Accelerating the search of fraudulent XBRL Instance Document

Guang Yih Sheu

Abstract


This study presents the preliminary development of a new mobile computing application. Android and iOS apps are created to find XBRL (eXtensible Business Reporting Language) instance documents conforming unacceptably to the Benford's law. Such XBRL instance documents are more possibly fraudulent. Required input data are the uniform resource locator of an XBRL instance document and significance level for concluding Chi-square, Kuiper, and Kolmogorov-Smirnov test statistics. Except for these three types of test statistics, XBRL instance documents conforming unacceptably to the Benford's law are found based on visual comparison of actual and theoretical digital probabilities and mean absolute deviation test statistics. The proposed smartphone apps are executed without needing any XBRL taxonomy or definition file. Two practical examples demonstrate that they can be employed to quicken the audit of numerous XBRL instance documents. In conclusion, we can use a smartphone as a tool of reducing the burden of accountants.


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