Quantifying Financial Crime in the Asia Pacific



John Walker

Director, John Walker Crime Trends Analysis;

Research Fellow, Centre for Transnational Crime Prevention, University of Wollongong, Australia


For the Financial Crime Forum Asia Pacific Hong Kong

5-7 June 2006



I’ve grown up in a period that coincides with the development of computers and computer science, from the point where they became “useful”, even though they were the size of a small football stadium.  The chief accountant at my first jobs had a computer to do the company’s accounts and print out the payslips.  It was difficult to imagine anything else they could be good for, because they were just adding up machines attached to high-volume printers.


I was also lucky enough to be born a number cruncher at the right time, and I learnt to program these machines, using long-forgotten languages like Algol and Fortran, and I soon discovered that you could get them to do far cleverer things than just add up the wage packets.  I used matrix algebra to work out how much extra traffic you would get along a certain road if you allowed a certain development to take place.  Few of my school mates ever imagined that there was a real-world use for matrix algebra!  I even used probability theory to see if space satellites would achieve the scientific experiment they were designed for.  This now allows me to say, with at least some authority, “It’s not rocket science” whenever I think a problem is solvable and others think it’s too hard.


From there, I worked in regional government in the UK and Australia, using statistics to try to solve some riddle that needed a town planning solution (e.g. why are there traffic jams at “that” particular place every time our football team plays at home, or what will happen to the jobs situation here when the baby-boomers all leave school and start looking for work?  Working for the Melbourne Metropolitan planning authority in the early 1970s, I was given some local area crime statistics, and asked to identify the regional planning implications.  I found this stuff so fascinating that I came back to it a few years later, and was offered a research position at the Australian Institute of Criminology.


Most criminologists are not very numerate, so I found myself in demand and learnt on the job.  Part of my university training, however, was in economics, and I started to feel that there was a vast unexplored side to criminology.  It was clear that there are crimes perpetrated for economic gain, but what about the other aspects of it?  I was again blessed with good timing, because this was the time when criminologists were experimenting with randomised surveys as an alternative to police statistics to better measure the extent of crime.  I insisted that we ought to ask about the costs to the victims – how much property was stolen? – did you have to go for medical treatment? – did you have to take time off work? Etc.  We discovered that this was indeed an important question.  People often didn’t even report a crime to the police if it didn’t cost them much, or if the costs of reporting to police outweighed the cost of the crime.  At the other end of the scale, politicians, we found, didn’t do much about crime unless there was political mileage for them – but you could make them listen if you showed them how high the costs of crime actually were.  They then had a political “angle” to work with – they could tell the electors that their policy would save millions of their dollars, but more importantly they could convince their own Treasurers that their policy would be money well spent..


As well as individuals, I surveyed businesses about the costs of crime, and discovered that many of them are more worried about the damage to their reputations than to their immediate bottom line, and prefer not to report crimes to police – and the bigger the crime the more likely this appeared to be!  Presenting these findings to politicians in Australia resulted in some notoriety for me, and I was encouraged to continue.  My costs of crime estimates became the justification for a whole range of policy changes, not least of which was a demand for more police and for a shift towards a focus on fraud – the biggest single figure in criminal accounts. 


In the early 1990s, I began working as a consultant, and on the basis of my costs of crime work I was asked to try to estimate the extent of money laundering in Australia.  Although necessarily imprecise, my estimates supported vigorous action by the Australian government, and continue to do so to this day.  During that same decade, UK Prime Minister Tony Blair began to stress the need for action to be “evidence-based” as the fundamental principle of his U.K. government, and I realised that this was what I’d been searching for all along.  What he was saying is that governments should take in all the evidence before making their minds up on what to do about a problem.  It’s ironic then that his government is now in deep trouble for doing the exact opposite, but this paper is not about Iraq, it is about the need for evidence-based action to combat financial crime.

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In this paper, I’ll begin by giving a very simple definition of financial crime, but one that has very wide-ranging implications.


I’ll then present an analysis of the size of the problem – globally and in the Asia Pacific region.


I’m then going to have an audience question and answer session, but don’t worry – I’ve thought of some really good questions that you should ask me, and I’m going to answer them straight away, so you don’t go home disappointed with me.


Then I’m going to try to show the implications of these estimates, in terms of developing evidence-based strategies against financial crime.


I’m going to end up with a few words about how I would go about it – remembering that it isn’t rocket science – it IS achievable.

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I’m going to start with a short list of what might be included as financial crimes.  I’m sure you’ll be able to think of others, but for the purposes of this presentation, a very short list will be enough.


Financial fraud

The adverse impact of financial fraud, not only on individuals and the commercial sector but even on national economic systems, is increasing rapidly worldwide. Left unchecked, fraud could lead to the financial ruin of people and commercial enterprises as well as seriously damage economic systems.

Counterfeit currency

A major challenge confronting societies today is the growing integration of the world’s economies, which has sparked myriad transnational criminal schemes designed to exploit financial payment systems. Fraud schemes involving credit cards, identity theft, bank fraud or the most fundamental financial crime – the production of counterfeit currency – recognise no national boundaries.   Although counterfeiting has diminished substantially since the establishment of special law enforcement units assigned to fight it, the crime continues to present a potential danger to national economies and financial losses to consumers. Recent developments in photographic and computer technology, as well as printing devices, have made the production of counterfeit money relatively easy, thereby increasing the threat.

Intellectual property (IP) crime

Intellectual property (IP) crime is the generic term for a wide range of counterfeiting and piracy offences. These include trademark, patent and copyright infringement. The last decade has seen a steady worldwide increase in these types of criminal offences. One reason is the ready availability of modern technology to counterfeiters, who systematically use it to infringe trademarks and breach copyrights. Counterfeiting is so widespread that few legitimately manufactured goods are not copied in one form or another and the rights of the owners infringed.

Payment cards

As the use of payment cards continues to increase, it is likely that the frequency and extent of fraud will also continue to expand. The payment card industry and law enforcement community are constantly working to develop enhanced production and security measures to deter payment card fraud, but criminals continue to devise more sophisticated methods to override such features.

Money laundering

The international police community is aware that there is a need to achieve major results in the struggle against the financial criminal activities related to the organized criminal groups.  But now here’s the problem: money laundering is sourced from the proceeds of both financial and non financial types of crimes.  So if we’re serious about developing evidence-based policies to combat financial crimes, we’re going to have to collect evidence about the whole range of large-scale income-generating crimes, including all forms of organised crime – prostitution, drugs, people-smuggling, gun-running – the lot!  These aren’t of themselves financial crimes, but they generate the income that then requires further – financial – crimes in order to keep the money and the criminals themselves safe from justice.


Conclusion:  Any type of major income-generating crime can therefore contribute to the problem of financial crime. To understand financial crime, we have to understand All major crime.

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Just as in local politics, where properly-directed political action is much more likely if you can identify which problems are the costliest, so it is in international and global politics.  The right strategies against financial crime will not be found by merely acting on knee-jerk reactions, simplistic assumptions or by following a dominant viewpoint.  We need our policies to be based on evidence.  We need them to be focused on the most serious problems – the priority areas.  Any other approach is wasteful of scarce crime prevention resources.  Therefore we need to be able to identify the most serious problems.


But it is not possible to quantify financial crime by looking only at banking and other finance data because

·        Criminal origins can rarely be identified.

·        Layering and placement processes make it difficult to separate money obtained from crime from legitimate funds.

We need to know:

·        How much crime there is (and where and what sorts of crimes?)

·        How much profit is made from the crimes?

·        How much of the profit is laundered?

·        Where does it go to, and

·        How much trouble does it cause?

Financial crime is a whole set of different problems, on different economic scales, in different places.  You can’t prioritise a set of problems without knowing what they are, how bad they are and where they hurt most, and you can’t develop effective financial crime prevention programs without knowing what the priority areas are.


Conclusion:  By focussing on financial transactions, we are mostly looking in the wrong direction.  The only successful approach to quantifying Financial Crime will be to start from estimates of the profits of criminal enterprises.

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Unfortunately, this means we have to ask questions that are bigger than most criminologists ever dare to ask.


·        How much crime is there around the world, and how much of it is based in the Asia Pacific region?

·        How big are crime profits around the world, and where are they generated?

·        What factors make crime more profitable in some countries than others?

·        What factors make some countries more attractive to ML than others?

·        How much money is laundered each year around the world, and how much of it is generated in, or laundered through, the Asia Pacific region?

·        How much harm is caused by crime and ML in the Asia Pacific region, and who suffers most?


Conclusion:  These are the sorts of questions that economists – not criminologists – commonly ask and answer.  It is neither rocket science nor is it impossible.

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There are a number of options traditionally used by economists.  The first one is generally used by people who are being harassed by journalists who need “a figure before my deadline”. 


Guess - The “Wet finger” approach – you wet your finger and see which way the wind is blowing.


The second approach can be called the Top down approach: - examples include

·        What proportion of global GDP is likely to be Proceeds of Crime?

·        What proportion of global finance is held in dubious “offshore” accounts?

This approach is an improvement on the first, and has the advantage of only really needing two numbers, one of which is an actual and credible statistic, such as the global GDP estimate, and the other is generated possibly by option 1 but may be derived from real analysis.  Either way, since at least half of the calculation has credibility, it is a big step forward.


The third approach is, rather inelegantly called “Bottom up”, and involves the use of the five-step logic.

·        How much crime, proceeds, ML generated;

·        what offences generate the money;

·        how is it laundered;

·        where does it go?

·        How much trouble does it cause?


A few countries, including the USA, UK, Netherlands, Canada and Australia have produced credible estimates of the profits of crime.  In Australia, we have tried to track that through to the amount of money generated in Australia and laundered through the finance system . If you can do it for one country, can it be done for all?

This approach is far superior, because it promises to identify those problem areas that should be priority targets for action, and can therefore be the basis for a real evidence-based approach to financial crime prevention.  The downside is that is it a little more data-hungry that the other approaches.  Well – since we are being honest with each other today, it is a LOT more data hungry than the other approaches.


There are a couple of important points that are worth making here.

Firstly, Triangulation – cross-checking with other data – is an important technique.  If your data suggest that $X are generated by crime in a given country, but you also know that the economy of that country cannot support such a figure, then you have to reconsider whether $X is correct.  This could mean finding a third figure – triangulation – that can help decide where the true fogures are likely to be.


Secondly, and this will offend almost all statisticians in the Forum, Accuracy is (almost) meaningless; credibility is everything.  We waste our time trying to produce perfect statistics on financial crime.  We will never achieve it, and if we wait for perfect statistics before we take evidence-based action against financial crime, we will be waiting for ever.  The official data are mostly – as a famous New York District Attorney once delicately put it – crap, but there are large numbers of knowledgeable people out there in the world, with plenty of evidence and wisdom to analyse it.  What is of fundamental importance, however, is whether the estimate we come up with is credible.  If its is credible, then politicians of good intent will find them difficult to ignore, and evidence-based political action will (or at least might!) follow.


Conclusion:  Only the bottom-up approach, beginning with estimates of criminal proceeds, with triangulation across other statistics, shows any promise of producing meaningful estimates

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Here are some examples of the top-down approach.  They are at the very least useful as points of triangulation, and at best are worthy estimates that can be used to determine at what level to pitch an evidence-based financial crime prevention programme.


The first one is the notorious estimate of money laundering of 2-5% of global GDP, which comes from Michel Camdessus, then head of the IMF, in 1996.  While the IMF has never produced any supporting analysis for this figure, we should at least feel sorry for Camdessus, who was at the time being hounded by journalists for some sort of figure.  His estimate clearly has some credibility, since it is still repeated frequently by journalists who don’t have the time to do any better, as well as professionals in the financial advice industry who are trying to establish the value of their own services.  2-5% is globally equivalent to US $1-2.5 trillion;  AU$14.7 – 36.7 billion for Australia.  Half the world’s GDP is in the Asia Pacific region, either as members of the Asia Pacific AML Group or not.  This approach would suggest an Asia Pacific total for money laundering of $0.5 – 1.25 trillion US.  Financial crimes that do not involve money laundering – although this is probably rare – would be in addition to this range.


The second top-down approach is that of estimating the size of each country’s “shadow economy”.  This is, effectively, income generated in a country which is not reported to the taxation authorities.  Professor Friedrich Schneider or the University of Linz, Austria, has done some interesting work for the World Bank, suggesting that globally US$6.6 trillion of untaxed income is generated – some of which is probably generated by crime.  His estimates are AU$20 billion for Australia. Half the world’s GDP is in the Asia Pacific region, either as members of the Asia Pacific AML Group or not.  This approach would suggest an Asia Pacific total shadow economy of $3.3 trillion US.  But we do not know what proportion of “shadow economy” is criminal, and in particular what proportion could be described as financial crime.  If Schneider’s estimates of shadow economy are well-founded, we still need to identify how much of it is generated by crime.  Unfortunately, so far we don’t have any way to do this.


A third approach was presented last year in Raymond Baker’s very interesting book Capitalism’s Achilles Heel.  He analysed the offshore holdings of very high net-worth individuals around the world, based on Merrill Lynch’s World Wealth Report.  His total for the Asia Pacific region, including north and south America and Asia, amounted to $5.3 US trillion.  But again, we have no way of knowing what proportion of this money is criminally earned, or what proportion could be described as financial crime.


Holdings of High Net-worth Individuals Offshore (Raymond Baker 2005)


Total Holdings

(US $trillions)

% Offshore

Amount Offshore  (US $trillions)





N. America




Latin America








Middle East













These latter two estimates can therefore serve as “upper limits” on the extent of criminal proceeds and financial crime – the real figures must be something less than $3.3 US trillion a year.

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My approach in Australia was to follow the five-step logic, building on the growing body of work on the costs of crime in Australia.  Police data, crime victim survey data and insurance company data suggest that criminals in Australia earned over AUD11 billion per annum in recent years, mostly from fraud and drugs offences.  Expert surveys suggest that a fairly high percentage of the income from these crimes is laundered, and a figure of over $6 billion a year is consistent with the current data.


Crime Type







Number of





Stolen &








$ million


% Proceeds


(1995 Survey)





$ million




(a= 1995 Survey

b=2005 survey)





AUD million










Residential Burglary









Non-Res Burglary









Theft of Motor Vehicles


















Theft from Motor vehicle









Other Theft









Criminal Damage























75.0 – 90.0













Total Offences









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There is no reason why a similar approach would not be successful in other countries.  Most countries produce official crime statistics based on police data.  Many countries have now conducted crime victims surveys, and their findings can be extended to other, similar, countries that have not conducted surveys of their own.  Insurance data exist for many countries, which can be triangulated against the crime and victimisation data.  There is now a wealth of economic and demographic data available on virtually every country on the planet, which can be used to moderate or calibrate the estimates. And finally there is a growing body of information on the extent of known money laundering and other financial crimes, that can be used to construct a global model of financial crime and money laundering flows.


Just for fun, some years ago, I actually tried this, using mostly UN data and some heroic assumptions about, for example, the way the economy of a country would impact on the profitability of crime there, or the way the financial regulations and company laws might attract or deter money launderers from using a particular laundering route.  The results were quite revealing.

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In 1998, my model came up with a global figure of almost $US3 trillion per year – rather more than Camdessus’ maximum figure, but in the same ballpark.  Because the model works “bottom-up”, the output is presented country-by-country, and can therefore be aggregated to regional level: - the figure for the Asia Pacific Region is around $US1.8 trillion a year , and perfectly consistent with the “upper limit” of $US3.3 trillion mentioned earlier.  This suggests that the proceeds of crime amount to just over half of the value of the shadow economies in the Asia Pacific – is this credible?  Maybe!


But the model clearly suggested that any fixation the world’s anti-moneylaundering agencies had about the illicit proceeds of drug trafficking were misguided, because fraud and other organised crime is far more important than all the illicit drugs put together, and that the tiny countries like Nauru and the Cayman Islands were simply cashing in on a problem created by the rich countries themselves – not the cause of the problem, per se.  At the time, the suggestion that fraud could be far more important a generator of criminal proceeds was not well received in certain countries, but there has been a considerable shift of opinion since the events of September 11 2001.   (I could say I told them so, but I will refrain).


Estimates of the Generation of Laundered Money by Crime Type and  Region ($US bill/yr)

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So we can put these estimates for the Asia Pacific Region together, and we can see that in spite of its shortcomings, the model’s results are consistent with the top-down estimates. Conclusion:  Financial crime in the Asia Pacific is worth somewhere in the region of $US 2 trillion per annum  -  which is a rather worrying 15% of GDP.

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I want to step back a bit now, and look at the concept of triangulation.  My money laundering model produces a figure for global money laundering, which can be disaggregated by region or even down to country level.  How can we check to see if it is producing credible results?


Again, as so often in my career, I got lucky, and last year I was asked to try to construct an economic model of the global illicit drugs trades.  This invitation came after many years of frustration caused by high-powered researchers who said “it couldn’t be done”.   Well, we did it, and the results were presented in last year’s World Drug Report, and appear to be credible.  The estimated global income to illicit drug producers and traffickers is around a third of a trillion $US, and can be disaggregated to the regional, or even (with care!) country level.  This figure is clearly compatible with the results of my money laundering model, but the important thing is that it demonstrates the potential of a complete methodology for analysing transnational crime.  This model is based on a blend of official statistics, expert knowledge and triangulation, much of it derived from an annual international expert survey.

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The UNODC now has a model of the illicit drugs trades around the world, which can be used to identify patterns of trafficking, degrees of harm caused by the trades, in the countries of origin, transit and destination, and priorities for action.


This U.N. Model independently suggests that the global illicit drugs trades are worth around $US 300bn per annum (Income to traffickers).  The profit margins must be high, to cover the risk, so perhaps as much as 25% of this $US300bn is launderable profit: - $US75bn.  The Walker Moneylaundering Model – even with its “dodgy data and heroic assumptions” - estimated around $US69bn in 1998.


If we can do this for illicit drug trafficking, why can it not be done for the other major economic and financial crimes?

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Another potential triangulation analysis has been developed by Professor John Zdanowicz at the University of Florida.  In 2004, he estimated that USD156.2 bn was moved out of the United States annually by way of pricing discrepancies during 2001.  USD4.27 bn involved countries which are listed on the United States State Department ‘Al Qaeda watch-list’.  USD 213 bn was moved into the United States by the same mechanism.  Is this fraud or laundering – or both?  Are the beneficiaries in America or elsewhere?  We don’t know, but this analysis highlights the fact that studying financial statistics alone cannot even tell us how much money is being laundered, because launderers have clearly got other options now.  You buy a business with your criminal proceeds and then fiddle the books to both launder your money and make even more.

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Another source of valuable triangulation information is Schneider’s shadow economy analysis.  His analysis in 2004 produced some interesting – and as-yet unpublished – results by comparing the estimates of shadow economy against GDP per capita.  Unsurprisingly, his analysis suggests that the poorer countries have higher percentages of shadow economy than the rich countries, and there appears to be a nice “J”-curve on the graph. But this analysis seems to identify “excess” shadow economy in some countries, often those with a reputation for “mafia-type” organised crime, including Italy, Russia and Colombia, and the excess can be measured as a proportion of the countries’ GDPs.  It is too soon to know whether this form of analysis can successfully identify the proceeds of organised crime in specific countries – but it is at least extremely interesting.

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I now briefly return to Ray Baker’s interesting work on cross-border flow analysis.  It is based only on a review of studies of transnational crime, and the data may not be internally consistent, but this is another essential research technique in its own right.  Each of these figures can potentially serve as a credibility check on any estimates we are able to generate.

Global Flows

Low ($US bn)

High ($US bn)




Counterfeit goods



Counterfeit currency



Human trafficking



Illegal arms trade









Crime Subtotal






Abusive transfer pricing



Fake transactions



Commercial Subtotal






Grand Total



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A further source of triangulation for estimates of money laundering is the UN’s statistics on services exports.  When computed as a percentage of GNP, these figures clearly show which countries have unusually strong financial services exports.  These countries include most of the Caribbean tax havens and some of the larger centres including Luxembourg, Switzerland and Singapore.  In many countries, these data can be interpreted as measuring the capacity of the financial sector to support money laundering.

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The next slide shows how a study of finance and business regulation in each country can measure the “willingness” of a country’s financial sector to support money laundering.


This work, conducted for the European Union by the Italian research group TransCrime, led by Professor Ernesto Savona, analyses the provisions of the Criminal Law, Administrative Regulations, Banking Law, Company Law and International Co-operation Provisions.  Using a simple questionnaire-type framework, the analysis gives a sort of credit rating to each country’s finance sector, depending on how laundry-friendly or laundry-proof it is.  As the chart shows, the results of such analysis can identify those countries whose regulations leave gaps for money launderers to wriggle through.

Criminal Law

1. Money laundering punished in your criminal system?

2. Legislation provides for a list of crimes as predicate offences?

3. Predicate offences cover all serious crimes?

4. Predicate offences cover all crimes?

5. Provision allowing confiscation of assets for an ML offence?

6. Special investigative bodies or investigations in relation to ML offences?

Administrative Regulations

1. Is there an anti-ML law in the jurisdiction?

2. Banks covered by the anti-ML law?

3. Other financial institutions covered by the anti-ML law?

4. Non-financial institutions covered by the anti-ML law?

5. Other professions carrying out a financial activity covered by the anti-ML law?

6. ID requirements for the institutions covered by the anti-money law?

7. Suspicious transactions reporting?

8. Central authority (for instance, an FIU) for the collection of suspicious transactions reports?

9. Co-operation between banks or other financial institutions and police authorities?

Banking Law

1. Prohibition to open a bank account without ID of the beneficial owner?

2. Limits to bank secrecy in case of criminal investigation and prosecution?

Company Law

1. Minimum share capital required for limited liability companies?

2. Prohibition on bearer shares in limited liability companies?

3. Prohibition on legal entities as directors of limited liability companies?

4. Registered office exists for limited liability companies?

5. Any form of annual auditing (at least internal) for limited liability companies?

6. Shareholder register exists for limited liability companies?

International Cooperation Provisions

1. Extradition (at least of foreigners) for ML offences?

2. Assistance to foreign law enforcement agencies in investigation of ML cases?

3. Law enforcement may respond to a request from a foreign country for financial records?

4. Provision allowing the sharing of confiscated assets for ML offences?

5. The 1988 UN Convention been ratified?

Transcrime & Walker Attractiveness Indices



The potential for money laundering through a country’s banking sector is the product of its capacity to launder and its willingness to launder.  These charts suggest that it may be possible to measure both of these aspects.

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Naturally, we would also wish to use whatever official statistics exist on financial crime and money laundering in each country, but as the example of Australia shows, this is not very revealing.  Official statistics tend to reflect official activity, and this usually means successful prosecutions and seizures.  Even in Australia, where we have an active programme to detect and prosecute financial crimes, our statistics show only a fraction of what is believed to exist.  Nevertheless, it is a “lower limit” on the estimates.

Proceeds of Crime - Orders made and recoveries achieved, Australia 1999/00 – 2003/04

Year                       Orders made (AUD)     Recoveries (AUD)

1999-2000               13.1 M                          17.3 M

2000-2001               17.3 M                          4.3 M

2001-2002               25.2 M                          4.0 M

2002-2003               21.8 M                          13.5 M

2003-2004               77.3 M                          4.0 M

TOTAL                 154.7 M                      43.1 M

Source: Australian Federal Police Annual Reports

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OK – now to our question and answer session, and as I mentioned I have selected some very good questions for you to ask:



Mr Walker, these numbers look credible, but so what?  We know it’s a big problem.


Good question! We also know that the implementation of measures to prevent financial crime is very costly.  We need to ensure that those costs are commensurate with the real size of the problem.  The size of the problem is important to determine the appropriate level of response. Commonly, crime prevention and control costs are around a third of the costs of the problem itself.  Underspending is ineffective; overspending inefficient use of resources.



Mr Walker, governments, businesses and other organisations have to determine the level of investment in financial crime prevention independently.  How do these analyses at the regional level help us address the problem?


Another very good question!  I’m so pleased you asked me that!

The answer is that you need more detailed analyses than those I’ve presented here so far.  My model in fact is calculated at the country level and then the results aggregated to regional level.  But the country-level data is too poor to produce credible country level results.  In a perfect world, each country would be measuring its own levels of exposure and a coordinated, evidence-based approach could be developed.

In fact, if we still have time, I’ll show you what we should be doing…

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We need a coordinated, evidence-based approach to financial crime prevention.  It needs to be focused on the most serious problems – the priority areas.  Any other approach is wasteful of scarce crime prevention resources.  Therefore we need to be able to identify the most serious problems.  Actions need to be based on evidence, not simplistic assumptions or a dominant viewpoint.  Different countries face different problems, but all are interdependent.  A coordinated approach is essential.  The situation is continually changing – priorities will change too.  We need a mechanism that will identify when priorities need to be changed.  We need to monitor (and anticipate!) changes carefully.


As Neil Jensen, Director, AUSTRAC, said (2005): It is difficult to explain the importance of a problem without quantifying it.


We always need to remember the problem with official statistics, however, perfectly described by English economist Sir Josiah Stamp, in 1929: 

“The government are very keen on amassing statistics.  They collect them, add them, raise them to the nth power, take the cube root, and prepare wonderful diagrams.  But you must never forget that every one of these figures comes, in the first instance, from the village watchman, who just puts down what he damn pleases”. 


Conclusion: New sources of On-going transnational crime information are required.

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We need a systematic approach to compiling estimates of the proceeds of crime in every country in the region, and the countries to which the proceeds of crime are believed to be sent for laundering

Then we need to look at the same data from the other end of the telescope:

Compile estimates of the proceeds of crime being sent to every country for laundering, and their criminal origins.

We can then cross-check the estimates of crime in country 1, by comparing with other countries’ estimates of flows of illegal goods and laundered money from that country. 


If experts in one country believe that $X are being sent to a second country for laundering, but the experts in that second country have a very different perception, then the disparity must be resolved. The methodology therefore contains an automatic self-checking mechanism.  Exact statistics are not required.  Triangulation is used to assess and improve data quality.  The result is effectively an international input-output model of the financial crime “industry”, with the same analytical characteristics as the input-output models used around the world to measure GDPs and the contributions of their different industrial sectors.

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In Australia, we have tried to pioneer this technique in respect of money laundering – with some success. Questionnaires addressing these topics directly, but in ways that do not require statistical precision, could populate an “Input-Output” style of economic model, similar to that adopted successfully by UNODC for global drug trafficking. The responses do not require comparability in official statistics, and could provide data for cross-jurisdictional analysis of causative factors.

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The best indication of feasibility is, however, the UNODC’s Annual Reports Questionnaire on illicit drugs and the global economic model that has been built from this valuable and growing database.  This methodology works, it isn’t rocket science, it produces valuable and credible results, and there is no technical reason why it could not be expanded to cover the whole range of financial crimes.

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What is lacking is funding and organisation. A Regional (or even Global) network of researchers is needed to conduct such an ongoing survey, coordinated from centres of excellence like BCTCP Beijing and CTCP Wollongong, would form a powerful source of current information and knowledge.  A research programme like this  could produce annually updated risk assessments for Governments, Law Enforcement Agencies and Financial Institutions.  I could turn knowledge into evidence to support preventive measures based on informed international collaborative action.  Groups of governments can jointly address common problems identified through the survey.  Common solutions can be identified and adapted to new circumstances.


In summary, the methodology uses the framework of an “international input-output” economic model.

This is a recognised analytical technique used by national governments and international economics agencies to monitor, measure and predict changes in the legitimate economy;

This proposal involves applying the same approach to the world’s illegitimate industries.


UNODC has used the questionnaire methodology for many years

·        to monitor the global illicit drugs markets,

·        to form the basis of a global input-output model, and

·        to assist in the development of preventive measures


Australia has already tested and used the questionnaire methodology to estimate money laundering.



Conclusion: The methodologies have been successfully tested in closely related areas.


Thank you for your attention!