Research on Identification Model of Financial Fraud of Listed Company Based on Data Mining Technology

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1 208 2d Iteratioal Coferece o Systems, Computig, ad Applicatios (SYSTCA 208) Research o Idetificatio Model of Fiacial Fraud of Listed Compay Based o Data Miig Techology Jiaqi Hu, Xiao Che School of Busiess, Zhejiag Uiversity City College, Hagzhou, 3006, Chia Keywords: Accoutig iformatio distortio, Fiacial fraud idetificatio, Data miig, Classificatio, Support vector machie Abstract: Corporate fiacial iformatio fraud is a difficult problem ad a great hazard sice the birth of the listed compay system. The classic method of fiacial fraud detectio is of high accuracy, but it requires eormous mapower ad material resources, which also eeds a relatively log time for ivestigatio. With the developmet of computer techology, data miig techology ca be used as a very importat tool to idetify the distortio of eterprise fiacial accoutig iformatio. Through the modelig work of differet classificatio ad clusterig algorithms, a set of idetificatio models ca be costructed with low cost ad high efficiecy. This paper uses the popular support vector machie as a data miig tool to idetify fiacial fraud ad achieves good results.. Itroductio Although govermets are actively dealig with fiacial fraud i various idustries, the situatio at home ad abroad is still ot optimistic []. The study of fiacial fraud discrimiatio has udergoe may chages. The mai research subjects i the early years ca be cosidered as "applicatios of aalytical procedure law". I the later period, the extesive applicatio of statistical models ad the popularity of data miig algorithms make the research directio has chaged. The mai directio of this study is to use data miig techology to idetify fiacial frauds [2]. 2. Selectio of Research Samples ad Variables 2. Samples Selectio Except some eterprises liked to iterim reports, quarterly reports ad temporary reports, we chose compaies that were publicly puished by the CSRC for their aual reports from 2000 to The 6 compaies have varyig degrees of deceit i their fiacial statemets. If a compay takes a lot of fraud, it will oly calculate its first fake year. We must take the ecessary measures i lie with the priciple that the sample cotrol eterprises caot be false. First, to esure the similarity betwee the fake sample ad the cotrol sample i the idustry ad the aual distributio, we should choose the same busiess as the idustry ad the year of the fake compay. Secodly, it is ot possible to cosider the eterprises of ST, S ad PT; fially, withi 3 years, the audit report colum of the cotrol sample is the stadard ad ureserved. Opiio. Based o the above priciples, after a series of selectio, we fially chose the same umber of trustworthy eterprises ad fake eterprises. Both of them are 6 compaies. The Tai'a database provides us with all the sample data. 2.2 Variables Selectio O the choice of fiacial idicators, this paper maily uses Relief algorithm to aalyze ad scree the existig fiacial idicators. Accordig to the algorithm requiremets, first, a sample is radomly selected, ad the sample used for the study is from the sample set. Secodly, two samples, Near Hit ad Near Miss, were selected from differet groups of samples. Compared with the target samples, the two selected samples belog Copyright (208) Fracis Academic Press, UK DOI: /systca.8.045

2 to the same kid ad the same kid. However, it should be oted that these two samples are ot radomly selected but are the most relevat samples. Thirdly, to update the weights of each feature, we eed the followig rules. First, the distictio betwee class attributes is the core of the algorithm ad is positively related to the weight size. Therefore, to kow whether the weight should be icreased, the key poit is to calculate the degree of associatio with two selected samples o a certai feature of the target sample. I geeral, it is to calculate the degree of the correlatio betwee the Near Hit ad the feature first, ad the calculate the Near Miss i the same way ad compare the relative size of the two correlatio degrees. If Near Hit is smaller, the weight of the feature ca be improved, because it shows that the feature ca be used to distiguish betwee the samples of the same class ad the differet categories; o the other had, the weights should be reduced. Secodly, because the average weight is more covicig, it is ecessary to calculate the average value may times. For example, to coduct radom samplig, we first eed to have a overall sample. The object that is selected by the feature i the algorithm is X={x,x2,,x}. Secodly, m is a radom sample umber. The the N characteristic value of the i sample is expressed as x{xi,xi2,,xin}. Based o the above coditios, we ca use the followig formula to express the sum of the the weighted value of all the features:. Amog them, the represetative feature is j. [:N] is the rage of its value, ad the samples that are radomly selected accordig to the requiremets are represeted by i. M (x) represets a heterogeeous earest eighbor sample, ad H (x) represets the earest eighbor sample of the same kid. To elimiate the ifluece of radom samplig, we coducted five experimets i this experimet. Ad whe the weights of each experimet are more tha zero at the same time, we ca reach the followig eight idicators: () asset liability ratio, (2) cash ratio of operatig icome, (3) asset reward rate (4) total et profit rate (ROA), (5) receivable turover, (6) ivetory turover, (7) the rate of cost durig sales. (8) the turover of mobile assets [3]. 3. Basic Theory of Support Vector Machie To improve geeralizatio ability of classificatio machies, we geerally believe that small sample size has absolute advatages. I the past, however, to make the expected risk meet the requiremets, we ofte speak with experiece that the experiece risk determies the expected risk, but this is oly applicable to the case with large sample cotet. I the support vector machie (SVM) idea proposed by Vapik et al., the fixed experiece risk has abadoed the previous view. The result is ot oly to reduce the cofidece rage to a certai extet, but also to miimize the impact of the sample cotet [4]. For the learig of machie geeralizatio ability, the traditioal cocept is differet from SVM [5]. The former focuses o all traiig samples, ad the latter focuses o collectig small samples. This part is ofte the support vector at the boudary of differet classes of samples, called SV. For the source of support vector machies, the first is liear separable, followed by the optimal classificatio surface; the followig graph ca basically reflect the two ideas. Figure Schematic diagram of SVM model Solid poit: sample ; hollow poit: Sample 2; solid lie (except coordiate axis): classificatio

3 lie; dotted lie: meet three coditios: () through samples, (2) parallel to the solid lie, (3) ad the miimum spacig of classified samples. The classificatio iterval refers to the distace betwee two dotted lies, ad the real lie i the graph is also the optimal lie. Oe reaso: it successfully separates two samples, ad the reaso is two: the distace of the two dotted lies is maximized. For the equatio where the classificatio lie is located, the expressio ca be expressed, ad the the equatio is ormalized. The purpose is to make the equatio set for a liearly separable set of samples d ( xi, yi),,..., x, R, yi + {, }. Whe we have the coditio of yi[( wx i) + b] 0,,...,ν, the formula is also correct. Uder this coditio, the classificatio iterval we calculate is 2/ w. It is ot difficult to fid the 2 maximum classificatio iterval we require is set up at the miimum value of w /2.That is, the two items are iverse ratio. I the above picture, the dotted lie is the set of samples o the class boudary (SV), ad the distace reflects the classificatio iterval, ad the optimal classificatio surface is the classificatio surface whe the classificatio iterval is the largest. Accordig to the above aalysis, the optimal classificatio surface is related to the maximum classificatio iterval, ad is also take at the miimum value, so we have the iequality: 2 mi Φ ( W) = W = ( WW ) 2 2. It is difficult to solve the problem directly, so i practical applicatio, the problem of the complex spherical optimal classificatio surface is coverted to a simple dual problem (Formula ). This method is also called the Lagrage optimizatio method. But at the same time, the solutio of this formula has its restrictive coditios (Formula 2). W( α) αi αα i jyy i j( xi xj), Formula : 2 i j= Formula 2: yα = 0, α 0, i =, 2,..., i i i Combied with the two-type solutio, we get the formula 3, which represets the optimal classificatio fuctio: Formula 3: ( ) = sg{( ) + * * } = sg{ αi i( i ) + } f x w x b y x x b Liear separable is the basic idea of support vector machie ad is also a prerequisite for solvig the optimal classificatio plae. However, to fully reflect the advatages of SVM, we ca ot oly solve the liear separable problem, but also cosider the liear iseparable coditio, which meas that the liear costrait coditios will ot be applied. Therefore, we have itroduced the liguistic symbols i hyperplaes, which are called relaxatio variables. It is hoped that all samples ca be correctly classified by hyperplaes, so that we ca get yi[( w xi) + b] + xi, i =,...,. I this way, we go back to the steps metioed earlier, but the equatio becomes ito Formula 5: mi φ( W) = ( W W) + C ξi 2. The costrait coditio is [( W Xi) + b] + ξi 0, i =,2,...,. I the process of machie learig ad popularizatio, the error of the classificatio error ofte appears, which belogs to the system error; ad because the system error has some regularity, the costat C is itroduced, the purpose is to make up the error. O the priciple of simplificatio of complex problems, the above-metioed problems are trasformed ito dual problems. The W( α) αi αα i jyy i j( xi xj), maximum of the followig formula 2 i j= whe solvig the optimal

4 yiαi = 0,0 αi Ci, =,2,..., classificatio surface. The costrait coditio is. From the three cases above, we kow that the costrait coditios must exist regardless of the liear oliear solutio. For this problem, a coditio that satisfies all cases - Kuh Tucker coditio (KKT) appears. I terms of cocrete practice, liear classificatio is ot very commo. Therefore, to better solve the problem, how to use oliear hyperplae becomes the key. 4. Model Costructio ad Aalysis Based o Support Vector Machie This experimet is implemeted with R laguage as the tool ad relyig o the e07 package. Idetifyig variables still selects the variables selected before, represetig false fiacial statemets, ad - idicatig ormal samples. After the completio of data stadardizatio, cosiderig that ay experimet has errors, the pealty parameter C is itroduced ad its value is 0; ad i may types of kerel fuctios of SVM, we choose the Gauss kerel fuctio ad make =2, accordig to the above coditios, the followig results are obtaied: Sample Table. Idetificatio effect of support vector machie T sample size Number of correct recogitio Accuracy of recogitio F sample size Number of correct recogitio Accuracy of recogitio Traiig sample % % Test sample of % % Test sample of % % Test sample of % % Test sample of % % Test sample collectio % % From the table, we ca see that the total recogitio accuracy of T sample is higher tha that of F sample, while the total recogitio accuracy fluctuates at 85%. The effect is still available. However, the recogitio of class F samples ad the recogitio of T class samples always show great differeces. The recogitio rate of data samples i 204 is the highest, ad it is possible to geerate the possibility of model cotigecy. The overall recogitio rate ca reach 85%, which reflects the recogitio effect of the model. 5. Coclusio Usig data miig techology ca speed up the judgemet of fiacial reports ad its mai tool is the data miig model. The support vector machie model used i this paper has obtaied the better experimetal results with a average recogitio rate of approximately 80%. It ca show that data miig techology has good work effect i the field of fiacial fraud idetificatio. Compared with the traditioal high labor cost fraud ispectio, data miig techology has a sigificat advatage i margial cost. Regulators ca use cloud techology to coduct real-time moitorig ad checkig of fiacial data ad stregthe the checkig of fiacial fraud. Refereces [] Marcel, Jeremy J., ad Amada P. Cowe. "Cleaig house or jumpig ship? Uderstadig board upheaval followig fiacial fraud." Strategic Maagemet Joural 35.6 (204): [2] Throckmorto, Chadra S., et al. "Fiacial fraud detectio usig vocal, liguistic ad fiacial cues." Decisio Support Systems 74 (205): [3] Yag D, Jiao H, Bucklad R. The determiats of fiacial fraud i Chiese firms: Does

5 corporate goverace as a istitutioal iovatio matter?[j]. Techological Forecastig ad Social Chage, 207, 25: [4] Albashrawi M, Lowell M. Detectig fiacial fraud usig data miig techiques: A decade review from 2004 to 205[J]. Joural of Data Sciece, 206, 4(3): [5] Petraşcu D, Bucur M A, Dobre E. Aalysig the Maagemet of Huma Resource i Ecoomic-Fiacial Fraud Ivestigatio[J]. Procedia Ecoomics ad Fiace, 205, 27: