Development Of Internet Finance Research Articles

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.

Internet financial risk management and control based on improved rough set algorithm

With the development of the Internet, Internet finance in new P2P modes will face a great many difficulties and opportunities; so, relevant risk early-warning models need to be researched and analyzed. The early-warning analysis will not only be helpful for P2P, the new mode, but will also be worth learning by the whole Internet financial industries, and there will be a particular demonstration effect. Deep researches have been made on Internet financial risk precautions mainly through analyzing and researching the risks in the leading P2P online debit and credit model within the scope of Internet finance; therefore, risk factors that influence the development of Internet finance are obtained. Next weighting KNN Internet financial risk management and control algorithm with the variable precision rough set is out forward. Training sets of different categories are divided into positive regions and boundary regions through the upper and lower approximation concept of variable precision rough set, thereby acquiring the affiliation regions of the samples based on the similarity between test samples and the sample center. In this way, the category of samples belonging to the positive region can be directly judged, and that of other regions can be judged through the KNN algorithm based on quantitative weighting. Experimental results have verified the effectiveness of the mentioned algorithm.

An Analysis of the Impact of Financial Technology on the Development of Internet Finance

<p>Nowadays, with the continuous development of modern technology, technologies such as blockchain, artificial intelligence and big data have promoted the rapid development and transformation of my country's traditional financial industry. From the traditional gold and silver standard and bank notes, to the rapid rise and development of electronic payment, mobile payment, digital currency and wealth management, China's Internet finance has achieved better and faster development. This article mainly studies the relative impact of fintech on the development of Internet finance, and aims to make better use of fintech for financial management, to promote the better execution efficiency of financial-related circulation, and to promote the development of China's financial industry, which is also more informatized, modernized and intelligent.</p>

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.

An Analysis of the Opportunities and Challenges of Internet Finance in the New Normal of Economy

<p>The vigorous development of Internet finance is very important for the sustainable development of China's economy. In recent years, Chinese government has vigorously supported the development of enterprises, and issued a series of policies related to finance, tax and financing guarantee support. For enterprises, this is not sufficient. In addition to the support of external policies, enterprises need more self-examination. The financial management system of many enterprises is not perfect and the quality of financial management is low, which will have a negative impact on the operation and management of enterprises and can not effectively guarantee the quality of financial information of enterprises.</p>

Effect of China Coastal Aera's Income Distribution Based on the Network Financial Environment

Lan, G.; Lv, Y.; Kuang, X., and Wu, Y., 2020. Effect of China coastal aera's income distribution based on the network financial environment. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 246–251. Coconut Creek (Florida), ISSN 0749-0208.Given increasing studies on the impact of financial development on income distribution at home and abroad, this paper conducts a multi-level, multi-angle theoretical and empirical study on the impact of China's financial development on income distribution based on the historical background of China's financial system and financial market development. In recent years, China's Internet finance has experienced explosive growth, and its unprecedented heated discussion has reflected, to some extent, the expectations of the practical circles and even the public for its financial function deepening. In the empirical analysis, model regression analysis method is employed to study the income distribution effect of Internet finance development in China. The results show that the urban residents' education rate of return is much lower than the average rate of return. For the most significant proportion of groups, those who have signed the labor contract have higher income than those not. Finally, some policy suggestions are proposed on standardizing income distribution from the aspects of education, technical training and law.

Assessing the efficiency of financial supply chain for Chinese commercial banks: a two-stage AR-DEA model

PurposeThe purpose of this paper is to evaluate Chinese commercial banks efficiency based on different non-performing loans in the process. Moreover, we identified the difference among different types of banks (state-owned commercial banks, joint-stock commercial banks and city commercial banks) and different operation stages (deposit producing sub-stage, profit earning sub-stage and overall stage).Design/methodology/approachAssurance region (AR) restrictions are combined with a two-stage data envelopment analysis (DEA) model. The efficiency scores of 26 Chinese commercial banks (listed banks) are analyzed by a two-stage AR-DEA model in the study period of 2013–2017.FindingsThe results show that state-owned commercial banks had better performance than joint-stock commercial banks and city commercial banks over the five-year study period. The development of Internet finance has positive impact on deposit producing sub-stage and insignificant non-homogeneity existed among the different groups in the circumstances of considering different non-performing loans.Practical implicationsThe research findings provide practical insights that help bank managers find the defects in operation process, which need to be improved.Originality/valuePrevious studies viewed non-performing loans as an integrated whole variable. The paper divides non-performing loans into three categories based on the risk and investigates the effect of different types of loans on bank efficiency scores.

Discussion of the Risks and Supervision of Financial Big Data Applications

With the innovation of big data finance, the existing financial industry and financial institution structure have been affected to a certain extent, and the risk of financial market has undergone a profound change. In the development of Internet finance, big data technology has been recognized and widely used in various industries, financial big data and Internet finance are two important players to transform the competitiveness of the financial industry for Chinese financial ecology. In order to identify the risks in financial markets in the application of financial big data, we need to make clear the characteristics of financial big data. The essence of big data finance is financial innovation. In the process of financial innovation, it not only increases the volatility of the financial system, but also causes the homogenization of financial market. The purpose of this paper is to analyze the risks of big data in finance, to clarify the important matters of financial big data supervision, and to improve the relevant laws and regulations, strengthen the information sharing mechanism and other big data financial supervision measures in view of the existing data monopoly, personal privacy leakage and many other issues, and to provide theoretical reference for the development of big data finance and the ecological stability of financial market in China.

The Influence of Internet Finance on the Sustainable Development of the Financial Ecosystem in China

As a new species in the financial ecosystem, internet finance has significantly impacted traditional finance and has improved the diversity and ended the long-term stability of the financial ecosystem. From the perspective of the interaction between the ecological subjects of the Internet and traditional finance, this study examines the influence of internet finance on the sustainability of the financial ecosystem in China. We tested the dynamic correlation and risk transmission at the volatility level between the ecological subjects of internet finance and the banking, securities, and insurance industries by establishing a dynamic conditional correlation-generalized autoregressive conditional heteroskedasticity (GARCH) model of Baba, Engle, Kraft, and Kroner (DCC-GARCH-BEKK). The result indicates a positive dynamic correlation between internet finance and traditional finance almost all of the time. The introduction of internet finance has changed the risk transmission effect among the ecological subjects of traditional finance. Based on empirical findings, this study provides suggestions to promote the sustainable development of internet finance and the whole financial ecosystem. Our research not only has strong practical significance but also contributes significantly to the literature on internet finance and sustainable development.

Prediction of fundraising outcomes for crowdfunding projects based on deep learning: a multimodel comparative study

As a new financing model, crowdfunding has been developed rapidly in recent years and has attracted the attention of investors and small- and medium-sized enterprises and entrepreneurs. However, many projects fail to be funded; thus, crowdfunding project fundraising outcomes forecasting and multimodel comparisons are meaningful ways to identify project quality and reduce market risk. It is important to reduce participation risk through automated methods, which is of great significance to the sustainable development of Internet finance. First, based on the data from the Kickstarter, preprocessing and exploratory analysis are conducted. Then, we introduce a deep learning algorithm (multilayer perceptron) and apply it to the prediction of crowdfunding financing performance. We compare deep learning with other commonly used machine learning algorithms, including decision tree, random forest, logistic regression, support vector machine, and K-nearest neighbors algorithm. We tune each machine learning algorithm to get the best parameters. The experimental results show that the deep learning model can obtain the best prediction results, with an accuracy of 92.3% when predicting the fundraising outcomes of crowdfunding financing, followed by the decision tree. Deep learning shows significant advantages in many evaluation criteria, which demonstrates the potential for crowdfunding project financing predictions. This study combines machine learning with Internet finance, providing inspiration for future research and resulting in many practical implications.

Countermeasures for the Development of Internet Finance Based on E-commerce Platform

“Internet Finance” is an organic combination of contemporary information, network technology and financial transactions. At the same time, it is also a new type of financial model. E-commerce has made a huge contribution to the development of Internet finance, providing massive amounts of customer data and e-commerce for the development of Internet finance. Platforms and Internet technologies, and Internet finance’s role in promoting e-commerce are also obvious. The development of Internet finance based on e-commerce in China has been rapid, and it has now become an important force in the financial industry, and has maintained a rapid development. Through analysis, this article proposes countermeasures for Internet finance development for e-commerce platforms.

Research on the Influence of Internet Finance on the Allocation of Household Assets in the New Middle Class: Setting Shandong Province as an Example

In the last few years, China's Internet finance has been extremely hot, and Internet financial products develop rapidly. Duo to having the advantages of high profitability, low threshold, various types, convenience and fast transaction, Internet financial products is favored by more families. Especially for the new middle class who have high education and high income, they are more likely to accept Internet financial products. Therefore, Internet finance may have a significant impact on the change of asset allocation structure of the new middle class households. So this paper is based on the Allocation of Household Assets questionnaire data of the new middle class in Shandong Province, analyzed the difference in assets structure between the new middle family of considering Internet financial investment and no consider the Internet financial investment. It is concluded that believes that Internet finance can increase the degree of diversification of the new middle class family assets, encourage households to take part in the financial market, and promote the proportion of investment in risky financial assets. Finally, this paper proposes to some policy suggestions that strengthening the publicity of Internet finance, continuing to encourage the innovation of financial products, and preventing risks of Internet finance, which is to promote the healthy and stable development of Internet finance in China.

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.

Research on Internet Financial Consumer Rights Protection in China

Along with the rapid development of Internet finance in China, the irregularities in Internet finance industry has violated the Internet financial consumer rights at many levels. Learning from domestic and foreign experience, the improvement on the legislation on Internet finance, the financial supervision system and the Internet financial consumer dispute resolution mechanisms should be achieved, so as to enhance the protection of Internet financial consumer rights.

FCM-Based P2P Network Lending Platform Credit Risk Dynamic Assessment

With the rapid development of Internet finance, Peer to Peer(P2P) network lending has also developed as a new type of financing. However, as the P2P network lending platform becomes more and more popular, problems such as running, shutting down, and withdrawing cash are emerging. One of the most common problems is caused by credit. This paper addresses the issues such as credit problems and existing risks of online platforms by proposing a Fuzzy Cognitive Map (FCM) that is used to establish a credit risk assessment model for the P2P network lending platform. It provides a new idea for the credit risk assessment of online lending platforms. The paper uses Lu Jinfu as an example to evaluate its credit risk and selects two other platforms to rank its credit risk. The results show that the fuzzy cognitive map considers the mutual influence and feedback between the indicators. The index weight matrix learned by historical data represents the relationship between the indicators. After inputting the initial data into the inference, the index data reaches a steady state. The steady-state refers to the state that we expect the indicator to reach after a certain period. As an objective evaluating method for the credit risk of the P2P network lending platform, the proposed method has demonstrated its advantage and effectiveness.

Trade openness, internet finance development and banking sector development in China

This paper investigates the impact of trade openness on Internet finance development in China. Using a unique panel data set for 29 Chinese provinces, we find that trade openness has a positive impact on Internet finance development and is a leading indicator of Internet finance development. Further analyses show that higher trade openness is associated with faster Internet finance development in wealthier provinces. Moreover, we investigate the effect of Internet finance development on the banking sector development. The empirical results suggest that Internet finance development has a positive effect on the banking sector size and efficiency.

On the Development of Rural Internet Finance and Countermeasures

<p>This article analyzes the degree of the Internet development in rural areas, discusses the current development status, and provides some feasible suggestions of economic, policy, and public issues in the development, so as to promote the development of rural Internet finance. It also summarizes its current development situation</p>

The exploration of internet finance by using neural network

In order to find out the risks of Internet finance as much as possible, and to ensure the rapid and healthy development of Internet finance, random forest (RF), a common classification algorithm in machine learning, was applied to analyze the risk factors of Internet finance. Additionally, the results of traditional statistical methods were compared with those of RF and back propagation (BP) neural network methods and their performance was evaluated. Finally, some suggestions were given for these risk factors, especially for the problems with high risk. The results showed that the RF algorithm model had the best classification effect and could accurately analyze the risks of Internet finance in terms of market, law, credit, personal information, and professional knowledge. It was found that credit and personal information risk were the most important factors in the future development of Internet finance when BP neural network was used to evaluate these risks. To a certain extent, they would also hinder the use and development of Internet finance. At the same time, it proved that BP neural network had a good prediction effect. To sum up, using the RF algorithm and BP neural network method in machine learning to explore the problems of Internet finance is of great significance for risk prediction for other financial institutions.

Performance of Internet Finance Companies in China

Internet finance experienced fast development in the world including China. With around 2000 internet finance companies currently operating in China there is extensive competitions among internet finance companies offering financial services to investors. The paper empirically investigates the determinants of internet finance companies performance based on 330 randomly selected sample data using a linear regression method based on one-month information collected from an online internet finance database. The findings show that the interest rate level, number of projects, and average loan periods play a significant role in the performance of internet finance companies. This investigation could help internet finance companies to adopt effective competitive strategies in order to enhance market performance and at the same time help individual investors to identify good performed companies from the internet finance market…. The joint efforts from both internet finance companies and individual investors will promote the development of internet finance in China and accelerate the revolution of China traditional financial system.

Has Internet Finance Decreased the Profitability of Commercial Banks?: Evidence from China

ABSTRACT This paper explores the popularity of internet finance and its potential shocks to the business of traditional commercial banking in China. Using data on 200 commercial banks in China during the period 2011 to 2016, this paper investigates whether internet finance, measured by P2P (peer-to-peer lending) lending and third-party payment, has a negative impact on the profitability of commercial banks. It concludes that P2P lending and third-party payment has significant negative influence on the profitability of loans and deposits at China’s commercial banks. By intensifying competition, the development of internet finance has decreased the interest income of loans, increased the interest cost of deposits, lowered the growth rate of loans and deposit, and brought about more risk. City and rural banks and unlisted banks are more vulnerable to internet finance than large government-owned and joint-stock banks.