How can data analytics support organisations in the accounting and audit sector?
For professionals working in the audit and accountancy sector, the quality, consistency, and scale of the data they work with presents a significant challenge, more so when distributed across multiple siloed data sources. In response to this challenge, Analytics Engines has created an industry-specific solution called Xamine, which is designed to provide audit and accountancy professionals with a data-driven approach to audit.
The Journal of Accountancy published an article exploring the potential impact and application of data analytics on the audit and accountancy world. Since its publication, however, adoption of these technologies has been slow.
In March 2021, a report from First Intuition found that only 41% of accounting organisations surveyed had begun to adopt data analytics. Of this 41%, none felt that they had done so to the “maximum extent”. The survey also found that 40% of respondents had taken little to no action regarding the adoption adopt data analytics within their organisation.
Much like the legal sector, there is significant scope for change within the audit and accountancy space. In a 2021 interview with Accountancy Today, Chris Knowles, Chief Digital Officer at RSM UK discussed the growing digital skills gap within the accountancy sector and said: “We’ve got to experiment, and we’ve got to try things out with regard to digital innovation.”
In this blog post, we’ll discuss some of the challenges that data analytics can help to solve within the audit and accountancy sector and showcase how organisations can adopt a data-driven approach to audit.
Identifying financial risk is a major responsibility for organisations in the audit and accountancy sector. Oftentimes, however, these organisations rely on samples rather than whole datasets, making it difficult for them to develop a “holistic” understanding of a client. This in turn makes it difficult for accountancy and audit professionals to identify anomalous patterns and behaviours.
This challenge is exacerbated further by the quality, scale, and consistency of the data that many modern data professionals are working with (a point explored further in this article from the ACCA). Large volumes of data from multiple siloed sources often inhibit an organisation’s ability to perform comprehensive audit checks efficiently and effectively.
Addressing the Skills Gap
An article from Accountancy Today found that 31% of global CFOs don’t have enough people with software and technology experience within the finance function. The article goes on to state that this skills gap “threatens” accountancy and audit organisation’s ability to provide “the consultancy, analysis, planning and due diligence required to support broader business goals”.
A Data-Driven Approach to Audit
Analytics Engines have developed Xamine, a holistic data-enabled audit approach capable of capturing, extracting, cleansing, formatting, interpreting, analysing, and presenting data from a range of sources. The solution is designed to:
- create efficiencies that can be re-invested to other audit tasks;
- identify insights that are not yet known;
- support audit professionals through a no-code user interface;
- add value to the audit process and outputs; and
- safeguard quality as risk factors and thresholds can be easily amended across audit files as a result of findings or developments in the audit profession.
To learn more about Xamine, you can download the product fact sheet using the link below.
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Specialists in data integration, machine learning, AI, in-depth analytics and visualisations, Analytics Engines leverages connected technology and data analysis to deliver better insights and outcomes for customers.
We break down our customers’ data silos and enable organisations to understand how, where, and why their data is connected. Our team provides customers with the resources and capability they need to search, understand, and act upon their data.
To learn more and to arrange a Data Discovery Session, contact us using the form below.
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