Answer:
Part 1
Data analytics refers to a technique of using software and specialized systems to examine data and draw a conclusion regarding the information. Auditors inspect and model data to assess population data, identify potential risk and also avail quality audit evidence (Sun, 2018). Data analytics enables auditors to understand the environment of the entity and perform a comprehensive risk assessment (Brandas, 2018). The task will discuss application of data analytics in identifying audit risk of Woolworth’s group limited and also identify barriers and challenges in using the data analytics.
Auditors use data analytics to identify information that supports audit procedures, familiarize with the entity, and also identify the overlying risk. Woolworth’s group limited is affected by both control and detection risk (Gonzalez, 2018). Data analytics is used to identify key risks in Woolworth’s group limited by extracting, manipulating and analyzing data to identify fraud and errors in the data. Risk is identified by auditors through inquiry, inspection, and observation. Detection risk is identified by discovering and analyzing patterns, deviations and inconsistencies to extract meaningful information in the data (Chan, 2016). Trends and correlation is evaluated to extract a conclusion about the audit risk. Therefore, data analytics reduces audit risk and adds value to Woolworth’s group limited.
Data analytics is also used to identify control risk by assessing population data. Control risk occurs due to ineffectiveness of internal controls in detecting and preventing material misstatements. Internal controls involve separation of duties, physical inspection, authorization and approval, and reconciliation of financial statements (Hilorme, 2019). Data analytics thus enables auditors to deliver high quality audit evidence and identify operational risks that emanate from weak internal controls in the organization.
Auditors are facing several challenges and barriers while using data analytics; for example insufficient skills and expectation gaps. Auditors are faced with complex business model that does not operate as traditional ones (Mayes, 2018). Technological advancement has created sophisticated systems, and auditors need insight and understanding of how the systems work to perform an effective audit. Therefore, auditors are faced with the challenge of constantly updating their skills to perform an effective audit (Bengtsson, 2019). Expectation gap occurs when auditors encounter unrealistic expectation from stakeholders who claim that the client’s data is 100% because they are testing 100% of a population using data analytics.
In a nutshell, data analytics is used by auditors to profile audit risk, understand clients and also deliver relevant audit. Inherent risk and control risk are identified after utilizing data analytics technique to understand the client. Data analytics is used to identify control risk by assessing population data rather than sample data. Detection risk is identified by discovering and analyzing patterns, deviations and inconsistencies to extract useful information in the data. The risk leads to an incorrect opinion when the auditor is unable to detect errors and fraud. Additionally, control risk occurs when internal controls are ineffective to detect and prevent misstatements. Therefore, control risk can be controlled by sufficient internal controls.
Part 2
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Audit planning memorandum
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The lanning materiality
Materiality is chosen because misstatements involve errors and omission from financial statements and annual reports. Accuracy and reliability of financial statements and reports as required by the provider of financial capital. Listed Companies should disclose material information that can affect the market price of securities. Materiality is chosen because of human error and bounded rationality, willful misrepresentation, and timely disclosure of misstatements once discovered.
Materiality is chosen by determining the base and calculating the number; misstatements are then tracked on the summary of the unadjusted errors, the likely misstatement is then estimated and compared to the preliminary materiality and materiality is then allocated.
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Materiality base $ amount selected and percentage (%) applied:
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The base: 1 mark
Net income is the base of determining materiality threshold of the Company.
The amount of materiality is 50 million.
The base was chosen because of
There are several qualitative factors that determined the choice of the materiality base. For example, misstatements, conditions of the industry and violators of contractual agreements. Quantitative factors that determine the choice of materiality base include the quantity of fraud, staff turnover, and volatility of operating profit, liquidity and solvency.
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The base percentage
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The materiality threshold is 5% because the Company has legal proceedings which have adversely affected the reputation and financial performance. The Company has also reported cases of fraud as a result of misrepresentation.
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References
Bengtsson, E., and Zago, M., 2019. Big Data Analytics and Auditing-Implementation and knowledge.
Brandas, C., Muntean, M., & Didraga, O. (2018), Intelligent decision support in auditing: big data and machine learning approach. Proceedings of the IE 2018 International Conference, p.425.
Chan, D.Y. and Kogan, A., 2016. Data analytics: Introduction to using analytics in auditing. Journal of Emerging Technologies in Accounting, 13(1), pp.121-140.
González, H., 2018, November. ESIA Expert System for Systems Audit Risk-Based. In Advances in Artificial Intelligence-IBERAMIA 2018: 16th Ibero-American Conference on AI, Trujillo, Peru, November 13-16, 2018, Proceedings (Vol. 11238, p. 483). Springer.
Hilorme, T., Zamazii, O., Judina, O., Korolenko, R. and Melnikova, Y., 2019. Formation of mitigating risk strategies for the implementation of projects of energy-saving technologies. Academy of Strategic Management Journal.
Mayes Jr, C.R., Landes, C.E. and Hasty, H., 2018. Taking the Risk out of Risk Assessment: Properly Considering a Client’s Risks Is Essential to a Quality Audit. Journal of Accountancy, 226(2), p.38.
Sun, T., and Vasarhelyi, M.A., 2018. Embracing textual data analytics in auditing with deep learning. The International Journal of Digital Accounting Research, 18(24), pp.49-67.
