This document discusses how public sector organizations can use data analytics to enhance spend management control. It explains that while ERP systems provide a wealth of financial data, tools are needed to make sense of the information. The document then explores different data analytics tools and techniques that can help users analyze spending patterns and detect anomalies. As a case study, it describes how the audit software IDEA was used to analyze staff claims data from an ERP system. Specific analyses identified potential duplicate medical receipts, double payments, and transport claims that did not comply with expense policies. The document emphasizes that data analytics allows auditors to review all transactions for exceptions rather than relying on samples.

1. Using Data Analytics to Enhance
Spend Management Control in the
Public Sector
8 December 2010
Public Sector Finance World 2010
Yoong Ee Chuan, CPA CIA CISA CISM

2. Agenda
1. Analysing how data analytics will provide greater spend
management control
2. Exploring different data analytics and computer assisted
audit tools and techniques
3. Questions and answers

3. Data Analytics
• What is Data Analytics?
• Wikipedia:
“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.”
• Some examples
• Data mining
• Business intelligence
• Statistical applications

4. Data Analytics
“Making sense out of
nonsense!”

5. Data Analytics – Spend Control
• Public sector finance professionals – guardians of public
money
• Value‐for‐money
• Economy (Less is more)
• Efficiency (Input vs output)
• Effectiveness (Output vs outcome)
“If you know the enemy and know yourself you need not fear
the results of a hundred battles. “
Sun Tzu
• Enemy = Enterprise Resource Planning (ERP) systems +
information overload + tools to understand your data

6. Data Analytics – Spend Control
• ERP systems provide wealth of information…
• If you can access it!
• Typically requires IT dept + Finance + Operations to get
reports and analysis they want
• ERP  Worksheets  Info for decision making
• Data analytics tools and techniques make user (Finance or
Operations etc) make the data talk to them
• How…..? Let’s find out!

7. Data Analytics Tools
• You already have them!
• Data analysis software
• Key characteristics
• Slice and dice to what you want
• Filters, sort, summarise, total, count, chart, pivot
• Microsoft Excel, OpenOffice Calc, Google Docs, etc
• IDEA, ACL, SPSS, etc.
• Concept is that the data analysis tools help you make sense out of
the (non)sense of data flooding your organisation
• There is no “one perfect tool”
• Experiment and use what suits you

8. Data Analytics Tools
• Caseware IDEA ‐ Data analysis or generalised audit software
• Need to understand data generated.
• 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.
• How to report it in a meaningful way.

9. Data Analytics Tools – Case Study
• Audit of Staff Claims
• Medical Claims
• Transport Claims
• Why review using data analytics?
• Detect non‐compliances and help organisation save $
• Review ALL (100%) of transactions vs sample 30 claims
• How to use?
• Step 1: Import data from ERP system i.e. Excel or flat files
• Step 2: Define field definition (text, numeric, date)
• Step 3: Run analysis i.e. exceptions, duplicates, patterns
• Step 4: Report exceptions, anomalies, patterns

10. Use of IDEA in Audit of Staff Claims

11. Use of IDEA in Audit of Staff Claims
(Medical)
• Perform Analysis  Audit Observation #1
• Duplicate Receipt Numbers
• From same staff ID

12. Use of IDEA in Audit of Staff Claims
(Medical)
• Perform Analysis  Audit Observations #2
• Double‐payment detected
by IDEA

13. Use of IDEA in Audit of Staff Claims
(Transport)
• Audit Observations #1
• Non‐deduction of Normal Travel Expenses from Office to Home
for journeys Starting or Ending from Home
• IDEA analysis
• Extract FROM = “Home”, FROM_TO_HOME = “N” and OFF_DAY = “N”
• Do similar for TO = “Home” etc.
• Review exceptions
• Audit Observations #2
• Possible Duplicate Taxi Claims and Claims without Valid Taxi
Receipt Numbers
• IDEA analysis
• Extract data where “RECEIPT_NO” is not “” and test for duplicates
• Extraction exception conditions where business rules are relatively
clear

14. Use of IDEA in Audit of Staff Claims
(Transport)
• Audit Observations #3
• Unusual Multiple Journeys Within the Same Day by Same Officer
• IDEA analysis
• Summarise by RECEIPT DATE and ID
• Sort by NO_OF_RECS
• Identify those staff who make many trips on same day
• IA Approach
• Do Field Statistics – get the big picture of data
• Analyse for exceptions to business rules e.g. transport claim rules
• Review different scenarios where controls may be circumvented
e.g. duplicate claims, high or frequent transactions by same
individual

15. Questions & Answers
Yoong Ee Chuan CPA CIA CISA CISM
Email: yekker@gmail.com