Research on Credit Risk Evaluation for Small and Medium-sized Enterprises in Supply Chain Based on BP Neural Network Xiaoping Zhang a, Chen Chen b

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1 Internatonal Conference on Computatonal Scence and Engneerng (ICCSE 2015) Research on Credt Rsk Evaluaton for Small and Medum-szed Enterprses n Supply Chan Based on BP Neural Network Xaopng Zhang a, Chen Chen b Department of Accountng, Hebe Fnancal Unversty,Baodng, Chna a @qq.com, b vcacer2@163.com Keywords: Supply chan fnance; BP neural network; Small and medum-szed enterprses; Credt rsk assessment Abstract. Abstract More attenton to supply chan fnance as a new form of fnancng channel s pad wth the ncreasng hghlght of the fnancng dffculty of small and medum-szed enterprses (SMEs). It s crucal for scentfc and accurate rsk assessment to control the control of the credt rsk of supply chan fnance. BP neural network model s used to open up a new way for credt rsk assessment of SMEs based on the establshment and mprovement of an ndex system of credt rsk evaluaton for SMEs. BP neural network model s a smart algorthm wth self-learnng and adaptve characterstcs. The BP artfcal neural network model was analyzed and detected n a practcal case. The feasblty of the model was proved. The result s satsfactory. The model can be used to assess SMEs credt rsk n the supply chan. Introducton Small medum enterprse fnancng s a long-standng problem. In recent years, the rapd development of the supply chan fnance n the past two years to a certan extent, ease the small medum enterprse fnancng dffcultes. At present, the expert of supply chan fnance (SCF) are defned as follows: based on the grasp of the supply chan transacton detals, and the characterstcs of the structure, wth the help of core enterprse credt strength or sngle transacton self-lqudatng degree and crculaton of goods value, supply chan fnance provdes a comprehensve range of fnancal servces for a sngle or multple enterprses. Because the proporton accounted for the small medum enterprse fnancng lease busness n the bank s very small, fledglng plus supply chan fnancng mode, there s no establshed for supply chan n the small medum enterprse credt rsk evaluaton ndex system[1]. In order to be able to tmely, dynamcally and accurately evaluatng and montorng the rsk of small medum enterprse fnancng process, ths paper establshed a comprehensve ndex system for small medum enterprse credt rsk evaluaton, establsh a bengn development between enterprses, banks, and supply chan of ndustral ecosystem. The tradtonal rsk assessment methods nclude prncpal component analyss (PCA), analytc herarchy process (AHP) and fuzzy mathematcs method, etc. But the above methods have the followng defcences: the weght of evaluaton are easly nfluenced by subjectve factors, so the decson may be easy dstorton. To solve ths problem, ths paper adopts a rsk assessment method based on BP neural network whch has the characterstcs of self-learnng and adaptve ablty[2]. BP neural network Neural network s essentally a smulaton system for the human bran s thnkng process. Its core s the mathematcal models and algorthms, and the physcal mplementaton s computer software. Just lke thns are composed of atoms, neural network s composed of many nterconnected neurons[3]. Fg. 1 shows the basc work prncple of neurons, and the mathematcal expresson s as follows: y f w x. (1) The authors - Publshed by Atlants Press 213

2 Where x and y are respectvely the nput and output. w s weght coeffcent. f s characterstc functon, whch reflects the mappng relatonshp between nputs and outputs, and t s usually a nonlnear functon. X 1 X 2 X n f y Fg. 1 The neuron model Ths seemngly smple model wll produce a powerful neural network combng dfferent network topology and network algorthms together. Neural network conssts of nput layer, hdden layer and output layer, whch s shown n Fg. 2. X 1 y X 2 Fg. 2 The connecton model of neural network Wth a lot of development of neural networks, t was found that no matter how the organzatonal structure of the network t s, It s always has the followng two characterstcs: (1) Self-learnng Neural networks can be modfed accordng to the external envronment of ther own behavor n order to adapt to the external envronment, whch s manly due to ts learnng process. Learnng s often the frst step n usng neural networks. When a group of nformaton s nput, neural networks can contnue to adjust ts nternal parameters (or say weghtng coeffcent), and eventually produce a seres of consstent output. (2) Generaton Once after the self-learnng, the response of neural network, to some extent, to the reducng of nput nformaton and ther own local defects are no longer senstve. Ths mechansm can make the neural network has a strong fault tolerance and reduce the nput data qualty requrements. BP neural network s a multlayer forward network based on back-propagaton errors[4]. The network structure s consst of nodes and arrows lne. Nodes smulate the human bran 's neurons, and arrows lne smulate the connected relatonshp between neurons. Accordng to dfferent neurons locaton, the network can be dvded nto three layers, namely nput layer, hdden layer and output layer. Nodes n nput layer are responsble for data nput, and themselves have no computng functon. Nodes n the hdden layer and output layer have a ncreasng and a actvaton functon. Informaton processng mechansm on BP neural network s made up of nodes ncreases and the actvaton functons. The ncreasng functon means that the multplcatons of a node s all nput and ts correspondng connecton are all added up. For example, the ncreasng functon value of the frst node n hdden layer s defned as below. x1 D w 1. (2) 214

3 Where x 1 s the nput value of the frst node n hdden layer. node n nput layer. 1 D s the nput data of the th w s the weght value from the th node n nput layer to the frst node n hdden layer. The actvaton functon means that the added value mentoned above functon are transformed to the output value of the node through a certan mathematcal method. For example, the output value of the frst node n hdden layer s defned as below. x y1 g x 1. (3) Where g s named as actvaton functon. The common actvaton functons nclude Sgmod functon, proportonal functon, saturaton functon, hyperbolc functons and so on. The credt rsk evaluaton model based on BP neural network In the past, the evaluaton of banks fnancng objects are manly sngle enterprse, and the assessment are requred accordng to the statc fnancal data. Thus, there exsts many problems, because the fnancal data of small and medum-szed enterprses are scattered and not specfcaton, and the transparency s relatvely low, whch make t harder for banks to carry on the scentfc evaluaton[1]. Fnancal pont of vew of the supply chan thought s based on the correlaton between ndustry supply chan, assess to the enterprse supply chan operaton, n order to dynamcally assess the real rsks of credt project. Supply chan fnancal nterests of relevant subjects nclude: the supply chan core, the small medum enterprses of the core enterprse supply chan upstream and downstream, loan banks, and related logstcs enterprses. Accordng to the ndex desgn prncples and methods, on the bass of a large number of lterature research and expert nvestgaton, the credt rsk evaluaton ndex system of small and medum-szed enterprses under the fnancal perspectve of supply chan was establshed, whch contans four major categores and 21 ndcators, as shown n Table 1. Table 1. The credt rsk evaluaton ndex system of small and medum-szed enterprses The target layer Prmary ndcator The secondary ndcators Enterprse qualty Personnel qualty (A 1 ) Management qualty (A 2 ) Ablty to manage Total asset turnover (A 3 ) Small and medum-szed enterprses qualfcaton Proftablty Return on sales (A 4 ) Return on equty (A 5 ) Asset-lablty rato (A 6 ) Development potental Asset growth rate (A 7 ) Net proft growth rate (A 8 ) Credt ratng Credt ratng (A 9 ) Core enterprses qualfcaton Industry status Industry compettve poston (A 10 ) Proftablty Return on sales (A 11 ) Solvency Cash flow rato (A 12 ) Fnancng project Project product Inventory turnover (A 13 ) Qualty of vulnerablty (A 14 ) Accounts recevable stuaton Accounts recevable turnover (A 15 ) Rate of bad debts (A 16 ) Industry status Supply chan compettve poston (A 17 ) Supply chan strength The degree of close cooperaton The ndustry envronment (A 18 ) Cooperaton years (A 19 ) Transacton frequency (A 20 ) Informaton sharng degree (A 21 ) The key of BP neural network s to determne the number of network herarchy and the number of neurons n every level. Some research shows that the learnng of BP network wth 4 layered structure s better..therefore, ths paper uses the 4 level BP network structure to tran, the 4 level ncludes one nput data layer, two ntermedate hdden layers and one output data layer. 215

4 Case study From the RESSET fnancal database, 20 groups data were selected whch have the obvous features of supply chan, and were transformed nto Table 2. Sample data nvolves buldng materals, transportaton, ron and steel ndustry, botechnology, pharmaceutcal and other ndustres of small and medum-szed enterprses. Through the smulaton of Matlab software, the nformaton of 1-15 group are selected as the tranng sample of the model, and the sample of group are used as the test sample. The twenteth group of data s the object to be evaluated.. The learnng rate and error are respectvely set as 0.05 and Put test sample data nto the model, the results are shown n Table 2. The results show that, compared wth the experts expected results, the results of the model computaton has a hgh consstency, the fnal error s only , whch s wthn the set range and satsfes the requrements. Therefore, ths model s practcal and scentfc. Table 2. Sample data No. A 1 A 2 A 20 A Apply the proposed model to evaluate the fnal set of data, the data for the ffth column data are shown n Table 3, whch proved that the small and medum-szed enterprse credt rsk ratng s general, whch needs to strengthen rsk control. Table 3. The results comparson No Evaluaton results Expected results Conclusons The rapd development of supply chan fnance n the past two years to a certan extent, allevate the fnancng dffcultes of small and medum-szed enterprse. But due to not have a complete system of credt rsk and scentfc rsk evaluaton method, affected by the prevous rsk assessment model at the same tme, n the small medum-szed enterprse credt assessment, small medum-szed enterprse credt level s low, whch largely lmts the small medum-szed enterprse fnancng. In order to effectvely control the rsk of bank, at the same tme truly broaden the small medum-szed enterprse fnancng channels, acheve a wn-wn stuaton, ths paper establsh a comprehensve credt rsk evaluaton system, and adopts BP neural network to evaluate the rsk of small enterprses. The results s satsfed, ndcate that the proposed model can be used n practcal applcaton. 216

5 References [1] Ladslav Mura, Ján Buleca. Evaluaton of Fnancng Possbltes of Small and Medum Industral Enterprses. Proceda Economcs and Fnance. Vol. 3 (2012), p [2] Samuel B. Moore, Susan L. Manrng. Strategy development n small and medum szed enterprses for sustanablty and ncreased value creaton. Journal of Cleaner Producton. Vol. 17, Issue 2 (2009), p [3] Hu Junguo, Zhou Guomo, Xu Xaojun. Usng an mproved back propagaton neural network to study spatal dstrbuton of sunshne llumnaton from sensor network data. Ecologcal Modellng. Volume 266, Issue 24(2013): [4] Xuefeng Zhao, Qn Ba, Le Zhou, Weje L, Jnpng Ou. BP neural network recognton algorthm for scour montorng of subsea ppelnes based on actve thermometry. Internatonal Journal for Lght and Electron Optcs. Volume 125, Issue 18( 2014), p