Establishment of a Mathematical Model for Enterprise Credit Risk Recognition and Rating Based on Hybrid Learning Algorithms

Supply chain finance has broken through traditional credit modes and advanced rapidly as a creative financial business discipline. Core enterprises have played a critical role in the credit enhancement of supply chain finance. Through the analysis of core enterprise credit risks in supply chain finance, by means of a ‘fuzzy analytical hierarchy process’ (FAHP), the paper constructs a supply chain financial credit risk evaluation system, making quantitative measurements and evaluation of core enterprise credit risk. This enables enterprises to take measures to control credit risk, thereby promoting the healthy development of supply chain finance. The examination of core enterprise supply chains suggests that a unified information file should be collected based on the core enterprise, including the operating conditions, asset status, industry status, credit record, effective information to the database, collecting related data upstream and downstream of the archives around the core enterprise, developing a data information system, electronic data information, and updating the database accurately using the latest information that might be available. Moreover, supply chain finance and modern information technology should be integrated to establish the sharing of information resources and realize the exchange of information flows, capital flows, and logistics between banks. This should reduce a variety of risks and improve the efficiency and effectiveness of supply chain finance.

Download Full-text

Enterprise Credit Risk Management Using Multicriteria Decision-Making

Mathematical Problems in Engineering ◽

10.1155/2021/6191167 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Wenjuan Liu

Keyword(s):

Decision Making ◽

Risk Management ◽

Credit Risk ◽

Multicriteria Decision Making ◽

Final Decision ◽

Credit Risk Management ◽

Multicriteria Decision ◽

Risk Manager ◽

Enterprise Credit ◽

Weight Calculation

The purpose of this study is to reduce the rate of multicriteria decision-making (MCDA) errors in credit risk management and to weaken the influence of different attitudes of enterprise managers on the final decision when facing credit risk. First, several solutions that are suitable for present enterprise credit risk management are proposed according to the research of enterprise risk management in the world. Moreover, the criteria and matrix are established according to the general practice of the expert method. A decision-making method of enterprise credit risk management with trapezoidal fuzzy number as the criteria of credit risk management is proposed based on the prospect theory; then, the weight is calculated based on G1 weight calculation, G2 weight calculation method, and the method of maximizing deviation; finally, the prospect values of the alternatives calculated by each method are adopted to sort and compare the proposed solutions. Considering the difference of risk degree of managers in the face of credit risk management, the ranking results of enterprise credit risk management solutions based on three weight calculation methods are compared. The results show that as long as the quantitative value of the risk attitude of the enterprise credit risk manager meets a certain range, the final choice of credit risk management scheme ranking is consistent. This exploration provides a new research direction for enterprise credit risk management, which has reference significance.

Download Full-text

A GENETIC PROGRAMMING-BASED LEARNING ALGORITHMS FOR PRUNING COST-SENSITIVE CLASSIFIERS

International Journal of Computational Intelligence and Applications ◽

10.1142/s1469026812500113 ◽

2012 ◽

Vol 11 (02) ◽

pp. 1250011 ◽

Cited By ~ 4

Author(s):

ZAHRA NIKDEL ◽

HAMID BEIGY

Keyword(s):

Decision Tree ◽

Genetic Programming ◽

Learning Algorithm ◽

Fitness Function ◽

Learning Algorithms ◽

Hybrid Learning ◽

Experimental Results ◽

Programming Algorithm ◽

Hybrid Learning Algorithm ◽

Misclassification Costs

In this paper, we introduce a new hybrid learning algorithm, called DTGP, to construct cost-sensitive classifiers. This algorithm uses a decision tree as its basic classifier and the constructed decision tree will be pruned by a genetic programming algorithm using a fitness function that is sensitive to misclassification costs. The proposed learning algorithm has been examined through six cost-sensitive problems. The experimental results show that the proposed learning algorithm outperforms in comparison to some other known learning algorithms like C4.5 or naïve Bayesian.

Download Full-text