Background
Auditing can be transformed with machine learning leading to increased productivity and accuracy. Historically, audit is done on sample basis to identify risks. This approach does not take into account the total information available and relies mainly on manual processing. Audit firms can automatically process large volumes of data to identify anomalies and high risk transactions which can be further analyzed by humans.
How does AI impact the audit procedure?
Auditors can use AI to:
- Automate Manual Tasks of auditors such as documentation
- Analyze the complete volume of structured and unstructured data that come from financial records by analyzing data
- Identify Anomalies such as unusual payments or activities that would not be caught by manual auditing,
Traditionally the auditors face the challenge of evaluating multiple documents and contracts, reviewing the implementation of regulatory changes, and in deep diving in respect of uncertain transactions. Due to the large volume of data and expensive nature of manual work, auditors resort to judgmental sampling to achieve this goal to ensure wide coverage of checking. Machine learning can be utilized to go beyond sampling, reviewing all available information automatically to bring high risk documents to human attention.
With these AI and machine learning capabilities, auditors can do more advisory type of work such as understanding the entire ledgers and reporting on risk to management.
Why AI is important in today’s scenario?
The current business scenario is dynamic in nature. Businesses are growing and are getting more complex while they are trying to cut their audit budgets which encourage companies to automate more and more processes.
Using machine learning in audit improves the quality of the audit process. This is important because auditing scandals can spell the end of auditing companies or seriously harm their reputation, with the help of machine learning, auditors can focus on high risk issues and prevent such scandals.
Commonly AI is used for:
- Identification of material misstatements in the general ledger:
Because of various factors like large volume of data, limited time for doing audit and inherent limitations of internal control systems and accounting, auditors have been working with “reasonable assurance”.
Auditors examine a small set of data rather than the entire data set that’s why the risk of material misstatement increases. Use of machine learning improves the testing of ledger data by analyzing the entire dataset in a shorter time frame to identify material misstatements based on risk analysis rather than traditional audit rules. This means AI-based tools can flag transactional data based on how far they differ from the standard set.
- Automation of Expense Audits:
Another area where AI can be used to automate the audit process is checking of the expenses. AI-powered tools can help the auditor in detecting duplicates, out-of-policy spending, incorrect amounts, and excessive spending, and thereby helping the auditor to save his time and utilize the same in some other value creating activity for the client.
Conclusion:
At the end let us summarize the benefits of AI for an Audit Firm:
- The workload of auditors is reduced.
- Cost reduction: Use of AI reduces the cost involved in manual hours of research and analysis.
- Audit Quality: AI systems continuously learn and adapt to datasets so that they can improve accuracy and more data is processed. Therefore, use of AI increases the audit quality.