Question

1. Audit sampling requires that the auditor collect only a relatively small sub-sample of data, thereby initiating detection risk, i.e., the risk. . . that, based on the sample the auditor takes, the auditor will fail to detect a material misstatement in the financial statements. Data analytics seems to present a panacea to that notion of risk because, by auditing 100% of the sample, detection risk by definition is lower.
   1. It seems like data analytics is the perfect answer to the audit risk problem. So, comment on a variety of reasons that auditors might not be willing to rely on data analytics to drive audit risk down.
   2. Which do you think is costlier: sampling or data analytics? What are the different costs between sampling and data analytics?
   3. What role does cost-benefit play in the choice between employing statistical sampling versus data analytics?
2. Data analytics enables auditors to audit all transactions, rather than just a sample of transactions.
   1. Do you think that as the use of data analytics increases on audit engagements, the need for sampling will decrease?
   2. What role might the PCAOB or AICPA play in helping auditors determine when and how to incorporate data analytics into the audit?

Answer 1

Answer to (i) Reasons for auditors not to rely on data analytics

(1) Cost effectiveness of data capturing process :- In Audit sampling, data is retrieved straight from client and then assessed for its accuracy, completeness and relevance. While in data analytics, data extraction needs to be done at a deeper level using databases and then analysed into meaningful conclusions. As we see into corporations, databases are kept highly secured on a need to know basis along with multiple restrictions regarding usage, electronic transfer etc. In this scenario, clients make it tedious for auditors to capture required analysis and data banks in a cost effective and timely way.

(2) Audit Evidence conflicts :- Today Big Data analytics is capable of dealing with humongous volumes of data growing by the day and turn them into conclusive reports based on user requirements. To support their conclusions, auditors need to substantiate it with reasonable audit evidence. Nature of analytics is dependent on codes, rules, programs which makes it conflicting for the auditor as to whether to rely on their own professional judgment or to use it as an audit evidence.

(3) Limited Guidance from Auditing Standards :- ​​​​​​ Auditing profession is guided largely by internationally recognized auditing standards or country specific ones as well. Digital age has evolved dramatically after the conception of these standards. As a result, these standards do not talk much about using analytics as audit evidence or using them to deal with audit risk while determining fraud risk factors. Therefore, Auditors are apprehensive of using analytics as audit evidence

Answer to (II) & (III)Which is costlier : Sampling or data analytics & Different costs between both options

For a smaller establishment of Audit firm, sampling would be considered less costly as compared to data analytics. Reasons to support the statement are as under :-

1. If the competence of auditors is not there to work with data analytics, cost of hiring data scientists is involved.

2. Acquisition and maintenance costs for software compatible to work with data analytics.

3. Big data analytics involve unstructured data which may not be numbers; they may be video clips, images, punched biometric data etc. This kind of data needs to be converted in information using pre-defined parameters which means separation of irrelevant data involving further costs.

Answer to (iv) Role of Cost benefit analysis in selection for statistical sampling vs data analytics

Data analytics techniques allow the auditor to inspect 100% of the population for conclusions as opposed to audit sampling. The benefits of using data analytics are :- (a) Reduced Audit risk (b) Less time required for more extensive reports (c) Benefits of automation available like self correction of probable mistakes (d) Increased value added services can be provided to clients

The cons in using data analytics over sampling are :- (a) Costs involved in dealing with huge amounts of data (b) auditors still need to ensure the qualitative aspect of audit evidence to reduce risk of misstatement. (c) Lesser acceptability in auditing standards (d) Information overload while submitting conclusions (e) Technical skills required from all stakeholders involved in auditing process

Hence, Data analytics offers a more comprehensive approach towards audit sampling yet there is a long way to completely overthrow sampling out of the system. All the stakeholders need to fall in place and overcome the hurdles in the process.