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

The fraud: Textiles, mainly from Asian countries are declared with as low as one tenth of their real value -> Evasion of ad valorem duties: Declared value: 0.2$ x 30,000 Units = 6,000$ x 12% = 720$ Actual Value: 2$ x 30,000 Units = 60,000$ x 12% = 7,200$ During the investigation, sources speak of as many as 40,000 containers (scanned orig. invoice)

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.

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 EU27

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

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

Analytical approach: Period Cust. Proc. Product CodeOriginDestinationVolume Statistical Value Average Unit Price Chinese Export avg minimum Value Difference in PCT 31/01/ CNAT7968,783832,410,3617,532,05 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

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 -> Below 100% Pale brown to green -> Above 100% Period Product Codes Workbook: TableauPresentation.tbw / Worksheet: Sample Import Database

Period Product Codes Visualization: Filter on DIFF_PCT (Difference in %) set < 100% Workbook: TableauPresentation.tbw / Worksheet: Sample Import Database Filtered

Visualization: Overview all Member States Question 1: Can we identify clusters of cases?

Question 2: Can we see shifts between Member States? Event: Member States were informed regarding fraud cases Event: Mission to CZ, risk profile is introduced Event: Risk profile in DE is adapted Workbook: TableauPresentation.tbw / Worksheet: Shift MS05 – MS06

Question 2: Can we see shifts between Member States? Workbook: TableauPresentation.tbw / Worksheet: Product611211

Question 3: Can we identify an organized behaviour? Overview of all Member States combined, by Product Code Workbook: TableauPresentation.tbw / Worksheet: Overview Total

Other Findings: Differences between Member States regarding the distribution of unit values per volume -> Effect of differently designed risk profiles Workbook: TableauPresentation.tbw / Worksheet: Price per MS all Products