Huge Transaction Research Articles

The rise of livestreaming e-commerce in China and challenges for regulation: A critical examination of a landmark case occurring during COVID-19 pandemic

The devastating COVID-19 pandemic saw that the livestreaming e-commerce, which is a brand-new e-commerce model by combining online shopping with livestreams, emerged prominently in China. It shares some commonalities with other forms of e-commerce and traditional shopping channels like TV shopping one way or another, but the former is a disruptive iteration of the latter. Nonetheless, the arrival of livestreaming e-commerce also brings about significant regulatory challenges, due to opportunistic livestreamers coupled with other issues, resulting in all kinds of market failure acts, with false or misleading representations figuring most prominently. This is reflected by a landmark case occurring during the pandemic, in which Xin Ba as one of the most influential livestreamers sold cubilose products via Kuaishou, China's leading livestreaming e-commerce platform, in a false or misleading way. When the cubilose products touted as luxury foods were later proven to contain nothing but water and sugar, it attracted considerable public attention due to the large number of consumers affected and huge transactions it generated. This scandal was followed by an administrative investigation and a civil investigation. The Paper has an in-depth analysis of legal issues surrounding these investigations mainly centered on how Xin Ba as a livestreamer is liable for what, and finds that the laws applied to livestreaming e-commerce demonstrate legal inconsistencies and gaps, which a corresponding legal reform is proposed to address. As a way forward, the Paper also examines the issue of platform liabilities, a topic under-discussed under the landmark case. Upon the above deep analyses, the Paper concludes.

The Frameworks of Cross Border Cryptocurrency Transactions Between Two Countries

Cross border payments have been the biggest challenges in overseas business transactions. It is characterized by frauds, lack of security, money laundering, transactions delays, and huge transactions cost. The aim of this paper is to introduce new model of cross border digital currency transactions that would use the cryptocurrency from two different countries. The model has adopted the blockchain technology to authorize the smart contracts and proof of works under the monitoring of central banks of participating countries. In this model each country would adopt its own digital currency equivalent to the fiat/cash money value. Unlike the traditional blockchain networks which is considered to be decentralized systems, this model has deployed centralized blockchain networks under the control of central banks. All nodes in the network will be owned by central banks that would records and trace any transactions by using blockchain technology. Apart from central banks, this model includes local banks, and digital forex exchange market as their main stakeholders to serve the customers. As a centralized system, any smart contracts and proof-of-works resulted from any desired transactions is validated by the central banks of participating countries before execution. The customers have been losing their money during transactions through decentralized privately owned digital currency exchange vendors due to the lack of enough network policy to counter any fraud or suspicious transactions. Also, the cross-border transactions are encountered by the excessive of fees such as, agent fees, oversees fees, transactions fees, and exchange fees. This system is usefully to secure and speedup the transactions, prevent any frauds, and to avoid the overseas financial organizations costs that has been incurred by the customers over the years. The governments around the world would have the total control of the monetary transactions if they deploy digital currency on their economy. However, they are required to tighten the security measures to prevent any security vulnerabilities, frauds, threats, and get rid of money laundering activities.

Research on Legal Regulation of Internet Platforms

With the development of technology, Internet platforms continue to accumulate technological advantages and become super network platforms with rapid growth in the number of users and huge transaction scale, thus triggering problems such as platform monopoly, infringement of users’ rights and interference with social governance. When the state and the government face the challenge of platform governance, the existence of a technological divide makes it difficult to achieve the desired effect of Internet platform governance through the traditional unilateral regulatory model. When the law grants power, appropriate measures should be taken at the same time to avoid the abuse of power. With fairness as the regulatory goal of Internet platforms, it is necessary to start the restraint of platform power from multiple perspectives of self-regulation, cooperative regulation and external regulation to promote the standardized operation of Internet platforms.

Efficient Associate Rules Mining Based on Topology for Items of Transactional Data

A challenge in association rules’ mining is effectively reducing the time and space complexity in association rules mining with predefined minimum support and confidence thresholds from huge transaction databases. In this paper, we propose an efficient method based on the topology space of the itemset for mining associate rules from transaction databases. To do so, we deduce a binary relation on itemset, and construct a topology space of itemset based on the binary relation and the quotient lattice of the topology according to transactions of itemsets. Furthermore, we prove that all closed itemsets are included in the quotient lattice of the topology, and generators or minimal generators of every closed itemset can be easily obtained from an element of the quotient lattice. Formally, the topology on itemset represents more general associative relationship among items of transaction databases, the quotient lattice of the topology displays the hierarchical structures on all itemsets, and provide us a method to approximate any template of the itemset. Accordingly, we provide efficient algorithms to generate Min-Max association rules or reduce generalized association rules based on the lower approximation and the upper approximation of a template, respectively. The experiment results demonstrate that the proposed method is an alternative and efficient method to generate or reduce association rules from transaction databases.

Empowering voluntary approaches for environmental sustainability and resilient communities: a case study from Okinawa, Japan

What can government do to secure environmental sustainability and resilient communities? We discuss an emerging policy approach in natural tourism resource management in the Okinawa Prefecture using the case of the ‘Conservation and Use Agreement’ (CUA, hereafter) defined in the Act on Special Measures for the Promotion of Okinawa. The CUA is the certification by the Governor of Okinawa Prefecture, which endorses the self-regulation concluded by site-level eco-tour operators. We analysed the legal and administrative structure of the CUA and the implementation process of nine (existing/expired) CUA cases based on thorough literature reviews and in-depth interviews. We discuss three points from the analysis. Firstly, the number of CUAs remains small because of its huge transaction costs compared to the benefits of the status. Secondly, the CUA generally works as a precaution; however, it is not effective for solving the existing overuse issues for its voluntary characteristics. Thirdly, the administrative resources need to be strengthened for better communication with operators to further enhance the implementation.

INVESTIGATING MARKETPLACE CUSTOMER SATISFACTION:DATA MINING APPROACH

Customer satisfaction is perceived as business strategy’s key component and critical differentiation in a competitive marketplace. The recorded huge transactions per day in marketplaces can be used as useful information to evaluate customer satisfaction. A sophisticated method, such as data mining, is necessary to analyze this massive, multifaceted, and versatile empirical data generating accurate predictions. This research purported to investigate marketplace customer satisfaction as a reference to determine service and quality improvements. In conclusion this study draws two conclusive results: (1) The majority of marketplace consumers preferred the lead-time sensitive over price-sensitive; and (2) The Neural Net empirically showed as the most appropriate robust data mining technique among other techniques to predict marketplace customer satisfaction indicating by fittest accuracy, F score and ROC curve

Information Cartography in Association Rule Mining

Association Rule Mining is a machine learning method for discovering the interesting relations between the attributes in a huge transaction database. Typically, algorithms for Association Rule Mining generate a huge number of association rules, from which it is hard to extract structured knowledge and present this automatically in a form that would be suitable for the user. Recently, an information cartography has been proposed for creating structured summaries of information and visualizing with methodology called “metro maps”. This was applied to several problem domains, where pattern mining was necessary. The aim of this study is to develop a method for automatic creation of metro maps of information obtained by Association Rule Mining and, thus, spread its applicability to the other machine learning methods. Although the proposed method consists of multiple steps, its core presents metro map construction that is defined in the study as an optimization problem, which is solved using an evolutionary algorithm. Finally, this was applied to four well-known UCI Machine Learning datasets and one sport dataset. Visualizing the resulted metro maps not only justifies that this is a suitable tool for presenting structured knowledge hidden in data, but also that they can tell stories to users.

Classification of e-commerce financial transaction logs using machine learning approach

E-Commerce becomes inevitable in the current world. Especially in this pandemic period, almost activities have been carried out through digital mode to serve the customers with more safety. The financial transactions are much secured in the e-commerce payment. Still, many intruders are making these transactions into fail and hacking the customers’ information when the financial transactions are carried out. Along with, sometimes the transactions may get failed due to the network issues. Hence, the E-Commerce organizations are maintaining the transaction logs to check and take necessary action over the failed transactions. Out of huge transaction logs, identifying the suspicious failed transactions in manual method is not encouraged. Lot of technologies have come to support the detection of suspicious failed transactions such as Artificial Neural Network, Machine Learning, Deep Learning and other statistical methods. As the above methods are proved as more suitable for detecting and classifying the failed transaction logs, still the accuracy of classification has not been achieved much, which motivated to propose the machine learning based classification of E-Commerce Financial Transaction Logs.

On the Spatial Dimension of General Equilibrium

Problems related to the spatial aspects of economic development are considered within the framework of such scientific disciplines as "regional economy", "spatial economy", "new economic geography", "theory of international trade" mainly from the point of view of partial analysis. The article proposes a "basic model" designed to integrate the spatial dimension into the theory of general equilibrium in relation to a simple exchange economy. Its analysis shows why the choice by economic agents of the place of residence and production activities is as important as the selection of the field of specialization. Key modifications in the functional relationships of the main economic variables linked to the spatial dimension of economic activity are formulated. At the same time, it is shown that the effects of the social division of labor, identified using the "basic model", are potential, since the model ignores huge transaction costs that inevitably accompany the interaction of partially rational economic agents. That is why anchoring in space of the place of transactions, which is the result of experience, becomes a natural way of adapting institutional conditions to the real abilities of economic agents to receive and process information. And these adjustments, in turn, lead to changes in the toolkit of the market mechanism – in particular, adding to it rent by location.

A Critique of Bangladeshi Adoption of Basel Type Capital Regulation: An Institutional View

Abstract International concern on bank capital and minimum capital adequacy was first raised in 1980, in the G-10 countries governors meeting at the Bank for International Settlements (BIS) to respond to a series of bank failures and financial instability observed in Western developed economies. Later, the Basel Committee on Banking Supervision (BCBS) of the BIS proposed the Basel accord I, II and III in 1988, 2004 and 2010, respectively. Bangladesh Bank (BB) has introduced the ‘capital to risk weighted assets’-based approach for assessing the capital adequacy of banks in 1996 and later formally introduced the Basel framework in the early 2000s for its regulated banks. However, during Basel accord II and III implementation period (2009-2018), the banking industry accumulated huge non-performing loans which eroded its profitability. This creates a skepticism regarding any loopholes within the institutions. This paper argues that the naïve and excess reliance on External Credit Assessment Institutions (ECAIs’) credit rating in the process of adopting the Basel-type capital adequacy amounted to a risky strategy for the Bangladeshi banking industry in a sense that ECAIs allocate less efforts on accumulation of credit risk screening skills. We also document that the huge transaction cost and high coupon rate embedded within the debt instrument like the subordinated debt (sub-debt) issued by the regulated banks as Tier 2 capital might shrink the bank’s profitability and its contribution to the national exchequer. Little in the existing literature has been addressed to investigate the adoption of Basel regulations in Bangladesh from the institutional lens. This paper critically reviewed the Bangladeshi ECAIs regulations and sub-debt regulations to fill this research gap.

An Improved Distance Metric Clustering Algorithm for Association Rules

By mining association rules in large data, we can reveal useful information contained in the data and find out the relationship between things or the law of motion. However, because of the huge transaction data, the association rules obtained by mining are complex and massive. It is difficult to find useful association relations, especially when the re-demand is uncertain. To solve this problem, this paper first uses Apriori algorithm to mine association rules from a data set, then defines similarity measure between association rules, and applies DBSCAN clustering algorithm to association rules analysis. The analysis results show that this method is effective in association rules analysis.

Technology on the Banking Industry Operations & Its Impact

The aim of the present research is to know the impact of use of technology on the regular operations of the banking industry. The purpose of the banking industry is to assess the emerging trends of the application of the technology in providing services in the bank branches all over the country and the world. The large percentage of various sectors like financial markets, capital markets, money market and financial institutions undertake huge transactions of transfer of money to the various corners by providing movability of money transfer from one sector to another sector and from one bank and another bank and finally from one country to another country. Last couple of years have seen heavy use of technology in reducing the crowd and floating population in the bank branches so as to enable the branch to concentrate more on development activities like sanction of loans and advances, other customer services and facilities needed to improve the banking system. It goes without saying that without the use of technology the banking industry faces stalemate.

Crowd Funding - A New Horizon

In present competitive world it is very difficult to give direction to your dream, thinking and ideas. The biggest hurdle is converting the idea in to reality is generating funds. Traditionally, entrepreneurs have relied on family, friends and associates for seed capital to launch their ventures. Presently banks, venture capital firms, other financial institutions and intermediaries really option for finance. Financial intermediaries play vital roles in mobilizing of funds and make important contributions for the economy. But still funding is needed to bear direct and indirect expenses, huge transaction costs etc. To bridge this recurring demand of funds, nowadays technologies have given entrepreneurs a plethora of new ways to make the dream in to reality. Crowd funding is one of the option amongst them. It is the process of raising money to project or business venture through many donors using an online platform.

Mitigating risk of revenue leakages on the customer and vendor side in ecommerce sector

E-commerce is one of the rapidly booming sectors in India today, thanks to the rising internet user base and faster mobile penetration. The E-commerce industry is a complex ecosystem as it involves huge transaction volumes, complex procurement and logistics systems and reliance on new technologies for customer access and payment transactions. This complexity has given rise to frauds and revenue leakages, which is impacting the revenue for the ecommerce companies. Hence the major concern facing the Ecommerce sector today is how to mitigate the revenue loss. Very few studies have been done in academic literature in this area hence the objective of this study is to understand the sources of revenue leakage in the ecommerce sector and propose solutions for mitigating these revenue leakages. The study focusses on [2] major areas of revenue leakage viz. Customer side, Vendor side. The proposed revenue assurance model will be helpful to Ecommerce companies for detecting the sources of revenue leakages in the abovementioned areas and plugging the same thereby reducing losses. The study can also be helpful for consulting companies who are in the business of revenue assurance and fraud management for the ecommerce companies.

A reinterpretation of Coase’s land monopoly model: Locational specificity and the betterment potential of land as de jure and de facto property

On the grounds of two unique features of land, locational specificity and capacity for betterment through in-situ entrepreneurial transformation, this monograph uses three real world examples to qualify Coase’s idea, mentioned in two of his works on the Federal Communications Commission (FCC), that a state monopoly of land is undesirable for allocating land due to the huge transaction costs of non-price allocation. These two features of land enable the creation of institutional arrangements constraining such costs occasioned by rent-seeking or rent dissipation envisaged by Coase. Breaking new theoretical grounds in understanding planning beyond a matter of property rights assignment and attenuation, the three examples show that where the state has an effective monopoly of land supply, it does not behave like a private land monopoly but, subject to constrained rent-seeking, enables, and also possibly brings about the betterment of land and its redistribution by government planning. The examples, two of which testify to a Coase Theorem predicated on Coase’s first work on the FCC, also shed light on the question of property boundaries as an ex ante planning tool for de jure property or an ex post outcome of development. The monograph shows that the transaction costs of both dividing and recombining tradable land, as physically unitized into land parcels within a layout, are greater than partitioning and re-partitioning marketable segments of radio frequencies.

E-Commerce in Africa: The Case of Nigeria

Africa holds 15 % of world’s population with an across sector infrastructure investments average returns of 15 to 20%. The diverse location of people living within the continent makes intercity, interstate, or inter-country transactions difficult and where it does happens, comes with huge transaction cost. This creates a wide gap in Africa’s regional economy, resulting in wide spread of poverty, intra and inter regional conflicts within the continent. E-commerce provides a viable platform for bridging the gap in African economies. However, its adaptability and sustainability becomes a major concern. The level of development of e-commerce and its impact on the economy are analysed. Results show that e-commerce has made positive impact on the African economies but lack of trust on transactions within and from the continent is major challenge. Therefore, investment in e-commerce infrastructure in Africa (with more emphasis on Nigeria) will lead to rapid economic development within the continent.

P-FHM+: Parallel high utility itemset mining algorithm for big data processing

High utility itemset (HUI) mining is emerging as an effective pattern mining technique, which discovers itemsets with their utility more than user defined utility threshold. Most of the HUI mining algorithms discover high utility patterns of all length (k-itemset), while many applications require the HUIs of certain lengths. FHM+ is a HUI mining algorithm which includes the length constraint and produces HUIs of the user defined lengths. FHM+ is suitable to run small size of data on standalone system. In the era of big data, the standalone system are not efficient to process huge transaction data. To accommodate this, a parallel version of FHM+ is proposed and named as P-FHM+ that process big transaction data in distributed manner on a multi-node cluster. Extensive experiments are conducted with proposed P-FHM+ on multiple real time datasets and observed that P-FHM+ outperforms FHM+ in terms or speed and scalability.

Using generative adversarial networks for improving classification effectiveness in credit card fraud detection

In the last years, the number of frauds in credit card-based online payments has grown dramatically, pushing banks and e-commerce organizations to implement automatic fraud detection systems, performing data mining on huge transaction logs. Machine learning seems to be one of the most promising solutions for spotting illicit transactions, by distinguishing fraudulent and non-fraudulent instances through the use of supervised binary classification systems properly trained from pre-screened sample datasets. However, in such a specific application domain, datasets available for training are strongly imbalanced, with the class of interest considerably less represented than the other. This significantly reduces the effectiveness of binary classifiers, undesirably biasing the results toward the prevailing class, while we are interested in the minority class. Oversampling the minority class has been adopted to alleviate this problem, but this method still has some drawbacks. Generative Adversarial Networks are general, flexible, and powerful generative deep learning models that have achieved success in producing convincingly real-looking images. We trained a GAN to output mimicked minority class examples, which were then merged with training data into an augmented training set so that the effectiveness of a classifier can be improved. Experiments show that a classifier trained on the augmented set outperforms the same classifier trained on the original data, especially as far the sensitivity is concerned, resulting in an effective fraud detection mechanism.

Comparative Analysis of Association Rule Mining Algorithms in Mining Frequent Patterns

Finding frequent patterns in a huge transaction database has wide scope in research. Here in this paper we consider three of the algorithms in an association rule namely Apriori, Predictive Apriori and FP-Growth algorithm. Experiments are done to compare the result of these three algorithms with distinct datasets and present the result. Based on the results, we find that FP-Growth algorithm is more worthwhile than the Predictive Apriori and Apriori algorithms as time consumption is low and the need for candidate patterns is done away.

Semantic Cluster based Classification for Data Leakage Detection for the Cloud Security

novel approach for the data leak detection in the cloud environment is discussed in this paper. The paper uses the semantic based clustering for the anomaly detection to find the data leak. The Clustering is further used for the classification to add up for the semi supervised classification. After classification the threat patterns are stored in the database for further preventive actions in the data transmission. The necessary theory is discussed and the proposed approach is discussed with the results obtained. Keywordsleak prevention, semantic clustering, and semi supervised classification 1. INTRODUCTIONleakage is a problem which occurs intentionally or in an unintentional way. The intentional data leakage is the problem where the hackers try to disclose the data for fun or for any profit motivation. The unintentional damage is based on an accidental happening. Either intentional or unintentional act it involves in the loss of the data which leads to the loss and the reputation of the enterprise. This is common to every enterprise possessing the data and facing the trouble of leaking. It is also a global problem to think through since the data sharing is the mandate of the day for the communication. In the resource sharing enterprise which moves to the cloud computing for the distributed nature also severely suffer from the problem of data leakage. A survey says that by 2014 public IT services will exceed the traditional IT services by nearly five times (1). The forecasting based on the cloud computing predicts that worldwide revenue from the public IT cloud services will reach 55.5 billion dollars in 2014 (2). The cloud is prone to the data leakage because of its operational characteristics and its architecture, the chance is more because of the huge transactions which involves risks and challenges (3). The preventive mechanism is discussed in this paper which deploys the semantic clustering with the semi supervised classification for the updating of the threat history. The threat repository could be further used as the detection database to avoid the data breaches. This paper addresses the issue of proactive management through the learning from the previous data. The idea fills the gap of semantic based threat identification, which is lacking in the existing methods.