AUDIT ANALYTICS AUDIT

ESSAY 5: DATA ANALYTICS FOR FINANCIAL STATEMENT AUDITS
Figure 5-9: Distribution of (Current Ratio, ROA) Distances from the
Median Point
Distribution of (Current Ratio, ROA) Distances from Median
Where multivariate ratio analysis is used, the DA software would
compute distance of the entity’s multivariate ratio point from the median
point and the auditor would compare that distance with the distribution
of distances (for example, figure 5-9) to determine the extent to which the
entity is an outlier. Distance as measured in this multivariate example
accords with our everyday notion of distance—the length of the straight
line joining two points. Data science also recognizes other distance
metrics as well (Starkweather 2013; Tan, et al. 2006). Because ensembles
of multiple approaches have been shown to be particularly effective in
identifying true outliers (Zimek et al. 2014), DA applications seeking true
outliers in a multivariate ratio analysis might compute distance from the
median in several different ways and then calculate a composite outlier
score. For example, if four different measures are calculated, they can be
normalized to lie between 0 and 1 so that the composite outlier score lies
between 0 and 4, with 0 indicating that the point coincides exactly with
the median and 4 indicating that the point is a maximal outlier. Scores
might also be weighted in the construction of the composite.
The literature on multivariate ratio analysis in auditing is thin to
non-existent (Google hits: zero) and the discussion in the preceding
paragraph is speculative. The distance measures of data science are
principally used in applications such as text analysis, and the extent to
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