The traditional approach to collecting and managing corporate financial data for auditors has reached its limit. The overwhelming volume of data, tight audit timelines, and a growing regulatory and media focus on fraud have combined to significantly impact the quality and completeness of the audit.
Faced with these challenges, financial auditors are finding it necessary to turn to AI tools to overcome these limitations. However, many of the traditional and newer AI tools are only designed to analyze accounting entries, the structured financial data, during the audit. As a result, audit firms end up having to employ multiple different tools, against the available data, creating a patchwork of analysis and an incomplete picture.
What’s needed is a holistic platform for data collection, management and analysis that brings together all the client’s structured and unstructured data into a single pane of glass for review..
This article will explore how financial auditors benefit by leveraging a holistic platform at every stage of the audit process.
1) Faster Data Preparation and Accelerated Audit Timelines
The volume and diversity of data available during an audit is the most significant challenge facing auditors. Even simple transactions have multiple related documents, ranging from purchase orders and invoices to legal documents, contracts, shipping records, and more. Further, these documents could be from different jurisdictions, written in multiple languages, or saved in different formats.
A holistic platform allows auditors to quickly and easily prepare both structured and unstructured data for review. The drag-and-drop interface simplifies data collection and makes it easy for both clients and auditors to upload requested documents. From there, artificial intelligence (AI) automatically scans, tags, and cross-correlates documentation to every accounting entry selected for testing.
By spending less time collecting and preparing data, auditors can reduce the audit timeline and spend more time testing and verifying the legitimacy of transactions.
2) A More Complete and Independent Audit
In the past, it was simply not possible for auditors to test every transaction in every area of risk. Traditional audit methodologies relied on a sampling approach based on areas of likely statistical risk, such as year over year changes in revenue. Auditors would then request the appropriate business process documentation to confirm the selected transactions, but what could be reviewed was limited to the number of samples selected and the amount of documentation available to review in the fixed time set for the audit.
Enter AI, and now auditors can risk assess 100% of the accounting entries before the audit begins using a wide variety of numerical techniques, enhancing their sample. But what hasn’t yet been sped up is the number of business process documents that can be reviewed per audit cycle. That still requires an auditor reading the documentation to confirm selected transactions.
So now with risk being assessed at a 100% level on the numbers, auditors need a platform to help them assess the documentation at a similar rate.
A holistic financial data management platform enables the auditor to automatically test and confirm more transactions more rapidly, even before the audit begins. Auditors can therefore focus their time reviewing complex journal entries or entries that failed confirmation. Similarly, the platform allows auditors to sample a larger dataset without giving away their sample or indicating to the client what they are looking for. This is important for conducting an independent audit, especially in cases where there may be suspicious transactions that could indicate fraud.
3) Reduced Client Burden and Improved Client Experience
Clients accept the need for audits, but they generally want them to be completed as quickly as possible and with minimal disruptions to their business. Providing the requested data to the auditor can place a huge burden on clients, especially if their data is spread across multiple systems, departments, offices, or business units.
A holistic financial data management platform reduces the burden on the client by simplifying the data collection process. As mentioned above, clients can easily drag and drop requested data into the platform. In cases where data is siloed, user access can be given to each department or team to load their own documents.
Further, the client can continue to use the platform once the data has been uploaded. The fully contextualized data lake enables the client’s internal audit team to conduct financial analysis, risk assessment, and process optimization without building and managing another system.
4) Better Allocation of Skilled Resources
Audit firms do not want highly skilled personnel spending large amounts of audit time reading and reviewing documents to confirm stock transactions, especially as they deal with a growing skills shortage. Fewer people are graduating with accounting degrees, while large numbers of accountants and auditors are leaving the field for other industries.
A holistic data management platform helps reduce the labour required for a more complete financial audit. Because the majority of transactions are normal, artificial intelligence can conduct the initial review to pinpoint transactions that may need further testing. The auditor can then focus only on the potential areas of risk.
The reduction in labour can be significant. In one recent example, a forensic audit that was initially expected to require three auditors working for 10-12 weeks was completed by one auditor in only one week.
5) Laying the Groundwork for Continuous Auditing
Continuous auditing has always been the dream, but, until recently, the time and resources that would be required made it impractical compared to the traditional periodic audit.
However, a holistic financial data management platform lays the foundation for a continuous, real-time audit methodology.
Once the data has been uploaded and organized in the data lake, both clients and auditors can continue to access the system and upload new information. Transactions can then be monitored in real-time, with AI automatically flagging any discrepancies or anomalies for immediate review.
By reducing the lag between collection and analysis, auditors gain better detection of potential risks. The model continues to learn as more data is uploaded, resulting in a faster, more efficient, and more timely audit.
Leveraging A Holistic Data Management Approach for Financial Audits
As the volume of data grows and the business environment becomes more complex, financial auditors need a new approach to data collection and analysis.
A holistic financial data management platform replaces the wide range of disparate tools focused solely on structured data. By bringing all the client’s structured and unstructured data into a single, comprehensive platform, auditors can lower costs, reduce audit timelines, minimize the burden on clients, and deliver a more complete and independent audit.
Recent Comments