Summary
Nowadays, Audit Analytics is not limited to Ad hoc and to point-solution. Audit Analytics is embedded throughout the Internal Audit life-cycle delivering significant efficiency gains. Analytics is a core competency and value driver in IA. Established and proven operating model. Catalyst for IA’s broader adoption of leading technologies such as RPA, process mining etc.
Key Challenges in IA
Prioritizing Initiatives
- Articulating priorities
- Balancing conflicting agendas
- Monitoring progress
Quality
- Consistency in audit quality & depth of review
- Quality of audit communication & reporting
Value
- Tracking value added by IA
- Innovating to remain relevant
Coverage
- Balancing depth vs. coverage
- Planning and achieving coverage of diverse risks and geographies
Benefits of IA Analytics
The potential to utilize data analytics and “big data” to accomplish competitive advantage and manage operations and strategic plans ranks among the top risk issues for board members and C-suite executives worldwide. The true value of a mature analytics activity, integrated throughout the IA life-cycle, comes from being able to not only answer the questions of “what has happened” and “what is happening” but “what could happen next”.
Guiding principles for Audit analytics
- Define analytics goal in line with audit engagement objectives
- Know your data – availability and completeness
- Make it actionable and measurable
- Leverage existing insights
- Make it relevant to the stakeholders (process owners)
- Test, learn and improve continuously
Integration of Analytics in Internal Audit
Standard analytics are executed before the substantive test plan is defined for a specific audit ideally before a audit begins. The substantive test plan must include all analytics that have been executed, however the results of controls or substantive testing may require the substantive test plan to be modified or increased. Additional standard or customized analytics can be executed in order to support scope that had not been covered by the initial analytics run and cover additional transactions or risks.
Audit Analytics outcomes
Capital project stakeholders expect the use of real-time information to manage, monitor, and audit capital project performance. This requirement does not end with the project team, but rather extends to project controls, finance departments, project managers, and other internal stakeholders to ensure the successful, timely, and cost effective completion of capital projects. While traditional audit requirements remain the same (invoice auditing, monitoring for potential fraud, change order oversight etc). New audit approaches and technologies, such as the use of data analytics, are enabling organizations to perform 100% auditing of all costs and changes in a real-time manner. In addition to conducting more efficient and effective audit work, data and audit analytics are creating opportunities to improve overall project reporting and increasing value provided by audit across entire organizations.
Skilled Resources
- Foresee project challenges and mitigate project cost impacts
- Influence the project team to make decisions that accomplish overall project goals
- Control gaps in project execution through contractual agreements and real-time audit procedures
Cost Transparency
- Provide early indication of potential cost overages through audits and productivity analysis
- 100% audit of all monthly billings
- Provide real-time visibility into all project expenditures
- Monitor compliance with contract terms and conditions
Real-Time Data and Reporting
- Provide comprehensive data analysis that is not dependent on manual monthly revisions
- Identify and present metrics on-demand that demonstrate project control
- Provide accurate and categorized cost reporting that meets stakeholder requirements
Comprehensive Package
- Provide systems and software to be utilized by Internal Audit and other stakeholders
- Utilize systems that coordinate all aspects of cost, schedule, and risk management including budgeting, forecasting, and change management.
To read an article about “What is Data Analytics – Introduction & Types | Data Analytics Tools”.
