INTRODUCTION

In recent years, shadow banking has attracted increasing interest. The discussion focuses mostly on its contribution to the financing of the real economy – mainly in periods in which real bank finance dries up – and on its role as a source of systemic risk: that is, risk for the stability of the finance system (van der Veer et al. 2015). A less researched field is the role played by shadow banking in the laundering of illicit funds. The purpose of this chapter is to shed light on the ML risk of shadow banking and offer a view on whether ML can effectively be mitigated, and to what extent it is worthwhile doing so.

While reference is here generally made to ML, the same applies to terrorism financing too, as both utilize largely the same techniques. Terrorism financing can indeed be described as the inverse process of ML, where licit funds are diverted for the financing of illegal activities. The preparation of bribery offences also involves the diversion oflicit funds into dark pools, which can later be used to bribe officials making the detection of bribery more difficult. Hence, ML means here both concealing the unlawful source of criminal gains and concealing the existence oflicit funds for the purpose of preparing criminal offences.

Shadow banking: a definition

The Financial Stability Board Global Shadow Banking Monitoring Report 2015 describes shadow banking as credit intermediation involving entities and activities outside the regular banking system. It is also often referred to as “market-based financing”. A unique definition does not exist, as shadow banking activities can take very diff erent forms across countries, and definitions focus either on the entity performing the activity or the activity itself. Until 2014, the FSB adopted an entity-based definition and, in 2015, it introduced an activity-based “economic function” measure of shadow banking, which aims to narrow the focus to those parts of non-bank credit intermediation where shadow banking risks – from a systemic risk perspective – are more likely to occur.