Value-added tax fraud detection with scalable anomaly detection techniques
The tax fraud detection domain is characterized by very few labelled data (known fraud/legal cases) that are not representative for the population due to sample selection bias. We use unsupervised anomaly detection (AD) techniques, which are uncommon in tax fraud detection research, to deal with the...
Gespeichert in:
| Veröffentlicht in: | Applied soft computing Jg. 86; S. 105895 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier B.V
01.01.2020
|
| Schlagworte: | |
| ISSN: | 1568-4946, 1872-9681 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | The tax fraud detection domain is characterized by very few labelled data (known fraud/legal cases) that are not representative for the population due to sample selection bias. We use unsupervised anomaly detection (AD) techniques, which are uncommon in tax fraud detection research, to deal with these domain issues. We analyse a unique dataset containing the VAT declarations and client listings of all Belgian VAT numbers pertaining to ten sectors. Our methodology consists in applying AD methods to firms belonging to the same sector and enables an efficient auditing strategy that can be adopted by tax authorities worldwide. The high lifts and hit rates observed in most sectors demonstrate the success of this approach. Sectoral differences exist due to varying market conditions and legal requirements across sectors and we show that the optimal AD method is sector dependent. We focus on three methodological problems that show issues in the related literature. (1) Can we design suitable input features? We develop new fraud indicators from specific fields of the VAT form and client listings and show the predictive value of the combination of these features. (2) Can we design fast algorithms to deal with the large data sizes that can occur in the tax domain? New methods are developed and we demonstrate their scalability both theoretically as well as empirically. (3) How should fraud detection performance be assessed? A new evaluation methodology is proposed that provides reliable performance indications and guarantees that fraud cases are effectively detected by the proposed methods.
•Unsupervised anomaly detection shows high predictive power for VAT fraud detection.•Individual sector analysis reveals sectoral differences.•New value-added tax fraud indicators are proposed that successfully detect fraud.•Fast anomaly detection algorithms are developed and their scalability is shown.•Suitable evaluation methodology that guarantees fraudsters are effectively detected. |
|---|---|
| AbstractList | The tax fraud detection domain is characterized by very few labelled data (known fraud/legal cases) that are not representative for the population due to sample selection bias. We use unsupervised anomaly detection (AD) techniques, which are uncommon in tax fraud detection research, to deal with these domain issues. We analyse a unique dataset containing the VAT declarations and client listings of all Belgian VAT numbers pertaining to ten sectors. Our methodology consists in applying AD methods to firms belonging to the same sector and enables an efficient auditing strategy that can be adopted by tax authorities worldwide. The high lifts and hit rates observed in most sectors demonstrate the success of this approach. Sectoral differences exist due to varying market conditions and legal requirements across sectors and we show that the optimal AD method is sector dependent. We focus on three methodological problems that show issues in the related literature. (1) Can we design suitable input features? We develop new fraud indicators from specific fields of the VAT form and client listings and show the predictive value of the combination of these features. (2) Can we design fast algorithms to deal with the large data sizes that can occur in the tax domain? New methods are developed and we demonstrate their scalability both theoretically as well as empirically. (3) How should fraud detection performance be assessed? A new evaluation methodology is proposed that provides reliable performance indications and guarantees that fraud cases are effectively detected by the proposed methods.
•Unsupervised anomaly detection shows high predictive power for VAT fraud detection.•Individual sector analysis reveals sectoral differences.•New value-added tax fraud indicators are proposed that successfully detect fraud.•Fast anomaly detection algorithms are developed and their scalability is shown.•Suitable evaluation methodology that guarantees fraudsters are effectively detected. |
| ArticleNumber | 105895 |
| Author | Martens, David Vanhoeyveld, Jellis Peeters, Bruno |
| Author_xml | – sequence: 1 givenname: Jellis orcidid: 0000-0002-1975-0093 surname: Vanhoeyveld fullname: Vanhoeyveld, Jellis email: jellis.vanhoeyveld@uantwerpen.be organization: Faculty of Applied Economics, University of Antwerp, Prinsstraat 13, 2000 Antwerp, Belgium – sequence: 2 givenname: David surname: Martens fullname: Martens, David email: david.martens@uantwerpen.be organization: Faculty of Applied Economics, University of Antwerp, Prinsstraat 13, 2000 Antwerp, Belgium – sequence: 3 givenname: Bruno surname: Peeters fullname: Peeters, Bruno email: bruno.peeters@uantwerpen.be organization: Faculty of Law, University of Antwerp, Venusstraat 23, 2000 Antwerp, Belgium |
| BookMark | eNp9kE1LAzEQhoNUsK3-AU_7B7Ym2WY3AS9S_KTgRb2GaWaWpmx3Ndmq_fdmqQfx0NM7vMMzMM-EjdquJcYuBZ8JLsqrzQxi52aSC5MKpY06YWOhK5mbUotRmlWp87mZl2dsEuOGJ8hIPWZPb9DsKAdEwqyH76wOsMMMqSfX-67Nvny_zqKDBlYNZdB2W2j2f_Yp163_2FE8Z6c1NJEufnPKXu9uXxYP-fL5_nFxs8xdwXmfi8KVFUmlV84gVVVdgOMaOCpVokRFRW0cYg2qIi5TYYyQJhWmwJUUVTFl8nDXhS7GQLV9D34LYW8Ft4MNu7GDDTvYsAcbCdL_IOd7GD7oA_jmOHp9QCk99ekp2Og8tY7QhyTBYueP4T-MRX6P |
| CitedBy_id | crossref_primary_10_1016_j_eswa_2024_125216 crossref_primary_10_1080_16081625_2024_2356106 crossref_primary_10_1109_ACCESS_2023_3276761 crossref_primary_10_1057_s41599_024_03606_0 crossref_primary_10_1109_ACCESS_2020_3048018 crossref_primary_10_1016_j_asoc_2021_108160 crossref_primary_10_1080_08839514_2021_2012002 crossref_primary_10_1080_2573234X_2024_2438195 crossref_primary_10_1016_j_eswa_2021_116409 crossref_primary_10_1016_j_eswa_2024_126221 crossref_primary_10_1016_j_eswa_2023_121161 crossref_primary_10_3390_app13042707 crossref_primary_10_3390_risks12050074 crossref_primary_10_1007_s10614_024_10791_2 crossref_primary_10_1007_s13369_021_06116_2 crossref_primary_10_1109_ACCESS_2024_3471081 crossref_primary_10_3390_informatics7040050 crossref_primary_10_1007_s10618_023_00930_y crossref_primary_10_3390_app13095244 crossref_primary_10_1109_ACCESS_2025_3572741 crossref_primary_10_3390_su13158363 crossref_primary_10_1016_j_ins_2020_12_050 crossref_primary_10_1109_ACCESS_2022_3211528 crossref_primary_10_1109_ACCESS_2022_3221427 crossref_primary_10_1016_j_asoc_2021_107460 crossref_primary_10_1016_j_ins_2021_08_097 crossref_primary_10_3390_su16208866 |
| Cites_doi | 10.1016/j.cose.2015.09.005 10.3233/IDA-2006-10604 10.1214/18-EJS1474 10.1109/ICMLC.2002.1167400 10.1109/TKDE.2003.1232271 10.1057/rm.2014.2 10.17485/ijst/2015/v8i35/87306 10.1016/j.eswa.2014.02.026 10.1007/s10618-017-0517-y 10.1145/1541880.1541882 10.1214/ss/1042727940 10.1016/j.dss.2010.11.006 10.1007/s10618-012-0300-z 10.1007/s10618-014-0365-y 10.1057/palgrave.jors.2601920 10.1016/j.eswa.2012.01.204 10.1109/TKDE.2008.239 10.1007/s10618-015-0444-8 10.1016/j.ins.2011.08.011 10.1016/j.patcog.2017.09.037 10.1371/journal.pone.0152173 10.25300/MISQ/2014/38.1.04 10.1145/2523813 10.1109/TNNLS.2017.2736643 10.1016/j.eswa.2012.08.051 10.2307/3001968 10.1016/j.patrec.2005.10.010 10.1016/j.dss.2015.04.013 10.1016/j.dss.2010.08.006 |
| ContentType | Journal Article |
| Copyright | 2019 Elsevier B.V. |
| Copyright_xml | – notice: 2019 Elsevier B.V. |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.asoc.2019.105895 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1872-9681 |
| ExternalDocumentID | 10_1016_j_asoc_2019_105895 S1568494619306763 |
| GroupedDBID | --K --M .DC .~1 0R~ 1B1 1~. 1~5 23M 4.4 457 4G. 53G 5GY 5VS 6J9 7-5 71M 8P~ AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AAXUO AAYFN ABBOA ABFNM ABFRF ABJNI ABMAC ABXDB ABYKQ ACDAQ ACGFO ACGFS ACNNM ACRLP ACZNC ADBBV ADEZE ADJOM ADMUD ADTZH AEBSH AECPX AEFWE AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CS3 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ HVGLF HZ~ IHE J1W JJJVA KOM M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SDF SDG SES SEW SPC SPCBC SST SSV SSZ T5K UHS UNMZH ~G- 9DU AATTM AAXKI AAYWO AAYXX ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD |
| ID | FETCH-LOGICAL-c300t-13c67e258bc9de77f3ac08a0d556d2d5e3f9cddfa57e022d599129ddf93db2173 |
| ISICitedReferencesCount | 41 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000503388200068&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1568-4946 |
| IngestDate | Sat Nov 29 07:01:51 EST 2025 Tue Nov 18 22:22:14 EST 2025 Fri Feb 23 02:49:32 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Tax fraud detection Scalable algorithms Unsupervised anomaly detection |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c300t-13c67e258bc9de77f3ac08a0d556d2d5e3f9cddfa57e022d599129ddf93db2173 |
| ORCID | 0000-0002-1975-0093 |
| ParticipantIDs | crossref_primary_10_1016_j_asoc_2019_105895 crossref_citationtrail_10_1016_j_asoc_2019_105895 elsevier_sciencedirect_doi_10_1016_j_asoc_2019_105895 |
| PublicationCentury | 2000 |
| PublicationDate | January 2020 2020-01-00 |
| PublicationDateYYYYMMDD | 2020-01-01 |
| PublicationDate_xml | – month: 01 year: 2020 text: January 2020 |
| PublicationDecade | 2020 |
| PublicationTitle | Applied soft computing |
| PublicationYear | 2020 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Assylbekov, Melnykov, Bekishev, Baltabayeva, Bissengaliyeva, Mamlin (b30) 2016 Provost, Fawcett (b40) 2013 Couturier, Peeters, Van De Velde (b2) 2017 He, Garcia (b37) 2009; 21 Kollios, Gunopulos, Koudas, Berchtold (b57) 2003; 15 Campos, Zimek, Sander, Campello, Micenková, Schubert, Assent, Houle (b52) 2016; 30 (b9) 2012 European Commission (b12) 2017 Bolton, Hand (b35) 2002; 17 Branco, Torgo, Ribeiro (b38) 2016; 49 (b22) 2018 Eurostat (b11) 2016 Kumar, Nagadevara (b43) 2006 (b8) 2010 Chan, Mahoney, Arshad (b70) 2003 Mehta, Mathews, Kasi Visweswara Rao, Kumar, Suryamukhi, Babu (b31) 2019 Song, Takakura, Okabe, Nakao (b60) 2013; 231 Schneider (b4) 2015; 3 Fawcett (b65) 2006; 27 M. Gupta, V. Nagadevara, Autit selection strategy for improvig tax compliance - application of data mining techniques, in: Foundations of Risk-Based Audits. Proceedings of the Eleventh International Conference on E-Governance, Hyderabad, India, December, 2007, 28–30. (b5) 2017 Wu, Jermaine (b55) 2006 Chandola, Banerjee, Kumar (b32) 2009; 41 Eskin, Arnold, Prerau, Portnoy, Stolfo (b36) 2002 . H. Shao, H. Zhao, G.-R. Chang, Applying data mining to detect fraud behavior in customs declaration, in: Proceedings. International Conference on Machine Learning and Cybernetics, vol. 3, 2002, pp. 1241–1244 Govers, Deschacht (b68) 2017 Vanhoeyveld, Martens, Peeters (b1) 2016 Roussinov, Chen (b46) 1998; 15 Ghafoori, Rajasegarar, Erfani, Karunasekera, Leckie (b61) 2016 Ngai, Hu, Wong, Chen, Sun (b23) 2011; 50 Han, Ireland (b44) 2014; 16 Demšar (b64) 2006; 7 Breunig, Kriegel, Ng, Sander (b71) 2000 Basta, Fassetti, Guarascio, Manco, Giannotti, Pedreschi, Spinsanti, Papi, Pisani (b18) 2009 Rad, Arash, Rahbar, Rahmani, Heshmati, Fard (b48) 2015; 8 Zimek, Gaudet, Campello, Sander (b53) 2013 Akoglu, Tong, Koutra (b73) 2015; 29 presented at ICML2016 Anomaly Detection Workshop, New York, NY, USA, 2016. Agyemang, Barker, Alhajj (b49) 2006; 10 Phua, Lee, Smith-Miles, Gayler (b21) 2010 Gama, Žliobaitė, Bifet, Pechenizkiy, Bouchachia (b20) 2014; 46 (b67) 2016 L. Portnoy, E. Eskin, S. Stolfo, Intrusion detection with unlabeled data using clustering, in: Proceedings of ACM CSS Workshop on Data Mining Applied to Security (DMSA-2001), 2001, pp. 5–8. Tang, Mendis, Murray, Hu, Sutinen (b51) 2011 Castellón González, Velásquez (b17) 2013; 40 Martens, Provost (b74) 2014; 38 Goldstein, Uchida (b33) 2016; 11 Bekkar, Djemaa, Alitouche (b72) 2013; 3 (b10) 2016 West, Bhattacharya (b24) 2016; 57 Pozzolo, Boracchi, Caelen, Alippi, Bontempi (b19) 2018; 29 Matos, de Macedo, Monteiro (b29) 2014 Mittal, Reich, Mahajan (b13) 2018 (b7) 2011 Pozzolo, Caelen, Borgne, Waterschoot, Bontempi (b26) 2014; 41 Krempl, Hofer (b27) 2011 Junqué de Fortuny, Stankova, Moeyersoms, Minnaert, Provost, Martens (b3) 2014 Vanhoeyveld, Martens (b39) 2018; 32 R.J. Bolton, D.J. Hand, Unsupervised profiling methods for fraud detection, in: Proc. Credit Scoring and Credit Control VII, 2001, pp. 235–255. Van Vlasselaer, Bravo, Caelen, Eliassi-Rad, Akoglu, Snoeck, Baesens (b25) 2015; 75 Schubert, Zimek, Kriegel (b54) 2014; 28 (b6) 2017 Karlos, Fazakis, Kotsiantis, Sgarbas (b42) 2016 Verstraeten, Van den Poel (b14) 2005; 56 Bonchi, Giannotti, Mainetto, Pedreschi (b15) 1999 A. Thomas, S. Clémençon, V. Feuillard, G. Alexandre, learning hyperparameters for unsupervised anomaly detection, URL de Roux, Perez, Moreno, Villamil, Figueroa (b34) 2018 Domingues, Filippone, Michiardi, Zouaoui (b62) 2018; 74 Aggarwal (b75) 2017 Wu, Ou, Lin, Chang, Yen (b28) 2012; 39 Clémençon, Thomas (b47) 2018; 12 Vanhoeyveld, Martens (b56) 2018 Ravisankar, Ravi, Raghava Rao, Bose (b41) 2011; 50 Wilcoxon (b63) 1945; 1 (b66) 2013 Marques, Campello, Zimek, Sander (b59) 2015 Aggarwal (10.1016/j.asoc.2019.105895_b75) 2017 Vanhoeyveld (10.1016/j.asoc.2019.105895_b1) 2016 Breunig (10.1016/j.asoc.2019.105895_b71) 2000 Eurostat (10.1016/j.asoc.2019.105895_b11) 2016 Krempl (10.1016/j.asoc.2019.105895_b27) 2011 Wu (10.1016/j.asoc.2019.105895_b28) 2012; 39 10.1016/j.asoc.2019.105895_b69 Song (10.1016/j.asoc.2019.105895_b60) 2013; 231 Campos (10.1016/j.asoc.2019.105895_b52) 2016; 30 Vanhoeyveld (10.1016/j.asoc.2019.105895_b39) 2018; 32 West (10.1016/j.asoc.2019.105895_b24) 2016; 57 Bolton (10.1016/j.asoc.2019.105895_b35) 2002; 17 Ghafoori (10.1016/j.asoc.2019.105895_b61) 2016 Basta (10.1016/j.asoc.2019.105895_b18) 2009 Roussinov (10.1016/j.asoc.2019.105895_b46) 1998; 15 Zimek (10.1016/j.asoc.2019.105895_b53) 2013 (10.1016/j.asoc.2019.105895_b9) 2012 Assylbekov (10.1016/j.asoc.2019.105895_b30) 2016 Phua (10.1016/j.asoc.2019.105895_b21) 2010 Provost (10.1016/j.asoc.2019.105895_b40) 2013 Branco (10.1016/j.asoc.2019.105895_b38) 2016; 49 Matos (10.1016/j.asoc.2019.105895_b29) 2014 Kollios (10.1016/j.asoc.2019.105895_b57) 2003; 15 Verstraeten (10.1016/j.asoc.2019.105895_b14) 2005; 56 Chan (10.1016/j.asoc.2019.105895_b70) 2003 Pozzolo (10.1016/j.asoc.2019.105895_b19) 2018; 29 Clémençon (10.1016/j.asoc.2019.105895_b47) 2018; 12 Karlos (10.1016/j.asoc.2019.105895_b42) 2016 Akoglu (10.1016/j.asoc.2019.105895_b73) 2015; 29 Fawcett (10.1016/j.asoc.2019.105895_b65) 2006; 27 (10.1016/j.asoc.2019.105895_b67) 2016 Govers (10.1016/j.asoc.2019.105895_b68) 2017 (10.1016/j.asoc.2019.105895_b8) 2010 Goldstein (10.1016/j.asoc.2019.105895_b33) 2016; 11 Pozzolo (10.1016/j.asoc.2019.105895_b26) 2014; 41 Gama (10.1016/j.asoc.2019.105895_b20) 2014; 46 Mehta (10.1016/j.asoc.2019.105895_b31) 2019 Martens (10.1016/j.asoc.2019.105895_b74) 2014; 38 Van Vlasselaer (10.1016/j.asoc.2019.105895_b25) 2015; 75 (10.1016/j.asoc.2019.105895_b5) 2017 (10.1016/j.asoc.2019.105895_b7) 2011 Ngai (10.1016/j.asoc.2019.105895_b23) 2011; 50 Mittal (10.1016/j.asoc.2019.105895_b13) 2018 Marques (10.1016/j.asoc.2019.105895_b59) 2015 (10.1016/j.asoc.2019.105895_b66) 2013 Wilcoxon (10.1016/j.asoc.2019.105895_b63) 1945; 1 10.1016/j.asoc.2019.105895_b45 Bonchi (10.1016/j.asoc.2019.105895_b15) 1999 Chandola (10.1016/j.asoc.2019.105895_b32) 2009; 41 10.1016/j.asoc.2019.105895_b50 Wu (10.1016/j.asoc.2019.105895_b55) 2006 Eskin (10.1016/j.asoc.2019.105895_b36) 2002 Domingues (10.1016/j.asoc.2019.105895_b62) 2018; 74 Ravisankar (10.1016/j.asoc.2019.105895_b41) 2011; 50 Schneider (10.1016/j.asoc.2019.105895_b4) 2015; 3 Kumar (10.1016/j.asoc.2019.105895_b43) 2006 (10.1016/j.asoc.2019.105895_b10) 2016 He (10.1016/j.asoc.2019.105895_b37) 2009; 21 Han (10.1016/j.asoc.2019.105895_b44) 2014; 16 Castellón González (10.1016/j.asoc.2019.105895_b17) 2013; 40 Vanhoeyveld (10.1016/j.asoc.2019.105895_b56) 2018 de Roux (10.1016/j.asoc.2019.105895_b34) 2018 Tang (10.1016/j.asoc.2019.105895_b51) 2011 Bekkar (10.1016/j.asoc.2019.105895_b72) 2013; 3 (10.1016/j.asoc.2019.105895_b6) 2017 10.1016/j.asoc.2019.105895_b58 10.1016/j.asoc.2019.105895_b16 Couturier (10.1016/j.asoc.2019.105895_b2) 2017 Schubert (10.1016/j.asoc.2019.105895_b54) 2014; 28 (10.1016/j.asoc.2019.105895_b22) 2018 Junqué de Fortuny (10.1016/j.asoc.2019.105895_b3) 2014 European Commission (10.1016/j.asoc.2019.105895_b12) 2017 Rad (10.1016/j.asoc.2019.105895_b48) 2015; 8 Demšar (10.1016/j.asoc.2019.105895_b64) 2006; 7 Agyemang (10.1016/j.asoc.2019.105895_b49) 2006; 10 |
| References_xml | – reference: L. Portnoy, E. Eskin, S. Stolfo, Intrusion detection with unlabeled data using clustering, in: Proceedings of ACM CSS Workshop on Data Mining Applied to Security (DMSA-2001), 2001, pp. 5–8. – year: 2017 ident: b2 article-title: Belgisch Belastingrecht in Hoofdlijnen, Vol. 22 – volume: 57 start-page: 47 year: 2016 end-page: 66 ident: b24 article-title: Intelligent financial fraud detection: A comprehensive review publication-title: Comput. ‘I&’ Secur. – volume: 21 start-page: 1263 year: 2009 end-page: 1284 ident: b37 article-title: Learning from imbalanced data publication-title: IEEE Trans. Knowl. Data Eng. – year: 2018 ident: b56 article-title: Towards a scalable anomaly detection with pseudo-optimal hyperparameters – reference: presented at ICML2016 Anomaly Detection Workshop, New York, NY, USA, 2016. – year: 2010 ident: b21 article-title: A comprehensive survey of data mining-based fraud detection research – start-page: 596 year: 2011 end-page: 603 ident: b27 article-title: Classification in presence of drift and latency publication-title: 2011 IEEE 11th International Conference on Data Mining Workshops – year: 2010 ident: b8 article-title: Council regulation (EU) no 904/2010 of 7 October 2010 on administrative cooperation and combating fraud in the field of value added tax – reference: R.J. Bolton, D.J. Hand, Unsupervised profiling methods for fraud detection, in: Proc. Credit Scoring and Credit Control VII, 2001, pp. 235–255. – volume: 11 start-page: 1 year: 2016 end-page: 31 ident: b33 article-title: A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data publication-title: PLOS ONE – start-page: 149 year: 2017 end-page: 184 ident: b75 article-title: High-dimensional outlier detection: The subspace method publication-title: Outlier Analysis – volume: 8 year: 2015 ident: b48 article-title: A novel unsupervised classification method for customs fraud detection publication-title: Indian J. Sci. Technol. – volume: 50 start-page: 559 year: 2011 end-page: 569 ident: b23 article-title: The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature publication-title: Decis. Support Syst. – start-page: 584 year: 2006 end-page: 591 ident: b43 article-title: Development of hybrid classification methodology for mining skewed data sets - a case study of indian customs data publication-title: IEEE International Conference on Computer Systems and Applications, 2006. – volume: 231 start-page: 4 year: 2013 end-page: 14 ident: b60 article-title: Toward a more practical unsupervised anomaly detection system publication-title: Inform. Sci. – volume: 12 start-page: 2806 year: 2018 end-page: 2872 ident: b47 article-title: Mass volume curves and anomaly ranking publication-title: Electron. J. Statist. – reference: H. Shao, H. Zhao, G.-R. Chang, Applying data mining to detect fraud behavior in customs declaration, in: Proceedings. International Conference on Machine Learning and Cybernetics, vol. 3, 2002, pp. 1241–1244, – start-page: 24:1 year: 2018 end-page: 24:11 ident: b13 article-title: Who is bogus?: Using one-sided labels to identify fraudulent firms from tax returns publication-title: Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies – reference: M. Gupta, V. Nagadevara, Autit selection strategy for improvig tax compliance - application of data mining techniques, in: Foundations of Risk-Based Audits. Proceedings of the Eleventh International Conference on E-Governance, Hyderabad, India, December, 2007, 28–30. – start-page: 103 year: 2011 end-page: 110 ident: b51 article-title: Unsupervised fraud detection in medicare Australia publication-title: Proceedings of the Ninth Australasian Data Mining Conference - Volume 121 – start-page: 37 year: 2016 end-page: 49 ident: b30 article-title: Detecting value-added tax evasion by business entities of Kazakhstan publication-title: Intelligent Decision Technologies 2016: Proceedings of the 8th KES International Conference on Intelligent Decision Technologies (KES-IDT 2016) – Part I – volume: 56 start-page: 981 year: 2005 end-page: 992 ident: b14 article-title: The impact of sample bias on consumer credit scoring performance and profitability publication-title: J. Oper. Res. Soc. – volume: 29 start-page: 626 year: 2015 end-page: 688 ident: b73 article-title: Graph based anomaly detection and description: a survey publication-title: Data Min. Knowl. Discov. – volume: 40 start-page: 1427 year: 2013 end-page: 1436 ident: b17 article-title: Characterization and detection of taxpayers with false invoices using data mining techniques publication-title: Expert Syst. Appl. – volume: 41 start-page: 4915 year: 2014 end-page: 4928 ident: b26 article-title: Learned lessons in credit card fraud detection from a practitioner perspective publication-title: Expert Syst. Appl. – start-page: 369 year: 1999 end-page: 376 ident: b15 article-title: Using data mining techniques in fiscal fraud detection publication-title: DataWarehousing and Knowledge Discovery: First International Conference, DaWaK’99 Florence, Italy, August 30 – September 1, 1999 Proceedings – year: 2017 ident: b68 article-title: Btw-praktijkboek 2017 – year: 2017 ident: b5 – volume: 28 start-page: 190 year: 2014 end-page: 237 ident: b54 article-title: Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection publication-title: Data Min. Knowl. Discov. – reference: A. Thomas, S. Clémençon, V. Feuillard, G. Alexandre, learning hyperparameters for unsupervised anomaly detection, URL – volume: 50 start-page: 491 year: 2011 end-page: 500 ident: b41 article-title: Detection of financial statement fraud and feature selection using data mining techniques publication-title: Decis. Support Syst. – volume: 7 start-page: 1 year: 2006 end-page: 30 ident: b64 article-title: Statistical comparisons of classifiers over multiple data sets publication-title: J. Mach. Learn. Res. – year: 2016 ident: b67 article-title: Periodic VAT declaration form (document 625) – start-page: 312 year: 2019 end-page: 316 ident: b31 article-title: Identifying malicious dealers in goods and services tax publication-title: 2019 IEEE 4th International Conference on Big Data Analytics (ICBDA) – year: 2013 ident: b40 article-title: Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking – start-page: 77 year: 2002 end-page: 101 ident: b36 article-title: A geometric framework for unsupervised anomaly detection publication-title: Applications of Data Mining in Computer Security – year: 2018 ident: b22 article-title: Listed domestic companies, total. World Federation of Exchanges database – volume: 17 start-page: 235 year: 2002 end-page: 255 ident: b35 article-title: Statistical fraud detection: A review publication-title: Statist. Sci. – volume: 38 start-page: 73 year: 2014 end-page: 100 ident: b74 article-title: Explaining data-driven document classifications publication-title: MIS Q. – volume: 16 start-page: 25 year: 2014 end-page: 43 ident: b44 article-title: Performance measurement of the kcs customs selectivity system publication-title: Risk Manage. – year: 2011 ident: b7 article-title: Council directive 2011/16/EU of 15 February 2011 on administrative cooperation in the field of taxation and repealing directive 77/799/eec – start-page: 167 year: 2016 end-page: 212 ident: b1 article-title: Datamining voor fraudedetectie publication-title: Criminele Organisaties en Organisatiecriminaliteit – year: 2016 ident: b11 article-title: Tax revenue statistics – volume: 32 start-page: 25 year: 2018 end-page: 82 ident: b39 article-title: Imbalanced classification in sparse and large behaviour datasets publication-title: Data Min. Knowl. Discov. – start-page: 215 year: 2018 end-page: 222 ident: b34 article-title: Tax fraud detection for under-reporting declarations using an unsupervised machine learning approach publication-title: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining – volume: 75 start-page: 38 year: 2015 end-page: 48 ident: b25 article-title: Apate: A novel approach for automated credit card transaction fraud detection using network-based extensions publication-title: Decis. Support Syst. – start-page: 428 year: 2013 end-page: 436 ident: b53 article-title: Subsampling for efficient and effective unsupervised outlier detection ensembles publication-title: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – start-page: 1650 year: 2014 end-page: 1659 ident: b3 article-title: Corporate residence fraud detection publication-title: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – volume: 74 start-page: 406 year: 2018 end-page: 421 ident: b62 article-title: A comparative evaluation of outlier detection algorithms publication-title: Pattern Recognit. – volume: 1 start-page: 80 year: 1945 end-page: 83 ident: b63 article-title: Individual comparisons by ranking methods publication-title: Biom. Bull. – start-page: 7 year: 2009 end-page: 12 ident: b18 article-title: High quality true-positive prediction for fiscal fraud detection publication-title: 2009 IEEE International Conference on Data Mining Workshops – volume: 41 start-page: 15:1 year: 2009 end-page: 15:58 ident: b32 article-title: Anomaly detection: A survey publication-title: ACM Comput. Surv. – volume: 27 start-page: 861 year: 2006 end-page: 874 ident: b65 article-title: An introduction to ROC analysis publication-title: Pattern Recognit. Lett. – volume: 3 start-page: 7 year: 2015 end-page: 29 ident: b4 article-title: Size and development of the shadow economy of 31 european and 5 other OECD countries from 2003 to 2014: Different developments publication-title: J. Self-Gov. Manag. Econ. – start-page: 767 year: 2006 end-page: 772 ident: b55 article-title: Outlier detection by sampling with accuracy guarantees publication-title: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – year: 2017 ident: b12 article-title: Report from the commission to the council and the european parliament. Eighth report under article 12 of regulation (EEC, Euratom) nr 1553/89 on VAT collection and control procedures – volume: 15 start-page: 81 year: 1998 end-page: 112 ident: b46 article-title: A scalable self-organizing map algorithm for textual classification: A neural network approach to thesaurus generation publication-title: Commun. Cogn. Artif. Intell. Springer – start-page: 7:1 year: 2015 end-page: 7:12 ident: b59 article-title: On the internal evaluation of unsupervised outlier detection publication-title: Proceedings of the 27th International Conference on Scientific and Statistical Database Management – year: 2012 ident: b9 article-title: An action plan to strengthen the fight against tax fraud and tax evasion – start-page: 34:1 year: 2016 end-page: 34:6 ident: b42 article-title: Semi-supervised forecasting of fraudulent financial statements publication-title: Proceedings of the 20th Pan-Hellenic Conference on Informatics – year: 2017 ident: b6 article-title: Impact assessment. accompanying the document proposal for a council directive. amending directive 2011/16/eu as regards mandatory automatic exchange of information in the field of taxation in relation to reportable cross-border arrangements – reference: . – volume: 15 start-page: 1170 year: 2003 end-page: 1187 ident: b57 article-title: Efficient biased sampling for approximate clustering and outlier detection in large data sets publication-title: IEEE Trans. Knowl. Data Eng. – start-page: 183 year: 2016 end-page: 195 ident: b61 article-title: Unsupervised parameter estimation for one-class support vector machines publication-title: Advances in Knowledge Discovery and Data Mining – year: 2016 ident: b10 article-title: Revenue Statistics 2016 – start-page: 41 year: 2014 end-page: 48 ident: b29 article-title: An empirical method for discovering tax fraudsters: A real Case study of Brazilian fiscal evasion publication-title: Proceedings of the 19th International Database Engineering Nr 38, Applications Symposium – volume: 49 start-page: 31:1 year: 2016 end-page: 31:50 ident: b38 article-title: A survey of predictive modeling on imbalanced domains publication-title: ACM Comput. Surv. – volume: 39 start-page: 8769 year: 2012 end-page: 8777 ident: b28 article-title: Using data mining technique to enhance tax evasion detection performance publication-title: Expert Syst. Appl. – start-page: 93 year: 2000 end-page: 104 ident: b71 article-title: Lof: Identifying density-based local outliers publication-title: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data – volume: 3 start-page: 27 year: 2013 end-page: 38 ident: b72 article-title: Evaluation measures for models assessment over imbalanced data sets publication-title: J. Inf. Eng. Appl. – volume: 46 start-page: 44:1 year: 2014 end-page: 44:37 ident: b20 article-title: A survey on concept drift adaptation publication-title: ACM Comput. Surv. – volume: 29 start-page: 3784 year: 2018 end-page: 3797 ident: b19 article-title: Credit card fraud detection: A realistic modeling and a novel learning strategy publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 10 start-page: 521 year: 2006 end-page: 538 ident: b49 article-title: A comprehensive survey of numeric and symbolic outlier mining techniques publication-title: Intell. Data Anal. – volume: 30 start-page: 891 year: 2016 end-page: 927 ident: b52 article-title: On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study publication-title: Data Min. Knowl. Discov. – year: 2013 ident: b66 article-title: Study On the Feasibility and Impact of a Common EU Standard VAT Return – year: 2003 ident: b70 article-title: A Machine Learning Approach to Anomaly Detection – year: 2018 ident: 10.1016/j.asoc.2019.105895_b56 – volume: 3 start-page: 7 issue: 4 year: 2015 ident: 10.1016/j.asoc.2019.105895_b4 article-title: Size and development of the shadow economy of 31 european and 5 other OECD countries from 2003 to 2014: Different developments publication-title: J. Self-Gov. Manag. Econ. – start-page: 41 year: 2014 ident: 10.1016/j.asoc.2019.105895_b29 article-title: An empirical method for discovering tax fraudsters: A real Case study of Brazilian fiscal evasion – year: 2016 ident: 10.1016/j.asoc.2019.105895_b11 – volume: 57 start-page: 47 issue: Supplement C year: 2016 ident: 10.1016/j.asoc.2019.105895_b24 article-title: Intelligent financial fraud detection: A comprehensive review publication-title: Comput. ‘I&’ Secur. doi: 10.1016/j.cose.2015.09.005 – year: 2017 ident: 10.1016/j.asoc.2019.105895_b68 – start-page: 183 year: 2016 ident: 10.1016/j.asoc.2019.105895_b61 article-title: Unsupervised parameter estimation for one-class support vector machines – year: 2011 ident: 10.1016/j.asoc.2019.105895_b7 – volume: 10 start-page: 521 issue: 6 year: 2006 ident: 10.1016/j.asoc.2019.105895_b49 article-title: A comprehensive survey of numeric and symbolic outlier mining techniques publication-title: Intell. Data Anal. doi: 10.3233/IDA-2006-10604 – year: 2017 ident: 10.1016/j.asoc.2019.105895_b5 – volume: 12 start-page: 2806 issue: 2 year: 2018 ident: 10.1016/j.asoc.2019.105895_b47 article-title: Mass volume curves and anomaly ranking publication-title: Electron. J. Statist. doi: 10.1214/18-EJS1474 – ident: 10.1016/j.asoc.2019.105895_b45 doi: 10.1109/ICMLC.2002.1167400 – start-page: 369 year: 1999 ident: 10.1016/j.asoc.2019.105895_b15 article-title: Using data mining techniques in fiscal fraud detection – start-page: 767 year: 2006 ident: 10.1016/j.asoc.2019.105895_b55 article-title: Outlier detection by sampling with accuracy guarantees – volume: 15 start-page: 1170 issue: 5 year: 2003 ident: 10.1016/j.asoc.2019.105895_b57 article-title: Efficient biased sampling for approximate clustering and outlier detection in large data sets publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2003.1232271 – volume: 16 start-page: 25 issue: 1 year: 2014 ident: 10.1016/j.asoc.2019.105895_b44 article-title: Performance measurement of the kcs customs selectivity system publication-title: Risk Manage. doi: 10.1057/rm.2014.2 – ident: 10.1016/j.asoc.2019.105895_b69 – start-page: 34:1 year: 2016 ident: 10.1016/j.asoc.2019.105895_b42 article-title: Semi-supervised forecasting of fraudulent financial statements – start-page: 7 year: 2009 ident: 10.1016/j.asoc.2019.105895_b18 article-title: High quality true-positive prediction for fiscal fraud detection – year: 2016 ident: 10.1016/j.asoc.2019.105895_b67 – start-page: 149 year: 2017 ident: 10.1016/j.asoc.2019.105895_b75 article-title: High-dimensional outlier detection: The subspace method – year: 2010 ident: 10.1016/j.asoc.2019.105895_b8 – start-page: 584 year: 2006 ident: 10.1016/j.asoc.2019.105895_b43 article-title: Development of hybrid classification methodology for mining skewed data sets - a case study of indian customs data – volume: 8 issue: 35 year: 2015 ident: 10.1016/j.asoc.2019.105895_b48 article-title: A novel unsupervised classification method for customs fraud detection publication-title: Indian J. Sci. Technol. doi: 10.17485/ijst/2015/v8i35/87306 – volume: 3 start-page: 27 issue: 10 year: 2013 ident: 10.1016/j.asoc.2019.105895_b72 article-title: Evaluation measures for models assessment over imbalanced data sets publication-title: J. Inf. Eng. Appl. – year: 2017 ident: 10.1016/j.asoc.2019.105895_b6 – volume: 41 start-page: 4915 issue: 10 year: 2014 ident: 10.1016/j.asoc.2019.105895_b26 article-title: Learned lessons in credit card fraud detection from a practitioner perspective publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2014.02.026 – start-page: 167 year: 2016 ident: 10.1016/j.asoc.2019.105895_b1 article-title: Datamining voor fraudedetectie – volume: 32 start-page: 25 issue: 1 year: 2018 ident: 10.1016/j.asoc.2019.105895_b39 article-title: Imbalanced classification in sparse and large behaviour datasets publication-title: Data Min. Knowl. Discov. doi: 10.1007/s10618-017-0517-y – year: 2017 ident: 10.1016/j.asoc.2019.105895_b12 – volume: 41 start-page: 15:1 issue: 3 year: 2009 ident: 10.1016/j.asoc.2019.105895_b32 article-title: Anomaly detection: A survey publication-title: ACM Comput. Surv. doi: 10.1145/1541880.1541882 – year: 2012 ident: 10.1016/j.asoc.2019.105895_b9 – year: 2010 ident: 10.1016/j.asoc.2019.105895_b21 – volume: 17 start-page: 235 issue: 3 year: 2002 ident: 10.1016/j.asoc.2019.105895_b35 article-title: Statistical fraud detection: A review publication-title: Statist. Sci. doi: 10.1214/ss/1042727940 – volume: 50 start-page: 491 issue: 2 year: 2011 ident: 10.1016/j.asoc.2019.105895_b41 article-title: Detection of financial statement fraud and feature selection using data mining techniques publication-title: Decis. Support Syst. doi: 10.1016/j.dss.2010.11.006 – start-page: 7:1 year: 2015 ident: 10.1016/j.asoc.2019.105895_b59 article-title: On the internal evaluation of unsupervised outlier detection – year: 2013 ident: 10.1016/j.asoc.2019.105895_b66 – volume: 28 start-page: 190 issue: 1 year: 2014 ident: 10.1016/j.asoc.2019.105895_b54 article-title: Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection publication-title: Data Min. Knowl. Discov. doi: 10.1007/s10618-012-0300-z – volume: 29 start-page: 626 issue: 3 year: 2015 ident: 10.1016/j.asoc.2019.105895_b73 article-title: Graph based anomaly detection and description: a survey publication-title: Data Min. Knowl. Discov. doi: 10.1007/s10618-014-0365-y – start-page: 428 year: 2013 ident: 10.1016/j.asoc.2019.105895_b53 article-title: Subsampling for efficient and effective unsupervised outlier detection ensembles – volume: 56 start-page: 981 issue: 8 year: 2005 ident: 10.1016/j.asoc.2019.105895_b14 article-title: The impact of sample bias on consumer credit scoring performance and profitability publication-title: J. Oper. Res. Soc. doi: 10.1057/palgrave.jors.2601920 – year: 2018 ident: 10.1016/j.asoc.2019.105895_b22 – volume: 7 start-page: 1 issue: Jan year: 2006 ident: 10.1016/j.asoc.2019.105895_b64 article-title: Statistical comparisons of classifiers over multiple data sets publication-title: J. Mach. Learn. Res. – volume: 39 start-page: 8769 issue: 10 year: 2012 ident: 10.1016/j.asoc.2019.105895_b28 article-title: Using data mining technique to enhance tax evasion detection performance publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2012.01.204 – volume: 21 start-page: 1263 issue: 9 year: 2009 ident: 10.1016/j.asoc.2019.105895_b37 article-title: Learning from imbalanced data publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2008.239 – volume: 30 start-page: 891 issue: 4 year: 2016 ident: 10.1016/j.asoc.2019.105895_b52 article-title: On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study publication-title: Data Min. Knowl. Discov. doi: 10.1007/s10618-015-0444-8 – volume: 231 start-page: 4 year: 2013 ident: 10.1016/j.asoc.2019.105895_b60 article-title: Toward a more practical unsupervised anomaly detection system publication-title: Inform. Sci. doi: 10.1016/j.ins.2011.08.011 – volume: 74 start-page: 406 issue: C year: 2018 ident: 10.1016/j.asoc.2019.105895_b62 article-title: A comparative evaluation of outlier detection algorithms publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2017.09.037 – volume: 49 start-page: 31:1 issue: 2 year: 2016 ident: 10.1016/j.asoc.2019.105895_b38 article-title: A survey of predictive modeling on imbalanced domains publication-title: ACM Comput. Surv. – start-page: 24:1 year: 2018 ident: 10.1016/j.asoc.2019.105895_b13 article-title: Who is bogus?: Using one-sided labels to identify fraudulent firms from tax returns – start-page: 312 year: 2019 ident: 10.1016/j.asoc.2019.105895_b31 article-title: Identifying malicious dealers in goods and services tax – volume: 11 start-page: 1 issue: 4 year: 2016 ident: 10.1016/j.asoc.2019.105895_b33 article-title: A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data publication-title: PLOS ONE doi: 10.1371/journal.pone.0152173 – start-page: 215 year: 2018 ident: 10.1016/j.asoc.2019.105895_b34 article-title: Tax fraud detection for under-reporting declarations using an unsupervised machine learning approach – start-page: 77 year: 2002 ident: 10.1016/j.asoc.2019.105895_b36 article-title: A geometric framework for unsupervised anomaly detection – year: 2017 ident: 10.1016/j.asoc.2019.105895_b2 – year: 2003 ident: 10.1016/j.asoc.2019.105895_b70 – start-page: 596 year: 2011 ident: 10.1016/j.asoc.2019.105895_b27 article-title: Classification in presence of drift and latency – volume: 38 start-page: 73 issue: 1 year: 2014 ident: 10.1016/j.asoc.2019.105895_b74 article-title: Explaining data-driven document classifications publication-title: MIS Q. doi: 10.25300/MISQ/2014/38.1.04 – volume: 15 start-page: 81 year: 1998 ident: 10.1016/j.asoc.2019.105895_b46 article-title: A scalable self-organizing map algorithm for textual classification: A neural network approach to thesaurus generation publication-title: Commun. Cogn. Artif. Intell. Springer – volume: 46 start-page: 44:1 issue: 4 year: 2014 ident: 10.1016/j.asoc.2019.105895_b20 article-title: A survey on concept drift adaptation publication-title: ACM Comput. Surv. doi: 10.1145/2523813 – start-page: 37 year: 2016 ident: 10.1016/j.asoc.2019.105895_b30 article-title: Detecting value-added tax evasion by business entities of Kazakhstan – ident: 10.1016/j.asoc.2019.105895_b58 – volume: 29 start-page: 3784 issue: 8 year: 2018 ident: 10.1016/j.asoc.2019.105895_b19 article-title: Credit card fraud detection: A realistic modeling and a novel learning strategy publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2017.2736643 – year: 2016 ident: 10.1016/j.asoc.2019.105895_b10 – volume: 40 start-page: 1427 issue: 5 year: 2013 ident: 10.1016/j.asoc.2019.105895_b17 article-title: Characterization and detection of taxpayers with false invoices using data mining techniques publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2012.08.051 – volume: 1 start-page: 80 issue: 6 year: 1945 ident: 10.1016/j.asoc.2019.105895_b63 article-title: Individual comparisons by ranking methods publication-title: Biom. Bull. doi: 10.2307/3001968 – volume: 27 start-page: 861 issue: 8 year: 2006 ident: 10.1016/j.asoc.2019.105895_b65 article-title: An introduction to ROC analysis publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2005.10.010 – year: 2013 ident: 10.1016/j.asoc.2019.105895_b40 – ident: 10.1016/j.asoc.2019.105895_b16 – start-page: 103 year: 2011 ident: 10.1016/j.asoc.2019.105895_b51 article-title: Unsupervised fraud detection in medicare Australia – start-page: 93 year: 2000 ident: 10.1016/j.asoc.2019.105895_b71 article-title: Lof: Identifying density-based local outliers – start-page: 1650 year: 2014 ident: 10.1016/j.asoc.2019.105895_b3 article-title: Corporate residence fraud detection – volume: 75 start-page: 38 year: 2015 ident: 10.1016/j.asoc.2019.105895_b25 article-title: Apate: A novel approach for automated credit card transaction fraud detection using network-based extensions publication-title: Decis. Support Syst. doi: 10.1016/j.dss.2015.04.013 – ident: 10.1016/j.asoc.2019.105895_b50 – volume: 50 start-page: 559 issue: 3 year: 2011 ident: 10.1016/j.asoc.2019.105895_b23 article-title: The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature publication-title: Decis. Support Syst. doi: 10.1016/j.dss.2010.08.006 |
| SSID | ssj0016928 |
| Score | 2.451594 |
| Snippet | The tax fraud detection domain is characterized by very few labelled data (known fraud/legal cases) that are not representative for the population due to... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 105895 |
| SubjectTerms | Scalable algorithms Tax fraud detection Unsupervised anomaly detection |
| Title | Value-added tax fraud detection with scalable anomaly detection techniques |
| URI | https://dx.doi.org/10.1016/j.asoc.2019.105895 |
| Volume | 86 |
| WOSCitedRecordID | wos000503388200068&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: ScienceDirect customDbUrl: eissn: 1872-9681 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0016928 issn: 1568-4946 databaseCode: AIEXJ dateStart: 20010601 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3Pb9MwFLZg48AFxi-xDZAP3CpPSRzH8XGahqCHicNAvUWu_SyYSjo16dT993uOnTRMY9oOXKLKSZwo36fnz-7z-wj5zIHPLU81y6xzLM8kMOVMweYmsTj8A-TOdGYT8uysnM3U97hdsensBGRdl5uNuvyvUGMbgu23zj4C7qFTbMDfCDoeEXY8Pgj4n3qxBuYDip20ejNxK722EwstBFfwkJSO0HSbpnS9_KMX16PzQ1XXZixce7XaYNju8tDXbT_oBbuuX0u4vop211Nf5XMQ675SAUS5vs2g99HYp-I0kWH1crwAkSWjBYgYM4uS5SquJMagWo6jYuq9C8WdATusHVwcaeSiT7RTR9uL_66OfWvUGnIJ-zS1i8r3Ufk-qtDHU7KbSaEwXO8efzudTYd_lwrVee4OLx43U4W8v9tvcrdgGYmQ8z3yIs4e6HFA_RV5AvVr8rJ35qAxUL8h0xEJKJKAdiSgA8jUk4D2JKCRBKPzWxK8JT--nJ6ffGXRNYMZniQtS7kpJGSinBtlQUrHtUlKnVghCptZAdwpY63TQgIKOCtwhpApbFDcznGCyt-RnXpZw3tCwUjuZGryHJJcale63FmlINdpKUEm-yTtP01lYkl572yyqP4Nyj6ZDPdchoIq914t-i9eRUkYpF6FBLrnvoNHPeWQPN8S-wPZaVdr-Eiemav2d7P6FNlzA_zShXY |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Value-added+tax+fraud+detection+with+scalable+anomaly+detection+techniques&rft.jtitle=Applied+soft+computing&rft.au=Vanhoeyveld%2C+Jellis&rft.au=Martens%2C+David&rft.au=Peeters%2C+Bruno&rft.date=2020-01-01&rft.issn=1568-4946&rft.volume=86&rft.spage=105895&rft_id=info:doi/10.1016%2Fj.asoc.2019.105895&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_asoc_2019_105895 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1568-4946&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1568-4946&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1568-4946&client=summon |