INTO THE DATA MINE SEGMENTAUDIENCEDATE Presented by: Alicia Peters Fraud Prevention and Control Australian Taxation Office Into the (Data) Mine! GENERALCorruption Prevention Network 14 September 2006

INTO THE DATA MINE Organisation Tax Office Executive (Audit Committee / Integrity Advisory Committee) Internal Assurance Branch Fraud Prevention and Control Intelligence and Analysis Investigations Fraud Control Planning Fraud Awareness Training Standards and Reporting Coordination Internal Audit Security Policy and Services Official Conduct Team

INTO THE DATA MINE 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…’

INTO THE DATA MINE Data mining tools  There are many contemporary tools available  Tax Office -Microsoft Excel -Microsoft Access -ACL -Win IDEA

INTO THE DATA MINE The data  Who owns it? -Internal -External  Where is it?  Quality: -Duplicates -Inconsistencies -Incomplete

INTO THE DATA MINE What data mining can do  Compare different sets of data  Identify trends and outliers  Identify relationships

INTO THE DATA MINE 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

INTO THE DATA MINE 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

INTO THE DATA MINE 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

INTO THE DATA MINE 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

INTO THE DATA MINE 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

INTO THE DATA MINE Products  Threat assessments  Data mining reports  Fraud investigation referrals  Tax Office Executive reports  Weekly staff newsletter articles

INTO THE DATA MINE 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

INTO THE DATA MINE Data mining departmental systems  22,000 staff  60 locations around Australia  Projects -Cabcharge -Mobile telephones

INTO THE DATA MINE Cabcharge  Context -Cabcharge cards and vouchers -135,500 transactions a year

INTO THE DATA MINE 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

INTO THE DATA MINE Cabcharge  Focus -Large single expenses on cabs -Use of Cabcharge cards on Saturday

INTO THE DATA MINE Cabcharge Validation process:  Large fares -35 s sent to managers to confirm expenditure  Weekend use s sent to managers to confirm expenditure

INTO THE DATA MINE Cabcharge Results  Fraud -Suspected personal travel  Fraud risks -Incorrect amounts approved  Observations -High traffic routes

INTO THE DATA MINE Cabcharge  Reporting -Tax Office Executive -Process owners -Article in weekly staff newsletter

INTO THE DATA MINE 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

INTO THE DATA MINE Mobile telephones  Context -6,500 handsets -4 million transactions a year

INTO THE DATA MINE 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

INTO THE DATA MINE Mobile telephones  Focus -Calls to ‘190’ numbers

INTO THE DATA MINE 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

INTO THE DATA MINE 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

INTO THE DATA MINE 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

INTO THE DATA MINE 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

INTO THE DATA MINE 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

INTO THE DATA MINE Thank You  Questions?

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