Can you Leverage Analytics to Enhance Audit Quality and Efficiency?
As businesses continue to operate in one of the toughest and uncertain economic climates, Internal Audit (IA) is expected to deliver deeper business insights and ‘do more with less’. The use of data analytics allows IA to shift from the ad hoc review of transaction samples to the analysis of large data sets to draw meaningful audit conclusions.
Can you select the right tools that are cost-effective?
Can you ensure data quality, reliability and integrity?
Can you identify outliers and successfully detect fraud?
Be Equipped with the Latest Analytics Techniques to Boost Audit Performance
Join this practical 2-day workshop to acquire critical skillsets and tools to effectively leverage data analytics in the audit lifecycle. Learn how to use data analytics to enrich the risk assessment process, support planning and scoping decisions. Discover how to incorporate data analytics in fieldwork including designing data analytical tests. Examine how common analytics tools, advanced analytics and emerging technologies are used for audit. Find out how to create impact in your audit reports with analytics and visualisation.
Practical Hands-on Exercises
- Data cleaning
- Validating data quality
- Analysing large sets of data
- Automating procedures
- Detecting fraud and outliers
- Testing internal controls
- Performing visual analytics
- Building charts
Adopt Free and Paid Tools:
- Power BI
- Power Pivot
- Power Query
- How Data is Transforming Internal Audit
- Planning and Risk Assessment
- Data Analytics in Fieldwork
- Data Analytics Tools for Audit
- Advanced Analytics and Emerging Technology
- Data Analytics for Reporting
- Embedding Data Analytics Internally
Benefits of Attending
- Examine the critical components for effective data analytics
- Learn how to shift from manual audit to continuous monitoring and predictive analytics
- Find out the key phases in using data analytics as part of fieldwork
- Identify the different technology components that make up an infrastructure layer for analytics
- Weigh the pros and cons of tools including ACL, Qlikview, Qliksense, Power BI, Alteryx, SQL
- Discover how advanced analytics such as clustering is employed in IA
- Acquire analytical and statistical techniques to identify anomalies
- Uncover how to use visualisation to enhance the tracking of your audit plan
- Adopt the optimum people model when embedding analytics in your IA function
- Practice using Excel, Power Pivot, Power Query and data visualisation tools
Andre has set up the data auditing team for the PwC Singapore firm from scratch. In three years this has become a 25 person team supporting multiple types of data engagements, ranging from external and internal audits, special purpose assurance engagements, and advisory investigation assignments.
His clients include the breadth of industries, and range from MNCs to SMEs.
He has worked in the UK for ten years prior to joining Singapore, performing a similar role.
In particular, Andre has helped transform the Assurance practice through the injection of data techniques within audits. He has facilitated audit teams to identify areas to enable using data analytics, and has led initiatives of using global data tools to affect large scale changes within the audit strategy. Whilst doing this, Andre has devised the operating model to execute the work, employing both onshore and offshore resources.
Andre is an experienced tutor and have instructed in training and workshop sessions for seven years to a range of grades ranging from Associates to Partners.
Who Should Attend
Senior level executives responsible for Internal Audit, Finance, Risk Management, Fraud Management
Session 1: How Data is Transforming Internal Audit (IA)
- Key components for effective data analytics
- The driver and role of data analytics in recent years and megatrends
- How the IA function has evolved through the use of data analytics
- Examining the data analytics maturity continuum, and how to move from manual audit to continuous monitoring and predictive analytics
- How data analytics can transform all parts of the audit lifecycle
Session 2: Planning and Risk Assessment
- How data analytics can be used to enrichen the risk assessment process, and support planning and scoping decisions
- How to select areas of your audit lifecycle to apply analytics
Session 3: Data Analytics in Fieldwork
- How data analytics supports fieldwork
- Key phases in using data analytics as part of fieldwork
- Hands-on exercise: Designing data analytical tests
- Data analytics tests that can support fraud detection
Session 4: Tools
- Common tools (e.g. ACL, Qlikview, Qliksense, Power BI, Alteryx, SQL, etc.) that are used to execute data analytics for audit functions
- Different technology components that make up an infrastructure layer for analytics
Session 5: Advanced Analytics and Emerging Technology
- Use of advanced analytics such as clustering and its use in IA
- Different types of analytical techniques commonly applied
- Statistical techniques that can support identifying anomalies
- Other emerging technologies to look out for
Session 6: Reporting
- Creating impact in your audit reports through data analytics
- How analytics and visualisation can enhance the progress, monitoring and tracking of your audit plan
Session 7: Organisational Structure
- How the value of IA increases through the use of data and analytics
- Key components of a strategy to embed data analytics within the IA function
- Analysing the optimum people model for IA functions
Practical Hands-On Exercises
Sessions 8 and 9 will consist of real life application and hands on exercises. Using mock sample data, delegates will use data analytics tools to ensure data quality, detect fraud and outliers, test internal controls and perform visual analytics.
Session 8: Lab Session Using Data Analytics
- Data cleaning and validation using Excel formula
- How to validate data quality
- Using Power Pivot and Power Query to analyse large sets of data or automate procedures
- How to detect outliers, potential fraudulent transactions and attest to internal controls using data analytics
Session 9: Lab Session Using Data Visualisation
- How to use a data visualisation tool to perform visual analytics
- How to build charts in a visualisation tool