Accounting & Finance 2023, pp.1–20..
ISSN/ISBN: Not available at this time. DOI: 10.1111/acfi.13192
Abstract: Financial statement fraud is a costly problem for society. Detection models can help, but a framework to guide variable selection for such models is lacking. A novel Fraud Detection Triangle (FDT) framework is proposed specifically for this purpose. Extending the well-known Fraud Triangle, the FDT framework can facilitate improved detection models. Using Benford's law, we demonstrate the posited framework's utility in aiding variable selection via the element of surprise evoked by suspicious information latent in the data. We call for more research into variables that measure rationalisations for fraud and suspicious phenomena arising as unintended consequences of financial statement fraud.
Bibtex:
@article{,
author = {Gepp, Adrian and Kumar, Kuldeep and Bhattacharya, Sukanto},
title = {Taking the hunch out of the crunch: A framework to improve variable selection in models to detect financial statement fraud},
journal = {Accounting \& Finance},
volume = {n/a},
number = {n/a},
pages = {},
doi = {https://doi.org/10.1111/acfi.13192},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/acfi.13192},
}
Reference Type: Journal Article
Subject Area(s): Accounting