Online Finance Research Articles

Agency or reselling? Supplier’s online channel strategies with platform financing

Online finance implemented by e-commerce platforms assists small and medium-sized enterprises in overcoming capital shortages and advancing channel development. We investigate the interplay between channel strategy and platform financing within a dual-channel supply chain comprising a capital-constrained supplier, an e-commerce platform, and an offline retailer. The supplier has access to various sales services (i.e., reselling and agency selling) as well as financing services from the e-commerce platform. Using a game-theoretical model, we analyze the financing conditions under two channel strategies and explore the supplier’s optimal channel strategy. Our research results reveal several key insights. Firstly, whether to choose platform financing under varying channel strategies depends on the supplier’s capital constraints and the platform’s interest rate. High capital constraints allow the supplier to benefit from platform financing. Secondly, the determination of the supplier’s optimal channel strategy primarily hinges upon factors such as the supplier’s production cost and financial constraints, as well as the interest and commission rates set by the platform. Additionally, the e-commerce platform imposes stricter financial requirements on the supplier to offer financing under the agency selling strategy compared to the reselling strategy. Furthermore, in the case of relatively high (or low) production costs, choosing the reselling strategy (or the agency selling strategy) to secure retailer finance (or agency finance) can realize a Pareto improvement. Our findings underscore the significant role of platform finance and provide valuable insights for suppliers in developing channel strategies that effectively integrate both operational and financial considerations.

LightGBM Model for Detecting Fraud in Online Financial Transactions

With the rapid development of online finance, combined with its significant potential threats, nowadays it has increasingly drawn attention to the issue of fraud in online payment transactions. Due to the large and diverse nature of online payment data and the binary nature of fraud detection, this study implements the machine learning algorithm LightGBM to detect fraudulent activities. Through data exploration and data preprocessing, the data is refined. Leveraging violin plots to observe the distribution of variables in fraudulent and non-fraudulent activities, and then an insignificant variable is removed. Next, the processed data is fed into the LightGBM model. The result yields a fraud prediction accuracy of 99.5%, shows a very high precision. Additionally, the study verifies the algorithm's robustness and generalization ability. Overall, this research holds significant importance, given its findings and implications for addressing fraud in the rapidly evolving landscape of online financial transactions.

Financial Literacy of Street Vendors

Street vendors form a sector in the unorganized market of India. The advent of online payment, through platforms such as Paytm, PhonePe and Google Pay has helped them in understanding online finance, and assisted their business; however, there are milestones yet to be made in enhancing their financial literacy overall. As a part of this paper, we conducted a survey, not of the vendors themselves, but of certain members of the general public within our contact. It is known that the vendors lack a certain standard of living, and the survey was conducted in the direction of the public. We found that 58.3% of the respondents thought that the vendors contributed to the cultural vibrancy of the community, and that 75% of them thought that literacy was necessary. Despite this, 75% of them believed that the lack of education was the biggest barrier in financial literacy. It is our hope that this barrier ceases to exist for the vendors.

Customer Profiling Model for Ecommerce Apps

Abstract: Researchers are developing new ways to evaluate credit in e-commerce, combining traditional RFM analysis with machine learning such as SVM and random forests. This approach provides accurate information that distinguishes good customers from potential fraudsters, minimizing financial losses. Other models for online finance companies use clustering, advanced RFM feature selection, weighted averaging with logistic regression and random forest to better segment customers. These developments build on growing connections between traditional channels and technology between others, providing valuable insights for navigating today's financial challenges.

Blockchain Technology Mitigates the Role of Information Asymmetry -- Take P2P Online Lending Platforms as an Example

With the rise of online finance, P2P online lending platforms based on new lending methods have ushered in an era of rapid development, attracting the attention and research of scholars. This paper uses platform data comes from the home of online loans, the National Bureau of Statistics of the People's Republic of China, and the Wind database, and takes P2P online loan platform as an example to study its impact on information asymmetry. Empirical analysis shows that P2P online lending can alleviate information asymmetry and improve information transparency, and the robustness test verifies the results of benchmark regression. Finally, this article also makes some specific recommendations for enhancing the P2P online loan platform based on the findings of the investigation such as the use of blockchain technology to establish a regulatory system and improve information transparency.

Large Scale Data Influences Based on Financial Landscape Using Big Data

Managing massive amounts of data is a hot topic in the worlds of business and technology right now. This kind of activity occurs in the hundreds of millions every single day. The processing of large data events is greatly aided by the financial sector. This means that every day, the financial sector processes hundreds of millions of transactions. According to experts, this is becoming a major obstacle in the way data for various financial services and products is managed and analysed. The financial and insurance industries have also been impacted by big data. Big data has far-reaching effects on the economy, making it crucial to examine the consequences of this phenomenon. The goal of this paper was to use these ideas to illustrate the current state of affairs regarding big data in the financial sector and to explain how and why big data affects various subsets of the financial sector, such as financial markets, financial institutions, and the links between online finance, financial management, online credit service providers, fraud detection, risk analysis, and the management of financial applications. The link between big data and economic variables can be better understood after reviewing the secondary literature on the subject. Due to the novelty of the concept, this study will finish by proposing avenues through which more research into the use of big data in the f inancial sector could be conducted.

Retracted: Construction and Application of the Online Finance Credit Risk Rating Model Based on the Artificial Neural Network

Retracted: Construction and Application of the Online Finance Credit Risk Rating Model Based on the Artificial Neural Network

Deep resource allocation for a massively multiplayer online finance of tourism gamification in metaverse

Deep resource allocation for a massively multiplayer online finance of tourism gamification in metaverse

Sentiment Analysis of Neobank Digital Banking using Support Vector Machine Algorithm in Indonesia

Currently, in the industrial era 4.0, information and communication technology is very developed, whereas, in this era, there is an increase in complex activities, one of which is in the banking sector. With the ease and efficiency of online finance, people want to switch to using digital banks. Neobank is an online savings and deposit application from Bank Neo Commerce (BCN) that the public can use by using the Internet. One of the online services is mobile banking which can be used by both Android and iOS versions of customers. Users can review Neobank's performance and services through the Google Play Store to improve and evaluate Neobank's performance. Neobank application reviews on the Google Play Store are increasing. Therefore, a review analysis is needed by conducting a sentiment analysis on Neobank's review. The data amounted to 3159 user reviews collected from reviews of the Neobank application on the Google Play Store. This study aims to classify Neobank user review data, including positive or negative sentiments. The method used in this study is an experimental method using the Support Vector Machine algorithm. The accuracy results obtained using the Support Vector Machine algorithm are 82.33%, which is owned by the scenario of 90% training data and 10% test data. The precision results are 82%, and recall is 81%. Future studies can add datasets from various sources so that there are even more datasets so as to increase the accuracy of model classification.

Digital competency as a key to the financial inclusion of young people in complex scenarios: A focus groups study

Young people's financial and digital literacy have been studied independently and in-depth during the last decades. However, digital financial literacy as a compound concept is novel and still needs to be explored in the scientific literature. This work investigated young people's perception of their digital financial culture, identified factors that hinder or facilitate it, and explored their preferences in the training modalities for improvement. Twenty-two focus groups were carried out in different Mexican educational institutions based on diversity criteria. The evidence shows that: (1) young people perceive the need for digital financial education linked mainly to the understanding of critical concepts, the use of mobile applications, online financial operations, and digital financial security; (2) some voluntarily exclude themselves from online finance, this being one of the main obstacles in the development of digital financial culture; (3) the digital financial culture gap is accentuated more among young people in public educational institutions and upper secondary education; (4) they have a preference for emerging technological and digital resources for their training in digital finance. These findings make it possible to contextualize training proposals that favor the financial inclusion of young people in complex scenarios brought about by the digital transformation of the economy and society.

Entrepreneurial Financing in Africa during the COVID-19 Pandemic

Access to finance by small-to-medium-enterprises (SMEs) remains an enigma that still warrants further research. The COVID-19 pandemic has exacerbated the funding gap and necessitated the need for entrepreneurs to seek alternative financing due to tight credit rationing by the traditional finance institutions. There is a marked increase in demand for alternative online finance known as crowdfunding amid social distancing and lockdowns occasioned by the COVID-19 pandemic. The main objective of this study was to examine the trends in the financing of African SMEs during the COVID-19 pandemic with a particular focus on crowdfunding. The postpositivist research philosophy and deductive strategy was adopted in this study with the view to test an existing theory and hypothesis. Secondary data sourced from TheCrowdDataCentre were utilised for the study. Eight hundred and fifty-nine African crowdfunding campaigns were employed as the unit of analysis. The study employed econometric techniques to test the research objectives of this study. The probit model was employed in the analysis. The results of the study revealed that backers, the COVID-19 and social network variables were positively and significantly related to campaign success. On the other hand, duration was found to be negatively and significantly related to crowdfunding success. The study contributes to the growing literature on the impact of COVID-19 on crowdfunding performance, as well as the literature on alternative sources of finance.

Research on the Impact of Online Credit on College Students in the Context of Big Data Fintech

The integration of the Internet and big data has promoted the development and emergence of various industries, and the financial industry has combined with technology with the help of the Internet and produced new industries, one of which is online credit. In this project, we analyze the impact of Internet credit on college students in the context of big data and financial technology, analyze the current situation of campus Internet credit from multiple perspectives, and summarize the new problems and risks faced by Internet loans, help college students understand more about Internet credit, alert and guide them to establish a correct view of consumption, in order to seek the common healthy development of the Internet market and college students in the future. In order to seek the healthy development of the Internet market and college students, promote the construction of Internet credit products and services, and promote the healthy and rapid development of the credit platform of college Internet economy, and then promote the healthy and good development of new forms of financial services such as online finance and private finance.

Analyzing the Design and Promotion of Ideological and Political Content in Online Finance and Economics Courses

In the context of the new media age, the emergence and development of finance and economics courses along with online course resources have greatly enriched the presentation of teaching resources in colleges and universities. However, subjected to the flawed design and poor promotion of ideological and political contents, which are less popular, online finance and economics courses often do not reflect the meaning well or maximize the application of the courses’ ideological and political reform. This paper explores the dilemma and reasons for the promotion of the courses’ ideological and political content, in hope to provide suggestions for the design and promotion of ideological and political content in online finance and economics courses in colleges and universities.

Online Finance in a Dual-Channel Supply Chain with a Capital-Constrained Manufacturer

This paper investigates a dual-channel supply chain which consists of a capital-constrained manufacturer and an e-commerce platform. The manufacturer sells its products through an offline direct channel and an online platform. The platform can serve as a marketplace or a reseller for the manufacturer, while the platform provides online finance to the manufacturer. We discuss the equilibrium decisions of the manufacturer and the platform when the platform serves as two different roles. We find that when the total production quantity of the manufacturer who with an online marketplace channel exceeds a threshold, the manufacturer tends to choose the platform’s marketplace role for the online channel, unless the wholesale price and the upper limit of demand are high. When production quantity is below this threshold, unless the wholesale price and the upper limit of demand are low, the manufacturer tends to choose the platform’s reseller role for the online channel. The platform tends to choose the role whose production quantity exceeds the threshold. If both are exceeded, the platform chooses the marketplace role when the wholesale price is low, and if both are not exceeded, the marketplace role is better when the wholesale price is high.

The Relationship Between Performance and Trust in AI in E-Finance.

Artificial intelligence (AI) is fundamentally changing how people work in nearly every field, including online finance. However, our ability to interact with AI is moderated by factors such as performance, complexity, and trust. The work presented in this study analyzes the effect of performance on trust in a robo-advisor (AI which assists in managing investments) through an empirical investment simulation. Results show that for applications where humans and AI have comparable capabilities, the difference in performance (between the human and AI) is a moderate indicator of change in trust; however, human or AI performance individually were weak indicators. Additionally, results indicate that biases typically seen in human-human interactions may also occur in human-AI interactions when AI transparency is low.

Construction of new financial e-commerce model for small and medium-sized enterprises financing based on multiple linear logistic regression

This paper expounds on the development prospects of SMEs and E-commerce finance, and illustrates the significance of developing online finance. It also introduces the commonly-used research methods of the two kinds of financial models, such as multiple linear regression method and logistic regression method, and analyzes the reasons for the financing difficulties of SMEs. Currently, the high financing cost is the main reason for the financing difficulties of SMEs. Several reasons are account for the high financing cost. Among them, high financing cost,low-efficiency financial system,long financing cycle and the loan information asymmetry account for 35%, 21%, 19% and25% respectively. In addition, this paper clarifies the advantages and disadvantages of network finance and the necessity of developing online finance.

Construction and Application of the Online Finance Credit Risk Rating Model Based on the Artificial Neural Network

The low-cost, highly efficient online finance credit provides underfunded individuals and small and medium enterprises (SMEs) with an indispensable credit channel. Most of the previous studies focus on the client crediting and screening of online finance. Few have studied the risk rating under a complete credit risk management system. This paper introduces the improved neural network technology to the credit risk rating of online finance. Firstly, the study period was divided into the early phase and late phase after the launch of an online finance credit product. In the early phase, there are few manually labeled samples and many unlabeled samples. Therefore, a cold start method was designed for the credit risk rating of online finance, and the similarity and abnormality of credit default were calculated. In the late phase, there are few unlabeled samples. Hence, the backpropagation neural network (BPNN) was improved for online finance credit risk rating. Our strategy was proved valid through experiments.

Time-Varying Transmission Effects of Internet Finance Under Economic Policy Uncertainty and Internet Consumers’ Behaviors: Evidence from China

The modern finance industry is composed of not only numerous financial intermediaries but also internet-based mechanisms which are operated by mobile phone users and online consumers daily. In the coming 10 years, estimates suggest that over half of banks’ functions will likely be replaced by high-tech artificial intelligence. Given the great ongoing shifts in contemporary financial systems, the transmission effects of internet-based finance practices have introduced an important yet unaddressed empirical question on the coupling relationship between the internet finance industry and economic policy uncertainty (EPU). This paper adopts the time-varying parameter vector autoregressive model with stochastic volatility (TVP-SV-VAR) model and novel data from Alibaba Corp. to investigate this relationship. We find: First, the impact of internet-based financial approaches on EPU is greater than the reversal effect, indicating that China’s gross domestic product (GDP) is largely influenced by the online finance industry. Second, the lag impacts are time varying and become stable after 2016, corresponding to the current Chinese government’s long-term strategic plan that emphasizes maintaining the economy’s overall stability. Lastly, additional evidence shows that the online financial approaches are positively correlated with consumers’ behaviors, implying that the online finance industry is gaining its momentum when people are using e-currency rather than real cash. After all, it takes time to observe the real effect of these macro policies. With internet and information technology developing, artificial intelligence is being used in the areas of big data, credit lending, and risk control. This largely reduces the data analyzing cost for internet finance companies and makes risk control more convenient.

Research on the Challenges and Sustainable Development Strategies of Community Banks in China

With the rapid development of information technology and the Internet, online financial services are fast, equal, and flexible, which bring a lot of challenges to commercial banks, especially the subsidiary community banks. This thesis first clarifies related research at home and abroad and further expounds on the development status and existing problems of the community banks. Then it proposes to improve the supervision system, strengthen technical support and talent reserve, and promote the development of online finance and credit reporting, in order to increase the core competitiveness of community banks in China.

Construction of a New Financial E-Commerce Model for Small and Medium-Sized Enterprise Financing Based on Multiple Linear Logistic Regression

This paper expounds on the development prospects of SMEs and E-commerce finance, and illustrates the significance of developing online finance. It also introduces the commonly-used research methods of the two kinds of financial models, such as multiple linear regression method and logistic regression method, and analyzes the reasons for the financing difficulties of SMEs. Currently, the high financing cost is the main reason for the financing difficulties of SMEs. Several reasons are account for the high financing cost. Among them, high financing cost,low-efficiency financial system,long financing cycle and the loan information asymmetry account for 35%, 21%, 19% and25% respectively. In addition, this paper clarifies the advantages and disadvantages of network finance and the necessity of developing online finance.