data mining and audit tools - wirc-icai.org mining and audit tools.pdf · • concurrent audit...
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Data Mining & Audit Tools
© Nikunj S. Shah
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Data Mining and Audit Tools
• What is Data mining?
• Data mining – Two different meanings
– Common Sense – Every method that assists in
finding patterns in large data sets.
© Nikunj S. Shah
finding patterns in large data sets.
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Data Mining and Audit Tools
Technical – Data mining commonly involves four classes of task:
• Classification - Arranges the data into predefined groups with the help of algorithms
• Clustering - Is like classification but the groups are not
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• Clustering - Is like classification but the groups are not predefined, so the algorithm will try to group similar items together.
• Regression - Attempts to find a function which models the data with the least error. A common method is to use Genetic Programming.
• Association rule learning - Searches for relationships between variables.
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Gold is tested by fire, man by gold.
- Ancient Chinese Proverb
© Nikunj S. Shah
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Data Mining and Audit Tools
• Skill sets for a career in Investigative AuditAuditing
standards &
procedures
Evidence
gathering
Information
Technology
Accounting &
Business
reporting
systems
© Nikunj S. Shah
Investigative
techniques
Fraud
Detection
Litigation
processes &
procedures
gathering
Computer
Science
Technology
Criminology
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Data Mining and Audit Tools
• “Man is a tool making animal” – Benjamin Franklin
• Human redefined as
© Nikunj S. Shah
• Human redefined as
“Homo Faber”
• Are we alone in making
tools?
• Well, we have got
company…….
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Data Mining and Audit Tools
The need for CAATTs
• CAATTs – Only practical means for establishing what the facts really are as they:
© Nikunj S. Shah
they:
�Help apply intuition, knowledge and skill with far greater impact
�Identify indicators of fraud or red flags
�Document findings• Note: results of Data Analysis may not constitute a proof for fraud.
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Types of CAAT tools
• Spreadsheet software, Database management systems, Word processors (mobile phones?)
• Generalized Audit Software (GAS):
Data Mining and Audit Tools
© Nikunj S. Shah
• Generalized Audit Software (GAS):
�ACL, IDEA, SoftCAAT
• Concurrent Audit techniques
�Integrated Test Facility (ITF)
�Systems Control Audit Review File (SCRAF)
�Continuous and Intermittent Simulation (CIS)
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There are seldom small frauds; merely large ones
given insufficient time to grow.
- Michael J. Comer, Corporate Fraud, p. 18
© Nikunj S. Shah
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Data Mining and Audit Tools
• Data mining process with CAATTs has two
steps
– Defining patterns manually and recording it in
© Nikunj S. Shah
the tool.
– Testing patterns’ presence in the database
automatically.
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Data Mining and Analytic Techniques for
Investigative Audit1. Matching
Data
© Nikunj S. Shah
Investigative Audit
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Data Mining and Analytic Techniques for
Investigative Audit
Matching Data:
How do I do it?
• Use the VLOOKUP() function in Excel or Join Databases feature in
IDEA to match data from different sources
• Such data must have a common field, usually called as ‘Key field’
© Nikunj S. Shah
• Such data must have a common field, usually called as ‘Key field’
on which the data is joined.
Case studies:
• Match the payroll data with the card swipe data / server log on
data on the employee ID to detect ghost employees
• Match the transaction file received from the stock exchange with
the transaction data from the broker’s application on client ID to
detect possible money laundering
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Data Mining and Analytic Techniques for
Investigative Audit1. Matching
Data
2. “Same
Same Same”
&
“Same Same
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“Same Same
Different“
test
Investigative Audit
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Data Mining and Analytic Techniques for
Investigative Audit
“Same Same Same” & “Same Same Different” test:
How do I do it?
• Use the Sort, IF() & AND() functions in Excel or Duplicates /
Duplicates exclusion feature in IDEA to do these tests
Case studies:
© Nikunj S. Shah
Case studies:
• Use these tests to detect customers having same email address,
Pin Code but different Names
• Use these tests to detect customers having same address, Pin
Code but different FD Nos
• Use these tests to detect double payments i.e. payment made to
the same vendor against the same invoice no. and same date
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“There are three things in the world that deserve no mercy,
hypocrisy, fraud, and tyranny.”
- Frederick W. Robertson
© Nikunj S. Shah
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Data Mining and Analytic Techniques for
Investigative Audit1. Matching
Data
2. “Same
Same Same”
&
“Same Same
3. Benford’s
Law
© Nikunj S. Shah
“Same Same
Different”
test
Investigative Audit
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Data Mining and Analytic Techniques for
Investigative Audit
Benford’s Law:
How do I do it?
• Use the LEFT() and COUNTIF() functions along with other
mathematical functions in Excel or Digital Analysis feature
in IDEA to do these tests
© Nikunj S. Shah
in IDEA to do these tests
Case studies:
• Use these tests to detect unusual duplications of digits;
think hard to find the reasons thereof
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“The important thing is not to stop questioning.…..
Never lose a holy curiosity.”
- Albert Einstein
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Data Mining and Analytic Techniques for
Investigative Audit1. Matching
Data
2. “Same
Same Same”
&
“Same Same
3. Benford’s
Law
4. Relative
Size Factor
Theory
© Nikunj S. Shah
“Same Same
Different”
test
Theory
Investigative Audit
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Data Mining and Analytic Techniques for
Investigative Audit
Relative Size Factor Theory
How do I do it?
• Use the Sort, IF() & other mathematical functions in Excel
to do these tests
Case studies:
© Nikunj S. Shah
Case studies:
• Use these tests to detect “Out of Place” transactions in PO
to vendors, vendor payments, credits in vendor accounts,
etc.
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Data Mining and Analytic Techniques for
Investigative Audit1. Matching
Data
2. “Same
Same Same”
&
“Same Same
3. Benford’s
Law
4. Relative
Size Factor
Theory
© Nikunj S. Shah
Investigative Audit
“Same Same
Different”
test
Theory
5. Identify
high value
transactions
with 80-20
principle
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Data Mining and Analytic Techniques for
Investigative Audit
Implement 80-20 principle to identify high value
transactions
How do I do it?
• Use the Pivot table feature in Excel, drop the amount field
in the row and in the data area and group the amount in
© Nikunj S. Shah
in the row and in the data area and group the amount in
the row fields into appropriate strata.
Case studies:
• Use this test to identify high value (and may be high risk)
transactions in purchases, sales, vendor payments etc.
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“If you are out to describe the truth, leave elegance
to the tailor.”
- Albert Einstein
© Nikunj S. Shah
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Data Mining and Audit Tools
• Acknowledgements:
�Shri. Chetan Dalal Chartered Accountant
�Shri. Himanshu Vasa Chartered Accountant
�Shri. Jayesh Gandhi Chartered Accountant
© Nikunj S. Shah
�Shri. Jayesh Gandhi Chartered Accountant
�Friends and Colleagues