Transforming internal audit through data analytics
Adding insight to audit
Traditional internal audit (IA) methodologies have served their purpose well for decades. However, as the business landscape for most organizations becomes increasingly complex and fast paced, there is a movement toward leveraging advanced business analytic techniques to refine the focus on risk and derive deeper insights into the organization. Leading IA functions are embracing recent enhancements in data mining technology and data visualization tools to deliver results more dynamically in response to risk, dive deeper into organizational data, and deliver fact-based insights.
Challenges and expectations
As the business environment grows increasingly complex, IA is being asked to support the organization by delivering deeper insight and greater value more efficiently and effectively. Some of the more challenging expectations of IA include:
- Being more efficient and achieving more with less
- More effectively identifying and responding to risk
- Delivering more robust and effective analysis of key issues
- Providing more meaningful actionable insights
- Driving change within the business
Leveraging analytics for improved decision-making
Business analytics can help meet these expectations. Leading IA functions are embracing enhancements in data mining technology and data visualization tools to move beyond the traditional internal audit activities and toward an environment of more sophisticated risk analysis and monitoring. Learn how analytics allows companies to more skillfully slice and interpret their data, deliver insight, and add value—enabling executives to make more efficient and effective decisions.
Governance, Risk & Compliance
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