1. COST-BENEFIT ANALYSIS OF ANTI MONEY LAUNDERING REGULATION IN THE EU: AN EXAMPLE OF DATA COLLECTION ON MONEY LAUNDERING
by Barbara Vettori
A presentation prepared for theTask Force Meeting on Crime Data Availability (Money Laundering)
Luxembourg, 6-7 November 2008

2. The Study and its objective
COST BENEFIT ANALYSIS
OF TRANSPARENCY REQUIREMENTS IN THE COMPANY/CORPORATE FIELD AND BANKING SECTOR RELEVANT FOR THE FIGHT AGAINST MONEY LAUNDERING AND OTHER FINANCIAL CRIME
A study financed by the European Commission – DG JLS
(Contract no. DG.JLS/D2/2005/01 30-CE /00-93)
The Study and its objective

3. The Study and its objective
OBJECTIVE OF THE STUDY
The Study and its objective
To carry out a CBA comparing costs and benefits arising from two disclosure systems of beneficial ownership of private and public unlisted companies:
the intermediary-based disclosure system embodied in the Third AML Directive (Model 0)
a new upfront and ongoing disclosure system proposed by the EU Commission (Model 1)

4. “making something known”
SOME DEFINITIONS…
(a) DISCLOSURE
Some definitions…
“making something known”
(b) BENEFICIAL OWNER
“the natural person(s) who ultimately owns or controls the customer and/or the natural person on whose behalf a transaction or activity is being conducted”

5. (c) COST BENEFIT ANALYSIS
Cost Benefit Analysis is a policy assessment method that quantifies, in monetary terms, the value of all consequences (positive and negative) of a policy for all members of society.
Costs and benefits are assessed on market value.
Not all consequences can be expressed in monetary terms (qualitative approach).
Some definitions…

6. Model 0: intermediary-based BO disclosure system
THE 2 MODELS
Model 0: intermediary-based BO disclosure system
The 2 Models

7. Model 1: upfront and ongoing BO disclosure system
The 2 Models

8. BO disclosure mechanism Intermediary-based Self-disclosure
Main differences
Model 0
Model 1
BO disclosure mechanism
Intermediary-based
Self-disclosure
BO definition
> 25% shares
> 10% shares
BO identification approach
Risk-based
Systematic
BO data keeping
Intermediaries’ internal database
Publicly accessible central registry
The 2 Models

9. 8 areas of incidence identified:
STRUCTURE OF THE CBA
AREAS of INCIDENCE
= specific category or subject or institution on which the introduction of the two BO disclosure models is supposed to impact in terms of costs and benefits
Structure of the CBA
8 areas of incidence identified:
Government
Law Enforcement Agencies (LEA and FIU) – OF INTEREST TODAY
Intermediaries (Banks – NBA - and Accountants - NAA)
Individuals
Businesses
European Union and Member States
Wider Costs and Benefits
Human Rights

10. COST AND BENEFIT ITEMS VARIABLES
Cost and Benefit Items identified for each Area and Model
39 Cost Items and 16 Benefit items identified as for Model 0
20 Cost Items and 12 Benefit Items identified as for Model 1
Structure of the CBA
VARIABLES
 Identification of the variables relevant to assess in monetary terms the cost and benefit items in Model 0 and Model 1

11. DATA GATHERING PROCESS
The CBA is supported by 2187 data elements gathered through two main kinds of DATA SOURCE:
1) A set of 6 QUESTIONNAIRES per MS (=162 questionnaires): submitted to 6 experts in each MS (FIU, LEA, National Bankers Association, National Association of Accountants, Company Registry Office, National Employers Association)
in 20/27 MS: questionnaires disseminated by national referents
period of the data gathering process: October 2006/May 2007
2) OFFICIAL DATABASES, DOCUMENTS AND REPORTS
Data gathering process

12. CONCLUSIONS
overall, successful data collection: 84% of requested data were available (% higher in the LEA/FIU area)
overall, good level of cooperation by FIUs and LEAs
importance of triangulating different data sources: questionnaires + IMOLIN, FATF (and FATF-style organisations) mutual evaluation reports, INCSR, FIU/police reports, etc.
Conclusions

13. COST-BENEFIT ANALYSIS OF ANTI MONEY LAUNDERING REGULATION IN THE EU: AN EXAMPLE OF DATA COLLECTION ON MONEY LAUNDERING
by Barbara Vettori
A presentation prepared for theTask Force Meeting on Crime Data Availability (Money Laundering)
Luxembourg, 6-7 November 2008