risk based, we are looking for the most unlikely combinations across millions of account holders with billions of transactions. to find these exceptions, we would traditionally define tell - tale identifiers. for example, " multiple accounts on a single address " and " a single deposit per month and immediate withdrawal ". seen separately these are relatively innocent. together, however, they can indicate human trafficking of seasonal workers : the combination identifies subcontractors who organise housing for seasonal workers – which is a perfectly legal activity – but then at the same time immediately withdraw the wages deposited and only pay a fraction to the worker – which is illegal off course. however, this combination could to an extent also identify student housing : a large inflow when student grants and loans arrive and a relatively quick withdrawal rate. these are just two dimensions. in practice, there are many and these interact in non - linear and unpredictable ways. with the use of data science techniques we can identify those. our showcase has proved itself already : my colleagues in integrity supervision can now do their work in a more efficient mannerby selecting the most risky files using data science. so what can we learn from this example? let me draw a few conclusions : data science has great potential, and data scientists can definitely add value. the data scientists should however work closely with the business – that is the supervisors with the business knowledge. this is not only needed for providing input to the model, but also for interpreting model outcomes. considering the example i just gave ; while the algorithm flags fraudulent transactions that would otherwise never have been identified, left to its own devices the model is not – or not yet – able to make a distinction between illegal activities and student housing. how can you make sure that data science knowledge is always combined with business knowledge? in organisational terms this is a challenge : if you embed data science locally, how can you keep the data science knowledge up to date? conversely, if you set up data science centrally, how do you prevent empire building? at de nederlandsche bank, we chose a hub - and - spoke system for this – stressing joint development. our data scientists work in a data science hub, together with the spokes – which are the various divisions of de nederlandsche bank. this comes with the additional benefit of making optimal use of potential spill - overs – using the same code for different applications - while at the same time working on a variety of topics, from financial markets and pension funds to payment services and

the role of low short - term interest rates in the gfc through their impact on risk - taking incentives, see j. stein ( 2013 ) overheating in credit markets : origins, measurement, and policy responses. speech at the federal reserve bank of st. louis research symposium on “ restoring household financial stability after the great recession : why household balance sheets matter ”, 7 february. for a careful discussion of the channels through which a low - for - long scenario could still engender material risks to financial stability, see committee on the global financial system ( 2018 ) financial stability implications of a prolonged period of low interest rates. cgfs papers no 61. 13 the idea that low capitalization can induce risk shifting in response to shocks hitting bank balance sheets was developed in a seminal study by m. dewatripont and j. tirole ( 1994 ) the prudential regulation of banks, mit press. see also j. tirole ( 1994 ) on banking and intermediation. joseph schumpeter lecture. european economic review. 14 c. bean ( 2014 ). the future of monetary policy. speech at the london school of economics, london, 20 may 2014. this implies that an effective prudential policy is a necessary but not a sufficient condition for monetary policy to be effective in the medium to long run. central banks must be conscious of unintended consequences of a monetary strategy that keeps policy rates low for a long time. translating this to the current context, my main takeaway is that as long as inflation remains subdued, central banks are likely to maintain an ample degree of monetary accommodation. but the longer this accommodation is maintained, the more pressure will mount on financial regulators and supervisors to be able to preserve financial stability. 5. financial stability and the regulatory policy triangle so what would this mean in practice, and how does it relate to the international regulatory agenda? my key point here is that in an environment in which monetary policy is likely to remain accommodative for some time, it is important that the agenda for regulatory reforms is pushed ahead, along several dimensions. let me also elaborate here by referring to the same three elements : necessity, effectiveness and possible unintended consequences. necessity the first question is whether the regulatory reforms that have been adopted are still necessary. my short answer here is absolutely. and we actually need to strengthen our regulatory toolkit even further. as i mentioned before, the global financial crisis could develop in an environment with