Lifting the numbers game: identifying key input variables and a best‐performing model to detect financial statement fraud

Autor:	Adrian Gepp, Sukanto Bhattacharya, Kuldeep Kumar
Rok vydání:	2020
Předmět:	Actuarial science Ensemble forecasting Computer science business.industry media_common.quotation_subject Economics Econometrics and Finance (miscellaneous) Big data Business economics Empirical research Accounting Debt Default business Finance Financial statement Parametric statistics media_common
Zdroj:	Accounting & Finance. 61:4601-4638
ISSN:	1467-629X 0810-5391
DOI:	10.1111/acfi.12742
Popis:	This study enables practitioners and researchers to make an informed choice for a financial statement fraud detection model, rather than defaulting to popular, yet dated, models. Using a specifically devised performance criterion, our newly configured ensemble outperforms 31 others in the most comprehensive comparison to date spanning parametric, non‐parametric, big data and ensemble techniques. We use a large set of input variables and holdout data relative to prior studies. We find empirical support for financial and non‐financial variables covering the three Fraud Triangle factors. New findings include fraud risk being reduced with more debt, likely from increased monitoring by creditors.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::06e11f10fe42e632bb54d5842aeb6f94 https://doi.org/10.1111/acfi.12742 Zobrazit plný text záznamu