Application of support vector machine in the detection of fraudulent financial statements

Auditing practices nowadays have to cope with an increasing number of fraudulent financial statements. Data mining techniques can facilitate auditors in accomplishing the task of detection of fraudulent financial statements (FFS). Considering the character of FFS, this paper designs a FFS detection model based on support vector machine. To perform the experiment, we choose 44 FFS according to the auditing reports and 44 non-fraudulent financial statements(non-FFS) according to some specific standards from listed companies in China during 1999-2002 as training data set. Similarly, 73 FFS and 99 non-FFS during 2003-2006 are chosen as testing data set. We train the model using training data set and apply the trained model to the testing data set, good experimental results are obtained.