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2009 WRI World Congress on Computer Science and Information Engineering
Fraud Detection in Tax Declaration Using Ensemble ISGNN
Los Angeles, California USA
March 31-April 02
ISBN: 978-0-7695-3507-4
Fraud detection in tax declaration plays an important role in tax assessment. Using ensemble ISGNN (Iteration learning Self-Generating Neural Network) to solve the problem of fraud detection in tax declaration is presented in this paper. An ensemble ISGNN is trained using financial data of sampled enterprises, and the trained ensemble ISGNN is then employed to detect whether tax declared by an enterprise is legitimate or not. Experimental results show that proposed approach is effective: classification precision of proposed method is 96.7742% in 31 sample data, and it is 3.22 points higher than that of SGNN. The number of samples to train ISGNN of ensemble ISGNN is one third that of SGNN.
Index Terms:
ISGNN; Ensemble ISGNN; Fraud Detection
Citation:
Kehan Zhang, Aiguo Li, Baowei Song, "Fraud Detection in Tax Declaration Using Ensemble ISGNN," csie, vol. 4, pp.237-240, 2009 WRI World Congress on Computer Science and Information Engineering, 2009
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