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Lincoln Center campus, Manhattan ​well I'll be talking about deep learning in in in in various ways so basically I'm my back my background is in computer science then I moved into economics and Finance and and we did what I guess could be called deep learning already around around ten years back and I'm sure there are several in this room that that have done deep learning much much before that so it's it's really nothing new and and and basically what I will be will be doing now is that I'll take you through a few cases where we've been sort of doing research and the way that we work is that we basically publish everything that we do and and then we try to move move into products to industry work in info litical ventures and and and that involves mainly consulting work and a few of those cases all they'll take you through especially with with central banks and how our models relate to that so what well basically my background and my interest has been in measuring vulnerability and this is not to say that that that we can predict the the date of a financial crisis or a stress event what we say is that we can measure vulnerability when we can measure when you are vulnerable to shocks that trigger stress events we don't know precisely when these shocks happen and what the next shock is but what do we what we do know and there's plenty of historical evidence for this is how how likely or how vulnerable you are to to certain types of shots and this this then relates to to sort of standard deep learning algorithms as you would understand today related to text but also sort of standard machine learning tool boxes that we're applying on numerical data to standard classification problems so basically we have three cases now coming up and I'll try to be brief on these and then we can have a few for I did more detailed follow-up questions on the modeling techniques here and I'll try to take you through that basically the results of some of our in industry work there but we focus on well in networks we focus on measuring what we call interconnected risk measuring risk in interconnected financial systems in silo brain we're working on on basically visualization dashboards where we are joining visual processes and social processes so we are essentially focusing on capturing interaction among humans rather than only looking at interaction between the human and the computer and Aleks analytics which is pure deep learning from our side it's an event based trading system where we are plugging in certain deep learning algorithms so with matrix our objective is to come up with a standardized risk rating for interconnected financial systems it's obviously a interconnected risk in general is complex hard to quantify and there is no sort of standard for measuring system-wide impact in interconnected systems and this could be an example so this is an example that we normally look at where we have banking systems as nodes and linkages across banking systems defined by exposures across across the nodes and essentially what we're looking at here is a four dimensional data cube so if we forget linkages and networks we have a standard three dimensional data cube so basically we have on the y-axis here we have entities that could be countries banking systems or that could be banks here we have time that could be highfrequency data but it could also be annual data depending on what we're looking at and then we have variables it could be credit cap it could be various indicators depending on then whether we're looking at banks or countries and so forth now the fourth dimension that we're adding to this is is the linkages here so we're not only concerned in sort of crunching this three-dimensional data cube from several say risk indicators into a number but we're also concerned in aggregating this number based upon how these entities are interconnected and this is this is partly related to work that we did with with ECB some time ago so so this here is a bank level early warning model so it's in a sense of predictive model but basically what it's doing is that it's measuring vulnerability and in this case you are a banking system however it's a bank level model so you have you have granular output you have output at the Bank level and so so here you have granular out but here you have output at the bank level and basically what you can see here and the y-axis is distressed probability so this is measuring vulnerability at the Bank level you have the top I think top 20 banks here and this exercise is a pretty comprehensive assessment exercise so at this point I was with the ECB and we were building a an early warning model simultaneously with the comprehensive assessment the comprehensive assessment included I think around 9,000 supervisors so it was manual labor this model included two guys on the side of a few other things and basically what you can see here is with pre comprehensive assessment data so the same data that was used in the pre in the comprehensive assessment how the model basically maps to the outcome of of the comprehensive assessment this is not to say that that we could have done as well as the comprehensive assessment but we are not far off with with a model and then we are not at that point even taking into account any type of interconnectedness so that's based upon individual data so it's based on balance sheet income statement and market prices for these entities now what we've then done in in a recent paper and we've done some empirical work before


that this is a mathematical model but the empirical work before that showing that obviously we should write the vulnerability of a bank as a function of also its neighbors right so that's basically what we are doing here so you have a decomposable measure that accounts for individual effects of Bank C direct effects of Bank I own Bank C and indirect effects of time J by a bank I on Bank C right that's fairly obvious then the nice thing is that it's really decomposable so we have these measures decomposed the other thing is that we can go through this is a two additive case so we have basically decomposed into two different types of effect we can do a que additive case I'd out and we can do dynamic interation so we can simulate with this as well and to give you intuition what this means let's say if we look at the at the country level on imbalance or only in an early warning model so this is based upon imbalance indicators for Germany and the set of other other countries that we are using for learning but essentially here the blue part looks at individual risk it's basically for Germany it means that this much vulnerability descends from the indicate indicators of Germany right now this is what a German policymaker would agree with they said they would say that they had an important crisis so you should not even find vulnerability in domestic indicators here and that's precisely what you find when you account for direct effects because when you also account for the vulnerability of the countries that the German banking sector was exposed to then you add up the red component which would be softer in Europe in this case and the states and we have plenty of empirical evidence that shows that if you account for these linkages then you improve performance so this is basically how we move to how we move to not only accounting for four vulnerability but also for accounting also accounting for for vulnerability in a an interconnected system so from a machine learning perspective basically what we're doing is a two class classification problem we had two class classifiers and what we have on top of that is network aggregation I would not say this is very deep learning but I mean what this essentially is is ensemble learning it's based upon a very large number of different models and we have a web interface to interact with methods and the exercises and this is something that we work with central bank's already for around five years or so and there is a public version which relates to one of our papers so if you want to have a look you can have a look over there so the second project basically is related to a research project funded by the Swift in 2013 that within that project we built an interactive visualization platform and the idea with that was to basically run an awareness project where we were putting systemic risk models and data side-by-side in order to better understand the models themselves and in order to be able to compare existing models and data with each other you have a large number of models measuring the same thing but giving quite different out and and I guess this relates quite well to Tetris Cerreto project measuring system Inc risks so so this this then turned into a sort of generalpurpose infrastructure for setting up especially interactive network visualization for network analytics and with this we worked with several central bank's and several banks with this infrastructure setting up ad hoc dashboards especially around around Network data and again you find certain showcases that are line publicly-available like Crysis metrics comm but this the idea behind this obvious is that we want not only to rely on artificial intelligence but also to rely on human intelligence we want to rely on the human visual system as and in its own way as an efficient processor of information now now the question then is we how should we together analyze data if we are a sort of data-driven community and looking at the same problem and what we've been working on before this is only involves only interaction between one analyst and one screen like this and and this where we want to get now is is much more than just collaborative so say the accountant that spots an irregularity in a graph of retail sales and then converse with a colleague shows it to a lawyer and percent percent sits to a jury and this is a strange example collaboration here is not really the problem you can send their own emails you can even print this on paper and show it to these guys the question is how are you capturing the knowledge of these people on the way and storing that structuring that and that's where we are basically not doing artificial intelligence but intelligence augmentation by combining visual and social processes because we know that there is a large role for human experts in in data-driven organizations analytics relies on on our visual abilities to a large extent because we do rely on visualization but not so much on social abilities and we are not facilitating human interaction documenting it and structuring it so essentially what we want to do is under table visualization dashboards what we're already doing is which unattainable visualization dashboards where we are basically linking expert discussion to the data we don't see silos as a bad thing in that sense silos are just a way to connect the organization with the work of the analyst silo 2.0 is highly interconnected it's just one place where you are connected and that's where you are managing soft and hard knowledge in one accessible place so I'm not sure whether this really shows to the back but this this is a this is a an early version of an an example of an interactive chart where you have sort of full possibilities to zoom pan filter and all of that as you normally do with with interactive charts but also the capability of annotating data points or the data cube that you saw in the beginning you are now just tagging and linking your expert knowledge to that which in the background bills Viki and they feed where you are basically not only following people but also following data and essentially building a knowledge base of of your


organization okay the final part relates more directly to deep learning and this this was a research project that we focused on on we focused on event extraction in with a large supervisor in Europe looking at looking at around five five six thousand banks across Europe and what we basically built for them was a deep learning approach that detected distress events or elevated stress levels for banks based upon news data and then provided automated descriptions for these events and that was at the keyword level a text snippet level and document level at at news or article level so essentially it's it's it's a fairly at the beginning it was a fairly stand now we've moved a bit forward but I'll come to that soon but at the beginning it was a fairly standard algorithm we we did a sort of dr. vac type of a transformation from from textual vectors to the word to two numerical vectors which gave us the semantic vectors that we then use in a supervised way to learn a distress signal for individual banks and that basically functioned as an alert system to look at banks that have stress-related discussion it doesn't mean that we're predicting with this it means that we are basically pointing out where we have stress-related discussion but what that means is that you don't have to follow the news for 5000 banks but instead basically you can look at the basically places where we are pointing with this and you already get an understanding when you get the keywords the text snippets and the articles that are top ranked in terms of stress levels so here you basically see these are percentile distributions of the stress score in Europe so for all banks all articles how how that evolved over time the the darkest line here is the mean of the stress score giving an understanding of how stress related discussion has evolved over time and now this relates directly to almost analytics which is an event in a sense can be used as an event event based trading system now the difference is that the events are not distress events but the events are let's say let's generalize the events can be anything basically any binary events that you can predefined so say for a certain stock they could be new projects mergers and acquisitions and so forth now all MUX has what basically launched yesterday and it is based based upon a very powerful event extraction engine that is to a large extent rule-based but we are component by component replacing with data driven approaches and that's where we are also plugging in some of these deep learning approaches and improving on them so for instance one way that we improve this lately is that we are not only incorporating textual data here but we're also incorporating numerical data so in a bank case you would have the fundamentals coming in here as well so it's basically understanding the document in the context of the fundamentals for the bank at the same time okay so a final final shameless marketing is that we are organizing in Helsinki a conference on systemic risk analytics lots of similar approach is covered with the Bank of Finland with the European systemic risk board which is under the ACB and then we're also organising an executive FinTech education with actually several speakers coming from from London and a large extent different most coming from abroad okay thank you very much you Montefiore Medical Center and Yeshiva University.

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