Financial statements fraud identifiers

Abstract

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.

Keywords:

• Financial statement fraud
• fraud detection models
• logistic regression analysis
• SMOTE algorithm
• ROC analysis

JEL CODES:

• M41; M42

Notes

1 Simple model’s quality assessment could be done by comparing their classification results with critical value. Critical value in this case is theoretical probability increased by 25%.

2 The authors differ according to the proportions of area under ROC curve and appropriate discrimination power estimation. First group proportion of area is shown outside while other group proportion is shown inside the brackets.