Rapid Development Of Internet Finance Research Articles

Financial Accounting and Tax Management under the Background of the Internet

With the rapid development of Internet finance, the fields of financial accounting and tax management show new characteristics, which further promotes the reform and development of financial accounting and tax management. Based on this, starting from the current problems of Internet financial accounting and tax management, this paper discusses the countermeasures to strengthen Internet financial accounting and tax management. The so-called learning activity is a series of teaching and learning activities in a specific environment. It includes both teaching and learning. Using a study plan to summarize can better reflect an attitude toward students' study plans. There are seven main perspectives on discussing teaching and learning activities in relevant translated works. They are the Angle of teaching content, the Angle of learning or cognitive psychological process, the Angle of teaching principle, the Angle of teaching mode, the Angle of teacher- student relationship, the Angle of stage or process and the Angle of discipline. In the past 20 years, the curriculum and teaching reform of primary education in China has taken teacher-student as the starting point, but its practical effect in curriculum implementation is worth rethinking. At the same time, based on the study of translated works, the author analyzes the exploratory learning activities under the situation of concise problems.

The Transformation and Development of Internet Finance to the Lifestyle of Chinese Residents

In the context of the rapid development of Internet finance, researchers can understand the needs of users according to their behaviors and consumption habits, which can help enterprises and governments formulate relevant strategies. In addition, financial technology in the field of Internet finance continues to innovate, and researchers are committed to exploring the future application prospects of new technologies. However, there are still some research gaps. The research theme of this paper is the impact of Internet finance on low-income groups and residents in rural areas. The specific question that this paper wants to explore is what impact Internet finance has on low-income groups and residents in rural areas in which aspects. The result of the study is that Internet finance has a positive impact on low-income groups and rural residents through the convenience of financial services, the convenience of financial management, the expansion of lending channels and the popularization of financial knowledge education.

Internet Finance and Risk-Taking of City Commercial Banks: Evidence From China

China’s urban commercial banks are more likely to face risks with the rapid development of Internet finance. However, few studies unfold the impact of Internet finance on the risk-taking level of urban commercial banks and explore their role path in depth. To study the relationship between the two and their path of action, this paper takes 62 city commercial banks in China from 2011 to 2020 as research objects and makes empirical tests by Stata. The intermediary effect model is used to study its impact path from the perspectives of assets, liabilities, and intermediate services. The conclusions are as follows: (1) Internet finance aggravates the risk-taking of urban commercial banks; (2) This effect shows heterogeneity in listed and non-listed, trans-region and non-trans-region urban commercial banks; (3) Net interest margin, debt structure, and non-interest income show the mediating effect when Internet finance affects the risk-taking of urban commercial banks. The results are beneficial for urban commercial banks to change their business strategies with their characteristics and achieve sustainable and healthy development.

Based On “Long Tail” Theory to Solve Finance Difficulties of SMEs with Internet Finance

Base on the “long tail” theory put forward by Anderson, this paper analyzes the availability of Internet finance to solve the financing difficulties of small and micro enterprises (SMEs). The SMEs mentioned above is the “long tail” demanded by finance. On the one hand, the application technology of big data in the Internet can be used to identify the financing needs of “long tail” SMEs. On the other hand, with the effects of external economy, the effects of scale economy and those of scope economy exerted on the markets of the financing of SMEs, the fund supply to SMEs will increase. In traditional financial markets, the SMEs, due to the “hyper-normal” credit rationing, were often rationed out of standard financial system. As a result, the financial supply curve bent backward. However, with the rapid development of Internet finance, the rationing degree of financial credit is relieved, the financing gaps in SMEs is offset to some extent, and the allocation of financial resources is optimized, providing a new idea to solve the financing difficulties in SMEs. Finally, this paper puts forward relevant proposals about Internet finance to solve the financing difficulties of SMEs.

Internet Financial Risk Management Under the development of Deep Learning

With the rapid development of Internet finance in both quantity and scale, various challenges have emerged. Deep learning is a promising tool to explore the optimal algorithm to analyze input variables that affect Internet financial risks, and corresponding classification to manage and minimize those risks. This study employs a questionnaire and data analysis to evaluate Internet financial risks, with a focus on psychological risk, social risk, technical risk, moral risk, and material risk. The research provides theoretical and practical value in the field of Internet financial risk management. The results of the study demonstrate that psychological risk, social risk, technical risk, material risk, and moral risk are all significant factors that contribute to Internet financial risks. Additionally, higher education is found to be a protective factor against Internet financial risks, while higher income is associated with greater risk. Furthermore, psychological risks were found to have the most significant impact on Internet financial risks.

Intelligent SME Financing Risk Control Platform Algorithm Based on Intelligent Internet Financial Architecture

Due to their poor conditions and the high loan threshold of banks, small and medium-sized enterprises are faced with the problem of difficult and expensive financing. With the rapid development of Internet finance, small and medium-sized enterprises can use Internet finance to obtain higher transparency, higher participation, and lower intermediate costs. It provides a certain reference for the development of SME financing model in the future. It further diversifies the financing channels of SMEs and facilitates the financing process, and finally puts forward countermeasures and suggestions based on the problems that may arise in the process of Internet financing.

Exploring Innovative Promotion Models for Financial Products Based on Big Data Analysis

In the era of big data, financial products are constantly innovating, which directly triggers a significant transformation in the traditional financial industry and demonstrates high innovation value. Currently, it is essential to utilize big data analytics to analyze and enhance the innovation and resource allocation efficiency of financial products. There is also hope to optimize the innovation promotion model of financial products and address various crisis issues faced during the rapid development of internet finance. This paper explores the opportunities and challenges of financial product innovation in the context of big data analytics and studies the development approach of innovation promotion models. Furthermore, it investigates the specific innovation promotion model for short-term trading products in internet finance under the background of big data technology.

Research on the Optimization Path of the Management of Financial Support for University and College Students from the Perspective of Financial Security—Reflections on the Illegal Campus Loan

The rapid development of Internet finance has spawned a number of non-compliant campus lending platforms. These illegal institutions or platforms take advantage of the immature consumption concept and lack of discernment of college students under the banner of solving urgent needs, resulting in them falling into tremendous economic pressure, which undoubtedly buries major hidden dangers for their academic development and mental health. Accordingly, the causes and harm of illegal campus loans are analyzed from the perspective of financial security, and the widespread popularity of illegal campus loans has sounded the alarm for college students. Subsequently, the current situation of the management of financial support for university and college students is intensely studied, and the ways to optimize the management are explored, which may provide a new idea for contemporary college students to meet their necessary expenses through financial support for university and college students, such as part-time study, student loans, bursaries.

Internet Finance in the Big-data Age

With the flourishing development of e-commerce, Internet enterprises have accumulated a huge amount of user data, and gradually obtained the needs and preferences of users. It provides financial services from the initial simple payment to transfer remittances, credit loans, asset management, and insurances, besides, has core competitiveness of ‘data utilization’, constantly mining, analyzing and studying the increasing amount of data, so as to tailor services for customers. In the era of big data, the rapid development of Internet finance also has a certain impact on the traditional financial industry, because it has altered the operation level. Furthermore, the application of big data in Internet finance is personalized, and a customer precision recommendation model based on network is established. The goal of this essay is to find out multiple applications of Internet finance and differences with banks. In respect of research result, the public really relies on it and meanwhile faces to risks. Last but not least, the continuous exploration of Internet finance is essential, as it can take the era of big data to the next level.

Can the modified ESG-KMV logit model explain the default risk of internet finance companies?

With the rapid development of internet finance in China, the risk management of internet finance has become an urgent issue. This study analyzes the factors that affect the default risk of Chinese internet finance companies based on measuring the distance to default of companies. This study incorporates ESG rating into the evaluation model to comprehensively reflect the default risk factors. The traditional KMV model is modified with ESG rating, and results are used to construct the panel logit model. Based on internet finance firms listed on China A-Shares data from 2016 to 2020, our results show the following: first, the modified ESG-KMV logit model can effectively analyze the influencing factors of the internet finance default risk. Second, ROE, accounts receivable turnover ratio, asset-liability ratio and z-value are important factors that affect the default risk of internet finance companies. Third, it is also found that COVID-19 has significantly impacted the default risk of internet finance companies. As a policy implication, the regulator can incorporate ESG into the measurement of the default risk to create more awareness among internet finance companies on the importance of the environment and sustainability to human societies.

Loan Default Prediction based on Machine Learning (LightGBM Model)

With the rapid development of Internet finance, the number of online loan platforms has been increasing, and the scale of loan business is gradually expanding. At the same time, the bad debt and non-performing loan ratios have also risen sharply, the reasons for which include incomplete information of lenders, imprudent management of capital chain, inaccurate default prediction, etc. Therefore, loan platforms need a set of efficient and accurate loan default prediction solutions to ensure the healthy operation of Internet finance and avoid huge losses for platforms and investors. Current loan default prediction models mainly contain traditional machine learning methods such as logistic regression and decision tree, as well as integrated models such as Random Forest and GBDT. In this paper, we use LightGBM machine learning model to process massive loan default information using the lender information dataset on Tianchi Platform, and the process includes data preprocessing, feature engineering, model training and evaluation. The experimental results show that the LightGBM algorithm has a strong ability to predict loan default, with a final AUC value reaching about 0.73. This method is helpful for banks and Internet loan platforms to conduct background investigation and default prediction of loan applicants, so as to strengthen the ability of loan risk management.

Optimal Investment Portfolios for Internet Money Funds Based on LSTM and La-VaR: Evidence from China

The rapid development of Internet finance has impacted traditional investment patterns, and Internet money funds (IMFs) are involved extensively in finance. This research constructed a long short-term memory (LSTM) neural network model to predict the return rates of IMFs and utilized the value-at-risk (VaR) and liquidity-adjusted VaR (La-VaR) methods to measure the IMFs’ risk. Then, an objective programming model based on prediction and risk assessment was established to design optimal portfolios. The results indicate the following: (1) The LSTM model results show that the forecast curves are consistent with the actual curves, and the root-mean-squared error (RMSE) result is mere 0.009, indicating that the model is suitable for forecasting data with reliable time-periodic characteristics. (2) With unit liquidity cost, the La-VaR results match the actuality better than the VaR as they demonstrate that the fund-based IMFs (FUND) have the most significant risk, the bank-based IMFs (BANK) rank 2nd, and the third-party-based IMFs (THIRD) rank 3rd. (3) The programming model based on LSTM and the La-VaR can meet different investors’ preferences by adjusting the objectives and constraints. It shows that the designed models have more practical significance than the traditional investment strategies.

Chinese consumers’ intention to purchase tourism financial products

ABSTRACT With the rapid development of internet finance, building a “tourism + finance” ecosystem is gradually becoming a theme of reform in the tourism industry. In this study, the technology acceptance model (TAM) is used to explore which factors, in addition to perceived usefulness and perceived ease of use, influence consumers’ intentions to purchase tourism financial products. The study conducts a questionnaire survey including basic questions and a Likert scale with 19 indicators covering 5 dimensions. The results are drawn from a structural equation model that supports five out of six proposed hypotheses. The findings suggest that perceived usefulness, perceived ease of use, trust, and perceived risk significantly influence purchase intentions. Additionally, perceived ease of use has a significant impact on perceived usefulness. Suggestions regarding future tourism financial products development are made, and the study’s limitations are discussed.

Innovation of Internet Finance and Small and Micro Enterprise Financing Mode

With the rapid development of the Internet, Internet financial services are also constantly popularizing, constantly occupying an important position in the financial market, the rapid development of Internet finance has improved the way of social financial services, enriched the types of financial products, effectively solved the problems of small and micro enterprises in financing difficulties, high financing costs, slow financing speed, and played an important role in the financing of small and micro enterprises. But under the development mode of Internet finance, the financing mode of small and micro enterprises still faces some shortcomings and problems. The purpose of this paper is to introduce the current situation of Internet finance and small and micro enterprise financing, take this as the starting point to study, further analyze the problems and reasons, and analyze the relevant countermeasures according to the problems.

The Impact and Influence of Internet Finance on the Banking Industry

In recent years, with the rapid development of Internet finance, Internet technology has become increasingly mature and popular with the general public. The Internet is subtly changing our lives and economic activities as well as people's thinking habits. With the continuous development of the wave of financial innovation, the traditional financial industry is facing huge crises and challenges. Whether the banking industry of traditional financial institutions can meet the challenges from Internet finance and whether the banking industry can rely on Internet finance to innovate and improve its own business not only determines the future of the banking industry but also relates to the healthy development of financial industry in the future.

Supply chain financial service management system based on block chain IoT data sharing and edge computing

The implementation of the “Internet +” policy advocated by the state has also led to rapid development of Internet finance. In order to promote changes in business development models, as a pioneering work for banks serving the real economy, supply chains are being developed to address small and medium-sized enterprises. The financing of enterprises, the transformation and development needs of banks themselves, and the promotion of logistics technology. Edge computing refers to an open platform that integrates network, data processing, storage and application core functions, and can provide the closest end-of-page service near the object data source to meet real-time, application intelligence, security and privacy Sexual needs. The core of supply chain financing is to establish an optimized plan that can effectively control supply chain financing. By integrating the financing literature of the supply chain, the settlement cost in the supply chain can be solved. Based on theoretical research, this article analyzes supply chain financing and block chain technology. Combined with the current specific situation of block chain in supply chain financing, the management system, cash flow of the supply chain, and risk control system are analyzed. All parties to the supply chain financing optimize the supply chain financing risk control system while reducing business costs and improving corporate efficiency, which greatly reduces the risks of all parties in the supply chain financing. The block chain Iota environment based on shared data and advanced data processing has very powerful theoretical and practical significance for promoting the development of commercial banks and enterprises.

Optimization of China’s Financial Advertising Regulation System: Based on Behavioral Finance and EU Experience

With the rapid development of Internet finance, the volume of China’s financial advertising has exploded. On the one hand, the problems caused by false and misleading information contained in some financial advertising have already affected the stability of China’s financial market. On the other hand, China’s current regulation system has inherent flaws. Under the framework of protecting financial consumers’ rights and interests, it is combined with the traditional false advertising regulation system and premised on sellers’ duties of appropriateness and informing. However, its theoretical premise-rational investors-is rocky, which has already been challenged by the behavioral finance theory: While making investment decisions, investors are tend to be affected by their behavioral deviations. The heuristics and behavioral deviations of the typical retail investors are often exploited by financial advertising, including representativeness bias, availability bias, framing bias, anchoring bias, and affection bias. Therefore, the system, which is based on the rational investor theory and centered on the proper fulfillment of information disclosure obligations, is flawed by nature and cannot fundamentally solve the problem of regulating financial advertising which takes advantage of investors’ limited rationality and behavioral deviations. It is, thereby, necessary to establish a specialized financial advertising regulation system , so as to protect the stable operation of the financial market and the legitimate interests of financial consumers. In practice, taking advantage of investors’ limited rationality and behavioral deviations is the common problem of financial advertising in China and the EU. In order to transform the theoretical achievements of behavioral finance into practical system effectiveness, China may learn from the practical regulation experience of the EU’s New Markets in Financial Instruments Directive in this regard. In terms of the optimization idea of the regulation system, an informed financial advertising regulation model should be established by taking investors’ psychology and information processing mode into consideration. In terms of the optimization path of the regulation system, the basic requirements of financial advertising information should be neutral, clear and non-misleading. In addition, specific regulation systems should be further formulated for typical problems in practice, including the regulation system of financial advertising information involving historical performance, future performance forecasting, comparative financial advertising, and taking advantage of investors’ affection bias.

Impact of internet finance on the performance of commercial banks in China

The rapid development of Internet finance has certainly affected the operation of commercial banks. This paper investigates the impact of Internet finance on commercial banks. First, a theoretical influence mechanism of Internet finance on commercial banks is explored, and the Internet finance index and integrated performance index of commercial banks are constructed using factor analysis. Then, a static panel and a dynamic panel model are established to empirically examine the impact of Internet finance on the profitability, security, liquidity and growth as well as the comprehensive business performance of commercial banks. Finally, the heterogenous impacts of Internet finance on city commercial banks, joint-stock banks and state-owned commercial banks are discussed. The results show that the development of Internet finance has a positive impact on the profitability, security and growth of commercial banks, and has a negative impact on the liquidity of commercial banks. In addition, Internet finance has promoted the improvement of the comprehensive business performance of commercial banks. Moreover, the impact of Internet finance on different types of commercial banks is heterogeneous with the impact on state-owned commercial banks being the weakest and the impact on city commercial banks is the most significant.

Early Warning for Internet Finance Industry Risk: An Empirical Investigation of the P2P Companies in the Coastal Regions of China

Ma, L.; Wang, Y.; Ren, C.; Li, H., and Li, Y., 2020. Early warning for internet finance industry risk: An empirical investigation of the P2P companies in the coastal regions of China. In: Gong, D.; Zhang, M., and Liu, R. (eds.), Advances in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 106, pp. 295–299. Coconut Creek (Florida), ISSN 0749-0208.Recent years have seen the rapid development of Internet finance in China, and various peer-to-peer (P2P) lending platforms have been released. However, the emergence of diverse problems, such as absconding with the money and fraudulent platforms, has exposed the lack of supervision of P2P lending platforms. This paper collected 26 dimensions of data from 1292 P2P companies in the coastal regions of China from third-party websites. Risk warning and machine learning were incorporated to construct a risk warning model for P2P lending platforms. This model consists of a decision tree model, naive Bayes model, and support vector machine model. Correlation analysis was used to analyze the data source, which is the data predicted by the above models, and the target variables. Type I error rate was utilized to determine the optimal model. The conclusions are as follows: The indicators of registered capital, changes of business scope, and platform background have a great impact on the accuracy of P2P risk prediction; the decision tree model is capable of predicting problematic platforms; and the decision tree model, support vector machine model, and naive Bayes model have a descending order in accuracy. Hence, the decision tree model is the best model for risk warning.

LAW: Learning Automatic Windows for Online Payment Fraud Detection

The rapid development of internet finance has caused increasing concern in online payment fraud due to its great threat. It is typical to employ rule systems or machine learning-based techniques to detect frauds. For the most significant features of such fraudulent transactions are exhibited in a sequential form, the sliding time window is a widely-recognized effective tool for this problem. With a sliding time window, features about the transaction characteristics can be extracted, and the latent patterns hidden in transaction records can be captured. However, the adaptive setting of sliding time window is really a big challenge, since the transaction patterns in real-life application scenarios are often too elusive to be captured. As a matter of fact, the practical setting usually needs to be updated and refined with manual intervention regularly. This is time-consuming indeed. In this article, we pursue an adaptive learning approach to detect fraudulent online payment transactions with automatic sliding time windows. Accordingly, we make efforts on optimizing the setting of windows and improving the adaptability. We design an intelligent window, called learning automatic window (LAW). It utilizes the learning automata to learn the proper parameters of time windows and adjust them dynamically and regularly according to the variation and oscillation of fraudulent transaction patterns. By the experiments over a real-world dataset of the online payment service from a commercial bank, we validate the gain of LAW in terms of detection effectiveness and robustness. To the best of our knowledge, this is the first work to make a sliding time window for fraud detection capable of learning its proper size in changing situations.