Third-party Payment Research Articles

When friends become enemies: co-opetition relationships between banks and third-party payment providers

PurposeThe purpose of this paper is to investigate the conditions under which working with an incumbent downstream competitor could be a beneficial strategy for upstream firms as the case of the relationship between banks and third-party payment providers.Design/methodology/approachUsing a game model, this study considers a market with two upstream firms (banks) and two downstream firms (third-party payment providers). One downstream firm is an incumbent that poses a competitive threat to the upstream market, and the other downstream firm does not.FindingsThe results show that the optimal decision for banks depends on the number of loyal users the incumbent third-party payment providers and banks have. When the bank has more loyal users than the competitive third-party provider to a certain level, it would terminate cooperation with the provider; otherwise, the bank would maintain cooperation. This is true whether the duopoly banks are symmetrical or asymmetrical.Originality/valueThis study makes contribution to the theory of co-opetition lies in the fact that it examines a special case of competition and cooperation between vertical enterprises in the bank context. This study investigates how the upstream firms do when threatened by a downstream firm while the upstream firms have other options. This study also contributes to bank marketing theory through providing explanations for some of the incomprehensible cooperation in China's payment market, which is characterized by consumer loyalty. This study extends previous new-entry competition for banks by differentiating between incumbent and new-entry downstream firms.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/IJBM-11-2019-0414

Game Problem and Nash Equilibrium in the Electronic Payment Market with Multi-agent Participation

The rapid development of e-commerce has greatly promoted the popularization of electronic payment methods in China. The electronic payment service of online banking solves the problem of online transaction payment in the early stage of e-commerce development. After the rise of third-party Internet-guaranteed payment services, the problem of mutual trust in online shopping was solved in a breakthrough way, and the electronic payment service model was continuously innovated, and the electronic payment service scene was rapidly expanded and popularized. In this process, the relationship between third-party payment service providers and online banking has evolved from cooperation to competition, and has been developing in the contradiction between competition and cooperation. At the same time, the third-party payment model has brought great challenges to financial supervision. Based on the respective interests of the main participants in the electronic payment market, this paper applies game theory and its Nash equilibrium theory to analyze the optimal strategy choices of the participating entities in the electronic payment market, and gives their respective development strategies.

Understanding Chinese Millennials' Adoption Intention Towards Third-Party Mobile Payment

Different user groups will tend to value different third-party mobile payment offerings differently, and so make different adoption decisions. Millennials are regarded as creative, socially conscious and more willing to provide opinions on the advantages and disadvantages of new technologies. Therefore, it is important to investigate the key factors that affect Chinese Millennials' adoption behavior of third-party mobile payments as they grow in popularity in China. This study establishes a new conceptual model based on the Technology Acceptance Model incorporating additional variables such as perceived risk, personal innovativeness, compatibility, and social influence. This research collected quantitative data by questionnaire (N=380). The data was analyzed by SPSS. This study suggests that social influence and attitude directly affect adoption intention towards third-party mobile payments, whereas perceived usefulness, compatibility, perceived ease of use and personal innovativeness only indirectly affect adoption intention towards third-party mobile payments.

New normal and new economy: a new growth engine for China

As the economy stepped into the “new normal,” the Chinese government has implemented various policies to transform its economic development model to pursue an innovation-driven growth. Against this background, the most innovating field is the Internet-based “new economy.” A major feature of the Chinese new economy is that it is based on a new payment system. Various new businesses have been developed owing to third-party payment platforms (particularly Alibaba's Alipay and Tencent's WeChat Pay) and a vast ecosystem has been created around them. Moreover, the development of the Chinese new economy is helping to expand consumer spending and is becoming a new economic growth engine.

'I Access Your Data You Access Mine'. Setting a Reciprocity Clause for the ‘Access to Account Rule’ in the Payment Services Market

Article 36 of the EU Payment Services Directive 2 No. 2015/2366 (hereinafter also referred to as PSD2) awards digital operators, so-called third-party payment providers (TPPs), the right to freely access users’ payment accounts data (known as the XS2A rule), typically retained by incumbent banks to provide payment initiating services (PIS) or account information services (AIS). The stated goal of the XS2A rule is to help an infant industry grow: that of Fintech payment services. However, mounting evidence suggests that unlike Fintech startups, big techs may benefit from the norm, putting a much stronger challenge to established banks. The latter indeed have less access to data about consumers’ behavior (preferences, habits and conducts), less analytics capacity, and thus are placed in a competitive disadvantage by the XS2A, compared to their big tech rivals. According to many, that may result in competition harm and consumer welfare reduction in the medium and long run. The article revisits the rationales for introducing the XS2A rule by critically assessing its asserted goals (Part 1). It then suggests (Part 2) that to level the playing field among the market players involved and increase its overall proportionality, the norm be revisited. In the relationship between big tech and the banks, we propose to complement the XS2A rule with a “reciprocity clause”. Instead of free access to customers’ account data, big tech would pay by counter-providing access to the behavioral data in their possession pertaining to the same customers concerned by the XS2A, and upon their consent. Data thus transmitted would only be available upon condition of reuse to increase the efficiency of the payment service provision. Reciprocity would greatly enhance data circulation among market participants, while fostering competition among them, creating incentives for more innovation and strengthening the overall proportionality of the XS2A rule.

Research on brush face payment system based on internet artificial intelligence

The facial information of a person has constant invariance, and at the same time, it is an ideal basis for identifying identity information through differences between different individuals. The use of face recognition in third-party payment platforms will greatly improve the efficiency and securit y of payment. Based on this, the paper studies and designs the third-party payment system for face recognition in the context of Internet artificial intelligence to improve the convenience and security of face payment.

Forecasting third-party mobile payments with implications for customer flow prediction

Forecasting customer flow is key for retailers in making daily operational decisions, but small retailers often lack the resources to obtain such forecasts. Rather than forecasting stores’ total customer flows, this research utilizes emerging third-party mobile payment data to provide participating stores with a value-added service by forecasting their share of daily customer flows. These customer transactions using mobile payments can then be utilized further to derive retailers’ total customer flows indirectly, thereby overcoming the constraints that small retailers face. We propose a third-party mobile-payment-platform centered daily mobile payments forecasting solution based on an extension of the newly-developed Gradient Boosting Regression Tree (GBRT) method which can generate multi-step forecasts for many stores concurrently. Using empirical forecasting experiments with thousands of time series, we show that GBRT, together with a strategy for multi-period-ahead forecasting, provides more accurate forecasts than established benchmarks. Pooling data from the platform across stores leads to benefits relative to analyzing the data individually, thus demonstrating the value of this machine learning application.

Research on user satisfaction of third-party mobile payment under the IoT environment——based on the structural equation mode

This paper establishes a theoretical model of third-party mobile payment user satisfaction based on the ASCI model and proposes research hypotheses. Then, the questionnaire survey method is used to collect sample data and carry out reliability and validity test. After the test is passed, structural equation modeling (SEM) is used to verify the research model and hypothesis. Finally, the analysis concludes: perceived quality and perceived value will have a positive impact on user satisfaction; user expectations will affect user satisfaction through perceived quality and perceived value; the impact of perceived risk on user satisfaction is not obvious. Propose reasonable suggestions for third-party mobile payment companies based on the research conclusions.

A perspective on categorizing Personal and Sensitive Data and the analysis of practical protection regulations

Abstract The modern era is marked with users' comprehensive manners in interaction and data mining. This phenomenon is an extraordinary aspect of modern digital age. The digital path of a user associated with surfing the webpages on internet is unconditionally interconnected with a small dialogue box asking about permission to use cookies from the website. Afterwards, the relevant user's web-findings of items or information that had been browsed before is recommended automatically from the searching engine again. It is clearly acknowledged that after the announcement of the EU General Data Protection Regulation (GDPR), which was enforced in 2018, the worldwide impact has been evolving on the awareness and specification of the data protection. Therefore, data protection at the present era focusing on the area of fintech services is the research object. The objective of this study is to classify categories and definitions of users' sensitive data. Moreover, it analyses methods of defining sensitive data of the user and their realized activities especially as on-line transactions such as Zhifubao-the biggest “third-party electronic payment service platform”. In the meantime, we evaluate the GDPR, compare to the Data Protection Act (DPA), and discuss whether and to what extent it marked a milestone progress above the current legislation of processing data standards, or whether some obstacles have occurred in law implementation process.

Vulnerability Evaluation Method for E-Commerce Transaction Systems With Unobservable Transitions

E-commerce transaction systems have become an important factor in trading activities. However, e-commerce systems are still undergoing development. Unobservable actions and attacks on systems are frequent problems that increase the vulnerability of e-commerce systems. Most existing approaches to addressing these issues cannot describe or analyze the overall structure of a local specification and unobservable actions well. The vulnerable e-commerce transaction net (VET-net) is a useful model for describing the unobservable actions, online transactions and third-party payment platforms of e-commerce systems. Based on a VET-net, we focus on the detection and evaluation of e-commerce transaction systems to attacks. We propose the concept of vulnerable transitions, which include not only vulnerable actions but also unobservable transitions. Then, we use an improved slice method to locate the vulnerable transitions. For these vulnerable transitions, we propose a vulnerable transition evaluation method based on a hidden Markov model along with a reachability graph (HMM-RG). The HMM-RG uses hidden Markov models (HMMs) to approximate the state reachability graph of a VET-net. By calculating the firing probability, the HMM-RG can evaluate the vulnerability degree of malicious states. We use a real-world case to show our method’s effectiveness and reasonability.

State of the Art: Secure Mobile Payment

With mobile payments popular around the world, payers can conduct a payment anytime and anywhere. While providing great convenience, mobile payment also brings many payment security issues. This paper is the first comprehensive review of secure mobile payment. We classify the mobile payment into TPC (third-party payment company)-led mobile payment and Bank-led mobile payment, and based on this, summarize the system structure of mobile payment. Then we discuss the mobile payment security technology framework from Tokenization, PAN (bank card primary account number) binding, and Secure Payment Authentication, respectively. Besides, this paper introduces secure technologies(hardware and software) used in these procedures, discusses and analyzes the security issues that they have been encountered, summarise open issues, and proposes future development directions. In the end, we give the discussion and comparison of popular and representative mobile payment applications, including Alipay, Wechat Pay, Apple Pay, Samsung Pay, and Google Pay.

A Survey on Various Online Payment and Billing Techniques

Over the past few decades, Internet technology has shaped almost everybody’s life. Business banks set up internet workstations and provide customer information requirements, internet payment fund diversion financial services, credit, investment, etc. An online payment system is a means for conducting economic transactions based on the Internet. This allows a seller to accept payments over the web or other internet connections, such as direct network connections between retail stores and their suppliers- a common way of keeping inventories just in time. Online payment systems are greatly expanding a company’s scope and selling potential. Usually, online payment services are operated by third-party firms like PayPal, Google, or Click2Pay. Such companies make a profit by taking a small portion of each transaction, or by signing contracts with institutions that require a large number of transactions. Without the ability to make online payments, a large Internet retailer, like Amazon.com, could not exist. Online payment systems have expanded the playing field between big and small companies, as each of them can adopt the same payment methods when they sign up with third-party payment processors.

Research on current situation, risk and Control Strategy of Third-Party payment in China

With the rapid development of third-party payment in China, platform operation safety has become the most concerned issue. Based on the analysis of the expansion of third-party payment business in China, the scale of users and the scale of payment transactions, this paper explores the operational risks of the third-party payment platform in China. The influence factors of risk and the evolution of causality are analysed. On this basis, how to control the third party payment risk is discussed.

Factors Influencing Continuous Use of Third-Party Mobile Payment Services in China: An Empirical Study in Shenzhen

Factors Influencing Continuous Use of Third-Party Mobile Payment Services in China: An Empirical Study in Shenzhen

Costs of Severe Maternal Morbidity During Pregnancy in US Commercially Insured and Medicaid Populations: An Observational Study.

To estimate the maternity-related cost of health care services in women with and without severe maternal morbidity (SMM). Women with a live inpatient birth in the calendar year 2013 were identified in the MarketScan® Commercial and Medicaid health insurance claims databases. Costs were defined as the amounts paid by insurers plus out-of-pocket and third-party payments. Costs were calculated as total maternity-related costs and categorized as prenatal, delivery, and postpartum costs. SMM was identified using the CDC algorithm of 25 ICD-9 diagnostic and procedural codes. Variables associated with higher delivery costs were determined by multivariable linear regression analysis. A total of 750 women met the criteria for SMM in the Commercial population. The total, per-patient mean costs of care for women without and with SMM were $14,840 and $20,380, respectively. Delivery hospitalization costs were 76-77% of total mean costs for women without and with SMM. A total of 99 women met the criteria for SMM in the Medicaid population. The total, per-patient mean costs of care for women without and with SMM were $6894 and $10,134, respectively. Delivery costs were 71-72% of total costs. Variables independently predictive of increased delivery costs in both Commercial and Medicaid populations were delivery by cesarean section, multifetal gestation, gestational hypertension/preeclampsia, and obstetric infection. The occurrence of SMM was associated with an increase in maternity-related costs of 37% in the Commercial and 47% in the Medicaid population. Some of the factors associated with increased delivery hospitalization costs may be prevented.

Research on Two Decision Models in Third-Party Payment Platform Transaction

In transaction platform, because of competing for market share, this will inevitably bring a series of problems and risks for companies. Decision of the managers and consumers in third-party payment platform, are issue of great concern to platform development. Using rough set theory, this paper constructs the third-party payment platform transaction’s rough complex network, establishes a rough complex networks trust model for platform managers to enhance efficiency, a customer select commodities rough decision model for customer’s benefit maximization. In this paper, rough set theory is used to study the uncertainty of complex network trust model, but also given attribute reduction algorithm based on time series analysis to solve the attribute rules extraction in dynamic knowledge system. Simulation examples show that the models and algorithms provide the science decision-making approach for a direct participant of the platform transaction.

Foreign Students’ Intention towards a China’s Third Party Mobile and Online Payment Platform Based on Alipay

As the times progressed, so many features of smartphones that are developed by producers in manufactures. Hardware and software are fused to deliver comfort and increase the productivity of users’ smartphone. The example of software that can help users for a convenient transaction is Alipay and Wechat Pay, a China’s third-party mobile and online payment platform developed by Alibaba Group and Tencent respectively. This research aimed to observe ex-pats’ motives in the use of a Chinese third-party online payment platform with the sample was some foreign students in Nanjing as the Alipay users. Based on Unified Theory and Acceptance and Use of Technology (UTAUT) which consists four key constructs (effort expectancy, performance expectancy, social influence, and facilitating conditions), the results found that behavioral intentions were significantly carried out by effort expectancy, performance expectancy, and facilitating conditions, while social influence factors were found to be insignificant.

A Fraudulent Data Simulation Method Based on Generative Adversarial Networks

In view of the positive and negative sample imbalance in the process of establishing anti-fraud rules and models of the third-party payment, this paper simulated the fraudulent transactions by means of generative adversarial networks. In the design of the model, the output of the discriminator is optimized where classification results are set to three categories; the loss function of the GAN model is changed to include two parts: the source loss function and the category loss function. The generated data and the real business data are mixed according to a certain ratio, to train a fraud detection model. Verify the generation effect of this simulation method by comparing the detection effects of different detection models.

Security analysis of third-party in-app payment in mobile applications

The massive growth of smart mobile devices has attracted numerous apps to embed third-party in-app payment, which involves more sophisticated interactions between multiple participants compared to traditional payments. Therefore, such payment is error prone and could be exploited easily, leading to serious financial deceptions. To investigate current third-party mobile payment ecosystem and find potential security threats, we conduct an in-depth analysis against China–world’s largest mobile payment market. We study four mainstream third-party mobile payment cashiers, and conclude unified process models. We also summarize the security rules that must be regulated by cashiers and merchants and illustrate four types of attacks if violating these rules. Besides, we also detect seven cases of security rule violation on both Android and iOS platform. Our detection result shows that hundreds of popular apps violate at least one security rule, and hence face with various security risks, allowing attackers to consume commodities or services without purchasing them or deceiving others to pay for them. Our further investigation reveals that cashiers as well as merchants should be responsible for those vulnerable cases. We also performed proof-of-concept attacks in real world, reported these issues to all involved parties and helped them fix the vulnerabilities.

Investigating the nature of competition facing private healthcare facilities: the case of maternity care in Uttar Pradesh, India.

The private healthcare sector in low- and middle-income countries is increasingly seen as of public health importance, with widespread interest in improving private provider engagement. However, there is relatively little literature providing an in-depth understanding of the operation of private providers. We conducted a mixed methods analysis of the nature of competition faced by private delivery providers in Uttar Pradesh, India, where maternal mortality remains very high. We mapped health facilities in five contrasting districts, surveyed private facilities providing deliveries and conducted in-depth interviews with facility staff, allied providers (e.g. ambulance drivers, pathology laboratories) and other key informants. Over 3800 private facilities were mapped, of which 8% reported providing deliveries, mostly clustered in cities and larger towns. 89% of delivery facilities provided C-sections, but over half were not registered. Facilities were generally small, and the majority were independently owned, mostly by medical doctors and, to a lesser extent, AYUSH (non-biomedical) providers and others without formal qualifications. Recent growth in facility numbers had led to intense competition, particularly among mid-level facilities where customers were more price sensitive. In all facilities, nearly all payment was out-of-pocket, with very low-insurance coverage. Non-price competition was a key feature of the market and included location (preferably on highways or close to government facilities), medical infrastructure, hotel features, staff qualifications and reputation, and marketing. There was heavy reliance on visiting consultants such as obstetricians, surgeons and anaesthetists, and payment of hefty commission payments to agents who brought clients to the facility, for both new patients and those transferring from public facilities. Building on these insights, strategies for private sector engagement could include a foundation of universal facility registration, adaptation of accreditation schemes to lower-level facilities, improved third-party payment mechanisms and strategic purchasing, and enhanced patient information on facility availability, costs and quality.