How to Use Predictive Analytics to Improve the Audit Process

Conventional audit techniques entail obtaining a small amount of data and evaluating the results to determine the inconsistencies for an investigation. In contrast, predictive analytics involves using facts about the past to make assumptions and then deploying the inferences to make predictions.

This method allows auditors to work with 100 percent of the transactions of a particular data set to quickly identify patterns and correlation in large amounts of data, provide graphical findings, and determine high-risk areas for improvement. Subsequently, predictive data analytics lets auditors improve substantive procedures, risk analysis processes, and test controls.

The Concept Predictive Analytics in the Audit Process

The major difference between traditional reporting and predictive analytics is inference. The historical or fact-based assessment informs us where we have been and where we are at the moment. But it doesn’t elaborate on the current budget, the volatility of the portfolio, or revenue change per product and region. On the other hand, predictive analytics combine past and present occurrences with inferences to predict the future. It explains how much budget will be left in a given period, provides clarity in portfolio volatility and sales changes to enhance data accuracy, timing, and completeness.

Data analytics is typically the process of data extraction using fields within a given data structure instead of existing accounting records. The auditing team then sorts, filter, highlight, and then present the data visually via graphs and charts. The application of data analytics enables auditors to produce quality and statistical projections to help understand risks associated with accounting transactions’ frequency and benefits.

While some data analytics models are simple, others may involve complex procedures. Auditing teams employing these techniques have to exercise professional assessment to identify statistical and mathematical patterns to determine further testing exceptions.

Where is Predictive Data Analytics Used in Auditing?

Auditors use data analytics models in many ways. The most common applications include matching orders to cash, payment transactions, and evaluating capital expenditure against maintenance and repairs. These procedures can also be used to examine items with negative margins and evaluate gross margins and sales. It’s also valuable for payable aging and receivables, reduction in overdue by customers, and testing to see if segregation of responsibilities is appropriate.

Benefits of Data Analytics to Businesses

If performed appropriately, data analytics can produce more efficient and high-quality audit findings that benefit various departments within an organization. From management to boards and audit committees, properly executed audits can enhance granularity and transparency. Furthermore, data analytics delivers valuable insights that can improve communication between management and auditors. It provides information that helps in internal benchmarking, root causes of exceptions, control gap deficiencies. The audit insights can also help control failures, evaluate the effect of manual interventions, consistency of control application, and the extent to which procedures are being applied.

Notably, data analytics performances are more granular and can be utilized much faster and more widely, allowing the auditing team to focus on other more important things. It helps to keep in mind that the quality of data analytics procedures lies in the tools used, plus the judgments and analyses the tools can facilitate. The method used in data extraction and analysis is also as crucial as the visualizations that data analytics deliver.

Predictive data analytics facilitates audit professionalism, providing a way to rethink the whole auditing process. Most of the historical and fact-based reporting models are slowly vanishing. However, auditors need to ensure that the new auditing standards can improve audit value to stakeholders and investors, improve assurance, and accommodate new tools. Ultimately, it is evident that producing better insights and risk management controls can enhance quality audits despite the operational challenges.

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