Izvorni znanstveni članak
https://doi.org/10.1080/1331677X.2023.2218916
Financial statements fraud identifiers
Robert Zenzerović
Josip Šajrih
Sažetak
Contemporary research among fraud professionals indicates that
organizations lose 5% of revenues from fraud every year which
makes the research in this area and the derivation of fraud detection
models very important. The purpose of the article is to
develop a new accounting tool that will help companies and
investors in prompt fraud detection and prevention which can
finally result in the preservation of financial stability as well as
more efficient capital allocation. In this context the main objective
of the research is to test the significance of some financial statements
positions’ relations that has not been used in the previous
research using the dataset from SEC AAERs presented and
included in Bao et al.’s research as well as to combine them with
existing ones and consequently develop new financial statement
fraud detection model. Another objective consists of presenting
some of the most significant and contemporary research in the
field of financial statement fraud detection models and comparing
their quality using the ROC analysis. Research results were
generated by using the SMOTE algorithm and logistic regression
analysis on the dataset of 146,045 cases for a period from 1982
to 2014 and point out five independent variables used by Bao
et al. The financial statement fraud detection model comprised of
change in free cash flow, percentage of soft assets, sale of common
and preferred stock, change in cash sales, and change in
receivables shows a sufficient level of discriminant power with
67% area under ROC curve. The model derived could be used as
a starting point for fraud detection preventing the significant
losses the company and stakeholders could face.
Ključne riječi
Financial statement fraud; fraud detection models; logistic regression analysis; SMOTE algorithm; ROC analysis
Hrčak ID:
314894
URI
Datum izdavanja:
13.6.2023.
Posjeta: 66 *