Transcript (Data) Mine! - Corruption Prevention Network

GENERAL
Corruption Prevention
Network
14 September
2006
SEGMENT
AUDIENCE
DATE
Into the (Data) Mine!
Presented by:
Alicia Peters
Fraud Prevention and Control
Australian Taxation Office
INTO THE DATA MINE
www.ato.gov.au
Organisation
Tax Office Executive
(Audit Committee / Integrity
Advisory Committee)
Internal
Assurance
Branch
Fraud
Prevention and
Control
Intelligence
and Analysis
Investigations
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Official
Conduct
Team
Internal Audit
Fraud Control
Planning
Security
Policy and
Services
Fraud
Awareness
Training
Standards
and
Reporting
Coordination
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What is data mining?
 ‘The nontrivial extraction of implicit, previously unknown,
and potentially useful information from data.’
 ‘…analysing data to show patterns or relationships;
sorting through large amounts of data; and picking out
pieces of relative information or patterns that occur…’
http://en.wikipedia.org/w/index.php?title=Data_mining&oldid=59371931
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Data mining tools
 There are many contemporary tools available
 Tax Office
-
Microsoft Excel
Microsoft Access
ACL
Win IDEA
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The data
 Who owns it?
- Internal
- External
 Where is it?
 Quality:
- Duplicates
- Inconsistencies
- Incomplete
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What data mining can do
 Compare different sets of data
 Identify trends and outliers
 Identify relationships
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What data mining can’t do
 Data mining cannot
-
identify fraud in isolation – generally requires further scrutiny
verify individual transactions
avoid false positives and / or false negatives
work without access to data
 Data mining is less effective if the quality and quantity of
available data is reduced
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Outcomes
 Data mining needs to be directed by the type of outcome
sought
- Identification of potential fraud incidents
- Identification of fraud risks
- Identification of system / process vulnerabilities
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Where do the ideas / projects come from?
 Criminal’s perspective of our systems and processes
 Communication between Internal Investigations, Fraud
Control Planning and Official Conduct Team
 External information from liaison with other departments
 Research
 Media
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Planning data mining projects
 Projects are based on emerging risks
 No strict work plan - maintains flexibility to deliver
products and address emerging risks
 Connections with Internal Audit
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What we do with the results
 Suspected fraud incidents
- Internal fraud investigation referrals
 Fraud risks and vulnerabilities
- System / process owners
- Fraud Control Planning as an intelligence product
- Tax Office Executive
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Products
 Threat assessments
 Data mining reports
 Fraud investigation referrals
 Tax Office Executive reports
 Weekly staff newsletter articles
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Data mining in the Tax Office from an internal fraud
perspective
 Two types are undertaken
- Departmental – What makes the department work
- Administered – What the department exists for
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Data mining departmental systems
 22,000 staff
 60 locations around Australia
 Projects
- Cabcharge
- Mobile telephones
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Cabcharge
 Context
- Cabcharge cards and vouchers
- 135,500 transactions a year
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Cabcharge
Data:
 Internal data quality was poor because of different
management systems
 Data sought directly from Cabcharge
- Many duplicates
- Incomplete data population
- Inaccuracies around pick up and destination locations
- Differences in data for card and voucher transactions
 Imported 315,500 transactions into IDEA
 Got rid of duplicates
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Cabcharge
 Focus
- Large single expenses on cabs
- Use of Cabcharge cards on Saturday
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Cabcharge
Validation process:
 Large fares
- 35 emails sent to managers to confirm expenditure
 Weekend use
- 301 emails sent to managers to confirm expenditure
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Cabcharge
Results
 Fraud
- Suspected personal travel
 Fraud risks
- Incorrect amounts approved
 Observations
- High traffic routes
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Cabcharge
 Reporting
- Tax Office Executive
- Process owners
- Article in weekly staff newsletter
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Mobile telephones
 Liaison meeting with Defence
 Identified personal use of mobile telephones as an
emerging risk – especially in relation to ‘190’ numbers
 Identified that these types of calls were not being checked
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Mobile telephones
 Context
- 6,500 handsets
- 4 million transactions a year
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Mobile telephones
 Data
- Internal data quality was good but did not provide a means
of identifying who destination numbers belonged to
- Imported 8 million transactions into IDEA
- Got rid of duplicates
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Mobile telephones
 Focus
- Calls to ‘190’ numbers
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Mobile telephones
 Identified ‘190’ calls which reduced the population size to
just under 3,000 transactions
 Used Internet to identify the services provided by each
‘190’ number
 Excluded reasonable personal uses such as time and
weather services
 Identified telephones used to access large volumes of
adult content / gambling hotlines
 Identified other uses which constituted code of conduct
issues, for example, competition hotlines
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Mobile telephones
Results:
 Fraud
- 7 phones calling adult / gambling numbers
 Misuse
- 14 phones calling other services
 Fraud risks
- No current review system
 Observations
- Reporting lost / stolen phones
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Mobile telephones
 Reporting
-
7 internal fraud investigation referrals
14 letters to managers to deal with individual minor matters
Tax Office Executive
System owners
Article in weekly staff newsletter
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Data mining of administered systems
 Longer term projects
 Current project involving BAS processing
- Initial data set – 6.5 million transactions
- First cut – 3 million
- 11,000 records based on risk algorithm
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Conclusion
 Focussed on emerging risks
 Flexible approach
 Requires intelligence support
 Work with what you have
 Takes time
 Good planning
 Record keeping
 Information sharing
 Marketing of results
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Thank You
 Questions?
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