Research On XBRL Financial Report Audit

Since “Enterprise Accounting Standards of General Classification Standards”had been promulgated by the Ministry of Finance in2010and eXtensible BusinessReporting Language (referred to as XBRL) financial report had been mandatorydisclosure in SSE and SZSE, XBRL will be extended in accounting field of ourcountry. From technical point of view, uniqueness of XBRL data makes XBRLfinancial report information is easy to extract, in the meantime, enterprises couldprovide specific financial information according to auditor’ s unique needs, evenimproving auditee’s information feedback frequency. From costing perspective, theXBRL technology not only simplified information sharing and transfer procedures,but also reducing financial information acquisition cost and transition cost betweenauditor and auditee, even make audit evidence of XBRL financial report easiertracking. From audit procedure perspective, using computer assisted audittechniques which including data testing, integrated testing, parallel simulation onXBRL financial report audit, the auditor will transfer their focus from control testingto substantive procedure. From audit scope perspective, auditor will focus ontraditional financial reporting in addition to the control of XBRL systemeffectiveness, as well as XBRL specification, XBRL classification criteria whetherused appropriately，whether contents of XBRL instance document is consistent withtraditional financial report.This article is based on XBRL financial report, firstly, according to condition ofexisting XBRL financial report disclosure content can not meet the need of financialreport users, it expand XBRL financial report disclosure content from theperspective of stakeholders, which in order to meet the needs of the stakeholderswho use XBRL financial report, ensure to provide information users morecomprehensively, timely scientifically and effectively access to enterprise financialinformation. Secondly, construct corresponding audit strategy separately from twolevels of XBRL system effectiveness and XBRL financial report authenticity. Thirdly,proposing audit strategy separately from authenticity and validity perspective whichconstruction of Levenberg-Marquardt algorithm based on BP neural networkmaterial misstatement risk identification model when XBRL financial report andtraditional financial report were both effective. Lastly, according to auditingstandard No.1211includes28indication of material risk misstatement, making an empirical analysis from several indexes of continue operating, financing ability,operating ability, major affiliated transactions, key personnel changes, and internalcontrol environment aspects. Empirical analysis result proves that in materialmisstatement risk identification Levenberg-Marquardt algorithm based on BP neuralnetwork model is better than Logistic algorithm model, which has higher predictionaccuracy and practicability.