Management Science and Engineering
Vol. 08  No. 01 ( 2019 ), Article ID: 29180 , 8 pages
10.12677/MSE.2019.81009

Logistics Financial Risk Assessment Model Research

Mingming Fan, Shimin Wang

School of Computer & Information Engineering, Beijing Technology & Business University, Beijing

Received: Feb. 15th, 2019; accepted: Mar. 1st, 2019; published: Mar. 8th, 2019

ABSTRACT

In this paper, the development of the core enterprise in the logistics financial system has strong guarantee for the stability of the supply chain, and the problem of financing of logistics enterprises has not been well solved, in which the difficult point of pain is not a large measure of the business ability of the bank, but the small sample problem is also a key factor in the failure of bank to analyze accurate analysis. This paper studies the risk assessment model of the core enterprise, namely the logistics enterprise loan, from the perspective of the bank in the supply chain finance. Oriented by the bank risk and the development of the logistics enterprise, this paper constructs a multi-index assessment model from the perspective of economics. Under the background of considering the enterprise size and operation status, this model uses the principal component analysis to determine the stability of the supply chain and upstream and downstream enterprises and other factors to confirm the evaluation index of the logistics enterprise. Finally through the concrete example simulation test, the results show that the bank under the restricted condition of the small sample can better comprehensive evaluation of logistics enterprises to financing risk.

Keywords:Logistics Enterprises, Internal Supply Chain, Principal Component Analysis, Support Vector Machine

1. 引言

2. 物流金融信用风险指标体系构建

Table 1. Logistics financial risk assessment index system

Table 2. Three-level indicators and index description

Table 3. Indicators with large correlation coefficient

3. 基于支持向量机物流金融信用风险评估模型

3.1. 数学建模

1) 线性分类：

$\mathrm{max}Q\left(\alpha \right)={\sum }_{i=1}^{n}{\alpha }_{i}-\frac{1}{2}{\sum }_{i=1}^{n}{\sum }_{j=1}^{n}{\alpha }_{i}{a}_{j}{y}_{i}{y}_{j}{x}_{i}^{\text{T}}{x}_{j}$

s.t. ${\sum }_{i=1}^{n}{a}_{i}{y}_{i}=0$ ，且 ${a}_{i}\ge 0,i=1,2,\cdots ,n$

$\mathrm{max}Q\left(\alpha \right)=\frac{1}{2}{\sum }_{i=1}^{n}{\sum }_{j=1}^{n}{\alpha }_{i}{a}_{j}{y}_{i}{y}_{j}{x}_{i}^{\text{T}}{x}_{j}-{\sum }_{i=1}^{n}{\alpha }_{i}$

s.t. ${\sum }_{i=1}^{n}{a}_{i}{y}_{i}=0$ ，且 ${a}_{i}\ge 0,i=1,2,\cdots ,n$

s.t. ${\sum }_{i=1}^{n}{a}_{i}{y}_{i}=0$ ，且 $0\le {a}_{i}\le C,i=1,2,\cdots ,n$

$\mathrm{min}\omega ,b,\xi \varphi \left(\omega ,x\right)=\frac{1}{2}{\omega }^{\text{T}}\omega +C{\sum }_{i=1}^{n}{\xi }_{i}$ s.t. 

2) 非线性分类：

$\mathrm{max}Q\left(\alpha \right)={\sum }_{i=1}^{n}{\alpha }_{i}-\frac{1}{2}{\sum }_{i=1}^{n}{\sum }_{j=1}^{n}{\alpha }_{i}{a}_{j}{y}_{i}{y}_{j}K\left({x}_{i},{x}_{j}\right)$

s.t. ${\sum }_{i=1}^{n}{a}_{i}{y}_{i}=0$ ，且 $0\le {a}_{i}\le C,i=1,2,\cdots ,n$

$f\left(x\right)=\mathrm{sgn}\left({\sum }_{i=1}^{n}{a}_{i}^{*}{y}_{i}K\left({x}_{i},{x}_{j}\right)+{b}^{*}\right)$

3.2. 模型构建

1) 数据归一化：

${x}_{i}=\frac{{x}_{i}-{\mathrm{min}}_{i}}{{\mathrm{max}}_{i}-{\mathrm{min}}_{i}}$

2) 核函数的选取和参数的选择：

4. 实证研究

Table 4. Distribution of sample sets

Figure 1. Support vector machine output results

Table 5. Reliability statistics

Figure 2. Test set accuracy and accuracy

5. 总结

Logistics Financial Risk Assessment Model Research[J]. 管理科学与工程, 2019, 08(01): 64-71. https://doi.org/10.12677/MSE.2019.81009

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