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

Abstract

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.

Introduction

As the third technological revolution progresses, the Internet industry has come into being [1]. The Internet has its technology platforms; moreover, it also has the characteristics of the connection functions endowed by a complete framework, making it free from time and space constraints. Compared to traditional industries, its coverage and transmission speed has been improved [2]. With the continuous development of society, Internet technology is also continually developing. Various industries are interconnected, and the original industry classification standards are becoming more blurred [3]. In the 15 years since 2015, Internet finance has developed rapidly, even showing a blowout phenomenon [4]. The development of Internet finance in the debit and credit sector is growing at an annual rate of 3 times. In the field of Internet finance, with the rise of Peer-to-Peer (P2P) network platforms in various countries since 2005, people are paying increasingly more attention to online loans [5]. First, when traditional finance is combined with the Internet, Internet finance appears; second, its development provides financial support for some small businesses. With the vigorous development of small-, medium-, and micro-enterprises, as well as the continuous growth of financing funds and public investment needs, P2P online loans have developed rapidly. Such a development trend is undoubtedly the excellent development potential brought by the integration of finance and the Internet in Internet finance. It is the most critical development model that genuinely breaks the limitations of traditional geographical, time, and space conditions, which realizes the free interconnection of all aspects among people [6], [7]. Therefore, exploring Internet finance is of considerable significance to the development of the financial industry.

The rough set was proposed in 1982. It is adopted to analyze the expression, learning, and induction of incomplete data and inaccurate knowledge. The rough set can be utilized for classification to find the internal connection between inaccurate data [8]. On the industrial chain of Internet finance, financing institutions of various sizes are uneven. Many small- and medium-sized financing institutions are significantly different from traditional banks [9]. Therefore, rough sets should be fully utilized to mine the data and extract useful knowledge from historical business data, as well as evaluating and modeling the system risks that may occur in the Internet financial industry under the current mode [10]. Reports on the application of rough sets in the field of Internet finance are various. Karimi (2018) used the conditional features in the rough set model to classify the ten components of the most critical financial risks; finally, through the Rosetta software, the risk prediction model for Internet finance was proposed. Simulations confirmed that this model had high prediction accuracy, which could measure Internet financial risks excellently [11]. Mu and Xiong (2019) scored the risk size and importance of initial indicators through a questionnaire survey of Internet finance professionals; then, they adopted a mining method of rough set based on improved genetic algorithms to improve the search efficiency of the Internet finance [12]. García et al. (2020) developed a rough set model, which could accurately predict the financial risks of enterprises by correlating model scores with financial performance indicators of Internet enterprises through comprehensive scoring of European listed enterprises, as well as industry and financial variables [13]. Therefore, applying rough sets to the financial field and predict risks are prevalent. Rough set methods can be used as a supplement to subjective risk evaluation, which can enhance the objectivity and interpretability of evaluation results. Consequently, it can combine objective attribute importance with subjective prior knowledge to determine attribute weights and then conduct risk assessment [14]. Various historical business data and external environmental data of Internet financial institutions are analyzed to obtain regulatory knowledge associated with system risk assessment, providing theoretical guidance for management departments of financial risks in evaluation system construction, as well as a scientific decision-making basis for the financing activities of Internet financial institutions. In this way, the overall operating risks of these institutions are reduced. Finally, system risk management and decision-making for Internet financial institutions are realized, and the ability of Internet financial institutions is improved to resist system risks.

Peer-to-peer (P2P) mode is of a broad range of participants and high flexibility of capitals due to its relative freedom. As there is insufficiency in the relative early-warning mechanism of our country, there have been an increasing number of events like fund disruption and escape, for Internet finance of China, as its relative businesses started later than other countries, it is still in an initial stage with many problems to be solved and improved. Thus, research on Internet financial risk early-warning will effectively stifle the potential risks in the cradle and enable our country to adequately respond to the necessary works in Internet finance, the newly-emerged development mode, which will also provide specific references to the development of the whole Internet field.