It has been a decade since banks, insurance companies, and retail sectors, large corporations started using data mining with technology companies’ help for analysis, design, and model.
The larger audit firms, especially started the data analytics usage in increasing audit quality in the past decade. Lately, large firms and mid-tier firms have changed the audit approach and methodology by adopting data analytics for 100% verification on smaller audits.
The established concepts on audit and its verification regarding the way it is regulated and executed are now challenged by data analytics. The evolution of audit testing started with 100% of transactions being tested in early days, then risk analysis and control testing were introduced with the help of materiality later, and now it is the time of data analytics as it is giving a big picture to the auditors on what is happening with the clients’ accounts.
Due to the results and increased adaptation and evident results; even though it is a new area for auditors, many smaller firms are starting to explore data analytics in audits. But the challenge for smaller firms rather than the software cost or availability, is the skills required to do the data analytics, especially audit verifications.
Data analytics in public accounting practice is not limited to external audit engagements and can be used in various assurance engagements. The selection or developing data analytics tools is critical; then we can apply to complete data sets to validate and analyze all the transactions to draw conclusions and decide on the next steps of verifications.
The auditors will perform more productive risk assessments and internal control procedure testing to decide the substantive test with data analytics.
The Visualization of data is also essential as after analyzing the data, it should be visualized for better understanding by using bar and pie charts, cluster, and other diagrams. There are many connector platforms available to perform a proper visualization like Microsoft Power BI and Tableau.
To establish a proper data analytics procedure for external audits; we need to address many operational issues, such as
Onboarding right talent and expertise
Extracting good quality evidence from the client system
Accessing to the client systems and related confidentiality issues
Cleaning of data and deciding the type of analysis to be performed.
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