Transforming internal audit with data analytics

Unlock the value of internal audit function and improve decision-making through quality analytics

As the world of business grows in complexity, the Internal Audit process has become more time consuming and difficult. Internal auditors are often unable to validate that a business is being run effectively and regulations-compliant despite conducting thorough audits. The huge volume of information that they must process within a relatively short period is a key reason why that is so.

Benefits of Data Analytics for Internal Audit

In recent years, data analytics has helped overcome above challenge and transformed the internal audit process. Used effectively, data analytics delivers significant performance uplift and value-add to an organisation and increases the credibility of internal audits with stakeholders. It also improves the quality by automating the process and provides management with optimal assurance.

A key benefit of data analytics is the comprehensiveness of audit and elimination of sample testing. This leads to more accurate audits and greater assurance to management.

Data analytics also helps improve audit efficiency by processing large volumes of data within a limited timeframe. An unprecedented level of efficiency can be achieved if we use analytics to review low-value activities while auditors focus on critical processes. Automation of time consuming tasks helps internal auditors establish more controls and improve adherence to standards and guidelines.

Data analytics also helps identify hidden patterns and irregularities. It assists internal auditors to identify risks efficiently and improve internal control. Conducting real time risk assessment and continuous auditing helps in improving risk management.

Quality Analytics

High-quality and impactful analytics are proven assets for any business. It also enhances the credibility of internal audits. However, it is equally very easy to lose trust due to unreliable and inaccurate results that may be caused due to poor data quality and misrepresentation of data.

It is hence critical to develop an effective data analytics approach. The process includes data quality assessment and validation or results. Organisations using data analytics for internal audits should develop data standards and conduct quality assessments.

Besides ensuring data quality, it is important to pay heed to data sensitivity, storage and jurisdictional regulations. This is especially relevant for organisations with offshore centres for data analytics.

Collaboration between the analytics team and internal auditors is critical to audit quality. Not involving analytics in defining the scope of the audit can lead to suboptimal, or even inappropriate, insights.

— Shailendra Ranjan is senior director – consulting at MBG Corporate Services