technology is not entirely free of new problems. or, put more optimistically, challenges. for instance, the algorithm could exhibit biases and lead to discriminatory outcomes. also, as with any data - dependent method, it is inherently backward - looking since it takes time to collect data and train the models. furthermore, since many of the techniques are new, users often feel they are forced to use black boxes with very low explainability. up to a point, they are right, since approaches are often a - theoretical, making it difficult to establish not just correlation but also causality. therefore, more and more work is being done to create'explainable ai '. lastly, since we often have to turn to cloud - based implementation – primarily because of computational demands – data security is an issue. i don't want to sweep these issues under the rug, but i do feel that careful application can yield benefits and that the least we can do is explore the possibilities. these possibilities have implications for how we supervise. in terms of supervision – the topic closest to my experience – these developments will have a significant impact on how we work. let me first turn to a showcase of how we, at de nederlandsche bank, have applied these new techniques in actual supervision. in the last year we have employed an outlier detection algorithm in " know your customer " - deep dives at several institutions. as you all may know, a great deal of media attention is given these days to preventing financial institutions from being used by criminals for activities such as money laundering. the standards that were designed to protect these institutions against money laundering were implemented in 2012 and are also referred to as " know your customer " or kyc in short. financial institutions have to comply with the standards, and as a supervisor, de nederlandsche bank needs to ensure that banks have incorporated the minimum standards in their business operations and that their systems work properly in fighting financial crime. but how to detect potential fraudulent transactions in a dataset that contains millions of customers and bank accounts and billions of transactions? data science has a clear role to play here. to identify exceptions we applied an isolation forest algorithm, and i probably don't need to explain it to this audience. but please indulge me while i take a few minutes to explain to you what i take away from the intuition and workings of an algorithm like this. 3 / 5 bis - central bankers'speeches since we have limited resources and since our supervision is

banking 5 and elsewhere. the bank's committee is called the governing council. it consists of the governor, the senior deputy governor, and four deputy governors. the governing council is advised by staff and by a monetary policy review committee, which looks at the evidence and the projections, and raises issues. open discussion and debate are encouraged. decisions benefit from the exchange of differing views among members – what james surowiecki calls the " wisdom of crowds. " 6 these differing views arise largely from different weightings of various aspects of the analysis presented by bank staff. the bank's clear goal – meeting the inflation target – helps to focus the deliberations. and with decisions made every six to eight weeks, there are frequent opportunities to take new soundings and adjust course. a good example of how we apply these strategies is the way we estimate potential output. potential output is important in understanding inflation dynamics over time, but, as i said earlier, it cannot be measured directly. we're not sure that our models fully capture potential output, especially when the economy is undergoing significant structural change. to add to the challenge, some of the data used in these models are frequently revised. so, in addition to the readings we get from our models, we also consider a number of alternative indicators of pressure on capacity. these indicators include the rate of capacity utilization, measures of labour shortages, building vacancy rates, reports from our quarterly regional surveys of firms, and the recent behaviour of inflation relative to expectations. of course we also consider more direct influences on inflation such as import prices, cost pressures such as wages relative to productivity, and inflation expectations. i should also mention that we meet, on a regular basis, with a wide variety of business people to discuss their investment and hiring intentions, their costs and their pricing decisions, and any constraints they may be facing. the information we obtain from these meetings helps us to interpret the numbers and better informs our judgment. of course, the strategies i have mentioned complement one another. taken together, they help the bank to arrive at a comprehensive and balanced view of developments in the canadian economy, the outlook for inflation, and the appropriate monetary policy action to keep inflation on target over the medium term. conclusion let me conclude. in my remarks today, i've described how the bank of canada deals with the uncertainty that underlies the conduct of monetary policy. the task of monetary policy is, and is sure to remain, a challenging one, given the limits of understanding