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Lifting the numbers game: identifying key input variables and a best‐performing model to detect financial statement fraud
Author(s) -
Gepp Adrian,
Kumar Kuldeep,
Bhattacharya Sukanto
Publication year - 2021
Publication title -
accounting and finance
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.645
H-Index - 49
eISSN - 1467-629X
pISSN - 0810-5391
DOI - 10.1111/acfi.12742
Subject(s) - default , financial statement , key (lock) , parametric statistics , computer science , creditor , set (abstract data type) , financial fraud , debt , finance , statement (logic) , actuarial science , econometrics , accounting , business , economics , computer security , statistics , mathematics , audit , programming language , political science , law
Abstract 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.