{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T01:48:03Z","timestamp":1784598483376,"version":"3.55.0"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T00:00:00Z","timestamp":1731888000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T00:00:00Z","timestamp":1731888000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s10489-024-05861-9","type":"journal-article","created":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T08:43:55Z","timestamp":1731919435000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Calibrating TabTransformer for financial misstatement detection"],"prefix":"10.1007","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2417-3307","authenticated-orcid":false,"given":"Elias","family":"Zavitsanos","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dimitrios","family":"Kelesis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Georgios","family":"Paliouras","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,18]]},"reference":[{"issue":"6","key":"5861_CR1","doi-asserted-by":"publisher","first-page":"1487","DOI":"10.2308\/accr.2008.83.6.1487","volume":"83","author":"KM Hennes","year":"2008","unstructured":"Hennes KM, Leone AJ, Miller BP (2008) The importance of distinguishing errors from irregularities in restatement research: The case of restatements and ceo\/cfo turnover. Account Rev 83(6):1487\u20131519","journal-title":"Account Rev"},{"issue":"4","key":"5861_CR2","doi-asserted-by":"publisher","first-page":"995","DOI":"10.1016\/j.eswa.2006.02.016","volume":"32","author":"E Kirkos","year":"2007","unstructured":"Kirkos E, Spathis C, Manolopoulos Y (2007) Data mining techniques for the detection of fraudulent financial statements. Expert Syst Appl 32(4):995\u20131003","journal-title":"Expert Syst Appl"},{"issue":"2","key":"5861_CR3","first-page":"104","volume":"3","author":"S Kotsiantis","year":"2006","unstructured":"Kotsiantis S, Koumanakos E, Tzelepis D, Tampakas V (2006) Forecasting fraudulent financial statements using data mining. Int J Comput Intell 3(2):104\u2013110","journal-title":"Int J Comput Intell"},{"issue":"02","key":"5861_CR4","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1142\/S0219622008002958","volume":"7","author":"B Bai","year":"2008","unstructured":"Bai B, Yen J, Yang X (2008) False financial statements: characteristics of china\u2019s listed companies and cart detecting approach. J Inform Technol Decision Making 7(02):339\u2013359","journal-title":"J Inform Technol Decision Making"},{"key":"5861_CR5","doi-asserted-by":"crossref","unstructured":"Deng Q, Mei G (2009) Combining self-organizing map and k-means clustering for detecting fraudulent financial statements. In: 2009 IEEE International conference on granular computing, IEEE, Nanchang, China. IEEE, pp 126\u2013131","DOI":"10.1109\/GRC.2009.5255148"},{"issue":"2","key":"5861_CR6","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1016\/j.dss.2010.11.006","volume":"50","author":"P Ravisankar","year":"2011","unstructured":"Ravisankar P, Ravi V, Rao GR, Bose I (2011) Detection of financial statement fraud and feature selection using data mining techniques. Decis Support Syst 50(2):491\u2013500","journal-title":"Decis Support Syst"},{"issue":"3","key":"5861_CR7","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1002\/1099-1174(200009)9:3<145::AID-ISAF185>3.0.CO;2-G","volume":"9","author":"EH Feroz","year":"2000","unstructured":"Feroz EH, Kwon TM, Pastena VS, Park K (2000) The efficacy of red flags in predicting the sec\u2019s targets: an artificial neural networks approach. Intell Syst Account, Finance Manag 9(3):145\u2013157","journal-title":"Intell Syst Account, Finance Manag"},{"issue":"4","key":"5861_CR8","doi-asserted-by":"publisher","first-page":"1293","DOI":"10.2307\/41703508","volume":"36","author":"A Abbasi","year":"2012","unstructured":"Abbasi A, Albrecht C, Vance A, Hansen J (2012) Metafraud: a meta-learning framework for detecting financial fraud. MIS Q 36(4):1293\u20131327","journal-title":"MIS Q"},{"key":"5861_CR9","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1007\/s11142-020-09563-8","volume":"26","author":"Using machine learning to detect misstatements","year":"2021","unstructured":"Using machine learning to detect misstatements (2021) Bertomeu, J., Cheynel, E., Floyd, E., W., P. Rev Acc Stud 26:468\u2013519","journal-title":"Rev Acc Stud"},{"key":"5861_CR10","doi-asserted-by":"crossref","unstructured":"Perols J (2011) Financial statement fraud detection: An analysis of statistical and machine learning algorithms. Audit A J Pract Theory 30(2):19\u201350","DOI":"10.2308\/ajpt-50009"},{"issue":"1","key":"5861_CR11","first-page":"11","volume":"39","author":"A Sharma","year":"2012","unstructured":"Sharma A, Panigrahi PK (2012) A review of financial accounting fraud detection based on data mining techniques. Int J Comput Appl 39(1):11","journal-title":"Int J Comput Appl"},{"key":"5861_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.accinf.2022.100572","volume":"46","author":"C Zhang","year":"2022","unstructured":"Zhang C, Cho S, Vasarhelyi M (2022) Explainable artificial intelligence (xai) in auditing. Int J Account Inf Syst 46:100572","journal-title":"Int J Account Inf Syst"},{"issue":"1","key":"5861_CR13","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1111\/j.1911-3846.2010.01041.x","volume":"28","author":"PM Dechow","year":"2011","unstructured":"Dechow PM, Ge W, Larson CR, Sloan RG (2011) Predicting material accounting misstatements. Contemp Account Res 28(1):17\u201382","journal-title":"Contemp Account Res"},{"issue":"1","key":"5861_CR14","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1111\/1475-679X.12292","volume":"58","author":"Y Bao","year":"2020","unstructured":"Bao Y, Ke B, Li B, Yu YJ, Zhang J (2020) Detecting accounting fraud in publicly traded us firms using a machine learning approach. J Account Res 58(1):199\u2013235","journal-title":"J Account Res"},{"key":"5861_CR15","doi-asserted-by":"crossref","unstructured":"Zavitsanos E, Mavroeidis D, Bougiatiotis K, Spyropoulou E, Loukas L, Paliouras G (2021) Financial misstatement detection: a realistic evaluation. In: In 2nd ACM International conference on ai in finance (ICAIF\u2019 21), pp 1\u20139. Association for Computing Machinery, November 3\u20135, 2021, Virtual Event, USA","DOI":"10.1145\/3490354.3494453"},{"issue":"2","key":"5861_CR16","first-page":"349","volume":"4","author":"M Puttarattanamanee","year":"2023","unstructured":"Puttarattanamanee M, Boongasame L, Thammarak K (2023) A comparative study of sentiment analysis methods for detecting fake reviews in e-commerce. High Tech Innov J 4(2):349\u2013363","journal-title":"High Tech Innov J"},{"issue":"1\u20132","key":"5861_CR17","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1002\/isaf.284","volume":"15","author":"B Hoogs","year":"2007","unstructured":"Hoogs B, Kiehl T, Lacomb C, Senturk D (2007) A genetic algorithm approach to detecting temporal patterns indicative of financial statement fraud. Intell Syst Account Financ Manag Int J 15(1\u20132):41\u201356","journal-title":"Intell Syst Account Financ Manag Int J"},{"key":"5861_CR18","unstructured":"Kiehl TR, Hoogs BK, LaComb CA, Senturk D (2005) Evolving multi-variate time-series patterns for the discrimination of fraudulent financial filings. In: Genetic and evolutionary computation conference, ACM, Washington, DC, USA. Citeseer, pp 1\u20138"},{"key":"5861_CR19","doi-asserted-by":"crossref","unstructured":"Chai W, Hoogs BK, Verschueren BT (2006) Fuzzy ranking of financial statements for fraud detection. In: 2006 IEEE International conference on fuzzy systems, IEEE, pp 152\u2013158. IEEE, Vancouver, BC","DOI":"10.1109\/FUZZY.2006.1681708"},{"issue":"7","key":"5861_CR20","doi-asserted-by":"publisher","first-page":"650","DOI":"10.1108\/02686900810890625","volume":"23","author":"F-M Liou","year":"2008","unstructured":"Liou F-M (2008) Fraudulent financial reporting detection and business failure prediction models: a comparison. Manag Audit J 23(7):650\u2013622","journal-title":"Manag Audit J"},{"issue":"7","key":"5861_CR21","doi-asserted-by":"publisher","first-page":"1146","DOI":"10.1287\/mnsc.1100.1174","volume":"56","author":"M Cecchini","year":"2010","unstructured":"Cecchini M, Aytug H, Koehler GJ, Pathak P (2010) Detecting management fraud in public companies. Manage Sci 56(7):1146\u20131160","journal-title":"Manage Sci"},{"issue":"2","key":"5861_CR22","first-page":"157","volume":"14","author":"HA Ata","year":"2009","unstructured":"Ata HA, Seyrek IH (2009) The use of data mining techniques in detecting fraudulent financial statements: An application on manufacturing firms. Suleyman demirel university journal of faculty of economics & administrative sciences. 14(2):157\u2013170","journal-title":"Suleyman demirel university journal of faculty of economics & administrative sciences."},{"key":"5861_CR23","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1016\/j.knosys.2015.08.011","volume":"89","author":"C-C Lin","year":"2015","unstructured":"Lin C-C, Chiu A-A, Huang SY, Yen DC (2015) Detecting the financial statement fraud: The analysis of the differences between data mining techniques and experts\u2019 judgments. Knowl-Based Syst 89:459\u2013470","journal-title":"Knowl-Based Syst"},{"issue":"1","key":"5861_CR24","first-page":"14","volume":"16","author":"BP Green","year":"1997","unstructured":"Green BP, Choi JH (1997) Assessing the risk of management fraud through neural network technology. Auditing. 16(1):14\u201328","journal-title":"Auditing."},{"key":"5861_CR25","unstructured":"Fissette M, Vries T (2017) Text mining to detect indications of fraud in annual reports worldwide. In: Benelearn 2017: proceedings of the twenty-sixth benelux conference on machine learning, technische universiteit eindhoven, Eindhoven University of Technology, Eindhoven (the Netherlands), pp 69\u201371"},{"key":"5861_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2020.113421","volume":"139","author":"P Craja","year":"2020","unstructured":"Craja P, Kim A, Lessmann S (2020) Deep learning for detecting financial statement fraud. Decis Support Syst 139:113421","journal-title":"Decis Support Syst"},{"key":"5861_CR27","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.knosys.2017.05.001","volume":"128","author":"P Hajek","year":"2017","unstructured":"Hajek P, Henriques R (2017) Mining corporate annual reports for intelligent detection of financial statement fraud-a comparative study of machine learning methods. Knowl-Based Syst 128:139\u2013152","journal-title":"Knowl-Based Syst"},{"key":"5861_CR28","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1016\/j.eswa.2017.08.030","volume":"90","author":"I Dutta","year":"2017","unstructured":"Dutta I, Dutta S, Raahemi B (2017) Detecting financial restatements using data mining techniques. Expert Syst Appl 90:374\u2013393","journal-title":"Expert Syst Appl"},{"key":"5861_CR29","doi-asserted-by":"crossref","unstructured":"Karpoff J, Koester A, Lee D, Martin G (2012) A critical analysis of databases used in financial misconduct research (working paper). SSRN Electron J","DOI":"10.2139\/ssrn.2112569"},{"issue":"3","key":"5861_CR30","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1016\/j.dss.2010.08.009","volume":"50","author":"SL Humpherys","year":"2011","unstructured":"Humpherys SL, Moffitt KC, Burns MB, Burgoon JK, Felix WF (2011) Identification of fraudulent financial statements using linguistic credibility analysis. Decis Support Syst 50(3):585\u2013594","journal-title":"Decis Support Syst"},{"issue":"3","key":"5861_CR31","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1016\/j.dss.2010.08.010","volume":"50","author":"FH Glancy","year":"2011","unstructured":"Glancy FH, Yadav SB (2011) A computational model for financial reporting fraud detection. Decis Support Syst 50(3):595\u2013601","journal-title":"Decis Support Syst"},{"key":"5861_CR32","doi-asserted-by":"crossref","unstructured":"Spathis C, Doumpos M, Zopounidis C (2002) Detecting falsified financial statements: a comparative study using multicriteria analysis and multivariate statistical techniques. Account Rev 11(3):509\u2013535","DOI":"10.1080\/0963818022000000966"},{"issue":"1","key":"5861_CR33","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1108\/02686900410509802","volume":"19","author":"KA Kaminski","year":"2004","unstructured":"Kaminski KA, Wetzel TS, Guan L (2004) Can financial ratios detect fraudulent financial reporting? Manag Audit 19(1):15\u201328","journal-title":"Manag Audit"},{"key":"5861_CR34","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.eswa.2016.06.016","volume":"62","author":"YJ Kim","year":"2016","unstructured":"Kim YJ, Baik B, Cho S (2016) Detecting financial misstatements with fraud intention using multi-class cost-sensitive learning. Expert Syst Appl 62:32\u201343","journal-title":"Expert Syst Appl"},{"issue":"6","key":"5861_CR35","doi-asserted-by":"publisher","first-page":"2213","DOI":"10.1111\/j.1540-6261.2010.01614.x","volume":"65","author":"A Dyck","year":"2010","unstructured":"Dyck A, Morse A, Zingales L (2010) Who blows the whistle on corporate fraud? J Financ 65(6):2213\u20132253","journal-title":"J Financ"},{"key":"5861_CR36","doi-asserted-by":"publisher","unstructured":"Huang X, Khetan A, Cvitkovic M, Karnin Z (2020) TabTransformer: Tabular Data Modeling Using Contextual Embeddings. arXiv https:\/\/doi.org\/10.48550\/ARXIV.2012.06678","DOI":"10.48550\/ARXIV.2012.06678"},{"key":"5861_CR37","unstructured":"Liu H, Dai Z, So D, Le QV (2021) Pay attention to mlps. In: Ranzato M, Beygelzimer A, Dauphin Y, Liang PS, Vaughan JW (eds) Advances in Neural Information Processing Systems, vol 34, pp 9204\u20139215. Curran Associates, Inc., Virtual-only Conference. https:\/\/proceedings.neurips.cc\/paper\/2021\/file\/4cc05b35c2f937c5bd9e7d41d3686fff-Paper.pdf"},{"key":"5861_CR38","doi-asserted-by":"publisher","unstructured":"Cholakov R, Kolev T (2022) The GatedTabTransformer. An enhanced deep learning architecture for tabular modeling. arXiv. https:\/\/doi.org\/10.48550\/ARXIV.2201.00199","DOI":"10.48550\/ARXIV.2201.00199"},{"issue":"2","key":"5861_CR39","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","volume":"42","author":"T-Y Lin","year":"2020","unstructured":"Lin T-Y, Goyal P, Girshick R, He K, Doll\u00e1r P (2020) Focal loss for dense object detection. IEEE Trans Pattern Anal Mach Intell 42(2):318\u2013327. https:\/\/doi.org\/10.1109\/TPAMI.2018.2858826","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5861_CR40","unstructured":"Mukhoti J, Kulharia V, Sanyal A, Golodetz S, Torr P, Dokania P (2020) Calibrating deep neural networks using focal loss. In: Larochelle H, Ranzato M, Hadsell R, Balcan MF, Lin H (eds) Advances in Neural Information Processing Systems, vol 33, pp 15288\u201315299. Curran Associates, Inc., Virtual-only Conference. https:\/\/proceedings.neurips.cc\/paper\/2020\/file\/aeb7b30ef1d024a76f21a1d40e30c302-Paper.pdf"},{"key":"5861_CR41","doi-asserted-by":"crossref","unstructured":"H, B, S, K (2020) Topics in financial filings and bankruptcy prediction with distributed representations of textual data. In: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Springer, Virtual Conference, Belgium, pp 306\u2013322","DOI":"10.1007\/978-3-030-67670-4_19"},{"key":"5861_CR42","doi-asserted-by":"publisher","first-page":"876","DOI":"10.1038\/s41597-024-03605-5","volume":"11","author":"E Zavitsanos","year":"2024","unstructured":"Zavitsanos E, Mavroeidis D, Spyropoulou E, Fergadiotis M, Georgios P (2024) Entrant: A large financial dataset for table understanding. Nature Sci Data 11:876. https:\/\/doi.org\/10.1038\/s41597-024-03605-5","journal-title":"Nature Sci Data"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05861-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-05861-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05861-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,2]],"date-time":"2025-01-02T15:03:12Z","timestamp":1735830192000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-05861-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,18]]},"references-count":42,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["5861"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-05861-9","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,18]]},"assertion":[{"value":"6 November 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 November 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"3"}}