Discuss about the Big Data and Analytics in Audit Engagement.
Modern engagements of audit regularly require assessment of clients which is using big data and analytics so that they can remain competitive and applicable in the current business environment. The system of clients is now integrated with cloud namely internet and exterior sources namely public media. Additionally, several customer engagements are nowadays upgrading to big data with the help of fresh and composite business systematic methods to apply intellect in decision making.
According to (Yoon, Hoogduin and Zhang (2015) there is a widespread recognition of audit profession that the rise of big data along with the increasing usage of data analytics in corporate procedure has introduced a new set of worries for the audit committee. It is necessary to realize that the present possibility and restraints to the profession of public audit prior to envisaging the role of additional compound analytics and big data in the involvement of audit. As audit is viewed as the profession of high regulation, the anticipations regarding data collection and analytical process must be addressed.
Big data is referred as the vibrant, large and dissimilar sizes of data being produced by the individuals, instruments and machineries (Alles 2015). Big data needs new, ground-breaking and accessible techniques to gather, host and analytical procedure from the large sum of facts that is collected to generate real time commercial understandings that is associated to customers, management of productivity and improved shareholder value. The word big data has turned into a chief subject of the technological media however big data has made their way to the several compliances, interior audit and management of fraud associated discussion. Several respondents have believed that the rise of big data technologies can serve a pivotal role in the prevention of fraud and detection.
The auditor is still required to test for the elementary affirmations to ensure that the purpose of audit is met irrespective of the natural surroundings of the information and the method the data are being assembled (Flood, Jagadish and Raschid 2016). The test for specific affirmations might change under the present new atmosphere with the diverse form of evidence and the manner in which the evidence is composed and assessed. In the modern world of composite IT and situation of big data, the capability and nature of audit proof has transformed. With the introduction of big data electronic evidence turns out to be more dominant in equation and might be very challenging to validate. For instance, with the availability of supplementary evidence from the peripheral big data, the intangible assets may be partly estimated by the customers.
Business makes the use of the Big Data Life Cycle for the making of assured forms of data that have captured from long time. With the improvement in the technology progressive instruments and tailored software can record the info for the examination (Zhang, Yang and Appelbaum 2015). Changes in a manner through which a business communicates have also increased the ability of the business to assess the areas of consumer sentiments.
Large amount of data has traditionally not been able to capture the process for numerous reasons most because of the worth of the insights corporations can obtain from the examination. Nevertheless, numerous aspects and new tools have reduced the cost and technological obstacle for the purpose of effectual processing of data that enables business of all size to solve the worth enclosed in the diverse bases of data (Cao, Chychyla and Stewart 2015). For example, it is difficult to maintain the conventional rational database to handle the unstructured data. As this would help in distributing the data and parallel processing of large datasets which is introduced to process the non-structured high speed data that helps in making easier performance of comprehensive analysis of big data.
There are many business that looking forward to cloud in order to provide large storage of solutions which is swift and allows unmatched scalability. However, these corporations are required to make sure that the control and risk administration practices on the cloud are maintained for the type of info that is gathered (Vasarhelyi, Kogan and Tuttle 2015). Cloud computing allows the companies to make use of the prebuild big data solutions or speedily create and employ the influential group of servers without the considerable amount of cost incurred in possessing the software.
Another example how business use the big data is through big data and analytics. Big data has both the prospects and challenges for the businesses challenges. To obtain the worth from the big data, the data should be administered and analysed in time as this would make the result available in a manner which would create a positive change or influence business decision (Appelbaum, Kogan and Vasarhelyi 2017). The effectiveness is also depended upon the business having the correct mixture of persons, procedure and expertise. Accordingly, analytics constitutes the detection and statement of meaning configurations in the data but in case of corporate analytics must be considered as the widespread usage of data and measureable examination by employing the analytical and explanatory models to obtain detail based business administration decision.
Analytics helps the business in optimizing the key procedure, functions and roles. It can be leveraged to combine the external and the internal data. It helps the business in meeting the stakeholder’s demands for reporting, managing of huge volume of data and improving the organization performance by turning the information to intelligence (Krahel and Titera 2015). Data driven decisions can help in reducing the inefficiency for the business, legal and IT, optimizing the current information assets by addressing the disconnections among the different functions of organization.
The profession of audit has long been identified on the effect of data analysis to enhance the relevancy of audit and quality. The mainstream use of the big data technology has been hampered because of the lack of effective technology solutions, problems with the data capture and concerns regarding the privacy. With the recent improvement in the technology solutions, problems with capturing of data and concerns regarding privacy has come into existence (Cai and Zhu 2015). The current improvement in the big data and analytics are offering opportunities to rethink in the manner in which is audit is executed.
The audit procedure would expand beyond the sample based method of testing to take into the considerations the examination of whole population of audit pertinent data by means of the intelligent analytics. This would provide higher quality of audit evidence and more reliable form of business insights (Rose et al. 2017). The analytics of big data provides the auditor to better recognize the financial reporting business operations and fraud risk to tailor the approach of delivering more relevant audit.
Advancement in the data science can be implemented to execute more well-organized audits and offer new form of audit proof (William, Glover and Prawitt 2016). To enhance the work of audit, the auditors can apply the methods of audit data analytics in the audit plan to recognise and evaluate the hazard by assessing the data to recognize the outlines, associations and variations from the models.
The data analytics approach can provide the auditors with the new sight of the company and its risk atmosphere to progress the superiority of the logical process in all stages of the audit. Expertise helps in the establishment of Big Data that can be used to enhance the familiarity of auditors regarding the businesses and balances fundamental in the monetary reports (Arens et al. 2016). This can help the auditors in obtaining the better evidence regarding the audit sentiments and comprehend the central reasons of repetition, fraud and issues of going concern.
To form an opinion of auditor’s audit evidence information is necessary as it should be sufficient and appropriate. Fundamentally, if the underlying information is not dependable or strong enough and if the origin is not certifiable, then more amount of evidence should be collected and reviewed. Inferior class of audit information cannot be compensated simply by gathering the large quantity of data (Leung et al. 2014). However, in the modern world of complex information technology and environment of big data, the feature and competency of audit proof has changed. With the implementation of big data quantity of evidence is considered hardly an attribute with which is to be concerned. The quality of information obtained turns out to be more dominant in the equation and may be challenging in the audit procedure. A large number of transactions are generated by computer and recorded which can only be substantiated by the electronically.
The issue for the electronic accounting data and electronic audit evidence is considered significantly dissimilar from that of the physical and paper based audit assessment. Several of the features possess strength with the paper based evidence acting as threat for the electronic evidence (Zhou, Simnett and Hoang 2016). The paper based document cannot be easily changed, under the big data the automated data might be effortlessly altered and these kinds of modifications may not be identified with the absence of suitable controls.
Under the paper based collection of audit evidence, sources that are substantiated externally to the client are regarded to be extremely dependable. While under the exterior automated evidence it is problematic to authenticate for the source and dependability. Paper based evidence is considered as difficult to assess and comprehend, while big data evidence might need a higher level of practical knowledge for the auditor (Leung et al. 2014). As big data is regarded as electronic data, it offers the scenario where the complexities are expanded significantly. Hence the type of test that the auditors undertake to assess the basic assertions may change.
Issues related to fraud might be challenging for the audit team under the big data environment. More amount of data cannot be effectively considered as the more effective information and the increased amount of complexity could result in complicate evaluation of audit evidence for fraud (Zhou et al. 2016). The detection of fraud emphasis on the evaluation of internal control irrespective of whether the analytics are based on the sample or 100% handing out of population. Under the environment of big data it is likely that the volume and data that are complex may act as the hindrance for audit teams.
Conclusion:
On a conclusive note, big data and business analytics is significantly altering the atmosphere of business and the ability of the corporate processes. The tasks of business are varying, trade functions are being added with processes being substantially accelerated. The same must happen in the functions of auditing and assurance functions since the auditing process requires automation of integrated analytical models. Auditors can gain benefit as they would be able to spread the work of audit all through the year and can identify the potential issues with improved audit plans in response.
Reference List:
Alles, M.G., 2015. Drivers of the Use and Facilitators and Obstacles of the Evolution of Big Data by the Audit Profession. Accounting Horizons, 29(2), pp.439-449.
Appelbaum, D., Kogan, A. and Vasarhelyi, M.A., 2017. Big Data and analytics in the modern audit engagement: Research needs. Auditing: A Journal of Practice & Theory, 36(4), pp.1-27.
Arens, A.A., Elder, R.J., Beasley, M.S. and Hogan, C.E., 2016. Auditing and assurance services. Pearson.
Cai, L. and Zhu, Y., 2015. The challenges of data quality and data quality assessment in the big data era. Data Science Journal, 14.
Cao, M., Chychyla, R. and Stewart, T., 2015. Big Data analytics in financial statement audits. Accounting Horizons, 29(2), pp.423-429.
Flood, M.D., Jagadish, H.V. and Raschid, L., 2016. Big data challenges and opportunities in financial stability monitoring. Banque de France, Financial Stability Review, 20.
Krahel, J.P. and Titera, W.R., 2015. Consequences of Big Data and formalization on accounting and auditing standards. Accounting Horizons, 29(2), pp.409-422.
Leung, P., Coram, P., Cooper, B.J. and Richardson, P., 2014. Modern Auditing and Assurance Services 6e. Wiley.
Rose, A.M., Rose, J.M., Sanderson, K.A. and Thibodeau, J.C., 2017. When should audit firms introduce analyses of Big Data into the audit process?. Journal of Information Systems, 31(3), pp.81-99.
Vasarhelyi, M.A., Kogan, A. and Tuttle, B.M., 2015. Big Data in accounting: An overview. Accounting Horizons, 29(2), pp.381-396.
William Jr, M., Glover, S. and Prawitt, D., 2016. Auditing and assurance services: A systematic approach. McGraw-Hill Education.
Yoon, K., Hoogduin, L. and Zhang, L., 2015. Big Data as complementary audit evidence. Accounting Horizons, 29(2), pp.431-438.
Zhang, J., Yang, X. and Appelbaum, D., 2015. Toward effective Big Data analysis in continuous auditing. Accounting Horizons, 29(2), pp.469-476.
Zhou, S., Simnett, R. and Hoang, H., 2016. Combined assurance as a new assurance approach: is it beneficial to analysts. In 26th Audit and Assurance conference-Thursday 5 May 2016.
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