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Transforming internal audit through data analytics

Shift your internal audit analytics program into drive

Internal audit continues to search for opportunities to provide deeper insights and value across the organization to address rising pressure from stakeholders, increased regulations, and a dynamic business landscape. To that end, 82% of internal audit functions surveyed in PwCs 2017 State of the Internal Audit Profession Study have increased their investment in data mining and data analytics to facilitate monitoring of key trends and support continuous auditing. However, many functions are finding their analytics programs stalled and in need of a jump start.

PwCs internal audit analytics professionals are helping companies shift their internal audit analytics programs into drive and embed analytics in a way that supports the entire audit program. We bring a combination of audit, data analytics, technology, and business knowledge to identify patterns, trends, and anomalous behavior using data. This leads to richer business insights and enables internal audit to position themselves more strategically within the organization.

Of internal audit functions have taken steps to integrate data analytics according to PwC's 2017 State of the Internal Audit Profession Study. However, a long road lays ahead as many seek to fully embed analytics into their audit program.

Benefits of embedding analytics into your internal audit approach

Deeper business understanding to assist in audit intelligence Advanced monitoring capabilities that offer a sustainable, quality approach to managing compliance, controls, and data validation requirements End-to-end testing for process and control verification Lower existing and future costs through automation of processes Earlier identification of anomalies and patterns of high risk, leading to enhanced coverage Effective communication of key issues and findings through dynamic dashboards Audit functions that are more relevant to the business and to boards

Perspectives

Internal audit analytics: The journey to 2020

Audit transformation through innovation and technology

Capitalizing on the wealth of data now availablefrom your own business activities as well as external sourcescan 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.

Benefits of an insights-driven approach

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

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.

Perform the same audit cheaper: Connecting the auditor directly to the process through the data with risk analytics and data visualization 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.

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.

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.

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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 theyll 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:

Assessment: Analyze current analytics capabilities both within IA and across the business and rapidly develop proof of concepts to identify challenges and opportunities.

Roadmap: Create a long-term strategy and vision for analytics; scope and prioritize projects to achieve this.

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.

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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 organizations 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.

Question:

How may the internal audit functions utilize data to improve audit efficiency and effectiveness?