Visualization and analysis of clusters in large populations of fraud cases. - PowerPoint PPT Presentation

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Visualization and analysis of clusters in large populations of fraud cases.

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Title: Visualization and analysis of clusters in large populations of fraud cases.


1
Visualization and analysis of clusters in large
populations of fraud cases.
2
  • The fraud
  • Textiles, mainly from Asian countries are
    declared with as low as one tenth of their real
    value -gt Evasion of ad valorem duties

Declared value 0.2 x 30,000 Units 6,000
x 12 720
(scanned orig. invoice)
During the investigation, sources speak of as
many as 40,000 containers
Actual Value 2 x 30,000 Units 60,000 x
12 7,200
3
  • The task
  • Identify all potential cases of undervaluation
    in the 27 EU Member States using the declared
    average unit prices
  • Create an overview of clusters of cases in order
    to
  • identify shifts between EU Member States
  • evaluate the effectiveness of counter measures
    taken by Member States
  • identify any organized behaviour across Member
    States.

4
  • The problem
  • Several statistical methods could identify these
    cases as outliers (e.g. t-test), but

Fraud appears to be so widespread that it biased
the declared average unit prices over time
Example Unit Prices for imported T-Shirts, by 27
EU-MS
Workbook TableauPresentation.tbw / Worksheet
Unit-Prices 610910 EU27
5
Comparison between Unit Prices declared during
export in China and import in Czech Republic
Solution Chinese export unit prices seem to be
reliable
Workbook TableauPresentation.tbw / Worksheet
Comparison Unit Prices CN MS05
6
  • Description of the data set
  • Contains import declaration data for four
    chapters with textile goods of the Combined
    Nomenclature 61, 62, 63 and 64
  • Date aggregation level Day
  • Starting from Jan 2007, last update Mai 2009
  • More than 6M records
  • Possibility for daily updates (monitoring
    function)
  • Contains Product Codes, Customs Procedures,
    Member State, Third Country, Volume, Statistical
    Value and Average Unit Value

7
Analytical approach
Period Cust. Proc. Product Code Origin Destination Volume Statistical Value Average Unit Price Chinese Export avg minimum Value Difference in PCT
31/01/2007 42 6204110000 CN AT 7968,78 3832,41 0,36 17,53 2,05
  • The lowest acceptable unit value is
    calculated average minimum values minus standard
    deviation over time (as declared at Chinese
    export)
  • Differences between this value and the declared
    unit value are calculated

8
  • Visualization
  • PERIOD set to All Values
  • Shape of the marks set to Country of Origin
  • Colour of the marks represents percentage of
    import values to export values
  • Red to pale brown -gt Below 100
  • Pale brown to green -gt Above 100

Period
Workbook TableauPresentation.tbw / Worksheet
Sample Import Database
Product Codes
9
  • Visualization
  • Filter on DIFF_PCT (Difference in ) set lt 100

Period
Workbook TableauPresentation.tbw / Worksheet
Sample Import Database Filtered
Product Codes
10
Visualization Overview all Member States
Question 1 Can we identify clusters of cases?
?
11
Event Member States were informed regarding
fraud cases
Event Mission to CZ, risk profile is introduced
Event Risk profile in DE is adapted
Question 2 Can we see shifts between Member
States?
?
Workbook TableauPresentation.tbw / Worksheet
Shift MS05 MS06
12
Question 2 Can we see shifts between Member
States?
?
Workbook TableauPresentation.tbw / Worksheet
Product611211
13
Overview of all Member States combined, by
Product Code
Question 3 Can we identify an organized
behaviour?
?
Workbook TableauPresentation.tbw / Worksheet
Overview Total
14
Other Findings Differences between Member
States regarding the distribution of unit values
per volume -gt Effect of differently designed risk
profiles
Workbook TableauPresentation.tbw / Worksheet
Price per MS all Products
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