Into the (Data) Mine!

Into the (Data) Mine! GENERAL Corruption Prevention Network 14 September 2006 SEGMENT AUDIENCE DATE Presented by:Alicia Peters Fraud Prevention and Control Australian Taxation Office www.ato.gov.au

Organisation

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

Data mining tools • There are many contemporary tools available • Tax Office • Microsoft Excel • Microsoft Access • ACL • Win IDEA

The data • Who owns it? • Internal • External • Where is it? • Quality: • Duplicates • Inconsistencies • Incomplete

What data mining can do • Compare different sets of data • Identify trends and outliers • Identify relationships

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

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

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

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

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

Products • Threat assessments • Data mining reports • Fraud investigation referrals • Tax Office Executive reports • Weekly staff newsletter articles

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

Data mining departmental systems • 22,000 staff • 60 locations around Australia • Projects • Cabcharge • Mobile telephones

Cabcharge • Context • Cabcharge cards and vouchers • 135,500 transactions a year

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

Cabcharge • Focus • Large single expenses on cabs • Use of Cabcharge cards on Saturday

Cabcharge • Validation process: • Large fares • 35 emails sent to managers to confirm expenditure • Weekend use • 301 emails sent to managers to confirm expenditure

Cabcharge • Results • Fraud • Suspected personal travel • Fraud risks • Incorrect amounts approved • Observations • High traffic routes

Cabcharge • Reporting • Tax Office Executive • Process owners • Article in weekly staff newsletter

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

Mobile telephones • Context • 6,500 handsets • 4 million transactions a year

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

Mobile telephones • Focus • Calls to ‘190’ numbers

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

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

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

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

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

Thank You • Questions?

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Into the (Data) Mine!