TIAPS ALB_Module 2E. Data Analytics for Internal Auditing

More documents

Recommendations

Info

This can help managers identify and react to problems or opportunities. Some causes of variances are purely random and can be eliminated. The comparison of two sets of data can help determine the extent to which they are correlated. Variance analysis is discussed further in Module 3. 2E.2.2 Trend Analysis Trends are changes (or variances) in data over time. The changes observed may be: • Random, to be identified and eliminated or ignored. • Cyclical, recurring over short cycles, such as higher demands for customer services at certain times of the day or week. • Seasonal, recurring over longer cycles, such as peaks and troughs in sales of ice cream over a year. • Underlying trends, being the true long-term pattern, often over multiple years, having isolated random, cyclical, and seasonal factors. Often underlying trends are most apparent when we can compare data from the some point in a cycle, season, or year over multiple cycles, seasons, or years. 2E.2.3 Reasonableness Testing Reasonableness testing is another form of variance or trend analysis in which reported or apparent performance is compared with what might reasonably have been expected, once the forecast is adjusted to take account of everything that is known. Variances may highlight errors or deliberate misstatements. When there seems to be no reasonable explanation, when things look too good or too bad to be true, then it deserves further investigation. 2E.2.4 Ratio Estimation Findings based on a sample of data can be extrapolated to make assumptions about the remaining data or used as the basis for forecasting. Larger samples help reduce the likelihood of bias but there is no guarantee the sample is representative of the whole population. Statistical modeling is used to calculate the degree of confidence in the analysis. 2E.2.5 Benchmarking Comparing actual performance with benchmarking data can help identify errors, weaknesses, and opportunities for improvement to align more closely with best practice. 2E.2: Reflection Consider these commonly used methods for analysis of data: Variance analysis Trend analysis Reasonable testing Ratio estimation Benchmarking Which of these do you commonly use? Which of these do you feel you need more help in developing your competency? 73
2E.3 Data Analytics Tools To help with the heavy lifting, auditors can take advantage of a wide array of technological solutions all of which are being used increasingly in service delivery, marketing, sales, human resources, finance, budgeting, and accounting. There are audit specific applications – both customized and off-the-shelf – for: • Smart apps. • Utilization of big data. • Artificial intelligence. • Machine learning. • Natural Language Processing NLP. • Robotic process automation. • Drones. • Artificial reality. Many proprietary tools are available. Vendors often provide consultation, technical support, and training to aid the adoption of solutions. Comparison websites are useful to help identify suitable options depending on organizational need and available resources. 65 For the internal audit function, such enablers may be used to support the following: • Remote auditing, using drones, artificial reality, robots, teleconferencing, and other similar tools. • Automated audit management, using audit software for planning, communication, storage and access, reporting, supervision, monitoring, and open issue tracking. • Advanced data analytics, using number-crunching, machine learning, artificial intelligence, and data visualization tools. • Internal cooperation and collaboration with other functions, especially assurance providers, through alignment with governance, risk management, and internal control platforms. • Significant reduction of manual and repetitive tasks and unnecessary duplication of data sets through robot process automation and reconciliation. • Continuous auditing through artificial intelligence. Artificial intelligence (AI) is a “hot topic” which much discussion about apps such as Chat GPT which may revolutionize many activities. It is not possible to predict exactly how AI will impact internal auditing but it is clear it creates new opportunities. Manual tasks such as data extraction can be automated much more quickly and without errors. Rather than relying on a sample, AI can perform analytics on complete population sets and in real time to identify trends, anomalies, errors, and potential fraud. AI can also anticipate new and emerging risks through rigorous interrogation of historical and recent data. Internal audit functions are often criticized for being slow to adopt technology in comparison with other functions. A recent article recounted the excuses commonly given by auditors: • Don’t have the budget. • Don’t have the time. • Don’t have the right people. 65 See for example, The Best Data Analytics Tools & Software of 2023, Forbes, 2023. 74
Page 1 and 2: 2E. Data Analytics for Internal Aud
Page 3: 2E.2 Data Analytics Methods Analyti
Page 7 and 8: aware of itself, know about its int
Page 9 and 10: Technique and Description A pie cha
Page 11 and 12: References and Additional Reading 7
Page 13 and 14: Lean Auditing (Part 2): Rethinking
Page 15: CIPFA: 77 Mansell Street, London E1

TIAPS ALB_Module 2E. Data Analytics for Internal Auditing

Create successful ePaper yourself

Delete template?

Save as template?