Analysis of Financial Data Anomaly Based on Data Mining Technology

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1 Revta de la Facultad de Ingenería U.C.V., Vol. 3, N 5, pp , 7 Analy of Fnancal Data Anomaly Baed on Data Mnng echnology Qn Xu * School of Economc & Management, Chengdu etle College, Chna *Correpondng author(e-mal: uqn97@6.com)) Abtract hrough the anomaly analy of fnancal data, th paper reearch on the method of anomaly analy n aocaton data mnng. hrough the hgh dmenonal multvarate tattcal analy of the mappng relatonhp between fnancal data, the author etract the relevant dmenon nformaton whch reflect the abnormal charactertc of fnancal data, and etablhe the dcrmnated tattc and npecton tandard. he emprcal analy reult how that ung the method of property Bune analy of abnormal data, can effectvely etract the abnormal charactertc of fnancal data to the nformaton flow, better accuracy of abnormal fnancal data mnng, trong ant-nterference ablty, ntellgence and Real-tme Anomaly Analy of fnancal data good. Key word: Data mnng, fnancal data, tattcal audt, correlaton feature. INRODUCION Wth the development of large data nformaton proceng technology, t provde trong data upport for the tattcal department and fnancal audt department to ue large data nformaton proceng method for fnancal data proceng and analy. he fnancal data nclude bank ournal data, fnancal ependture data, and fnancal ncome data etc. he data mnng and nformaton feature etracton technology are ued to analyze the anomaly of fnancal data. he attrbute feature reflectng abnormal nformaton of fnancal data are mned to realze centfc analy of fnancal data. he tudy of abnormal analy method of fnancal data ha mportant applcaton value n economc crme nvetgaton and audt nvetgaton. here are many factor affectng the abnormalte of fnancal data. he dtrbuton feature of abnormal attrbute have bg randomne, wth the feature of tme-varyng and autocorrelaton couplng. It dffcult to analyze the anomaly of fnancal data. he contructon of abnormal feature mnng model for fnancal data a knd of abnormal feature mnng and data predcton problem for a group of economc ample equence. In the tradtonal method, the anomaly analy method for fnancal data manly nclude upportng vector machne method, decon tree analy method, and tattcal feature analy and autocorrelaton feature mnng method. he abnormal attrbute mnng of fnancal data acheved wth the above method decompong the economc analytc model of fnancal data a the tattc ncludng mult-lnear component, o a to realze the feature reconttuton and multvarate lnear equaton of fnancal data by combnng the tattc nformaton proceng of fnancal data and emprcal data analy method, thu to tet your fnancal data anomaly analy, and acheve ome reearch reult. In th paper, a tattcal feature analy and abnormal behavor mnng method of fnancal data baed on CAR decon tree data mnng model preented n lterature. Semantc mlarty tattcal analy method combned to realze entty dentfcaton and attrbute correlaton analy of fnancal data to etract nformaton feature mutually for data flow and anomaly mnng of fnancal data, but th method cannot effectvely realze the decouplng of fnancal data n the analy. he ngularty n data mnng proce bgger and the cot of parallel computng larger. Lterature propoe a method for analyzng the abnormal fnancal data baed on upportng vector machne and analytcal method of PCA man component. Frtly, redundant nformaton of fnancal data fltered and carred out wth reduplcaton proceng to reduce computatonal overhead of fnancal data analy, etract pectral feature of fnancal data flow, combnng hgh order pectral analy for fnancal data abnormal feature etracton, to acheve nonlnear tme ere analy of fnancal data and gnfcant dfference analy of abnormal data, but the mmunty of th method not good n performng the anomaly analy of fnancal data, and accuracy of anomaly feature mnng not very hgh. In order to olve the above problem, th paper propoe a method of fnancal data anomaly analy baed on correlaton data feature mnng technology. Frtly, the phae pace reconttuton method ued to carry out hgh dmenonal feature epanon of fnancal data. he prncpal component analy method ued for auto-correlaton feature matchng of fnancal data, combnng wth the alternatve data method for randomzaton proceng of fnancal data, and then feature compreon method ued for non-correlaton redundancy proceng. Hgh-dmenonal multvarate tattcal analy combned for correlaton mappng of fnancal data, to etract the correlaton data reflectng the abnormal feature of fnancal data, contruct the 338

2 Revta de la Facultad de Ingenería U.C.V., Vol. 3, N 5, pp , 7 dcrmnant tattc and tet crtera. Fnancal data anomaly feature mnng carred out accordng to the gnfcant dfference of dcrmnant tattc to acheve abnormal fnancal data analy. Fnally, the emprcal data analy and mulaton eperment are carred out. he method n th paper ued to realze the abnormal analy of fnancal data, whch hown, to draw the concluon of valdty.. INFORMAION FLOW RECONSIUION AND PREPROCESSING OF FINANCIAL DAA.. Fnancal data flow reconttuton In order to realze the anomaly analy of fnancal data, nformaton flow model of fnancal data need to be contructed to etract the abnormal charactertc of fnancal data, analyzng the change and ncreang tuaton of fnancal data through the data mnng technology, to provde the reference nde for fnancal tattc and audtng department. he paper analyze the tattcal charactertc of fnancal data through contructng a mnng model for fnancal data and ournal trend effectvely and ynthezng fnancal data feature dtrbuton, adoptng certan mathematcal model contructon method to contruct nformaton flow model of amplng fnancal data, combnng economc equence analy method to make fnancal data flow reconttuton and feature reorganzaton. he fnancal data a et of nonlnear economc equence. he nonlnear economc equence analy method can be ued to mne the abnormal charactertc of fnancal data. hrough tattc and amplng of apror nformaton of fnancal department, nveron economc equence of unvarate fnancal data can be contructed a { } n. Hgh-dmenonal phae pace reconttuton technology ued for fnancal data correlaton feature reorganzaton. he abnormal charactertc of fnancal data analyzed n the phae pace. he nformaton flow model of fnancal data hgh-dmenonal charactertc. Optmal correlaton decompoton of the data done. he phae pace vector model of fnancal data obtaned n the reconttuted dmenon pace decrbed a: ( m) ( m) L N N N N ( m) () In the formula, the embeddng dmenon of fnancal data n the phae pace m. he amplng tme delay of fnancal data. In the m dmenon pace of reconttuted data map, the attrbute charactertc reflectng fnancal data category etracted. he tet and tattc model of fnancal data L contructed to yn ( ) generate a Gauan tme ere and a dcrmnant tattcal model of fnancal data contructed to generate the correpondng alternatve equence: p q n n n () In the equaton, fnancal data redundancy nterference tem wth mean value of, and varance of,,,, p known a randomzaton coeffcent,,, q known a tme wndow. he ngular value decompoton method ued to obtan the nveron rreverble value of fnancal data anomaly feature mnng. he ngular value decompoton proce L U * S * C, U and C a phae randomzed matr, and C ( c, c,, cn ) (3) Fourer converon carred out for the tattcal ere of orgnal fnancal data. Sgma tet crteron ued to obtan the thrd order autocorrelaton value of fnancal data to get the generalzed nvere potve oluton of fnancal data dtrbuted n the phae pace. Here, S the ngular value of L. he man component vector of fnancal data redundancy nformaton : S dag(,,, n ), n (4) For any orthogonal matr, n the lnear ubpace, phae pace reconttuton track matr L and elf-organzng map functonal ued to get the phae pace control matr of Nm dmenon through balance pont control: N 339

3 Revta de la Facultad de Ingenería U.C.V., Vol. 3, N 5, pp , 7 ( N ) J a c a c a c m a c a c a cm X 3 a Nc anc ancm In th paper, obtaned through ngular value decompoton of the th egenvector, combnng the non-lnear anomaly feature mnng method for the mamum Lyapunove nde functonal of fnancal data. () J Set up a eparaton coeffcent for the eparaton proceng of redundant nformaton of fnancal data accordng to the number of tranng ample of fnancal data and abundance of redundant data. Blnd ource eparaton technology adopted to get the blnd ource eparaton covarance matr C of fnancal data: In the equaton: l l N C X X X X l,, N N k N k (8) X X, X,, X m (9) Remove the dmenon of orgnal fnancal data to get the lack varable of fnancal data n the phae pace: () J () In the equaton, the vector quantzaton nformaton of the orgnal data ample equence n phae pace, and the correpondng m prncpal component feature ubet R. So let n n ( n ) be the tate nformaton parameter of abnormal dtrbuton of fnancal data. When the detecton tattc meet, autoregreon ARMA etmaton carred out through fnancal economc ample equence of the orgnal tattc [8] to get and. And when the number of prncpal k V component atfe the contrant condton S, cumulatve varance of fnancal data calculated n the reconttuted phae pace, and the Euler dtance of fnancal data flow dtrbuton track n hgh-dmenonal phae pace meet: () In the formula, ( a mall potve number), thu the relevance mappng vector of fnancal data determned, thu formng a ontology model wrtng matr form of fnancal data parallel mnng: B ( ), ( ),, ( ) Nb () hrough the above phae proceng, phae pace reconttuton method ued to carry out hgh dmenonal feature epanon of fnancal data, provdng the orgnal data nput ba for the abnormal analy of fnancal data... Autocorrelaton feature matchng of fnancal data Baed on hgh-dmenonal feature epanon of fnancal data by ung phae pace reconttuton method, the prncpal component analy method ued for auto-correlaton feature matchng of fnancal data, to mprove the accuracy for mnng of fnancal data anomaly. me-frequency analy method ued to obtan the correlaton dmenon of fnancal data a: fc n ( t) fct ( t) Re{ an ( t) e l ( t n ( t)) e } (3) In the formula, on the tme cale of nformaton flow dtrbuton of fnancal data, multple wavelet are decompoed to get the tranent dturbance of fnancal data a: fc n( t) c(, t) an( t) e ( t n( t)) n (4) a In the equaton above, n () t thee nveron ntegral functon of abnormal feature performed on the nth (6) (7) (5) ( N ) J m 34

4 Revta de la Facultad de Ingenería U.C.V., Vol. 3, N 5, pp , 7 phae pace dtrbuton traectory. n () t f the tme delay of fnancal emprcal amplng. c the amplng () frequency of man feature quantty of fnancal data, and l t the trantonal nformaton of mple component. he grd egmentaton method ued for tranent tate dturbance optmzaton of fnancal data, and the optmal correlaton dtrbuton functon of fnancal data obtaned a follow: P h( t) a p( t ) a In the equaton, and are the mlarty coeffcent and dturbance ampltude repectvely. he relevance mappng of fnancal data carred out n the reconttuted hgh-dmenonal feature pace of fnancal data, and the attrbute charactertc reflectng fnancal data category are etracted, to get the autocorrelaton feature dtrbuton functon of fnancal data a: y( t) ( t t) Wy( t, v) W( t t, v) vt y( t) ( t) e W ( t, v) W ( t, v v ) (5) y (6) hrough the tme-frequency calng, pectral analy technology ued for the abnormal feature cluterng proce of fnancal data. he data cluterng center for the contructon of fnancal data abnormal feature mnng decrbed a: y( t) k ( kt), k W ( t, v) W ( kt, v / k) y Here, k refer to the dturbance ampltude of data cluterng, v epreed a the amplng pectrum W value of fnancal data, the tme wndow functon. Set the ampltude of fnancal data anomaly feature mnng nformaton flow a A N ( ). Man feature quantty k of optmal correlaton data of fnancal data obtaned. he alternatve data analy method ued to get the nformaton clafcaton error of fnancal data: v W ( t, v) ( t / ) ( t / ) e d (9) In the fnte doman, the frequency doman dtrbuton of abnormal charactertc of fnancal data epreed a: W (, ) ( / ) ( / ) t t v X v X v e d () In the above equaton, the attenuaton coeffcent n the cluterng proce of fnancal data abnormal feature, and X the bfurcaton dtance of fnancal data clafcaton vector. X refer to lookng for the conugate of the fnancal data. Hyperbolc frequency modulaton carred out for fnancal data anomaly detecton ytem. Accordng to autocorrelaton r feature matchng technque, the matchng reult of abnormal feature : Q Q pq ak ep () a Here, k recognton coeffcent of fnancal data attrbute. If ak,t mean the kth abnormal data cluterng center tend to zero. If ak,t mean there a dturbance n the cluterng pace, t ndcate that there abnormal data. 3. REALIZAION OF FINANCIAL DAA ABNORMAL MINING 3. Randomzaton and de-redundancy proceng of fnancal data Baed on the nformaton flow reconttuton and autocorrelaton feature matchng of fnancal data performed above for abnormal feature mnng, th paper preent a method to analyze the abnormal fnancal data baed on the correlaton data feature mnng technology. Alternatve data method ued for the randomzaton of fnancal data proceng. Alternatve data method from the modern tattc Boottrap theory, and the realzaton proce decrbed a: yn ( ) () Phae-randomze the orgnal fnancal data to generate a et of Gauan economc equence to obtan a lnearly related Gauan proce; (7) (8) 34

5 Revta de la Facultad de Ingenería U.C.V., Vol. 3, N 5, pp , 7 n ( ) () ake the rank of the orgnal fnancal data a the aocaton rule mappng vector et, for the fnancal data aocaton rule mappng rn y( n ) yrank( n ), n,,, N () In the aocaton rule mappng ytem, the quaternary group of fnancal data attrbute clafcaton contructed; { rn ( )} (3) N n made Fourer tranformaton, and optmal matchng of fnancal data carred out to get a y( n) new equence, and the aocaton rule and autoregreve egenvector are the ame; y( n) (4) Accordng to the rank of, fnancal data rearranged to acheve the randomzaton proceng of fnancal data and generate alternatve equence. he attrbute charactertc reflectng abnormal nformaton of fnancal data are etracted, and elmnaton of redundancy made for the ubttute data k. he redundant vector of the fnancal data : Wy ( t, v) Wh ( t, v) W (, v) d (3) In the equaton, t data amplng tme, correlaton couplng coeffcent of fnancal data. In the data (e) W cluterng center, the elf-adapton weghtng, e of fnancal data clafcaton carred out non-correlaton redundancy removal proce by feature compreon method. K-L tranformaton ued to obtan charactertc compreon reult: ( t) y ( t) dt W( t, v) Wy ( t, v) dtdv (4) p p ( ) ( ) Nk p p Set the error of abnormal nformaton mnng of fnancal data a. he nonlnear abnormal feature mnng ued to get the non-correlaton redundancy blnd ource eparaton reult of the fnancal data a: vw ( t, v) dv f () t W ( t, v) dv (5) In the equaton, the number of man component reflectng abnormal nformaton n the fnancal data nformaton flow. 3.. Analy of abnormal charactertc of fnancal data In the nonlnear economc equence of fnancal data, hgh-dmenonal multvarate tattcal analy method ued to map the fnancal data. Aume that the fnancal data generated by the lnearly related nonlnear economc equence, and the followng ARMA model ued: MAR MMA a a b n n n a In the equaton, the amplng ampltude of ntal fnancal data, n the fnancal data calar b economc equence wth the ame mean, varance, the ocllaton ampltude of fnancal data. hrd-order autocorrelaton tattc ued a the npecton tattcal data of fnancal data abnormal analy: C n nd nd n or 3 3 In the equaton, n refer to non-lnear economc equence of fnancal data, d refer to the tme nterval for amplng fnancal data, D d, repreent the mean, n ( ) repreent takng the mean of the n ( ) fnancal data ample : N ( n) / N ( n) n (8) he traectory of non-lnear economc equence and vector feature economc equence of fnancal data n { ( t the hgh-dmenonal phae pace t)},,,, N. Decon tree model ued for the anomaly (6) (7) 34

6 Revta de la Facultad de Ingenería U.C.V., Vol. 3, N 5, pp , 7 of fnancal data. he bac dea to meaure the tme-varyng charactertc of economc data and randomne charactertc and n C( ). hrough the lnear correlaton proceng, the average mutual nformaton of fnancal data defned a: C( ) lm ( t) ( t ) d (9) In the equaton, the tme delay of fnancal data n the reconttuted phae pace, repreentng the correlaton degree of fnancal data change at the tme of t and t. he abnormal behavor trend and abnormal feature of fnancal data are mned baed on the correlaton degree to get the correlaton dmenon nformaton n vector pace of fnancal data economc equence a: p ( ) I( ) p ( )ln pp (3) X A pont n the economc equence phae pace of reconttuted fnancal data epreed a n, and the ( n) nearet neghbor pont n the et of tochatc phae of fnancal data epreed a, thu the economc equence eported by fnancal data anomaly feature mnng : X ( n) { ( n), ( n ),, ( n ( m ) )} (3) Fnally, Sgma tet method ued to contruct the tet crteron of abnormal feature mnng of fnancal data. Accordng to the gnfcant dfference of dcrmnant tattc, the accuracy of abnormal feature mnng of fnancal data teted. he tet crtera : Q Q pq ep (3) In the equaton, ~ dq p Q (33) p Q Q curve hown a fgure. X Fgure. ~ p Q Q curve dtrbuton of tet crteron Accordng to Fgure, fnancal data abnormal feature mnng dtrbuton meet the tandard normal Q Q dtrbuton, and f the dfference between and Q eceed a certan threhold c, makng: p Q Q Qc.5 (34) At th pont the confdence of fnancal data mnng 95%, becaue the normal dtrbuton ymmetrcal Q on both de of, there hould be: 343

7 Revta de la Facultad de Ingenería U.C.V., Vol. 3, N 5, pp , 7 z z p Q dq p Q dq (35).5 z In the equaton, z, when S., the abnormal dtrbuton of fnancal data not etablhed wth 95% probablty, acceptng the orgnal hypothe, the fnancal data anomaly mnng reult meet the convergence condton. 4. SIMULAION EXPERIMEN AND RESUL ANALYSIS In order to tet the performance of th method n the realzaton of fnancal data anomaly analy, mulaton eperment and emprcal data analy are carred out. he hardware envronment of mulaton eperment PC. he confguraton parameter are CPU 3. G, Core (M) CPU 66, G nternal torage. he emprcal data analy oftware nclude Ecel 7 and SPSS9.. he related parameter of fnancal data tet tattc are: Q c =, =3, c =, c r =,.,.,. 8. he fnancal data from a large group, and the tattcal tme from January to Aprl 7. he mlarty correlaton coeffcent of fnancal data =. he abnormal feature amplng rate of fnancal data f * fhz KHz, and the frequency band of data dtrbuton 4 ~ 5 KHz. Accordng to the F tet and correlaton analy of fnancal data carred out wth Hauman tet, the correlaton coeffcent hown n able. able. Analy Reult of Fnancal Data Correlaton coeffcent Accordng to the pror tattcal reult of fnancal data ample, the tme-doman waveform of the ample of 4 group of fnancal data are hown n Fg Fgure. Fnancal data ample he abnormal feature mnng carred out for the ampled fnancal data. he correlaton dmenon feature of fnancal data mned to get the abnormal feature mnng reult of fnancal data, a hown n Fgure

8 Revta de la Facultad de Ingenería U.C.V., Vol. 3, N 5, pp , 7 Fgure 3. Abnormal feature mnng of fnancal data After analyzng the reult of Fgure 3, t how that the method n th paper for the aocated nformaton mnng of fnancal data anomaly charactertc ha obvou beam drectvty, ndcatng that the ant-nterference ablty obvou. In order to compare performance, the method n th paper and tradtonal method are ued to analyze the accuracy of fnancal data anomaly feature mnng. he reult are hown n Fgure 4. he analy how that the method n th paper for fnancal data anomaly mnng more accurate. It mprove the rght mnng ablty for the abnormal data mnng. Fgure 4. Comparon of abnormal feature mnng accuracy of fnancal data 5. CONCLUSION In th paper, fnancal data anomaly analy method tuded. A method of fnancal data anomaly analy baed on correlaton data feature mnng technology propoed. he phae pace reconttuton method ued to carry out hgh dmenonal feature. he prncpal component analy method ued for correlated feature matchng, combned wth alternatve data method for randomzaton of fnancal data. Feature compreon method ued for non-correlaton redundancy removal proceng. Hgh-dmenonal multvarate tattcal analy method ued for correlaton mappng of fnancal data. he correlaton dmenon nformaton reflectng the fnancal data abnormal feature etracted to contruct dcrmnaton tattc and tet crteron. he abnormal feature of fnancal data mned accordng to the gnfcant dfference of dcrmnaton tattc to realze anomaly analy of fnancal data. he reult how that the accuracy of fnancal data anomaly mnng better than that of fnancal data, and the accuracy of fnancal data analyzed. he reult how that the accuracy of fnancal data anomaly mnng wth th method better. It ha good applcaton value n the fnancal audt and economc nvetgaton and other feld. Reference Yao Le, Yao Wangn. (6). Study on the Polcy Effect of Securte Margn radng - Baed on Multphae DID Model and Hauman et. Journal of Internatonal Fnancal Reearch, 349 (5),

9 Revta de la Facultad de Ingenería U.C.V., Vol. 3, N 5, pp , 7 Pan Jzheng. (7). he Improvement of Accountng Method Baed on Large Reearch Proect of Evolutonary Game. Management Engneer, (),36-4. aguch,h.,sahoo,p., Natara,G. (5). Captal Flow and Aet Prce:Emprcal Evdence from Emergng and Developng Econome. Internatonal Economc, 4(5),-4 SHEN L, SUN G, HUANG Q, et al. (5). Mult-level dcrmnatve dctonary learnng wth applcaton to large cale mage clafcaton, IEEE ranacton on Image Proceng, 4(), HIAGARAJAN J J, RAMAMURHY K N, and SPANIAS A. (5). Learnng table multlevel dctonare for pace repreentaton, IEEE ranacton on Neural Network & Learnng Sytem, 6(9), Ba Xuee, Sun Hongyn, Wang Hafeng. (6). M&A Behavor and Market Power: Baed on the Analy of Chna' A-hare Compane. Journal of Contemporary Economc Scence, 6(3),6-3. Luo La. (6). Study on the Detecton Method of Abnormal Data n Laer Communcaton Network,Laer Journal, 37 (), Lang Conggang, Wang Hongzhang. (6). Optmzaton Study on Dfferental Evoluton Algorthm and It Applcaton n Cluterng Analy. Journal of Modern Electronc echnology, 33 (3),3-7. L Nan, Sh Wehang. (4). Web Databae Securty Inde Baed on Mult-Layer Spatal Fuzzy Subtracton Cluterng Algorthm. Computer Scence, (),6-9. L Shuang, L Wenng, Sun Huanlong, Ln Zhongmng. (3). he Artfcal Fh Parallel Algorthm Baed on Multcore Machne. Journal of Computer Applcaton, 33 (),