Developers can leverage advanced analytics tools to streamline and improve software, compliance, and internal controls auditing. Several novel solutions solve existing pain points, suggesting their effect on audit processes could be permanent.

How Advanced Analytics Enhances Audit Processes

Whether auditors review contracts, financial statements, code, or internal controls, their traditional techniques are tedious — and inherently inaccurate to some degree. Take sampling, for example, where they test a database subset.

It is not uncommon for sampling portions to be trivial compared to the total population. For example, while testing 500,000 Amazon transactions may seem significant, it only accounts for 0.012% of the total — which may introduce unintended bias or errors, skewing audit process insights.

Advanced analytics tools like artificial intelligence, complex event processing, and data mining enable 100% population testing because of their rapid processing capabilities. They eliminate the need for sampling, thereby reducing bias and error risks.

These tools can also enhance fieldwork, risk assessment, document reviews, and report drafting. Autonomous technologies like AI are particularly beneficial since automation eliminates human error and increases quality. Moreover, they enable real-time data analysis or anomaly detection, streamlining processes.

Examples of Successful Analytics-Based Auditing

The Internal Revenue Service (IRS) recently leveraged advanced analytics for its audits. Since they are relatively rare, few people are audited yearly. While this undoubtedly benefits individuals, it creates persistent discrepancies and financial losses for the federal government.

The U.S. Government Accountability Office reports that the IRS is using AI-powered tools to close the multi-billion dollar tax gap. It uses multiple models to audit select taxpayers and partnerships by selecting large representative samples of high-risk returns. This automation eliminates tedious manual processes, enabling federal employees to expand their scope.

Many leading accounting firms have also seen success. For instance, PricewaterhouseCoopers has deployed AI auditing technology for financial procedure review to identify irregularities, enabling it to perform more in-depth analysis.

Similarly, Ernst & Young uses natural language processing (NLP) technology to minimize the need for manual contract reviews. It detects anomalies during review and explains its findings, minimizing the risk of material misstatements. A human-in-the-loop validates its model’s output, offering expertise and context awareness to maximize precision.

Deloitte developed an AI-powered chatbot named DARTbot for 18,000 audit employees, enabling real-time assistance. It provides source material alongside output, enhancing explainability and enabling auditing teams to analyze complex queries in greater detail.

Klynveld Peat Marwick Goerdeler — one of the largest global accounting firms — partnered with Mindbridge, an AI platform, to increase audit explainability. Its advanced rule-based and machine-learning technology integrates with the auditing platform KPMG Clara for real-time analysis. Consequently, its auditors gain more time to review high-risk items.

What Technology Can Be Used to Improve Auditing?

While many notable case studies revolve around financial, compliance, and consulting applications, this approach is also relevant for software auditing. Developers can leverage cutting-edge analytical tech to improve accuracy, explainability, and speed.

1. Data Visualization

With data visualization, developers can convert databases into graphics, charts, and tables, enhancing vendor collaboration and providing clarity to auditees. Since tools like AI can process unstructured data, they are not bound to text-based format.

2. Complex Event Processing

Since complex event processing analyzes numerous systems simultaneously in real-time, developers can detect software abnormalities swiftly. Consequently, pinpointing the source of issues becomes much more manageable.

3. Artificial Intelligence

AI tools can accelerate most auditing processes because of their autonomous and rapid processing capabilities. Developers can even use generative models or algorithm-powered chatbots to rewrite code or suggest solutions for errors.

Developers using advanced analytical tools benefit from being able to audit in real-time. Moreover, they can comprehensively review entire populations instead of sampling data. These benefits contribute to a more accurate, precise, and quality assessment.