INTO THE DATA MINE SEGMENTAUDIENCEDATE Presented by: Alicia Peters Fraud Prevention and Control...

transcript

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

Organisation

Tax Office Executive(Audit Committee / Integrity

Advisory Committee)

InternalAssurance

Branch

OfficialConduct

FraudPrevention and

ControlInternal Audit

SecurityPolicy and Services

Intelligenceand Analysis

InvestigationsFraud Control

Planning

FraudAwareness

Training

Standards and

ReportingCoordination

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

- 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?

We are committed to providing you with advice and information you can rely on. We make every effort to ensure that our advice and information is correct. If you follow advice in this publication and it turns out to be incorrect, or it is misleading and you make a mistake as a result, we must still apply the law correctly. If that means you owe us money, we must ask you to pay it. However, we will not charge you a penalty or interest if you acted reasonably and in good faith.If you make an honest mistake when you try to follow our advice and you owe us money as a result, we will not charge you a penalty. However, we will ask you to pay the money, and we may also charge you interest.If correcting the mistake means we owe you money, we will pay it to you. We will also pay you any interest you are entitled to.You are protected under GST law if you have acted on any GST advice in this publication. If you have relied on GST advice in this publication and that advice later changes, you will not have to pay any extra GST for the period up to the date of the change. Similarly, you will not have to pay any penalty or interest.If you feel this publication does not fully cover your circumstances, please seek help from the Tax Office or a professional adviser. The information in this publication is current at September 2006. We regularly revise our publications to take account of any changes to the law, so make sure that you have the latest information. If you are unsure, you can check for a more recent version on our website at www.ato.gov.au or contact us.

Our commitment to you

INTO THE DATA MINE SEGMENTAUDIENCEDATE Presented by: Alicia Peters Fraud Prevention and Control...

Documents