Loading [MathJax]/extensions/MathZoom.js
Credit Decision System for Micro, Small and Medium Enterprises (MSMEs) Based on Neural Network Algorithm and Nonlinear Programming | IEEE Conference Publication | IEEE Xplore

Credit Decision System for Micro, Small and Medium Enterprises (MSMEs) Based on Neural Network Algorithm and Nonlinear Programming


Abstract:

Micro, Small and Medium Enterprises (MSMEs) play an important role in economic development However, due to the prevalence of information asymmetry, MSMEs are hard to borr...Show More

Abstract:

Micro, Small and Medium Enterprises (MSMEs) play an important role in economic development However, due to the prevalence of information asymmetry, MSMEs are hard to borrow money and banks have difficulties in accurately assessing credit risks of MSMEs. In order to solve these problems in credit decision, we establish a credit risk assessment model for MSMEs based on the principle of back propagation (BP) neural network learning algorithm. After iteratively solve the nonlinear programming problem with the interior point method, a credit decision model is obtained. As the production and operation of enterprises and economic benefits may be affected by some unexpected factors, following that, the credit strategy is adjusted according to the enterprise's own business condition and market influencing factors.
Date of Conference: 15-17 April 2022
Date Added to IEEE Xplore: 24 May 2022
ISBN Information:
Conference Location: Xi'an, China

I. INTRODUCTION

In the stage where credit-based economic is developing rapidly, the borrowing and lending operations of enterprises have a significant pull on the economy. Among them, micro, small and medium-sized enterprises (MSMEs), despite their usually small business scale, are more active and play an important part of all types of economies [1], [2]. Considering the credit risk, banks generally provide different products and services for different volume of customers, which is consistent with the credit rationing theory [3]. Due to the general lack of collateral, small scale of operation, irregular corporate financial system, and low information transparency of MSMEs, banks provide them with more limited products and services [4]. Because the objective information asymmetry in the market economy, it is difficult for MSMEs to obtain sufficient financial support and for banks to find quality customers and expand their credit business. [5], [6] Currently, artificial intelligence is developing rapidly, and artificial neural network algorithms are a key component of research in the field of artificial intelligence [7]. The combination of artificial neural networks and bank credit rationing is an effective way to solve the traditional credit decision problem. The purpose of this research is to establish a credit risk assessment model for MSMEs based on the principle of BP neural network learning algorithm by studying the data mining of unprocessed information about corporate credit, and to establish a credit decision model by iteratively solving a nonlinear programming problem in a real lending market context using the interior point method. The artificial neural network has good adaptive learning capability. It works mainly by simulating biological neural networks, but the drawback is its poor ability to express special contexts [8]. Therefore, considering that the credit market is permanently affected by unexpected factors in all aspects of the economy and society, the credit strategy obtained based on the decision model is adjusted practically to obtain a fairer decision result according to the firm's own characteristics.

References

References is not available for this document.