Classification of the Priority of Auditing XBRL Instance Documents with Fuzzy Support Vector Machines Algorithm

摘要

Concluding the conformity of XBRL(eXtensible Business Reporting Language)instance documents law to the Benford's law yields different results before and after a company's financial distress.A new idea of applying the machine learning technique to redefine the way conventional auditors work is therefore proposed since the unacceptable conformity implies a large likelihood of a fraudulent document.Fuzzy support vector machines models are developed to implement such an idea.The dependent variable is a fuzzy variable quantifying the conformity of an XBRL instance document to the Benford's law;whereas,independent variables are financial ratios.The interval factor method is introduced to express the fuzziness in input data.It is found the range of a fuzzy support vector machines model is controlled by maximum and minimum dependent and independent variables.Therefore,defining any member function to describe the fuzziness in input data is unnecessary.The results of this study indicate that the price-to-book ratio versus equity ratio is suitable to classify the priority of auditing XBRL instance documents with the less than 30%misclassification rate.In conclusion,the machine learning technique may be used to redefine the way conventional auditors work.This study provides the main evidence of applying a future project of training smart auditors.