though it may require more time for verification but security is prior to time & cyber thefts. The efficiency of Software we Proposed came out to be 98.52%, 95.75%, 98.86% while performing an extensive testing of our algorithm with 3 datasets named UBIRIS.V1, UBIRIS.V2, IITD and the algorithms we have used in OpenCV For feature detection & extraction are ORB, brute force algorithms. With the hope of Interest in youth and additional feature of security to be embedded in online transaction is the motivation of the research.

Investigating the Security Threats on Using M-Payment Applications in Saudi Arabia: Exploratory Study

Online banking, debit cards, credit cards and mobile payments are the most common payment types in Saudi Arabia. This study explored security threats that affect m-payment applications in Saudi Arabia by interviewing 16 IT professionals to explore their insights and opinions about those security threats. Cybersecurity threats present the biggest challenge for most mobile systems, as mobile payments can be affected by cyber-attacks and require sophisticated approaches to achieve the desired security. In our study we report on the impact of security threats on the utilization of mobile payment applications and provide evidence related to those threats and their impact on the use of mobile payment applications. Evidence was provided regarding the security threats and their impact on using mobile payment applications. Information was also provided related to security threats such as Distributed Denial-of-Service, phishing attacks and malware. Although the participants in this study demonstrated a positive attitude regarding the safety and security of mobile payments, they also highlighted the security threats that impact m-payments. The results showed that the three main threats in Saudi Arabia were Distributed Denial-of-Service (DDoS), phishing attacks and Malware (Malicious software). This study makes two contributions. The first is to theory, by filling the gap in the literature because it is the first study to explore the threats to using m-payment in Saudi Arabia. Secondly, this study contributes to practice by providing a clear picture for service providers and users about threats they may face when using m-payment.

Increased E-commerce and Fintech Transactions During the Covid-19 Pandemic

Activity restrictions to suppress the spread of the Covid-19 virus have made people take advantage of existing technology as well as the emergence of e-commerce and the ease of transactions creating business opportunities that greatly assist people in meeting their needs without having to leave the house and interact with many people. Researchers want to know the influence of the development of fintech, e-commerce, delivery services, lifestyle and banking security on meeting the online needs of the millennial community among students. The research method used is quantitative research with a sample of millennial society from Tidar University and Stekom University students. Various e-commerce applications, social media, fintech and online transportation are the most widely used by respondents 94.2% shoppe, 49.7% whatsapp and grab. Technological sophistication affects lifestyle in meeting needs during the pandemic because more than 75% answered agree and 45% strongly agreed. The lifestyle of today's society by utilizing technology becomes faster, more effective and efficient because more than 75% answered agree and 50% answered strongly agree. Respondents from Tidar University and Stekom University operate e-commerce applications that are available by taking advantage of the ease of transactions, 90% agree. The results of student responses to promos or free shipping coupons made them interested in making online purchases, 80% answered strongly agree. The results of the survey of student respondents regarding the security and ease of payment from banking services to conduct transactions either through e-banking or top up answered 75% agreed.

Building agency: women vendors and gendered technology in informal markets in Assam

ABSTRACT Demonetisation of the Indian economy in 2016 facilitated significant changes in the lifestyle of people, particularly in adoption of digital transactions in everyday life. While there have been studies on mobile phones and the digital divide even prior to demonetisation, the growing gender disparity in the use of new technologies for secure payment via UPI (Unified Payment Interface) methods remains largely unexplored. Therefore, this paper explores the impact of new payment methods through digital means on street vendors in local markets through an ethnographic study based on narratives, unstructured interviews, and general observation. The study focuses on Assam while bringing out the larger socioeconomic context of the digital discrimination within the country. It locates how the government’s drive for digital economy post-demonetisation exacerbated the gender gap in access to technology in informal markets. The article observes how men use mobile phones and technology to sell their commodities while women vendors lag behind in ownership, usage, or access to such technology due to social norms and expectations. Subsequently, the study brings about the narratives on how women negotiate through such constraints to build their social ‘agency’ and identity in the market at both individual and collective level.

Costs or signals: The role of “Social insurance and housing fund” in the labor market — Evidence from recruitment platforms

In China's labor market, enterprises are allowed for some flexibility in deciding whether to provide “social insurance and housing fund” to laborers. This paper uses micro-data from two leading Internet recruitment platforms and finds that in a labor market with double-side information asymmetry, “social insurance and housing fund” serves as not only a cost but also a signal. Providing workers with “social insurance and housing fund”, enterprises send a signal of stable operation to the labor market while identifying high-quality workers for enterprises. We further construct an instrument variable (IV) of local average social security payment rate, and show that the signaling effect remains significant after accounting for the endogeneity issue using IV regressions. In addition, “housing fund” has a stronger signaling effect than “social insurance”. Heterogeneity analysis indicates that the strength of the two signaling effects is affected by the scale of the enterprises and the level of local payment rates. A theoretical framework capturing two micro-mechanisms — signaling and screening — is developed to fit our empirical findings. This paper provides explicit policy implications. It is suggested to strengthen the information disclosure and the propagation of social security payment, and further reduce the financial burden of enterprises.

Customer retention through service quality and satisfaction: using hybrid SEM-neural network analysis approach

Mobile commerce is a developing phenomenon, youth worldwide utilising the platform that provides flexibility, ease, and convenience of online shopping through mobile devices. The study investigates the influence of mobile commerce service quality dimensions on the perception of service quality and customer satisfaction. Satisfaction in Mobile commerce promotes the revisit intention among the customer. The study model was designed on the service quality model SERVQUAL, and the factors were adopted from the information and system quality dimensions. The hypotheses were tested with Jordanian adults, and data was collected through a survey from January 2020 to April 2020. The data was analysed using the variance-based statistical analysis tool with Smart Partial Least Squares 3.2. The non-compensatory analytical technique of artificial neural network analysis was employed to assess the study models. Resultantly, responsiveness and cognitive control factors were significantly related to the Mobile commerce overall service quality. The information quality dimension of content usefulness and adequacy was significantly related to the mobile commerce overall service quality. Mobile commerce system quality dimensions were significantly related to the overall service quality. Furthermore, overall service quality perception significantly influenced customer satisfaction, promoting the revisit intention towards mobile commerce. Multilayer artificial neural network analysis was applied. The result conclusively showed that website innovativeness, content usefulness, and ease of use were the three substantial mobile commerce platforms that impacted overall service quality. The e-business management should use state-of-the-art technology features to enhance the quality perception and develop payment security features to nurture trust among users. Future research opportunities and limitations were reported at the end.

Advancing Digital Payment Systems: Combining AI, Big Data, and Biometric Authentication for Enhanced Security

Digital payment solutions have recently gained extraordinary popularity across the globe. Many individuals in cities or urban areas have begun to use these payment systems for financial transactions. The combined use of different advanced technologies is indispensable for widespread adoption. Various obstacles to performing financial transactions using digital payment systems must be considered. Among these obstacles, security is the most remarkable point. Although this online payment system is fast and convenient, the chances of fraud are very high. A cybercriminal can hack into the system, stealing personal information like phone numbers and passwords. This article will focus on a new digital payment security system that will take advantage of artificial intelligence (AI), big data, and biometric authentication (BA). When using this security system, a transaction request will be sent. First, it will check whether the phone number is a registered user and if it is, further authentication will be carried out. The system will automatically authenticate the user’s identity with the help of facial recognition technology that will analyze the features of the face. Further, the user has to show various gestures that are analyzed by the system. This and other personal information will be converted into a number and then encrypted before being stored in the database. The incoming transaction request will be compared against the personal number. If the user is not unique, the transaction request will be immediately rejected. Secondly, if the number matches but the user is fraudulent, the transaction will also be rejected. Only if both are unique, the system will check for duplicate numbers among the other transactions for the particular period. If other numbers are not found, the transaction is executed; otherwise, it is still rejected. This is done to find out fake and auto-generated numbers. This security system will be helpful to ensure the authenticity of the user in digital payment systems.

ZERO-KNOWLEDGE PROOF APPLICATION IN ECOMMERCE PAYMENT

E-commerce plays a significant role in a country's economic condition. Since the COVID-19 outbreak, it has become more popular, along with concerns about its ability to handle information security. The Zero-Knowledge Proof (ZKP) method could be a possible solution to the e-commerce payment security issue that hampers customer trust. This paper investigates the viability of an online payment framework based on the ZPK method. This method is an upgrade for authentication during the payment process in online shopping. Experiments on customers' perspectives of the payment framework based on the ZKP method were conducted and supported the perceived usefulness, ease of use, trust, control, satisfaction, and loyalty aspects of a better e-commerce website. It allows advantages for both customers and e-commerce and prevents fraud, which will increase the trust level for both sides. zkSNARK speeds up and lowers the cost of the process, but there is a risk of DOS. Future work needs to be done to handle DOS in this method.

CrowdHB: A Decentralized Location Privacy-Preserving Crowdsensing System Based on a Hybrid Blockchain Network

With the advent of the Internet of Things (IoT), crowdsensing, as a new emerging application of the IoT that employs ubiquitous mobile users with smartphones for data collection and processing, has further deepened our knowledge. However, the problems of the current crowdsensing systems regarding system security, user privacy, and user payment (UP) raise serious privacy and security concerns, which affect participants’ adoption of the system. The Blockchain technology allows for nondeterministic multiple parties to interact with each other anonymously in a network that is not fully trusted. In this article, we propose a new decentralized crowdsensing system, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CrowdHB</i> . Unlike other blockchain-based crowdsensing systems, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CrowdHB</i> adopts a hybrid blockchain architecture and uses smart contracts to achieve location privacy preservation and ensure data quality while improving the system performance. Furthermore, to optimize task assignments to mobile users, we propose a location privacy-preserving optimization mechanism (LPPOM) and the approach of consistency optimization (ACO) to achieve a tradeoff between user privacy and system performance. The extensive experimental results show that the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CrowdHB</i> outperforms the other crowdsensing systems in terms of task success rate and performance for a large number of mobile users and tasks.

STRUCTUREAL DEASIGN OF SECURE E-COMMERCE WEBSITES EMPLOYING MULTI-AGENT SYSTEM

&#x0D; &#x0D; &#x0D; &#x0D; The vast growth in electronic trading and monetary transactions results to increase the importance of E-commerce applications rapidly. In order to accomplish the fully functioning, reliable, scalable, secure, efficient and user-friendly E-commerce applications, sufficient system analysis and design processes are important. Shopping over the internet requires offering secure payment method.&#x0D; Providing security for each individual online consumer at the same time (particularly for large websites) is a time-consuming effort that can result in a variety of issues, including response delays, customer orders being lost, and system freeze or crash, all of which impair system performance.&#x0D; This work seeks to present a new multi-agent system prototype structure that solves the challenge of security while avoiding issues that might degrade system performance. By creating integrated system taking into account the three parties the e-commerce, the e-bank and the customer, the main concentration is to create an archeticture that helps in decreasing the website traffic and supportng the security with effective performance. To simulate the real systems a recommendation system is created based on the customer behaviors to increase system performance in helping the customers to find their favorite products and solve problems like cold start, sparsity of user‐item matrix, scalability, and changes in user interest.&#x0D; This is done by creating a commercial environment (e-commerce and e-bank websites) and a software agent that is installed on the client's device to handle the purchase and ciphering procedures without the need for the consumer to intervene. The security process involve preprocessing phase followed by a modified lightweight AES employing chaotic tent map to generate encryption key.&#x0D; &#x0D; &#x0D; &#x0D;

HF-SCA: Hands-Free Strong Customer Authentication Based on a Memory-Guided Attention Mechanisms

Strong customer authentication (SCA) is a requirement of the European Union Revised Directive on Payment Services (PSD2) which ensures that electronic payments are performed with multifactor authentication. While increasing the security of electronic payments, the SCA impacted seriously on the shopping carts abandonment: an Italian bank computed that 22% of online purchases in the first semester of 2021 did not complete because of problems with the SCA. Luckily, the PSD2 allows the use of transaction risk analysis tool to exempt the SCA process. In this paper, we propose an unsupervised novel combination of existing machine learning techniques able to determine if a purchase is typical or not for a specific customer, so that in the case of a typical purchase the SCA could be exempted. We modified a well-known architecture (U-net) by replacing convolutional blocks with squeeze-and-excitation blocks. After that, a memory network was added in a latent space and an attention mechanism was introduced in the decoding side of the network. The proposed solution was able to detect nontypical purchases by creating temporal correlations between transactions. The network achieved 97.7% of AUC score over a well-known dataset retrieved online. By using this approach, we found that 98% of purchases could be executed by securely exempting the SCA, while shortening the customer’s journey and providing an elevated user experience. As an additional validation, we developed an Alexa skill for Amazon smart glasses which allows a user to shop and pay online by merely using vocal interaction, leaving the hands free to perform other activities, for example driving a car.

Empirical Study on the Influencing Factors of WeChat Business Integrity

With the increasing popularity of the smart phones and electronic payment, WeChat shopping has become a trendy lifestyle for many people. However, the issue of WeChat business integrity has gradually appeared due to the virtuality of the Internet. This paper analyzed the development of WeChat business and its business integrity. The influencing factors of WeChat business integrity and related hypotheses had been studied based on theoretical and practical analysis. The reliability and validity of the data collected through questionnaires were tested with SPSS24.0. Empirical analysis was done to the hypotheses by using the structural equation model. The results indicated that products and service quality, after-sale guarantee, payment security and Word-of-mouth had a prominent positive effect on the integrity of WeChat business.

Knowledge graph of mobile payment platforms based on deep learning: Risk analysis and policy implications

The Fintech mobile payment platform is expanding rapidly; this expansion, in turn, creates numerous risks. There is an urgent need to better understand these risks and to spur more secure payment behavior. This research aims to develop knowledge graphs of the mobile payment platform based on deep learning for risk analysis and policy inferences. We identify entities from collected policy documents, extract the relationships among the entities, and draw a risk knowledge graph on mobile payments. The use of unsupervised semi-automatic knowledge acquisition, we argue, can reduce the risk of mobile payment caused by a lack of knowledge. A significant benefit of this method is that risk knowledge can be acquired without supervision. Unlike other models, the absence of manual labeling allows for the relation extraction of triples to be unsupervised, while the previous triplet extraction was supervised. Compared with other unsupervised models, the precision of our model is improved, and the recall is the same as that of previous unsupervised shutdown extraction.Unsupervised relationship extraction can extract text relationships quickly and on a large scale, saving human resources for labeling.This method offers a potential solution to a fundamental problem; the content and quantity of policy documents exceed organizations’ and individuals’ ability to understand them. Our approach suggests the viability of developing a national policy risk knowledge graph to help mobile payment platforms understand national policies and reduce platforms’ operational risks while allowing users to quickly learn the risks of mobile payments and minimize the impact of those risks.

The Unintended Consequences of Welfare Reforms: Universal Credit, Financial Insecurity, and Crime

Abstract We evaluate the unintended effects of Universal Credit (UC), a monumental welfare reform that has increased the stringency of the UK social security payment system. We exploit the rollout of UC across constituencies, targeting first-time claimants, predominantly young males. Using monthly data from 2010 to 2019 for England and Wales, we first document the negative economic impacts of UC, showing it has led to an increase of around 6000 repossession orders from the social housing sector. We then focus on UC’s criminogenic effects, finding the reform has caused around 35,000 burglaries and 25,000 vehicle crimes. Collectively, our findings highlight the danger of welfare policies affecting the economic incentives of individuals at the bottom of the income distribution. They also have timely policy relevance considering 6.5 million benefit recipients will soon transition onto UC. (JEL K14, K42).

Secure Transaction System Using Face Recognition

The use of online payment is increasing dramatically in today's environment. As a result, this initiative was created to protect payments against internet scams. The project's main goal is to create a secure payment system that uses face recognition and Open CV in Python. It makes use of the confidence level, which is defined as the value generated by the system and used to determine the forecast's accuracy. Several graphic modules were combined with a pattern mirror to create this piece. Before entering a PIN, the system in this project uses face detection and a proxy. Because it embeds elements of the users' faces to increase system accuracy, the algorithm employed in this research will improve the security and efficiency of face recognition. To strengthen security, we have introduced three-fold encryption while the data is being transmitted for authentication. The user's bank balance is confirmed and the work is completed after the system has completed its verification. The suggested system outperforms existing functions, and its user-friendly interface makes it simple to use.

Secure Payment with QR Technology on University Campus

Secure Payment with QR Technology on University Campus

Implementation and Performance of Face Recognition Payment System Securely Encrypted by SM4 Algorithm

Face recognition payment is a new type of payment method, with AI face recognition technology as the core, and its speed and convenience are more in line with the users’ payment habits. However, the face is a biological feature with weak privacy, and the protection of user information security is particularly important. At present, face payment technology still has security risks, and the data transmitted during the transaction process are vulnerable to attacks. Aiming at the security problems in the payment process, a payment system that is jointly encrypted by the SM4 algorithm and the face liveness detection algorithm was proposed in this paper, which supports a variety of communication methods. The hardware platform adopts an octa-core 64-bit ARM processor with a main frequency of 1.8 GHz, which has powerful computing and processing capabilities. Based on the Android intelligent operating system, the development environment is more secure and convenient. It is also equipped with a liveness detection 3D structured light camera, which dynamically collects face information and accurately analyzes the characteristics of living bodies. Through the data encryption and decryption test and face performance index detection, the expected effect of the system was achieved, which greatly improved the performance of the face payment system currently studied. The SM4 encryption algorithm improved the running rate of encrypted data and the security of face transaction data transmission, the face detection algorithm improved the accuracy of living body feature recognition, and the payment system effectively improved the accuracy and security of face payment.

Trust Research on Didi Platform: About Influencing Factors and Willingness to Use

Along with the expansion of the scope of the “sharing” economy, creating more values by sharing exist resources is becoming increasingly common, facilitating energy saving and emission reduction and realizing mutual benefits in the ridesharing economy. Ridesharing, with Didi Chuxing as a popular ride-hailing and representative platform, has become a trend in recent years. However, the ride-hailing industry is faced with problems and challenges due to limitations and inadequate standardization of its institutions. In the industrialization of the ridesharing economy, lack of relevant policies and regulations and limitations of institutional environment leads to many problems. How to improve the trust of the users in the industry by the platform operation mechanism and thus ensure the steady growth of the transaction volume and users becomes the tasks that the whole industry needs to address. Taking "Didi platform" as the research object, this paper investigates the platform operation mechanism and consumers' trust in the third party ridesharing platform. The results show that : 1) the security mechanism of the ridesharing platforms, including payment security, price mechanism and feedback mechanism, as well as the influential factors of social system, including the reputation of platforms and government supervision. The platform reputation has the most significant impact; 2) payment security and shop owners certification mechanism can significantly reduce the transaction risk perceived by consumers, and then affect their intentions of use; 3) there is a positive transfer relationship between consumers’ trust in platforms and their trust in the driver group of the platforms.

Risk Prediction of E-Payment by Big Data Management Technology

E-payment has penetrated every aspect of people’s daily life, and the development and application of E-payment technology have made life more convenient. Besides, the process of E-payment has endless hidden dangers, posing a great threat to payment security. In this context, risk assessment and prediction of E-payment are particularly important. Therefore, this paper mainly studies the E-payment analysis and prediction method based on big data technology. Specifically, this paper uses the BP neural network to extract E-payment data feature and compare and analyze the characteristic of different methods; finally, the payment risks are predicted based on the features, the simulation experiments show that the best result is obtained by the method proposed in this paper, and thus the effectiveness of the new method is verified.

E-commerce Payment Security Strategies for Online Payments

E-commerce Payment Security Strategies for Online Payments