Hello, everyone. My name is Myung Jin. Thanks for coming.  Today, you know, I'm going to talk about the topic related to how to adopt the emerging day federal learning technologies.  But today, I will actually slightly touch upon a slightly different topic, which is called federal learning. For those who may not familiar with the federal learning, I will first briefly introduce the federal learning, and then shift gears towards the challenges that we face to adopt the, you know, new federal practices and approaches. Then I will present some of the approaches that we took and then finish my talk. Okay. So what is the federal learning? Federal learning is the machine learning technique that builds a model collaboratively with distributed data while keeping data privacy. So you may be very, very familiar with the, you know, centralized learning. All data is shipped to the cloud with, you know, some private data center. Then they use a lot of resources for the training. That's the centralized learning. As opposed to the demodel, the federal learning actually involves the devices which are distributed across, you know, many different places. And then without the sharing the data or the shipping it to the central location, each participant will do the training with the local data. Okay. This is kind of a very different model. So you may wonder where, what is the use case of the federal learning? The canonical use case that you might be familiar with is keyword recommendation. So when you actually use your mobile device with the keyboard, smart, the virtual keyboard, and when you type some keywords, the keyboard actually makes a recommendation on the next keyword. So that's actually driven by the machine learning model, which is trained through the federal learning practice. So I will actually work through the, very briefly about how the federal learning takes place.  And each device where the participants maintains some private or local data. Then by using this local data, the participants will do the training and build a local model. Then these local models will be shared with the central node called aggregation node or the parameter server. Then the parameter server will aggregate the individual model, the local models, and the build a global model. Then the global model will be shared again with the participants. On top of the global model, each participant will do the local training and this processor repeats until the certain criteria are met such as certain accuracy or certain number of rounds is over. So this is a slightly deeper look at with one of the famous, one of the most popular algorithms called the federated averaging. So on the client side or participant side, the local training takes place by applying the stochastic, the techniques such as stochastic gradient descent. Then the the once training is offered, the weights will be sent to the server. Then server will, you know, initially will take, select a certain number of clients at random. Then for each of the selected clients, it'll actually receive the weights from the participants. Once that is done, then in addition to that, it'll also use the number of samples or data points used for doing the weighted averaging. So by here this NK is the number of samples used by the particular participants K and then the MT is a total number of data samples used by the old participants. So with that we can actually conduct the weighted averaging approach. Then the WT plus one is the global model that can be used for the next round. So you may wonder why do we need the federated learning, although I already mentioned that the federated learning is actually used for the keyword recommendation. So one the challenges would be, you know, need for the federated learning is the privacy and related to the privacy and the data sovereignty issue. So you don't, you know, in many cases, in some cases, for example dealing with the medical record, we don't want to share the such the data with the, you know, other hospitals or the users, right? On the other hand, it may be very expensive to send the data to the central location. And thirdly, if none of these are issues, still the problem that faces by the organizations is that data silo problem. Even within the single organization, multiple teams may not be able to easily share the data because of the internal policies and so on. So even if data actually exists in the cloud. So in order to solve this, mitigated these challenges, the federated learning may be a good fit. So there are many different types of federated learning since it's exception, right? We have cross device federated learning, cross silo FL, vertical FL, synchronous FL, asynchronous FL. There are very large number of the approaches that exist today. And then the, you know, these different approaches, the more and more approaches are proposed. So one question we want to ask is, you know, as a ML practitioner, especially the federated learning practitioner, how do you actually use or choose one of these and then experiment these things quickly? So in order to kind of the, the facilitate it is experimentation or the use of the different federated learning technologies. We actually come up with the, we started a project called FLAME, which is an open source federated learning project that is led by, you know, Cisco Research, but it is a truly an open source project. So if you actually visit our repository through the disk QR code or URL, you can actually find out the more details. So the, I already alluded, you know, what would be the challenge, but today's conventional FL has this problem called the one size fits all. And the, let's say, let's say we have, we want to actually train in a model with a simple two tier topology. In a small scale, it may be a good enough, but as the number of participants increases, it may actually take longer to for the model to converge, train and converge. And also it may be hard to personalize a model. For example, if I want to maintain a model for the participants in Europe versus participants in the US, it is, you know, with this simple architecture, it's not the problem straightforward. So you may want to actually use the, you know, hierarchical topology where you have an intermediate aggregator, which can actually maintain the model for the particular group of users. And while the global aggregator can actually build a global model. And we also did some experiments with the, you know, various conditions like what if the, there are stragglers or the clients may actually fail or the aggregator or lead, lead or not fail across different schemes. I will not go over details about the individual schemes of, you know, we tested a classical FL, hierarchical FL and hybrid FL, but the, the one key takeaway from this slide is that depending on the conditions, experiments condition, the each approach actually, they exhibit a different performance metrics, like, you know, time to accuracy may be different, word, accurate self will be a different depending on the different approaches. So the, what are the challenges? We have many potential topologies, but which involve the complex and various training approaches. But then the framework are not the, the flexible or the, the extensible easily to support these various topologies. And then another problem is the, unlike the central running, the federal learning involves the, you know, how to deploy, the problem of how to deploy and where to deploy in what components. So we want to actually solve these, these challenges or the mitigated these challenges by introducing the flame. Flames are ultimate goal is to empower ML engineers by letting them focus on what they do best, which is the motor development, while the flame at the, the handles the monitoring functionality, multi-cloud support for torrents and so forth. So we already actually support various algorithms and topologies.  And the right hand side is the different topologies that the user can actually play and use. These are all the, you know, available, but the most important thing is that the flame actually exposes the APIs and, you know, the programming of paradigms so that the user can actually extend the flame easily. So, restfully, we actually published our work in a conference, in a conference called the ACM Symposium on Cloud Computing, which was published this year. So you can actually the look up the internet, search the internet and the find, the find out our work more detail. So please, you know, visit the, the, I mean, the read our paper for more details. So the, now let me shift the gears towards the, you know, more, more details. The flame, the, we want to decouple the ML code from the topology or the topology description in order to simplify the workload specification. So user need to, first of all, to think about what topology they want. And then they, they can be actually expressed as the topology section graph, which we call tag. Then our control plane will take the tag and then they will actually expand the tag into the real topology and workers based on the specification. What if, you know, user wants to change the topology, then user need to update the tag. Of course, you know, if we actually introduce a new components, then the new components will be associated with some code. But that this basically enables the online update and the fast turnaround because the user doesn't need to actually change, it doesn't need to actually develop the, the, the custom, you know, code at all. So the tag is represented with the two building blocks. One is the role and channel. Role defines the ML training behavior. Therefore, it is associated code. And then the channel, you can think of it as the abstraction for the communication. And the channel actually has the specific attribute called the group by, which allows the clustering of workers to support, you know, different topologies. So I will not go over details, but I will actually show how these features are used with this, you know, small toy example. Let's say I want to build a high-optical topology which has two groups. And this can be actually represented with this tag representation.  In this example, we have the west and the east group. And the, the, each group has the two datasets, A, B and the C, D. And then the, these, the groups, group, there are, we specify these group, grouping by using the group association and the group by attributes. So since we have, you know, four datasets, we need to actually, the, need four workers, four trainers. And then the each trainer basically inheriting a group information. And we have two groups. So we need to actually create two, the intermediate aggregators. Then at the top, we only have global aggregator. And, and, I mean, we only have one group, which is a default. And therefore, we only need to create one, the global aggregator. So this is the, you know, the very, very simple example, but we can actually, sorry, the, before I actually move to that, what if if you want to the, the, add more groups, right? So all we need to do is basically the introduce new group and then associate, you know, the new group with their data, new data sets. Then the, the, our system will automatically reprimand the, the new, new, new topology. So in addition to the, the, the example I showed, we can actually easily express the different topologies, like, you know, the distributed topology will be expressed with the simple south loop edge or, you know, hybrid approach on the right-hand side, on the right-most side, we can actually use the, the two tier model with the south loop so that we can actually create the hybrid approach, hybrid topology as well. Just to quickly summarize the, the flames, the abstraction provides the, provides the easy topology update by allowing the small code changes. And because of the abstraction, communication abstraction, the, we can actually use the different communication mechanisms, the, without disrupting the, the, the abstraction. I, I, I hope that these, the building blocks will help the, the transparent ML operations for federal learning. So now, let me shift the gears towards the, the, the how user can actually use our system. So let's say user want to build this simple two tier topology. It is basically expressed as, you know, a dumbbell topology. Then our library SDK actually support the base classes like aggregator and the trainer class, which basically implements the behaviors required for the aggregator or the trainer. So for aggregator, it needs to be able to, it should be able to aggregate the local models or the distributed global model. And then for trainer, it should be able to upload local model and fetch global model, right? All of these are actually supported out of box. And we also implemented, you know, other approaches like a hybrid approach or the hierarchical approach and so on. Then the, what the user need to do is, user actually inherit the, this base class and then implement their own, the training logic. For example, how to load data and how to train a model, how to evaluate. These are the, the, the, the abstract method that requires the users to implement. Then this will be packaged as the job that we, you know, through the CLI tool or the, you know, we recently released the user, the web-based GUI application in our repository. So then once the, the, the, the, the, all these, the specifications are specified as a job then it can be submitted to the control plan. Then the, the control plan will take care of the rest. So let's say the, the, the user submitted a job then the controller will save some state in database. Then it also interacted with this cluster manager such as a Kubernetes. Then the cluster manager will instantiate the, you know, workers. The, by using the, our container image and the image that contains the, an agent called the Flamelink, which connects with the control plane and fetches the, some details about the, the job itself and then launching the, you know, training app in the, in the Kubernetes cluster. So then each training app has its own specification. It could be, you know, in this particular example, the one training app is the, I mean, to train, to training apps or train has a role of trainer and the, the, the other one has the, the role of aggregator. Now, you know, with this, with the specification in each worker, it'll actually form the two tier topology and then the training, the federated learning prep, the, the procedure will be executed. Then the model will be generated. We can actually save the model into the model registry such as the ML flow. So the, now the, I will actually do a quick demo. It's, you know, UI based demo. As I already mentioned, the, we actually deployed the system components, I'm not the deployed. We released the system components, the library components and UI components in the monorebo. Therefore, users can actually use the, you know, any of these things, the, these components separately. If you want to quickly attach the, a flame on your local machine, you can use the library only. What if you want to see how the system works, then you can actually use the, the, the, the system components by using the mini-cube. We actually have the, the single machine setting called the Fiat, the flame in a single box. You can actually use that too. And also we have, you know, UI. So once they are the, the, the, if you have a, the Kubernetes cluster, you can actually use the, the UI as well. So let me quickly play the, the demo. Hello and welcome to the Flame dashboard application demo. The first section that I want to present is the design section. This can create a new design by clicking on the click new button. They have to input a unique ID, a name and an optional description. After the design is saved, the user can start to build the tag. There are two ways to build the tag. Building it from scratch, user can add roles. They can connect these roles with some channels and they can see the details of each entity. The user can set a role as the data consumer. Furthermore, adding more groups to a channel will lead to multiple groups for each corresponding role. Another way to create a tag is by using a predefined tag template. After the user uploads a valid tag schema, they can add the code files for each role. So I'll add a code file for the trainer, one for the middle aggregator and one for the top aggregator. After the user uploads the code files, then they can see how the tag can be expanded with some simulated workers. After all validation are complete, the user can save the changes by clicking on the save button. Once the design or tag is saved and valid, the user can create a job. Another important thing for a job are data sets. Flame manages data sets without leaking privacy. Users can register metadata for their data sets and make sure that users work or only access the data set during the training. Now let's move on and create a job. The user has to set a name to select the design. I'll go ahead with the one that I've just created. For the back and out two options, P2P and MQTT and the time out in seconds. On the next step, the user has to select data sets for each role that has data consumer. Moving forward on the last step, the user has, if the user has a pre-trained model, they can input the name and the version number. On the optimizers and selector sections, the user can select from the list of these, each coming with different set of args. Lastly, the hyperparameter section are some default parameters and the user can add some custom ones. Once the user filled everything, the save button can be clicked. After the job is created, we can start, start it by clicking on the start job option. After the job is completed, the user can check the results of the job by clicking on the name. So I'll go ahead to a completed job that was created with the same tag that I've used for the previous one. And here's a real topology expanded from hierarchical FL tag. In order to see more details about the job, the user can click on the graph icon. The first section represents the timeline of each worker metrics. For a better overview on how the job was executed. By clicking on each worker, the user can see more details about different metrics with their values. And by clicking on top aggregator, besides the metrics and hyperparameters, the user can check and download the model that resulted from the execution of this job. That would be all. Thank you. So the, this is the, you know, the short demo. We are still actively developing the UI. We are going to actually add the more features that are developed for the CLI. So, you know, stay tuned. We'll actually make, you know, release this pretty soon. It is currently our, you know, the overnight source code. So here's a list of, you know, awesome people who actually contributed to this project. You know, there are some backend developers and couple of interns over the last two years contributed to this project and shaped, you know, the current, the architecture of the project and then, you know, they build a lot of the other features as well. So just to finalize my talk, the Flaming is actually a community driven open source project for federated learning.  The, as opposed to the other frameworks that has, you know, low level APIs, we actually accept those and then provide a well defined structure for the software engineers to develop the different topologies or different approaches. And so we think that Flam can facilitate easy adoption of fast evolving the state-of-the-art FL technologies. So that is all. I'm happy to take any questions you may have. Yes? So, do you know that? Probably. So what are, oh, are you aware that's a real knife use cases who are using the learning? What are those use cases and why the distributed learning is is more suitable than the centralized one? So the one of the, these, the canonical use cases apart from the mobile, the keyword recommendation is the healthcare use case, right? The medical record are not easily shareable and therefore the individual hospitals will do the, you know, training with their local data set. But there's a problem with the such model, such approach is that the, you can actually train a model with your local data set, but that's not easily generalizable. Generizable. Therefore, you take a model and then try to deploy it to some other, for some of the hospitals. It's very likely that the model will not work, right? So that's a, that's a privately related one. And then in addition to that, let's say you actually shipped all the data, but then the, even if the hospitals are the, you know, put the data into their cloud, still they are reluctant to share. So even in the, although data is actually in the cloud, but it's not shareable. In such a case, federated learning would be a good solution. But this does not address the problem that the data itself could be leaked into the parameters in the model, right? Yeah.  So that's a, you know, different threat of research work that the research community has had to put a lot of effort on.  In such a case, you know, the model may actually leak the, you know, private information. So for that, it's not an extreme case, but for that, the research community tried to apply the differential of the, you know, privacy algorithm and so on. So do you see any use cases in IoT space, which this could be a good place, in my opinion? So I may not say that IoT use case, but if you think about the, you know, factory floor, a lot of sensors are sensors are deployed to kind of monitor the, the, monitor the process of the production, right? And then the, this predictive maintenance is an interesting area. And then the, you know, how to use the sensory data across different, you know, factory floors or across different factories. That might be a good use case for the federal learning too. Do you see any industry is using that? Last question. I am not certain. Yeah. But we, we are interested in this predictive maintenance use case. Okay. So if there is no further question, thanks for attending. I hope you enjoy the rest of the event. Thank you.


We have to go right now to the keynote speech by Maria Suntagianetti from Torcom School of Economics. I think you can use 40-45 minutes that would bring us to the close of the session that's still allowing for a couple of questions from the floor. So please go ahead. It's fine, okay. So I will change a bit the topic now. We have been talking a lot about climate risk and the way in which climate risk affects financial stability. But when we talk about climate change, I think that it's an even more important challenge how the economy will transit to lower emissions. And in this context, in financial markets, information and disclosures have a great role. Why? Well, as a financial economist, besides regulation and carbon taxes, we would want that also the preferences of market participants are reflected on the projects and the company that gets the funding. And European investors are widely believed to prefer a green and sustainable economy. But in order to invest, they need information. And here come the disclosure. And there is a lot of information that is provided to investors. Companies are providing sustainability reports. They discuss the sustainability policies in their investor reports, and so are banks. And there are also many credit rating agencies, one is rep-risk, that provide the information about how sustainable the policies of different corporations or different banks should be. There is a challenge here, however, that, well, it is widely debated how informative all this information that is being disclosed is. And indeed, it's not by coincidence that there is a huge policy debate on both sides of the Atlantic. So the European Central Bank came out with different reports about how banks' disclosures about environmental policies should be enforced and regulated. And this is a big issue also on the other side of the Atlantic, where, okay, now this is a big issue also on the other side of the Atlantic, where, for instance, the SEC is requesting opinions from market participants about whether at least the company's disclosures should be regulated and audited, okay. So this is a big issue, but we don't know about how much we can get in the current state, in which the corporations and bank disclosures are largely voluntary and they are not regulated. The information that firms and banks provide is, to some extent, cherry-picking. So this is the broad theme of my talk and I will base my talk on a paper that you see here. The paper is joint work also with Katerina Mendicino, who sits there, and Martina Yasova, Barnard College, and Maria Lumionti, who is a professor of accounting at the University of Texas at Dallas. You can see that paper is with Katerina, an economist at ACB, and we use data from an accredited, that is, data from the ACB, the usual disclaimer applies. So what we want to do here is to understand what extent banks are doing what they say. Now banks play an important role in everywhere and the Euro area in particular in funding investment. And since the European Commission, the European Central Bank, stress that they could play an important role also in the transition to a more climate-sustainable economy is important for us to understand how our banks sort of complying with this task. In words, banks are enthusiastic. I will show you evidence that banks are making extensive environmental disclosure in their reports. Basically, they discuss how they are changing their lending policy to contribute to fewer emissions. Of course, what we don't know is, we don't know how they are lending, how these lending policies are reflected in their portfolio. And this is precisely what we ask in this paper. Basically, what we are asking is, what is the relationship between the banks' environmental disclosures and their lending standards? So we start from the large banks in the Euro area, we look at all their environmental disclosures in annual investor reports and sustainability reports, and then we look at who is getting funding from these banks. Now a huge challenge that perhaps we didn't discuss enough during these two days is that it is extremely hard to be able to measure which borrowers are brown, green, how much they are emitting. Why? Well, because companies produce this information and sell information about emission only for the largest borrowers. And also banks have access to this information for the largest borrowers. But the banks in the European Union are facing the challenge to measure their carbon emission, their portfolios. So what are they doing? My understanding is that most banks are looking at industry level emission. And this is precisely what we will do in a larger part of our test. The European Commission provides information about emission by the EJT industry, and we will relate emissions to the industry value added in order to rank brown and green industries. However, before you were talking about transition, so it could be that a bank is getting more exposed to brown industries, but it's at the same time funding the transition of these industries. So we want that the banks and investors in general get engaged. So we could be picking this. I will show, so how do we do that? Well, fortunately for us, for a majority of firms in the European Union, there exists a business description in English. So we do a textual analysis of the business descriptions of the borrowers, for which we can gather this information, based on the taxonomy of green industries. And then we use also this information in order to check which firms are getting relatively more funding by companies that are busting more about their environmental sustainability of their policies and the changing data they are enacting. So this is the plan ahead. Let me give you some insight on what we expect, what we expect when companies disclose. Well, in principle, if the information provided by company could have a negative effect on the company reputation because this information is verifiable, we would expect that the company disclosed the truth. So in particular, we would expect that the banks that are stressing more their environmental policies are precisely those that are either changing most of the composition of their portfolio towards the green industries or funding to a larger extent of the transition. But I said that before, the huge issue about information about sustainability of banks and corporation policies in general is that we don't know how reliable this information is. And most of the reason is that it is very hard to verify this information. So there is lots of room for what I'm calling green washing. And here I am offering a definition of green washing that accountant use and is relatively broad. It's actually used also in some ECB report. So what is the definition of green washing we have in mind? Well, we have in mind the green washing of a bank that might be stressing in its report that is decreasing the exposure to a particular brown high emission sector, for instance, oil and gas. But that in other, not a visible part of their portfolio, because the loan portfolio of a bank is not visible, is actually behaving as before or funding relatively more brown borrowers. So what are our main funding? Let I will carry you through some of the actual results, but I would like to give you before an overview of our empirical analysis, because we have a lot of results. So we start from the analysis of the environmental disclosures of these banks. So we download all this information and we perform a textual analysis on the basis of a dictionary that we construct mostly based on the materiality of sustainability policies. And our dictionary aims to capture what banks say that how they are contributing to the environment, biodiversity as opposed to scenario related to risk. We validate this information. So we show that, for instance, banks in countries in which there is most social activism for the environment are disclosing relatively more, and that this information that we collect is related to proxy for the bank's reputation about being environmentally conscious. How do we measure this? Well, we have what rating agency have been doing. So we show that our measure for environmental disclosure is actually positively correlated with all ESG ratings that we could find. And also banks that portray themselves as more environmentally conscious in their highly visible activity appear to have a greener policy. So for instance, we show that these banks are more involved in underwriting on green bones. And this is also a sign of caution when we use the syndicated loan markets. Loans that are syndicated are a bit like bonds. They are highly visible. What we do is different. We look at the lending of the portfolio of a bank that is opaque without the fantastic data of the European Central Bank. There is no way that we could have written this paper. So what do we find when we do this? Well, we find that banks that portray themselves as environmentally and socially conscious issue relatively more loans to borrowers in brown industries, that is high emission borrowers. And this is the case according to all our measures. I'm sure that some of you might be thinking, OK, but those are the companies that need the capital in order to transit to greener technology. Unfortunately, this is not what we find. We find actually that the brown borrowers that get the loans from these banks are investing less. They are not doing much R&D. And these are the technologies that generally are fought to favor transition. Instead, these are borrowers that are relatively lower productivity in their industry. They are relatively lower profitability. So basically what we conclude on the basis of this analysis is that it appears that these banks are greenwashing. And there are characteristics of the banks' business model that is close relationship with borrowers that limit the extent to which these banks can favor the transition. So going back to what Martin was saying before, perhaps at least for what a bank funding is concerned, we need other type of regulation that directly affect the externalities. So let me get to the data. We have been seeing an credit a lot. So this is what we use here as well. And we focus on 553 banks, but the banks includes the subsidiary of the bigger funding group. And as I mentioned before, we will have in the papers three different ways of identifying brown and green borrower. The first one based on two-digit industries is the one that is more comprehensive because we can classify all borrowers in an credit depending on the two-digit emission in their country of origin. But then, of course, we are aware that there is a lot of heterogeneity within industries. So for this reason, we use SCOP-1 and SCOP-2 emission. And we chose to use urgentum that probably has the best coverage. And also we just added to the paper the analysis of the firms' business descriptions. That is, this allows us to highlight whether the bank is actually lending to the photovoltaic subsidiary of Shell.  So we download all these disclosures and we perform textual analysis after having created our dictionary. And again, our dictionary is mostly based on the sustainability accounting standards board classification. But it also, we look at the bank reports. We make sure to exclude words that are related to risk scenarios and so on. And again, the important words are related there is renewables, oil, natural gas, CO2, biodiversity. And I want to stress the characteristics of this report. While in the US, listed the companies are requested to disclose the risk, to my knowledge, in the European Union, this bank, at the date of our analysis, were not. So all the reports we read, and Maria basically read them all, have this positive slump, meaning that, yes, there are banks that discuss oil and gas, but they are portraying what they are doing good to move away from that. And indeed, so our measure for environmental disclosure, for which I will show you all the results, is based on quantity relative to the length of the reports. But I could use a measure based on the sentiment of the disclosures, and I would find all my results for all the positive sentiment, meaning that the banks are not saying in this investor report, well, if something, if there will be flood, we will be bust, okay. So, and this is the pattern of environmental disclosure over time. And what you see is that they have been increasing a lot of average. And this means that the banks, each single bank, disclose more, but there are also relatively more banks that are disclosing. So are we capturing just noise? So we do a validation analysis to provide evidence that no, our textual analysis is not just capturing noise. So what you observe first is that in countries in which there is more attention to the environment, there are banks disclosed relatively more. And this makes sense, because banks disclose in order to attract investors and customers, the depositors, okay. And this is what brings them to portray our reputation for environmental sustainability. We also find that the disclosures that we classify textually are higher for companies that committed to follow the GRI standard for their environmental disclosure. This is basically a voluntary commitment that the GRI is a website that they give you a questioner and the banks or companies would be supposed to disclose according to this questioner. But actually, even if you look at the GRI data, this is not the case. Some banks provide more quantitative information, other more qualitative. And then as I was mentioning before, these environmental disclosures are very highly correlated with the actual reputation of the bank that is captured by the ESG rating. And banks that try to portray, construct a reputation for environmental consciousness, also in their visible activity, here we are measuring the visible activity looking at green bond under writing, this is again correlated. So, basically our contribution here is looking at known visible activities. That is, what do they do when banks land? So if you want to remember only one slide of my presentation, this is the slide you should remember. It's not our actual analysis, but we look at the portfolio in the past of banks that are disclosing more. So what you see here is that the banks that disclose more tend to have a larger portfolio exposure to brown industries in the past. This is not surprising because we know from very different fields that any corporations disclose more if it thinks has to address a problem. So for instance, it has been shown in several papers that companies with higher gender gaps disclose more about diversity. So this is very similar. But this is fine because the specialization of a bank depends on the industry, the real activity in the main countries of operation. What we look at in our empirical analysis is whether besides disclosing, these banks are also trying to do something to move away from their past specialization to something that we classify as more sustainable. So how do we do that? Well in the previous figure, I was looking at the past loan, exposure in the past of the bank. Now we look at the new loan issuance. And basically we look at whether banks that on the basis of our textual analysis we classify as high environmental reporters lend relatively more to borrowers that we classify as brown. Of course this depends on the different demand that these banks face. So how do we address that? Well we have two methodologies that are widely accepted to control for the demand of borrowers in different industries. First we look at how different banks extend new loans to the same borrower during a year. And this is the usual Kwajamian methodology. But some of these borrowers are small, so some of what we do is based on a more generalized version of the Kwajamian methodology. That is we look at how banks lend to borrowers that being in a similar cluster are like to have similar demand for credit. So these are borrower in the same industry, country and year. So I will show you results with both methodology. So what do we find? Well we find that the banks that we classify as reporting more about their environmental policies lend relatively more to borrowers in brown industry. And this result is robust to using different proxy for unobserved heterogeneity. Now we know that the bank business model is such that they cannot cut the relationship. So perhaps these banks could try to engage relatively more borrowers in green industry perhaps that's what their environmental disclosures are inting to. But we find no evidence of that. So the results are very similar when we look at borrower level emission. That is scope one and scope two emissions that are widely considered to be the best indicator for a company brown. So why is so? So let's look at the channels and of course the first intuitive channel is that these banks are funding some brown firms that are investing in decreasing their emission and transitioning to a more sustainable economy. Now as you know an accredited has not a very long time series. So how to do that is very hard to look at whether the borrowers that are getting loans are decreasing their emission in the future. We could do that but I wouldn't consider these a very stringent test not to find anything. So what do we look at? Well we try to glean what the borrowers are doing with the money. And there is a very simple test that we can do for everyone because we have access to orbits that basically provides us a financial statement for the population of European borrowers. So if the borrowers are investing in transition we should observe that these borrowers are doing some R&D because many others for instance Philippe Paglione have told us well that the borrowers that invest more in green technology tend to file for more green patents. But then we should observe that the borrowers that get loans and they are in brown industries are investing in R&D or at the very least they should buy new machines that are greener. So they should invest more in fixed assets. So we also look at whether firms lend more to brown borrowers that have committed to decrease their emission in the future. And for this we use borrowers that are signatories of the science-based targets initiative. Of course this is a smaller sample of borrowers so we will adapt a bit our econometric methodology. But what you see here is that if anything these banks that discuss more of the environment in their reports are lending to a lower standard to borrowers that do higher R&D. And there is no evidence that they lend to borrowers that invest a lot in fixed assets or that have signed the science-based targets initiative. So what and we have basically the same result if we classify green and brown borrowers using company specific characteristics based on the textual analysis of business description. That is we do a textual analysis of the business description based on the eutaxonomy. And what you see is that at least when we control for demand using the cluster it is again brown borrowers that are getting relatively more credit from banks that disclose a lot about the environment. So then we try to get more around the question of why we observe this. And of course relationship are important. So the first step that we ask is that to ask to what extent the past is encumbering these banks leading them to tell something differently from what they are doing. For doing so we look at new relationship. And what you observe here is that well when banks are establishing a relationship with new borrowers they are indeed doing what they say. That is they are less likely to establish a new relationship with borrowers in brown industries. The problem is that when they have an existing relationship with a borrower in a brown industry they don't cut this relationship. And for this reason if we aggregate at the bank industry year level we do find that the banks that have high environmental disclosures each year extend more new loans to borrowers in brown industry as a result that you can see here. So why are these relationships so important? Can it be that well it's very hard to terminate some relationship? So we look at the quality of the borrowers. And specifically what we do is we try to identify firms that are less likely to be financially healthy. So these are firms with relatively low productivity, low profitability, low interest rate coverage. And what you observe here is that if anything these are the borrowers in brown industries that are receiving relatively more loans. We do some other things in the paper. For instance I am not showing the slides but I would like to highlight that of course banks may not penalize their borrowers in terms of quantity because of course they don't want the borrower to fail. But they could penalize them in terms of risk premium. However we do not find that these banks that are high environmental reporters charge a higher loan cost to these borrowers. Another aspect of the loan contract that we can look with an credit is the loan maturity. Okay and an idea would be well if the bank is trying to engage with the borrower and monitoring a lot. Well probably this bank would shorten the maturity because the renewal of the loan is what gives bargaining power to the bank. We don't find that. If anything the borrower in brown industry are getting longer maturity loans from these high environmental reporters bank. So we also find that this behavior is mostly enacted by banks that have relatively low capitalization. It's also surprising that they are more dependent on capital markets. And also large banks do this more. Again not surprising large banks are more likely to be listed. They are more visible. They are more subject to investor pressure. Then we try to look at the effects over time. If we look at the after parties agreement we find nothing but I wanted to leave you with this graph. Okay so you might be wondering well you start early on and some of you might be wondering why did all your slides say 2014. Well we look at the loan issuance. So even if an credit starts in 2018 we can construct our data set of new loan issuance. But it doesn't really matter. Our results are equally robust if we start in 2018. But what you observe here is that this behavior that we highlight is mostly driven by the last three years of the sample. That is when the environmental pressure has increased. So we are not talking about history or about a problem that has disappeared. So I am actually done so let me try to wrap up. What do we do in this paper? Well we look at the information that banks give about their environmental and social policies. This is important. Why? Well there is a chance that we as investors as customers we can convey our preferences to active market participant and affect investment. Okay so for instance there is mixed evidence but some institutional investors might be able to affect the companies precisely because of their preferences. Okay is this the case for banks? Well it looks like that some banks are trying to construct a reputation for being environmentally conscious. Okay but this reputation is reflected in visible market activity like under writing of green bones. Okay anyone can check that on Bloomberg's website and so on. But the way we don't see this information in the most important part of the business of the bank which is well is lending. Okay most of the portfolio of a bank is lending to small firms. So in this respect discussion and attempt to make to regulate environmental disclosure about what banks and company can say have to be supported if we think that this instrument if we think that information is relevant and this is what I wanted to share with you today. Thanks. Thanks a lot. Very impressive work especially on the data front and accredited or these textual analysis and also very interesting to settle these paradoxes in the in the conclusion. Congratulations also because you managed to catch up on our schedule more or less. So that's brilliant. Having said that we have therefore more room for questions either in the chat or in the room. I see already gentlemen at the end and then Tiana. Peter Raupach, Deutsche Bundesbank. I have a question on on a special sector where you possibly could observe more precisely whether banks have changed their activity which is power producers. So you know the energy mixes for basically all of them. Of course these are again the more visible companies so if the banks try to sort of cheat they would be correct or would tend to be more correct there but have you looked at that and if so what do you find? So we haven't looked at the power companies in particular. When we look at the business descriptions we try to go in that direction but it could be a good idea to look at those companies in particular and it is true that I stress the visibility. But when we use urgentum we really are basing our tests on 1000 of the largest borrowers in the euro area. Those are very visible and we still find the same result. So perhaps we can talk a bit more about the data sources and what we could use because I think it would be interesting. Thank you. We had another question here first. Whatever. So I just have a question for you because it strikes me that the period over which you're doing the analysis 2014 to 2020 is probably quite a heterogeneous period itself. So I wonder if you observe different behaviors over the period and I think it would be super interesting to see whether there are changes happening now especially with the signing of the net zero banking alliance by many of the banks which is quite late in 2021 and the objectives are now being set. So I wonder if you have any sense of a time factor in how the data may be changing or not. So this is what I was trying to convey with the last figure I showed. So within our sample that finishes in 2020 we see that is the last three years of the sample that largely drive the result. We don't focus on that particular climate alliance there have been many of these initiatives. So the GRI is one such and we don't find much there. But of course I can talk about my sample and it would be good if also with more enforcement and with the ECB attempts to regulate or at least morally convince banks these behavior might be changing. But I think that this attempt of regulation is probably as important. Vienna Moffin back up Portugal and ECB. So it's a great paper. I wonder if you looked into banks lending to different types of companies with the hypothesis being that when the banks land to large companies on which there's a lot of public information maybe syndicated loans on which it's easier to collect information maybe these banks are trying to look good for these firms they would land to greener firms but then for the private firms which are the bulk of the companies you have in anacredit then they could whatever and this would be consistent with the general results you have. So I don't have a sense of whether the loans in or what proportion of the loans to large borrowers in anacredit are syndicated that of course would make them more visible. But I can tell you that on average when we focus on large borrowers because we are using actual emission from urgentum we find the largely the same results. So of course when we look at the cross sectional effect we could use also borrower size and then yeah. Daniel Gruhe ECB SSM on your very interesting finding that that banks that do a lot of brown lending also do a lot of or more than the average amount of green reporting but also another finding which which comes from your research they tend to invest into new relationships that would be more green than average for the industry should we perhaps simply somehow see the greenwashing the over reporting of green activities as a sign of not the current policy but of an intended policy transition of a bank and in this way maybe optimistically see it as a positive sign. So I understand that the EBC cares about the average emission of the portfolio of a bank. We are being more generous here. We are looking at the average emission of new loans issued by a banks. Now what we find on the extensive margin is that when we look at new borrowers in brown industries those are the those that no one wants. And it's true these banks are even less likely to establish a new relationship with these brown borrowers. So that is a positive that goes towards the direction of what they are saying but if we look at the really green industry you know most of industries are in between they are neither brown nor green. In green industries those are the borrowers that everyone wants also probably they have also higher growth opportunities so that we don't find many cross-sectional effects. So it is true on the extensive margin if you look only a new relationship they are saying what they say they are doing what they say but if we aggregate using the new loans of these banks well they are not doing what they say so if the depositors are using this kind of advertising to choose their banks they are being cheated. Strong conclusion which would conclude our session. So thanks a lot to you and all of the speakers and discussions we are doing here.