Big Data Analytics and Discrete Choice Model for Enterprise Credit Risk Early Warning Algorithm

Jiangbo Yu

doi:10.1155/2022/3272603

Abstract

A business credit risk early warning algorithm based on big data analysis and discrete selection model is presented to address the issues of poor sample fitting performance, long warning time, and low warning accuracy that plague the traditional enterprise credit risk early warning algorithm. A-share listed enterprises in China were chosen as the credit data source for screening the samples based on big data analysis. After screening, financial failure firms were coupled, and paired samples were created. The credit risk variables, which included financial and corporate governance characteristics, were chosen based on the created samples. The enterprise financial risk submodel and the nonfinancial risk submodel were built based on the enterprise credit risk variables, and the financial and nonfinancial index scores of enterprise customers were evaluated separately to develop a discrete choice model of enterprise credit risk. The algorithm’s sample fitting performance was employed to achieve early warning of corporate credit risk. The algorithm based on big data analytics and discrete choice model is compared to the traditional method in order to verify its validity. The findings of the experiment reveal that the algorithm’s sample fitting performance is superior to the traditional one, making it more suitable for enterprise credit risk early warning. The proposed model depicts 85% accuracy.

Highlights

Credit growth is to promote investment, production, and consumption
Is paper expanded the current research status and relevance of financial risk early warning, as well as the development background, current position, and future difficulties, based on reviewing and assessing prior research works. is paper proposes a novel technique that can predict the potential risk related to finances and protect the organization from potential credit risks. e detailed sections are arranged as follows: Section 2 introduces the related works; Section 3 discusses the sample screening structure and variable selection process; Section 4 discusses the experimental results of big data analytics and discrete choice model for enterprise credit risk early warning algorithm; Section 5 is the conclusion
In [3] the authors proposed an enterprise credit risk assessment method based on improved genetic algorithm, which satisfies the adaptability of improved algorithm to corporate credit risk. e algorithm can accurately forewarn the credit risk of enterprises, but it takes a long time

Summary

Introduction

Credit growth is to promote investment, production, and consumption Traditional financing products such as medium- and long-term loans (mostly for infrastructure projects) and commercial-paper financing are still the major components of new loans for various enterprises at the moment. Better risk management might result in annual savings of hundreds of millions of dollars for financial organizations. Credit risk prediction is necessary because effective application of the above risk management measures necessitates banks’ ability to identify accounts that are likely to default. E detailed sections are arranged as follows: Section 2 introduces the related works; Section 3 discusses the sample screening structure and variable selection process; Section 4 discusses the experimental results of big data analytics and discrete choice model for enterprise credit risk early warning algorithm; Section 5 is the conclusion Is paper expanded the current research status and relevance of financial risk early warning, as well as the development background, current position, and future difficulties, based on reviewing and assessing prior research works. is paper proposes a novel technique that can predict the potential risk related to finances and protect the organization from potential credit risks. e detailed sections are arranged as follows: Section 2 introduces the related works; Section 3 discusses the sample screening structure and variable selection process; Section 4 discusses the experimental results of big data analytics and discrete choice model for enterprise credit risk early warning algorithm; Section 5 is the conclusion

Related Works

Sample Screening Structure and Variable Selection

Construction of Warning Model of Enterprise Credit Risk

Findings

Experimental Researches