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Threat, risk (organised) crime an d Crime-money (laundering)

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Presentation on theme: "Threat, risk (organised) crime an d Crime-money (laundering)"— Presentation transcript:

1 Threat, risk (organised) crime an d Crime-money (laundering)

2 Past, present and “OC threat/risks Past, present and “OC threat/risks”  2001: European Multidisciplinary Group declared: “We looked back; we must look forward!”   Therefore: “Future oriented” reporting: the future of OC = the future of its threat/risk  What is threat or risk?  And what is an ‘organised crime risk’?

3 The simple risk formula  Risk = p = ∑x i /N (per time unit) = threat =  the likelihood that an event x of a certain class Y will occur given the total set of events.  Could policy makers please substitute the x and Y?  Y is a closed definition of a class of events  x = single event of class Y  ∑x i-time = time series of events  Apply that to organised crime assessment

4 Finding an insurance policy against “organised crime”  Basic thesis: every determinable harm can be insured if a likelihood can be determined.  What does an insurance firm do with a new risk? (a) it determines the meaning of the class of events Y, then its total N (b) it designs a time series = past events x (c) the costs of events (classified harm) and fills the formula

5 Finding an insurance policy against organises crime: continued  What did the EU policy makers do?  (a) they formulated a fuzzy definition and (b) threw away the past. Just try to make a time series.  What can an insurance firm do?

6 The desperate insurance firm  What can an insurance firm do?  It cannot sell an OC insurance policy because there is no determinable risk! (Or serious crime): no x and no Y  On what basis to assess OC crime risk?  If no proper definition, no OC insurance risk  Only con men can sell such policies!

7 The desperate insurance firm (continued)  Are policy makers con men? They sold you multi-million policies   EUROPOL   Organised Crime Threat Assessments   Transnational Organised Crime Convention   Anti-money laundering regime All to make us feel secure!

8 The insurance firm perseveres!!  Continue with our insurance man. What can he do?  He must keep the OC banner: excellent commercial label   never abandon a winning formula!  Next: some correlation with a criterion variable.

9 The insurance firm perseveres!! (continued) For example:  Breakdown of social-economic or criminal variables against criterion variable = “Foreign direct investment” (Daniele and Marani; Italy)  OC and investment: negative correlation but ≠ causal relation, because  Underlying variable: mal governance and corruption.

10 The unmarketable exception clause  The underlying variable: mal governance and corruption.  The ‘Berlusconi exception clause’! How to sell such an insurance product?  Determining the threat of mal governance and sell corruption risk policies.  Commercial challenge for Transparency International, but otherwise unsalable.

11 The threat of crime money  The global threat since the 1980s.   Basic concern: threat to the financial system  integrity  Which criminal is going to cut the branch on which he is sitting?  Grubby banks are dangerous.... for launderers:

12 The threat of crime money (continued)   Calvi: hanging from Black Friars Bridge   Sindona (poison) + lawyer shot   Russian bankers (a too long series for a slide)   Nugan Hand Bank (Australia, suspicious suicide)   European Union Bank ($ 10 million lost)  Most recent launderers’ risk: unreliable bank employees selling CDs with names to the fiscal authorities!

13 The criminal risk industry  Instead of “threat thinking”: The real question: What is the role of crime money within the financial system?  Again: no data, but an abundance of threat images benefiting the compliance industry.  Lot of juggling with trillions by IMF, OEDC, World Bank, FATF: mutually copy-pasting figures and threats  A (financial) risk industry

14 Copy-pasting threats  ‘Affects currency movements’  ‘Destabilises banks by sudden withdrawals’  ‘Influences interest rate’.  ‘Distorts the GDP’.  ‘No optimal investment’ (remarked by “Ponzi-bankers”!)

15 The risk of laundered and unlaundered money  What is the harm of laundered money?   Part of the GDP: where is the danger?   Taxable   But there is moral harm: crime should not pay +  corrosion of morals

16 The risk of crime-money and corruption More corruption? white  All big corruption scandals in EU concerned white money!  Unlaundered money  What is the threat?  Luxury lifestyle? What is the difference with our greedy irresponsible Ponzi-bankers?  If laundered properly, no longer a threat!

17 The role of crime money on-going research  The Dutch confiscation database: statistical mud track since 1994  “Threatened” sector real estate: skewed division but: Mean € 182.000 / median € 150.000  mean value bank account: € 263.000 / median € 20.000   € 100.000 + : 90   € 1.000.000 + 11   94 % Dutch bank accounts < € 100.000 The role of crime money: less prominent, certainly not threatening, unless falsified by better data!

18 Do what you are (hopefully) paid for  Falsify, falsify, falsify, until the hypothesis do not crack.  Identify your ‘risk’ counting unit: no risk assessment without : ∑x i-n/time  Get to your database owners and hold them accountable:  they are your (democratic) knowledge source.

19 Thou should not hide knowledge  “We are the people”, researchers too,  And have the right to know.  If no data access: sue them under your Freedom of Information Act  If you don’t dare, just join the collective risk assessment ritual dance of the conferences.

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