Lifting the Numbers Game: Identifying key input variables and a best-performing model to detect financial statement fraud
Research output: Contribution to journal › Article › peer-review
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DOI
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.
| Original language | English |
|---|---|
| Pages (from-to) | 4601-4638 |
| Number of pages | 38 |
| Journal | Accounting and Finance |
| Volume | 61 |
| Issue number | 3 |
| Early online date | 26 Dec 2020 |
| DOIs | |
| Publication status | Published - 1 Sept 2021 |
| Externally published | Yes |