SYNOPSIS
In this paper we argue for the use of Big Data as complementary audit evidence. We evaluate the applicability of Big Data using the audit evidence criteria framework and provide cost-benefit analysis for sufficiency, reliability, and relevance considerations. Critical challenges, including integration with traditional audit evidence, information transfer issues, and information privacy protection, are discussed and possible solutions are provided.
Accounting scandals like Enron (2001) and Petrobas (2014) remind us that untrustworthy financial information has an adverse effect on the stability of the economy and can ultimately be a source of systemic risk. This financial information is derived from processes and their related monetary flows within a business. But as the flows are becoming larger and more complex, it becomes increasingly difficult to distill the primary processes for large amounts of transaction data. However, by extracting the primary processes we will be able to detect possible inconsistencies in the information efficiently. We use recent advances in network embedding techniques that have demonstrated promising results regarding node classification problems in domains like biology and sociology. We learned a useful continuous vector representation of the nodes in the network which can be used for the clustering task, such that the clusters represent the meaningful primary processes. The results show that we can extract the relevant primary processes which are similar to the created clusters by a financial expert. Moreover, we construct better predictive models using the flows from the extracted primary processes which can be used to detect inconsistencies. Our work will pave the way towards a more modern technology and data-driven financial audit discipline.
SUMMARY: Widely used probability-proportional-to-size (PPS) selection methods are not well adapted to circumstances requiring sample augmentation. Limitations include: (1) an inability to augment selections while maintaining PPS properties, (2) a failure to recognize changes in census stratum membership which result from sample augmentation, and (3) imprecise control over line item sample size. This paper presents a new method of PPS selection, a modified version of sieve sampling which overcomes these limitations. Simulations indicate the new method effectively maintains sampling stratum PPS properties in single- and multi-stage samples, appropriately recognizes changes in census stratum membership which result from sample augmentation, and provides precise control over line item sample sizes. In single-stage applications the method provides reliable control of sampling risk over varied tainting levels and error bunching patterns. Tightness and efficiency measures are comparable to randomized systematic sampling and superior to sieve sampling.
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