Audit Testing ERP Application and Connecting with CloudYoong Ee Chuan CISA, CISM, CPA, CIA doc

Audit Testing ERP Application and Connecting with Cloud
Yoong Ee Chuan CISA, CISM, CPA, CIA
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Agenda
1. Analysing how data analytics enhances audit testing of ERP
applications

2. Exploring different data analytics and computer assisted audit
tools and techniques

3. Understanding the risks of hosting ERP data with cloud
computing

4. Questions and answers

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Audit Testing – ERP Applications
What is Data Analytics?

“Analysis of data is a process of inspecting, cleaning, transforming, and
modeling data with the goal of highlighting useful information, suggesting
conclusions, and supporting decision making. Data analysis has multiple facets
and approaches, encompassing diverse techniques under a variety of names,
in different business, science, and social science domains.” Source:


• Some examples
• Computer-assisted-audit tools/techniques
• Data mining

• Business intelligence
• Statistical applications

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Audit Testing – ERP Applications
“Making sense out of nonsense!”
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Analysing How Data Analytics
Enhances Audit Testing
of ERP Applications

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Audit Testing – ERP Applications
Challenges of Audit Testing ERP Applications

• ERP systems provide wealth of information

• If you can access it quickly, efficiently and effectively

• Challenges include
• Lack of IT knowledge and skills by auditor
• Lack of knowledge of ERP package/module
• Lack of SQL, query language
• Overwhelming transaction volume from computerised records
• difficult to agreggate information for meaningful analysis
• cannot see the forest for the trees

• Access to data usually requires help of Information Technology , Finance
and Operations to obtain reports and analysis needed

• Use of data analytics allows the stories behind the data to emerge based
on the questions the auditor asks

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Audit Testing – ERP Applications
Why Use Data Analytics for Audit Testing ERP Applications?

• Increasing quantity and quality of data available

• Larger organisations typically have Enterprise Resource Planning (ERP)
implementations
• Human Resources/Payroll
• Financial Accounting/Management Reporting
• Accounts Payables
• Accounts Receivables
• Fixed Assets/Inventory
• General Ledge
• Project Management/Costing
• Core business applications for operations

• Business transactions captured in the bits and bytes of data residing in
ERP systems

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Audit Testing – ERP Applications

• Ability to analyse the underlying data representing business transactions
in meaningful ways:

• Empowers auditors to understand the business risks
• Use in audit planning and risk assessment
• Surveying audit universe from financial and operating data
• Summarisation of key fields by department, divisions, sections
• Helps to flag out areas of interest, potential misstatement,
non-compliance and potential fraud risks

• Ascertain compliance with business policies and procedures:
• Carry out detailed substantive and compliance auditing
procedures
• 100% testing instead of sampling
• Enhanced assurance and coverage

• Provides sufficient and appropriate evidence for audit reporting
• Exceptions are specific transactions flagged out by the data
analytics tools

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Exploring Different Data Analytics and
Computer Assisted Audit Tools and
Techniques (CAATs)

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Exploring Data Analytics & CAATs
• You already have them!

• Data analytics software

• Key characteristics

• Slice and dice to what you desire

• Filter, sort, summarise, total, count, chart, pivot

• E.g.s Microsoft Excel, Acccess, Open Office Calc, Google Docs etc.

• IDEA, ACL, SPSS etc

• There is no “perfect” tool

• Match the tools to the skillsets, experience, availability
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Exploring Data Analytics & CAATs
Example: Interactive Data Extraction and Analysis (IDEA)

• Caseware IDEA - Data analysis / generalised audit software / computer-
assisted audit tool

Caveats: Auditors / control professionals still need to:

• Audit objectives

• Need to understand business application and data residing in system

• Need to know what is the audit issue/business problem.

• Need to define that data needed and apply the right analysis to derive
the answers

• Answers may not always be 100% conclusive, still need professional
judgement and other corroborating evidence

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