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The purpose of this paper is to provide an approach to explore fraudulent financial reporting (FFR) via growing hierarchical self\u2010organizing map (GHSOM), an unsupervised neural network tool, to help capital providers evaluate the integrity of financial statements, and to facilitate analysis further to reach prudent credit decisions.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Design\/methodology\/approach<\/jats:title><jats:p>This paper develops a two\u2010stage approach: a classification stage that well trains the GHSOM to cluster the sample into subgroups with hierarchical relationship, and a pattern\u2010disclosure stage that uncovers patterns of the common FFR techniques and relevant risk indicators of each subgroup.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Findings<\/jats:title><jats:p>An application is conducted and its results show that the proposed two\u2010stage approach can help capital providers evaluate the reliability of financial statements and accounting numbers\u2010based decisions.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Practical implications<\/jats:title><jats:p>Following the SOM theories, it seems that common FFR techniques and relevant risk indicators extracted from the GHSOM clustering result are applicable to all samples clustered in the same leaf node (subgroup). This principle and any pre\u2010warning signal derived from the identified indicators can be applied to assessing the reliability of financial statements and forming a basis for further analysis in order to reach prudent decisions. The limitation of this paper is the subjective parameter setting of GHSOM.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Originality\/value<\/jats:title><jats:p>This is the first application of GHSOM to financial data and demonstrates an alternative way to help capital providers such as lenders to evaluate the integrity of financial statements, a basis for further analysis to reach prudent decisions. The proposed approach could be applied to other scenarios that rely on accounting numbers as a basis for decisions.<\/jats:p><\/jats:sec>","DOI":"10.1108\/02635571211204272","type":"journal-article","created":{"date-parts":[[2012,3,5]],"date-time":"2012-03-05T14:32:02Z","timestamp":1330957922000},"page":"224-244","source":"Crossref","is-referenced-by-count":22,"title":["Unsupervised neural networks approach for understanding fraudulent financial reporting"],"prefix":"10.1108","volume":"112","author":[{"given":"Shin\u2010Ying","family":"Huang","sequence":"first","affiliation":[]},{"given":"Rua\u2010Huan","family":"Tsaih","sequence":"additional","affiliation":[]},{"given":"Wan\u2010Ying","family":"Lin","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"key":"key2022012220285518000_b5","unstructured":"ACFE (2008), 2008 Report to the nation on occupational fraud & abuse, Association of Certified Fraud Examiners, Austin, TX."},{"key":"key2022012220285518000_b1","doi-asserted-by":"crossref","unstructured":"Ahn, J.Y., Seok, K.K. and Kyung, S.H. 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