In the late 1990s, the chairman of the Organisation for Economic Co-operation and Development's Financial Action Task Force (FATF) Working Group on Statistics and Methods stated that there was a “need to estimate the size of money laundering and quantify its constituent parts”. At least four areas were highlighted for further quantitative measures, including: understanding the magnitude of the crime, understanding the effectiveness of counter-money laundering efforts, understanding the macro-economic effects of money laundering and understanding money laundering ( Walker, 1998 ).
Today there is an abundance of data on global trends in financial crime, money laundering and terrorism financing and much work has been done in an attempt to produce accurate estimates of money laundering and terrorism financing flows, however, although a number of largely varied estimates have been offered, none of them can be irrefutably proven. Also, the quantitative issues that have been raised by anti-money laundering and the fight against terrorism financing have yet to be definitively answered ( Biagioli, 2008 ) and no broadly approved measurement methodology has yet been developed ( Fleming, 2009 ).
Quantifying money laundering and terrorism financing is a very necessary and worthwhile exercise, however, identifying and developing uniform procedures and techniques for quickly and easily describing, classifying and sharing new money laundering and terrorism financing techniques and behaviours with the wider international AML/CTF community is equally, if not more important, especially when the platforms, techniques and methods employed by adversaries change rapidly and are becoming more complex ( Nardo, 2006 ).
A number of programmes are already in existence for sharing information on money laundering and terrorism financing typologies. For example, annual typologies and case study reports are published by many AML/CTF agencies to assist reporting entities to meet their AML/CTF obligations. These reports contain details of sanitised, successfully detected money laundering and terrorism financing cases and provide a wealth of information on current threats and trends, techniques employed and, in many cases, the amount of funds involved. However, since these reports are published annually, a potential vacuum is created where new money laundering or terrorism financing schemes may go undetected until the next batch of typology reports is published. In addition, these typology reports only provide a limited snapshot of some of the types of money laundering and terrorism financing activity detected in individual jurisdictions in that year and often only include cases where large sums of money have been detected, thereby potentially omitting a number of significant or new money laundering and terrorism financing behaviours or techniques. The format that the typology reports take can also pose problems due to their over-descriptive and case-specific nature.
What is required is a collaborative, synergistic reporting system that can be updated in real-time; thereby informing AML/CTF experts and investigators immediately or soon after a new technique or method has been discovered.
This view is supported by a number of authors who believe that high-level collaboration ( Liu and Zhang, 2007 ), synergistic information sharing and knowledge management ( Biagioli, 2006 ; Biagioli and Nardo, 2007 ; Global Justice Information Sharing Initiative, 2006 ; Hardouin, 2009 ; Mueller, 2006 ; O'Connell, 2008 ) are important aspects of a successful AML/CTF system.
Much can be learned through the exchange of non-classified data and increased levels of communication and exchange of ideas between intelligence and law enforcement agencies, financial investigation units, researchers and the private sector at national and international levels as these have proven to produce good results in the past ( Hardouin, 2009 ).
An example of where a cooperative relationship paradigm has been successfully utilised is the “Spotlight” Project, a joint research project between Italy's Ufficio Italiano dei Cambio (UIC) and the London School of Economics (LSE) aegis of EC AGIS Program. The project relies on UIC methodological advice, the LSE's scientific direction and the expertise of a consortium of partners (including financial institutions, regulators and law enforcement agencies). The purpose of the project is to develop a methodology for creating effective money laundering monitoring tools. In line with its research objective, a holistic framework is proposed, so as to benefit from contributions coming from a wide range of quantitative, social and human disciplines ( Biagioli, 2006 ).
Spotlight attempts to combine a profiling methodology with a behaviour-led approach to modelling, as well as defining the modelling approach in a way that makes it usable in other national and regulatory contexts. Their ambition is to build a tool that, through some proper customisation procedures, can be exported to wider and different environments.
Biagioli and Nardo (2007) discuss the importance of collaboration and the sharing of information and competencies between bankers, lawyers, law enforcement officers and economists. They believe that the present professions might be stretched towards a new level of specialisation that may allow them to carry out osmotic flows of information through those contact lines and areas that, until now, have represented a restraining element and a potential obstacle. The authors imagined the dissolution of barriers that following a path of integration leading to a merging into one single unit, the excellent elements that have characterised each individual profession thus far. “Bridging the frontier” would mean, however, investing in education, building an area of shared information and knowledge, conceiving a unified territory, not just a common platform, where all potentialities deriving from the different disciplines, institutions and experiences might converge. In the views of the authors, this has become an operational need and, given the present institutional and regulatory framework, they believe that a critical mass has been reached.
Biagioli (2006) also looked at the evolution of organisational science in the application of knowledge sharing and knowledge management. He believed that it was necessary to proceed along the steps of enhancing knowledge capital to generate a knowledge base, identifying strategies of knowledge management and showing the costs and benefits in terms of content and procedures to generate a culture of knowledge management whereby a consensus for change could be reached.
Like Hardouin (2009) and Biagioli (2006) subscribed to the view that know-how should be derived from different sources, fields and experiences. However, he argues that a mere “paper-pushing” attitude should not be adopted; rather, it requires the proper management of different pieces of information to be used to build knowledge; a management strategy which goes far beyond the sharing of documents and news. The process Biagioli (2006) has in mind is quite unlike the concept of benchmarking, where one tries to adjust his actions to a declared objective: what one should aim for is the definition of a new paradigm; a (yet unknown) principle based on rules of integration and unified vision.
Therefore, synergy is essential for successful knowledge management and information sharing as every actor's skills and knowledge is precious and may prove vital to anti-money laundering and terrorism financing detection.
Better understanding of the current threat landscape (including current and emerging money laundering and terrorism financing schemes) allows us to model, simulate and forecast criminal action more accurately and intervene more effectively to prevent it. By collecting and crossing information from different aspects and approaches, information is transformed into systematic knowledge which, in turn, can be used to develop further information ( Biagioli, 2006 ).
A number of authors have highlighted behaviour modelling as a vital component in a successful AML/CTF system ( Biagioli, 2006 ; Biagioli and Nardo, 2007 ; Gao
Modelling helps us to develop a better understanding of complex environments. The greater the level of uncertainty that surrounds an environment, the more valuable modelling becomes.
The reason for modelling the environment dictates the type of tool(s) used. For example, is the purpose to communicate a set of behaviours or sequence of steps to a third party or is it to perform an in-depth analysis and verification of patterns identified.
The whole process of model building balances on the appropriate design of the path that goes from choosing the elements to describe the behaviour, to identifying the most accurate parameters to reflect them, and finally to defining proper queries that match parameters within the available data. Good design is logically and rationally grounded, reasonable in its basis as well as in its development, and sheltered from the risks coming from unsound basic assumptions, erroneous process or distortions, which can hamper the path and produce unreliable results. To this end, a proper methodology...