TY - JOUR

T1 - Lifting the Numbers Game: Identifying key input variables and a best-performing model to detect financial statement fraud

AU - Gepp, Adrian

AU - Kumar, Kuldeep

AU - Bhattacharya, Sukanto

N1 - Publisher Copyright: © 2020 Accounting and Finance Association of Australia and New Zealand Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2021/9/1

Y1 - 2021/9/1

N2 - 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.

AB - 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.

U2 - 10.1111/acfi.12742

DO - 10.1111/acfi.12742

M3 - Article

VL - 61

SP - 4601

EP - 4638

JO - Accounting and Finance

JF - Accounting and Finance

SN - 0810-5391

IS - 3

ER -