With the economic climate being uncertain, the role of auditors in a financial system gains paramount importance. While the use and benefits of data analytics have been acknowledged in financial audits, the tools to employ such data analysis were not available. However, with the advent of big data and the development in the field of data science, various methods can now be used in the field of financial audits.


Shift from the Sample Testing method

The manner in which financial audits usually function is that they work on the method of sample testing. However, with the use of data science and powerful computational tools, the entire audit data can be tested against the parameters that the auditors seek to impose. This will function in order to enable auditors to identify fraud and the failures in the operation of a business and create the business model in order to deliver pertinent results post the audit.


Barriers to integration of Data Science in Financial Audits

If auditors are unable to collect and use the data in an effective manner then data science cannot be applied for the purpose of financial audits. Often companies that are undergoing financial audits are reluctant to share all the data that they have for the purpose of the audit. The other issue is that companies employ a wide array of accounting systems for various purposes. If an array of different accounting systems are in operation across the board then the efficiency with which data science systems work will be hampered.

The manner in which data analytics needs to be designed in the context of financial auditing is to leave leeway for the auditor to apply his judgment to what the data is pointing out to as opposed to blind reliance on the results that the data shows. It should be used as a tool on which auditors rely on as a survey of the data in its entirety will give a much better picture of the financial system as opposed to testing of samples.