11 minute read 1 Jan 2018
couple meeting financial advisor office

How firms can advance analytics and automation within internal audit

By

EY Global

Multidisciplinary professional services organization

11 minute read 1 Jan 2018

To measure the advancements of analytics in IA, Ernst & Young LLP surveyed 16 banking and wealth management firms on their use of IA Analytics and compared these results to a similar study conducted in 2014. The results were telling. Roughly half of the 2017 survey respondents indicated leveraging data analytics in more than 50% of their audits, as compared to just 31% of survey respondents in 2014.

The 2017 survey results indicated an increased demand for IA Analytics, which led to additional questions: What is the current market landscape and maturity stage of the different IA Analytics programs? How can internal audit (IA) departments continue to be successful in advancing their analytics programs? To what level of maturity do IA departments want their analytics programs to be, and how can they get there? 

We sought to answer these questions and more by facilitating a roundtable discussion with IA leaders across 16 banking and wealth management firms. This article is a compilation of the insights from the 2017 survey results compared to the 2014 survey results, as well as from the roundtable discussion with industry leaders.

You can download the full report here.  

IA maturity stages and current market landscape 

Across the industry, IA departments have made significant progress in the development of their IA Analytics programs. Although the current development of IA Analytics programs may vary, the generalized path toward the growth of IA Analytics programs can roughly be categorized into three different stages: Analytics 1.0, Analytics 2.0 and Analytics 3.0.

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1

Chapter 1

IA maturity stages and current market landscape

The growth of IA Analytics programs can roughly be categorized into three different stages: Analytics 1.0, Analytics 2.0 and Analytics 3.0.

Across the industry, IA departments have made significant progress in the development of their IA Analytics programs. Although the current development of IA Analytics programs may vary, the generalized path toward the growth of IA Analytics programs can roughly be categorized into three different stages: Analytics 1.0, Analytics 2.0 and Analytics 3.0.

  • Analytics 1.0 focuses on incorporating analytics within the audit framework to increase efficiency through the development of documentation standards and show descriptive data analytics that may or may not be incorporated within advanced data analytics tools. 
  • The key focus of Analytics 2.0 is utilizing analytics to identify trends, develop thresholds and drive insights by incorporating advanced data visualization tools, shared cloud and big data services, and tools used to analyze unstructured data. 
  • Analytics 3.0 works to transform the “business of auditing” by using analytics to drive the audit process and risk identification. In this stage, the focus is on embedding analytics within the business, incorporating prescriptive analytics/risk identification and working to build a continuous auditing program.

While IA departments may currently be at different stages within this process, it is clear that the majority of respondents are continually working to enhance their analytics programs. Across 2017 survey respondents, the primary drivers for IA Analytics programs were increased audit depth, coverage and efficiency — a shift from previous years, which saw a rise in regulatory mandates as the primary motivation.

The primary drivers of analytics programs are focused on enhancing the audit program:

Driver Percentage of respondents
Audit coverage 100% of respondents
Efficiency in audit execution 88% of respondents
Audit depth 81% of respondents
Risk management 69% of respondents
Regulatory mandate 38% of respondents 

The current market landscape indicates that use of analytics in audit departments has become widespread. Respondents are implementing IA Analytics at a minimum stage of Analytics 1.0, specifically as a means to expand audit coverage and audit depth. Analytics lends itself to more easily repeatable processes, which reduces manual error and the level of effort required year over year, thus increasing the overall efficiency of an audit program. The following benefits are obtainable by audit programs that leverage analytics and are some of the distinguishing advantages of Analytics 1.0:

  • Ability to perform repeatable analytics
  • More population and control coverage; greater assurance
  • Deeper business understanding and focus on risk
  • More value to stakeholders
  • Ability to meet regulatory expectations

With IA Analytics becoming more of a standard practice, the industry as a whole is progressing along the analytics maturity road map with a majority of IA departments implementing programs at the Analytics 2.0 stage. In addition to accessing the advantages of Analytics 1.0, IA departments at the Analytics 2.0 stage are implementing analytics to conduct the following:

  • Examine a vast amount of data from both internal and external sources
  • Identify attributes that were previously unavailable
  • Discern relationships, anomalies and correlations that were never before visible
  • Focus on potential issues

A key focus for programs at the Analytics 2.0 stage is working to better integrate themselves with the business audit teams. To achieve this, some programs have developed products such as self-service tools and other integrated training programs. The creation of self-service tools allows business auditors to further incorporate data analytics within their programs by analyzing data in real time while the analytics departments address the more complicated data analytics requests, such as conducting trend analysis and identifying anomalies.

Over one-third of organizations have a high degree of integration between the business and analytics audit teams

Integration

38%

Number of organizations that have a high degree of integration between the business and analytics audit teams

Concurrently, analytics programs have also added more personnel and have increased the training and skill sets of their resources. A quarter of 2017 survey respondents stated that analytics teams make up 6%–10% of the IA department, compared to just 15% of 2014 survey respondents.

Furthermore, roughly 94% of 2017 survey respondents have incorporated, or are currently incorporating, data analytic concepts within both the business and advanced analytic training programs. By incorporating analytic concepts within general business auditor training, business auditors can receive guidance on the benefits and limitations of utilizing analytics for IA and gain an understanding of analytical concepts. Advanced analytical training programs may incorporate general business auditor knowledge through case studies and key business examples to provide further context and insight into the business audit process.

Though many IA departments have made advancements in their analytics programs and realized significant value from these advances, a majority of firms are continuing to improve their programs to be at maturity stage of Analytics 3.0. Firms are working to transform the “business of auditing” so that analytics is embedded within the business and will help drive the audit process and risk identification. Some major advantages and components of Analytics 3.0 include:

  • Incorporation of prescriptive analytics
  • Intelligent risk identification
  • Sophisticated audit planning and execution
  • Continuous auditing driven by analytics
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Chapter 2

Analytics challenges

Deployment of a successful analytics program poses several inherent challenges.

Across the industry, IA departments have made significant progress in the development of their IA Analytics programs. Although the current development of IA Analytics programs may vary, the generalized path toward the growth of IA Analytics programs can roughly be categorized into three different stages: Analytics 1.0, Analytics 2.0 and Analytics 3.0.

  • Analytics 1.0 focuses on incorporating analytics within the audit framework to increase efficiency through the development of documentation standards and show descriptive data analytics that may or may not be incorporated within advanced data analytics tools. 
  • The key focus of Analytics 2.0 is utilizing analytics to identify trends, develop thresholds and drive insights by incorporating advanced data visualization tools, shared cloud and big data services, and tools used to analyze unstructured data. 
  • Analytics 3.0 works to transform the “business of auditing” by using analytics to drive the audit process and risk identification. In this stage, the focus is on embedding analytics within the business, incorporating prescriptive analytics/risk identification and working to build a continuous auditing program.

While IA departments may currently be at different stages within this process, it is clear that the majority of respondents are continually working to enhance their analytics programs. Across 2017 survey respondents, the primary drivers for IA Analytics programs were increased audit depth, coverage and efficiency — a shift from previous years, which saw a rise in regulatory mandates as the primary motivation.

Summary

As IA Analytics programs continue to develop, there is an increasing shift away from utilizing analytics purely to increase audit efficiency/ coverage toward leveraging analytics to identify certain trends and anomalies, and ultimately to develop a continuous auditing framework. By incorporating new emerging technologies, analytics programs are less hindered by budget and time constraints and can focus their attention toward cross-firm risk identification and identifying key trends and anomalies that cannot be identified without testing the entire population.

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By

EY Global

Multidisciplinary professional services organization