INTO THE DATA MINE www.ato.gov.au

SEGMENT AUDIENCE DATE

Presented by:Alicia PetersFraud Prevention and ControlAustralian Taxation Office

Into the (Data) Mine!

www.ato.gov.au

GENERAL Corruption Prevention Network

14 September 2006

INTO THE DATA MINE www.ato.gov.au

Organisation

Tax Office Executive(Audit Committee / Integrity

Advisory Committee)

InternalAssurance

Branch

OfficialConduct

Team

FraudPrevention and

ControlInternal Audit

SecurityPolicy and Services

Intelligenceand Analysis

InvestigationsFraud Control

Planning

FraudAwareness

Training

Standards and

ReportingCoordination

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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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