Streamlining Financial Reporting with AI and NLG - with Emmanuel Walckenaer of YSEOP

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This is daniel fidel with emerge artificial intelligence research. And you're listening to the a in business podcast over the years. You've been a loyal listener with us. You've heard many use cases and trend explorations for the future of a in banking future. Vi insurance and wealth management we covered how compliance workflows or changing. How customer service is changing. How fraud and anti money laundering or changing. Today we're talking about reporting our guest. This week is manual. welcome year. Who is the ceo at easy peasy op is based in paris and a focus on natural language generation. They've been at it for a while. We actually had another member of their team on. I think it was some four long years ago. Something crazy like that and our focus in this episode is getting into detail as to the specific workflows where automating reporting really delivers value. We talk about two in particular one of which is credit risk understanding in assessing credit risk. What does that process look like today. What kind of data and insights required. Where does that fit into the mix and then we also look at improving report quality for folks who are controllers or analysts. We've talked from the perspective of compliance leaders but never really from the perspective of controllers. So a little bit of uniqueness here some of the use cases that were covering and i appreciate emmanuel's detail in exploring how the use cases work and also how the underpinning data infrastructure comes together to actually allow a natural language generated report to come to be a lot of detail in this episode of excited to get started. This episode is sponsored by easy. Op if you're interested in reaching emerges on go to e. m. e. r. j. dot com slash a. d. one that's eighty like add and then the number one m. e. r. j. dot com slash eighty-one and learn more about our creative services. What it is we offer in terms of content and reach and also how brands work with emerged. They wanna learn more about that process yemi rj dot com slash eight one without further. Ado this is emanuel decio easy. Opt here on the business. Podcast so emmanuel. I know we're going to be focused on some unique use cases in the financial services face in terms of where a i is starting to find its fit and add value or to talk first about the business units that you tend to work most closely with which again are somewhat unique. We cover a lot of different areas of banking but you guys hang out around the risk and compliance world as low as the finance and controlling world. I imagined kovic is change a lot of things for these folks the banking ecosystem insurance ecosystem or undergoing a lot of change. What do you see as some of those big kind of structural shifts that are changing and imposing new demands on on those business leaders. Well thanks well. It's a it's a great question so we have been working at easy up with at twenty five now tijuana large fashion institutions both in in europe. And the us. It's true that these guys are always intrigued by You know i could help us on so you know. I've been doing this project. Are you know. A lot of them are in production and pretty happy. Now with could be done. you'll think did accelerate. I think these guys are under massive pressure. You know they have given you know. Unprecedented amount of loan to help the economy a dramatic way. I mean they have. They have to be super giant. You know the certa tastes pretty high so they clearly have a lot of customers asking me. Okay how do you know how i could help me. I've let my team. How can i do more with the team. Have and you know. Could we be way more. If he shouldn't have to jail did a prediction that twenty five percent. You know of any companies with adopts on form of language a in g union natural language generation by twenty twenty two. We plan to double our install base this year so we actually way way way bigger. Yeah two things. I think some of the benefit of these kinds of applications that we're going to get into use cases in a moment is that there are some sorts of ai. Applications that require. I think a lot more data infrastructure overhauling than some of the stuff where we can kind of take what exists so we can actually just make it more simple. It's it's a little bit. More of a a circus. Accessible application of ai i take the jackhammer to the data. Infra overhaul what we're doing and by the way there's a place for that i think banks are going to have to evolve in a bigger way than surface but some of the accessible stuff right away. I think really great opportunities for you. These increased demands. Is it just due to. How much has changed about what they're reporting on is due to how many loans they have now out in the world. How much risk they have to manage. What are the things that covance brought onto them. That's made them forced to be more agile. Course being forced to work digitally. I think is probably part of it or do you see those being forces pressure from while you know. These guys have pressure from Everywhere yeah poor. Poor regulation england that now got into customers. Yup and because some say you know you need to help us to really get more data much more. Precise is more timely. And so on the buses who wants as well. You know what he's going to have any surprise this month. There's going to be you know a big change in your lab. thomas shoulders. Got the complaints you know. The governments you know compliance is is super era is not going to be lighter in the coming you know in the company and the sometimes you know these guys are. I'm talking about finance but even risk organization you know. They would love to become way more business driven organizations. Of course they can you know. Have the right data in your get things straight and i think they are very good at that but helped transform these huge that They have on hand how to use that to make a better business. How to debuts intial customers to actually drive their business more efficient way. This is super so lots of pressure from all directions. It sounds like a little bit. Glad i'm not in that business but obviously it's very important. Facet of the financial services world is make sure our reporting is working when we get the insights. We need to move on make smarter decisions so we can talk about some of those decisions. One category that. I know we wanted to dive into here. Was looking at risk reporting specifically around of credit risk tons to think about you know so many more loans out there in the world walk us through a little bit of kind of what the business workflow is in where is starting to find. Its fit into that particular workflow. That's To use case which is getting more and more popular so just before i stopped what we do in a nutshell. We do transform based on algorithms And the i. We do transfrom data into narrative that anybody can understand the letter in this type of ad. Kimmy trying to transform that. I in so the credit crate-raised departments for retail culprits fashion institutions. You know these are huge departments within the banks of course and the year every time you do what you have to grant a new loan and then afterwards every year have to reassess the risk based on the data fashion statements to the customer based on the cash flow based junior old type of financial information. They have to reassessment and so that they can. You know do some direct provision and so on. so where are we jump in. We say guys you know we can help you to actually do sixty to seventy percent of this reports automatically. We will produce a draft for the enemies so that all the section which are data driven will be actually drafted by the machine and the ice can actually dialogue with the machine. Say okay guys. Thank you for these highlights and you know this paragraph now a little more detail so yeah got slider you know and these interface and against say okay. I so to the machine was good morning until i want less detail the east you know dialogue who is a machine and makes the report the way it wants. The reports look like super efficient. You know have been deployed that three four banks. Now they are one of the banks you know. They told us well. We cut the the amount of time to do this report by half so it's a fifty percent efficiency gain. Which is you know. Pretty nice super high volume you the typically you have got two thousand two thousand five thousand and that is involved you know and you have to do in corporate banking you can. Do you know small and bean sized business. You can do so. It's you know super away again. The volume of our super high. So the arrow i on this type of solution are you know in millions of dollars a year and so i want to talk a little bit. We'll go a little bit more into the nitty gritty of the reporting in the use case itself in just a second but i want to kind of pull up to the business value side of things. Obviously you folks have experienced deploying these so i think there's lessons learned here you. You mentioned saving fifty percent on what it takes to do this report. I think maybe not everybody listening in will understand how much has to go into news reports together. Maybe if we could if you wouldn't mind painting a bit of a picture of i need a certain kind of report on my small business loans in the state of the risk profile of my small business loans kind of a high level. What kind of data has to get pulled in. How how did that process used to work before. Because i think people might not realize how much goes into this context helpful you know. It is of the time pretty tedious tasks to warn the updated fashion formation near the band the piano cash flow. You have to compare this year. Versus last year with us changed it's super tedious. And it takes you know two three hours to do that. You know more if it is you know. Of course there'd be other companies this and then the you know. This is a data crunching and this is really putting together. Okay let us change. What is important and so on. And this is a task that i can actually do automatic. You clever way. Intelligently he's not just describing. What's us change trying to say okay. This has changed because you know finding the underlying causes and sometimes guys you know. This is very strange. I mean this move or this. Change is very unusual in the you know in this type of cell so it went you know whatever so what is key. Is that the adoption by the end. User is absolutely oh yeah has be left to You know we take them with these type of solution out of their comfort zone. We appreciate report. Which is tedious. And so they not the most important part of the job that used to do it. Yeah and they're used to using what Microsoft excel and you know that the old bunch of tools they used to use right. oh uneven. Whoever out of tools and zone are used so it's a bit of a challenge for sure netease and the way you write has to be perfect the my god you know i told you you. This doesn't work you know. This sentence does a meanest So a perfect. It's definitely part of the challenge for us guys right. I mean if i'm not mistaken you have to design and interface where the feeding of the data and then the structure of the output on some level is clean and that kind of people put things in in that cleaning consistent way to get that clean consists now quote. You've got to also make that user friendly and that's not an easy job. I think that's one of the reasons a lot of a. I have a hard time. Yeah always spend enough time to actually find you on that by getting a lot of feedback from you know from the customers. Some seven we got some rates. We ask the customer cain the deployment phase as the. You know the enemies to rate from one to ten. You know they happy again. Yeah that that feedback is essential as a central. We can turn of another feature introduced to put some mesh. Earning somebody's may you know may be very comfortable with their some tone. You know some would love to have some Some seem some you know sentence structure so basically we gave them a few options you know. Would you like to prefer to rights like this or like that and we time we learn the style of the nfl so that we can actually something that you know. Grieve rights like-minded likes dan. Like shenault and that helps a lot in the option. Yeah i can imagine what. Because if if i recall correctly i think we interviewed folks three or four years ago or something like that. There is a lot of not necessarily machine learning in getting some elements of energy right. Because you don't want statistically we think this will work reporting what you want. Is you want like a reliable output which is a little bit more of an if then if then and structuring those things properly so rule fronts engine but then style in the way you ride this is where it can use machine and mission our mission so g is really getting better in that respect right i mean just look at the last three years you know i mean think about opening i. It's like it's incredible the kinds of things that are now possible even open source versus just a few years ago. Okay so that's the. Nfl fits in in sort of the the style of the output in terms of how the user would like to have it design. Okay so this is one example. We're talking about riskier and this is where we can take. Risks reports about credit risk and are these often very bespoke manual in other words. I can imagine there's almost an unlimited number of things to do reporting on do you have are there. Are there almost out of the box. Everybody has to do this kind of analysis in probably this kind of way boom baked into your product or is it more. Hey they're going to do an infinite number of different bespoke things based on what their internal and external stakeholders are needing and we just need a platform. That's more general if there are some regular ones i'd love to know what they are but if it's all the spoke that'd be interesting to us. This twist report is clearly an out of the box in a matter of days now every banks have their own rules of their own risk rules. You know rivers will allow no coach to joe so that none technician at the bank level can actually do the musician station themselves. They can actually fine-tuned the rules. They can actually build the reports. They want an every banks you know. Even though the same report you know every single banks of their own way for sure that we don't have to have a incredibly clever engineer to all the the whole the the business guy. Say okay guys. This is a standard way to do not based on our experience and were built and song now we completely understand you may do things slightly differently and that the risk level you want to you know whatever you want so all the parents. It's a three hour training and somebody can actually built his own report based on the mayor. We have prepared in sweet hours. They can actually choon these modern and have something that can really work. Yeah that's that's one of the things you only get from experience working with customers writing mean there's so many assumptions you'd have about what kind of reports would be needed a bank and how could a nontechnical person build it. It's like i imagine you learn so many things first couple of years in business learning about how to make this non tactically accessible right. That's that's a real a really important element of building products now every single of our customers once we build the first report hundred percent of them. Say okay guys. This is cool. This is working great. You know we have other ideas. Yeah yeah yeah well. And that's that's where you wanna get one. This ngo have other use cases in mind on progressive. You are building libraries of different reports that we can actually automate. Let's get one hundred percent. That's that's a tough number to hold onto. I feel the closer you can get better and certainly being able to have nontechnical people using an ai oriented tool at all is not an achievement. Anybody should should under. So that's risk. Now there's other spaces where you folks play as well. And i know that one of these is around improving quality of according for financial analysts in controllers. I think again the workflow for some of our listeners might be familiar if they work inside of that area of kind of the the controllers business unit. If you will but for some folks might not so. Maybe you could walk us through. What are these folks trying to report on what insights they looking for and then what is the process. Look like for them where we bring. Ai into the picture be an interesting project on many other projects folk controller or dna organization many reports echo than performance reports. They've got all at one. One project who have done for large Very large retail network in france. That they guys we would love to understand the performance or branches and they came up with twelve different. Kpi's you know what type of product y type of channel using which region and which branch which specific retail network and so on. So long. i. It's it's so got all these data what does does provide them a very. Are resisting tactic. Analyses of okay. What is the best branch where the customer customer satisfaction being battled out where the customer satisfied and they put some contest. You know the stat on the weekly and monthly basis. Though that every branch the branches are were actually waiting for that. He's not just competition for the sake of competition. The explaining guys you are now in the ba- five in your region or a national vesey's this she's trying. This is your key area of improvement This is the highlights of the mounts. You you know it's automated. It's absolutely you know we were talking about quality the information. Yeah the same rules for everybody not depend so fanatics restarted. Yeah this person uses. Microsoft salvage This other thing it's very well say explains why you know and that's as well very potter to explain okay. This is a big why you are unknown number one in customer satisfaction and this is what made the changes this you can count this product and saw happen to interview some of the brench measure. They saw that as a very powerful team even to motivate their teams guys. You these what we are you know and so it's pretty. You know very interesting. Quantity of the information timeline formation. Other banks exactly the same executives abdication. The made them to analyze their beck society. Spendings to be very specific. And so. it's you know. Less of a competition here is more to understand. Okay why did spendings. How does that compare to the last year or how compared to at what is forecast. You you can do that on. Sales know bbc on that you'd be was while the dodgers french banks are their production. Your mortgage production sale gate. Let's do this. That had a team that you know and they did. One is for the whole group. This was before zero and these guys super-busy they say okay we can just do. One is for the whole for the whole group. They've got tool major networks. They have got to. You know a lot of different departments and so on. They may not remorse for the branches for the retail thirteen different reports. Same team and they plan to do hundreds of different reports and the head of the You know the team told me. I want to be a business partner. I want to really bring to my internal customers detailed information how their business work just to colorado of this stuff. When i think controller i almost immediately and this is probably because i'm not a controller. I immediately think finance you mentioned customer service. I'm interested you know we talk about looking at the performance of a whole bunch of different branches and being able to compare them have healthy competition so that we can really drive business outcomes. I think anybody listening can understand. You can only manage what you can measure if you just have a bunch of spreadsheets that might be measurement. But it's not making it clear if you have really simple reporting that people can understand it compares things properly. You can now really everybody on the same page that makes sense for what a controller cares about. You mentioned mortgage production. I would imagine you know the. Pnl on the balance sheet would be where they're focusing. But it sounds like there's a lot more elements in there. What are some of the things that controllers need to pulse jerk raleigh. Nl and balance. She is as well. You know a very hot tub. 'age here that's time critical at real. The summit that any control is spending fifty percent obese time. Fifty one works on report. And this'll be porty pence fifty percent of his time building the report and fifty percent of. It's done understanding. And billy action plan discussing with his until stakeholders their sense on. So what he does is to just suppress fifty percent of i fifty percent who this but domestically don few minutes a can just focus on the more quantity work. I would essentially bet. Let's say my mother's life on the fact that it's not a few minutes for everybody. In all cases like this probably has got banks that ripping in data source xyz q r s. But you wanna make simple as you possibly can. Is this mostly from. Api's piping this stuff in is it from having the right places for humans to just manually input things what helps bring that time down. Ninety five percent of the time it is okay. Yeah you're you're fine the way to pipe on matica of information it can be from any different sources who have that can come from a c. p. can from your erp can comes from excel spreadsheet. Don't really care and fifteen different sources. We information's store that you know what we call a cuban which is a multi dimensional way to present the data and then our sta. That's that's a real challenge. What you're doing there. Because i think that i suppose we call it. The data engineering part of this problem. Right where you folks are working in different languages. Obviously you know your franchir. He english whatever you have different formats you know whether it's excel or csv or getting pipe from this database that database and there's gonna be a way where we take those through the right combs right filters to align the columns pool in the right data to be able to streamline that is really a non trivial task especially because there's probably some variance from bank to bank or even department to department. What are some things. Those are the big challenge. One of our biggest feels really significant to walk a very impressive these targeted use case his. Now i've got the data we are. We know what we're looking for if you want. So we got a targeted data structure. So we know we. Won't you know these. You know what we call an anthology prebuilt until g where we know initiative of all these different. Kpi's unsawn okay. Then what is left. I was says to do this quote. Unquote that mapping and discussing with a customer. Okay where's your arm. And then having data mapping so that we can match dinner specific environments with the targeted. You know easy ab- way of looking at things as men. There's a ton of challenge there. But i imagine you work with enough customers. You build on policy. That's pretty complete. And then you know customer number twenty a lot of the things you can just find the match right you can say oh well this lines up here and okay. We've used accel before. And he's kinda got something built where it can pipe band. But i imagine building. That system has been credit-risk. Is something like five hundred. Kpi's now to experience a lot. Yeah i would say well. So the customer say yo. You don't have that they are. I just do an upgrade and you know but most of the time we are. Yeah i would say hi. Runners want truck. I think one of the things that's important for people to understand listening in is that you know getting that data to make sense in a coherent way where we can start reporting on top of. It is absolutely not an easy in easy part of the job. Right for you to be able to put together paragraphs of what's happening in all these different branches that ontological that structure in the back end is really what makes what makes the magic happen and it sounds like for you to be able to pull in customer service or mortgage production. It's because there's an oncology that can support those differences again the customers can do his own. You know things in and then change the. And that's the magic. And we produce a texted medically now. We can say okay. I want very verbose. A you know this in french and english show on his paragraphs to be away lighter. I mean that's you know. Snow could studio super helpful so that they can. You know it will become their report. Yep we just provided a tool. Yeah like you said cutting down on that. I fifty percent right bringing that stuff down so they can focus on understanding and conveying those insights so final question will wrap up here but i think got to the media things. I think we really started to explain. The some of the the under girding technologies that are allowing to make this happen which is really instructor audience. The use case here quality improvement is the overall surname use case. It seems like the way quality improvement. The way that you folks facilitate that is basically by having a unified way to look at the data. It feels like in the data matching what makes quality happened because otherwise this person's got a pie chart they built off of google sheets. This person's got a pie chart from twenty day old data that was on their desktop. And it feels like that back end is what makes quality happened is that is that right or there other elements to thinking about quality improvement that we don't want to leave out here. Could he comes from quantity of the as you say don zone until the algorithm who plays quality comes from consistency so even language may be different. You know that the fundamentally were very consistent as we can go down to fifteen different levels you know doing some scroll down impossible for human to fifteen dollars. You know a fifteen levels downs. We can actually detect that. So it's a very fine and that is even you know. The narrative may look super clean. Always the you know different and edges and so on so critical from realtime disease very system or deductible so you know it's a lot of elements that come together here. But i like this general idea of cutting down on that i fifty percent and the importance of data engineering and even the place of machine. Learning in terms of the style. I think is all interesting for opening the eyes of the listeners. For this this use case so manual. I know that that's all we had for time. But i'm glad you could join us all the way from paris for this episode. Thanks so much for being able to be here on the show thank you. That's all for this episode of a in business podcast. I hope you got some good ideas from this episode. I appreciate emmanuel being able to join us and put some detail on where. Ai fits into reporting and also what makes it happen. Certainly a lot goes on in the background to make these reports. Come to life. And i want to appreciate you for listening all the way through to the end of this episode. If you're not already following on social please do been a real pleasure to see more folks engaging with us on lincoln and twitter and as our audience for the podcast has grown our social audiences grown in kind or creating a lot more content this year including some video kicking off in the coming months and i look forward to making sure that you see all of that. So stay abreast of all the latest trends in business. Our new best practices for our ally in ai. Adoption and other great use case episodes like this one by following us on social is just the m. e. r. j. on twitter very easy to find us or more at emerge artificial intelligence research on linked in or on facebook. If you wanna get all of our latest material whether it's infographics articles interviews and more stay plugged in on social. We appreciate you being here and if you want to show following us on social certainly helps so that's all for this episode. Look forward to catching the next one. You're on the a and business podcast.

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