U4 Anti-Corruption Resource Centre

This Anti-Corruption Helpdesk brief was produced in response to a query from a U4 Partner Agency. The U4 Helpdesk is operated by Transparency International in collaboration with the U4 Anti-Corruption Resource Centre based at the Chr. Michelsen Institute.


Please provide an overview of existing datasets on main sources of licit financial movement in and out of countries, what they cover and whether any analysts have previously used them to make any judgement about the scale of illicit financial flows or the potential for doing so


  1. Introduction
  2. Main methods of estimating IFFs
  3. Datasets of licit financial movement and their use in estimating IFFs
  4. Overall challenges
  5. References

Main points

  • Measuring IFFs is a challenging task, and estimates are mainly based on irregularities in data rather than direct measurement.
  • The main datasets used include the IMF’s Direction of Trade Statistics and Balance of Payments data, United Nations Commodity Trade Statistics Database, Bank for International Settlements datasets, Foreign Affiliates Statistics and Foreign Direct Investments datasets.
  • A major challenge in estimating IFFs from these datasets is the reliability of the data used. For instance, some IFF calculations have been revised due to the inaccuracy of data used. Most datasets cannot be used to measure crime and corruption-related IFFs, and these types of IFFs are mainly measured using data from investigations, suspicious transaction reports, prosecutions, convictions or surveys.


Given the clandestine nature of illicit financial flows, attempts to measure their scale and volume typically adopt an indirect approach. Current measurements are mainly based on estimates derived from irregularities in datasets of licit financial movements, including capital flows and trade flows. Though useful in providing estimates of illicit financial flows, there are challenges in using these datasets. These include the unreliability of data used, as well as the limitations of the datasets in estimating illicit financial flows related to corruption and other criminal activities.


Jorum Duri and Kaunain Rahman, [email protected]


Maira Martini and Matthew Jenkins, Transparency International

Sophie Lemaître, U4 Anti-Corruption Resource Centre




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