| Title |
Detection of fraudulent financial statements using the hybrid data mining approach
|
|---|---|
| Published in |
SpringerPlus, January 2016
|
| DOI | 10.1186/s40064-016-1707-6 |
| Pubmed ID | |
| Authors |
Suduan Chen |
| Abstract |
The purpose of this study is to construct a valid and rigorous fraudulent financial statement detection model. The research objects are companies which experienced both fraudulent and non-fraudulent financial statements between the years 2002 and 2013. In the first stage, two decision tree algorithms, including the classification and regression trees (CART) and the Chi squared automatic interaction detector (CHAID) are applied in the selection of major variables. The second stage combines CART, CHAID, Bayesian belief network, support vector machine and artificial neural network in order to construct fraudulent financial statement detection models. According to the results, the detection performance of the CHAID-CART model is the most effective, with an overall accuracy of 87.97 % (the FFS detection accuracy is 92.69 %). |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 235 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 235 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 40 | 17% |
| Student > Ph. D. Student | 24 | 10% |
| Student > Bachelor | 23 | 10% |
| Researcher | 12 | 5% |
| Student > Doctoral Student | 12 | 5% |
| Other | 33 | 14% |
| Unknown | 91 | 39% |
| Readers by discipline | Count | As % |
|---|---|---|
| Business, Management and Accounting | 57 | 24% |
| Computer Science | 39 | 17% |
| Economics, Econometrics and Finance | 21 | 9% |
| Social Sciences | 6 | 3% |
| Medicine and Dentistry | 5 | 2% |
| Other | 19 | 8% |
| Unknown | 88 | 37% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 December 2017.
All research outputs
#13,965,269
of 22,844,985 outputs
Outputs from SpringerPlus
#737
of 1,849 outputs
Outputs of similar age
#201,367
of 396,846 outputs
Outputs of similar age from SpringerPlus
#62
of 217 outputs
Altmetric has tracked 22,844,985 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,849 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.7. This one has gotten more attention than average, scoring higher than 58% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 396,846 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 217 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.