Internal audit analytics: The journey to 2020


Internal audit analytics: The journey to 2020

Audit transformation through innovation and technology

Capitalising on the wealth of data now available—from your own business activities as well as external sources—can help internal audit (IA) generate valuable new insights, provide greater assurance, and rewrite the rulebook on traditional auditing techniques. By embedding analytics in every phase of the audit process, IA can assist the business in navigating a world that has become more volatile, uncertain, and complex. We call this new approach to embedding analytics into internal audit "insights-driven auditing."

Insights-driven auditing: A multidisciplinary approach

Internal audit analytics is more effective when delivered as an integrated team. This means your core IA professionals are working together with the data science and analytics professionals and calling on subject matter specialists as appropriate. By co-developing scope, risk objectives, and approach for the internal audit and jointly participating in walk-throughs, internal auditors significantly enhance effectiveness of the analytics. In addition, a shared understanding of the process and outcomes ultimately results in an audit with a greater impact on the business.​

The success of any analytics-embedded internal audit is linked to those demonstrable results that can transform your organization, particularly when they translate to financial benefits. When seeking insight from data, it is important to ask the right questions and to always challenge yourself with “so what?” for any insight produced. Linking questions to key testing hypotheses, or “what could go wrong,” can help drive the internal audit analytics approach.

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Benefits of an insights-driven approach

The benefits of insights-driven auditing can be summarized into four simple statements:

  1. Perform the same audit faster: Improving your access to data and developing key insights before fieldwork commences; making connections and comparing performance and key benchmarks between products, processes, and business units means you focus only on what is of utmost importance and avoid merely confirming the obvious; or assessing transaction risks in real time.
  2. Perform the same audit cheaper: Connecting the auditor directly to the process through the data with risk analytics and data visualisation allows exploratory analytics to drive a more focused audit, while still testing 100 percent of the population. Moving to automated routines over manual saves time and money.
  3. Perform better audits: Combining data from inside and outside your organization to add new richness and granularity to insights and understanding of risk. Benchmarks, comparative analysis, and trending enhance on-the-job learning and development while delivering a more impactful result to business stakeholders.
  4. Make innovation a centerpiece: Providing a rich combination of data science disciplines and using a new generation of technologies to enhance; automate; and continuously improve the audit process, reporting, and service delivery.

Becoming an analytics-enabled function

For many IA leaders, knowing where to start on the internal audit analytics journey is one of the tougher decisions they’ll have to make. It will begin with an owner who sets out a vision and who remains ultimately accountable for decision making at every stage; a strategy in the form of a roadmap, which describes and sets out the vision and objectives two to three years in the future; and an agreed set of processes that take into account everything from the order and priority of key tasks to the steps required to identify, map, and extract data for use in your first analytic embedded audit.

If a key element is missing, the vision will likely not be met, and your brand, along with the business, could be damaged. To overcome this, we recommend a simple three-stage approach:

  1. Assessment: Analyse current analytics capabilities both within IA and across the business and rapidly develop proof of concepts to identify challenges and opportunities.
  2. Roadmap: Create a long-term strategy and vision for analytics; scope and prioritise projects to achieve this.
  3. Deliver and monitor: Initiate the program, deliver the roadmap, and monitor your implementation successes against key performance indicators.

Becoming analytics-enabled relies on the fundamental building blocks of people, process, data, and technology, all being informed by an analytics strategy. This enables the embedding of analytics into the audit lifecycle, focusing on the right risks at the right time while aligning analytics to the IA strategy and value drivers of the business.

The path forward

While traditional IA functions may leverage analytics to select samples, extrapolate results, or identify exceptions, insights-driven auditing goes beyond this basic process in order to better address business issues and risks and provide new and valuable insights to management. It can help IA professionals ask the right questions, improve confidence in audit results, and identify the most appropriate actions.

While few organisations are on the cutting edge right now, our experience suggests that insights-driven auditing will become pervasive among leading companies by 2020. Soon, effective IA departments will integrate analytics as a core capability across their function and throughout the audit lifecycle. By acting now, IA leaders can get ahead of this trend, generating valuable new insights and more effectively helping their business to navigate the future.

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