Thank you very much for the kind of introduction. So once again, I'm Anthony. I work for the Connected Corridors Project, and we're trying to build a decision support system to help transportation agencies to respond to traffic congestion caused by incidents. I'm going to explain a little bit about what that means, and then I'm going to turn it over to Brian, and he's going to talk about the systems engineering required to actually build a decision support system. So our subject area is the I-210 corridor in Southern California. This is one of the busiest corridors in Southern California, and it's also a very exciting corridor.  Here's one from maybe two years ago. I have some pictures of it. What was really interesting about this incident is that a big rig ended up in the middle of the median and just blocked the gold line, which is kind of a metro rail that runs through there for an entire day, and that was kind of a mess. I have a couple more pictures here. Very bad day for commuters, very good day for traffic scientists. This was interesting that there are people trying to use the on-ramp as an off-ramp, and if you saw the blog post, something similar to this happened just last month. Okay, so we imagine a future where these different cities, county, state, department of transportation, other stakeholders can work together to respond to an incident in a holistic and organized way, and part of this might involve warning folks about what's going on. It might also involve changing your ramp metering and changing the signal plans that you have on your arterial so you can quickly flush out the queues and maybe get people back on the freeway and on to where they're trying to go. And so I imagine the system looks something like this. Brian's gonna show you some different pictures, but in this picture, I imagine from the bottom, you bring in all kinds of data. You estimate your traffic state, so that's a yellow box, I don't know if you can read it, but you estimate your current conditions as best as you can. Hopefully you've studied the corridor, you know something about demand, and then you have a model to predict what might happen if you reroute this way, reroute some other way, or if you do nothing. And maybe a human in a loop selects a response and deploys it. So I'm just gonna talk a little bit about some of the ingredients that go into this. One is we're building a model, we're building a model of this entire corridor that includes the freeway, arterials, and transit. It has about 450 intersections and contains about 1,000 lane miles of roadway in glorious detail. We use this to simulate the effect of incidents and try to put together the elements of the response plans that will be needed to address these. I talked a little bit about estimation. What I mean is on the left there, I have a picture of, it's a time-space diagram, which is basically showing space on the vertical axis time on the horizontal. Black is where you have no data, and what you would like to do, and green and red is faster, slow, and you would like to fill in the gaps so that when you kick off your model, you're starting it with the current state of your transportation network. So we have algorithms to do that on the freeway. We have other algorithms for taking in data on arterials, whether they're stop bar detectors or advanced detectors, and estimating the cues, the average cues. And then I talked a little bit about demand. Demand means developing a table that defines the trips that people make within a network or between zones in a network. And we're in this process now where we're figuring out how many matrices do we need, how many days, what periods of the day, what types of vehicles, what really needs to go into this to be able to perform accurate simulations. Okay, this is my last slide. We have a lot of research themes, have a lot to do with data, estimation and probe data and machine learning. But we also, on the bottom there, part of it involves studying people's behavior. How do people select their routes? How might they respond to guidance or incentives? With that, I'm gonna turn it over to Brian who's gonna talk about some of the details of how we're trying to build this thing. Thank you, Anthony. So I say Anthony's a science part of this which I know very little about. I'm not a traffic engineer. I'm a systems developer so I'll talk a little bit about the systems development but I'm also gonna talk about Anthony described a lot of different data that's coming in here and I know that's of interest to the people who are here at BID. So I'm gonna talk a little bit about the data itself as well. So this is a diagram put together years ago by my boss. It's still quite interesting. For this group here, that outer green bar is a set of data that we're going to be collecting. So if you just work your way around it, there are travel time sensors. A lot of those are like Bluetooth. So they'll pick up the different Bluetooth MAC addresses and they'll see where individual elements are and then try to estimate travel times for those. There are sensors on board vehicles or sitting in your pocket. So there are providers of information that will give us cell phone data or GPS data from GPS units. There are the ramp meters themselves providing the flow of traffic through the ramps as well as what the ramp meters are actually scheduled to meter at. There are the traffic signals and I make sure the traffic signals aren't just the state of the signal itself there's also how much traffic's flowing through there and how large the queues are getting. We plan on getting information from parking lots. So the park and ride lots, how many commuters are essentially carpooling, there's some sense of that, but also where is parking available if you want to reroute people onto transit and change the mode, you have to know is there parking available for those travelers. So we're hoping to get parking data, transit data, that's key, the message signs, what those are currently saying and how you get information out. The different information providers, 511, those sorts of services will be in contact with those. Different communication devices, the highway activity radios, those sorts of things. And the traffic detectors, not only the arterial, but also the freeway sensing is there as well. So there's a lot of data that's gonna get collected and brought into the system. So this is another little more detailed drawing of the same sort of thing. So the interesting thing here is a lot of that data, if you look at intersection signals and you look at the blue, those are all entities that are sending as intersection data. So a lot of that data, it's not standardized in a perfect sense. So there's a lot of transformations that'll be going on in the system to clean it up. There's also a lot of data quality checks because with that many systems, you often get a lot of chunk. So you have to go through and clean all that up. You can see the green, there are several different transit agencies, different modes of transit you'll be taking a look at. So you'll have that sort of information. You can see in the red, the different law enforcement agencies where you get your incident information. There are the information providers for radio and weather and that sort of thing. And the other data suppliers are those sorts of folks, a lot of them that are giving us the probe vehicle data, the cell phone data, that sort of thing. So that's position and velocity and time for where they're seeing. And it's all anonymized, okay. So how do you process all that different sets of information? This is what we're building, okay? So the green is still all the different entities that are providing us information and are consuming the information we're creating, okay? The red is what we're calling our data hub. So in there is where we bring in all this different data, depending on how they want to send it to us, different modes of getting that information. We essentially are gonna take all the information, put it on one big central data bus. It's a messaging bus or a rest service bus, one of the two. And we'll just hook up processes onto those buses that'll essentially do the processing for us, all right? So anyone can pick up onto any one of those streams they want where they want the raw, the processed data, pick that off and use it as they need to. You can see Anthony mentioned the models and doing the estimation and the prediction, those are in that big blue box. So that's where the modeling is running and what it's doing is it's just gonna pick up all those data streams off those messaging buses and do the work it's supposed to do. Which means you have to update the models for the intersection signals that are in the models, the ramp meters, all that sort of control information has to go to the models, but also for the estimation, you have to give it all the sensing. So it knows, it can give you an estimate about the traffic as you fill in all those holes. That purple box is essentially a commercial traffic management system. That's an off the shelf sort of box that'll actually, I mean, we don't want to write software that's sending the signals out to different field elements, you can buy that. So that will be listening to that stream of information and that also will be listening to the stream of information from that decision support system on the predictions and the response plans it generates so that it can go ahead and execute those response plans for us. You can also see down at the bottom, their PEMS is the state system for freeway sensing. They're also looking to put a lot of this data in essentially as a large EDW and business intelligence type of system for longer term analysis. The other thing I'll point out here is there are three boundaries I've put in there. The idea is this is a pilot, but that pilot is done for a purpose to put it on other corridors. So the way we're seeing it right now is you see the inner boundary is the corridor system boundary, that's a local boundary. That's sort of like the 210 freeway that we're doing. There'll be one of those for every corridor. The regional system boundary is more of a regional area that's gonna handle multiple corridors. So that data hub is more of a multiple corridor idea after we get past the prototype stage. And then the state system boundary is where you're starting to essentially aggregate all that data at a state level and let people look at the big picture. So that data hub essentially what we're doing is categorizing into three different types of pipelines. There's a streaming or a sensing pipeline for that's the stuff that's coming in hot and heavy. Lots of volume of data has to be handled in real time. That's the sensing information and the probe data, that sort of thing. What we call heterogeneous data sources, those are the intersection signals. If you remember, there are multiple jurisdictions sending as intersection signals, not all exactly the same. They have a common standard format, but their implementation may be a little different. So we have a stream to handle those. And homogenous data sources, Caltrans only has, I mean they handle all the ramp meters, so they're all gonna look the same. I don't need quite as much transformation. So we're breaking the data into three different types. And I'll show you what the streaming looks like. This is one that may have some interest in that this is where sort of a lot of the machine learning and that sort of stuff will happen. Okay, so you can see freeway sensing, the GPS data, arterial sensing. That's the type of information that will go in here. We'll essentially write a stream reader for each one of the data sources that we get. And then each one of those stream readers, our only job is to stick that onto a real time queue system. We're using Kafka. So it'll put it on the Kafka topic and then we're gonna put Spark at the end of that. And Spark is where all the transformations occurs and the standardization, that's where all the data quality checks will go. That's also where if we get to the demand prediction and those sorts of things, that's where those algorithms will live to run the demand predictions, the route selection predictions, those sorts of things. And then the only thing that that does is it goes ahead and tips that information back onto a set of Kafka topics for all the downstream system for decision support and that purple box, the quarter control box to actually go ahead and act on that information. So at no time do we really persist the information. This is a real time production stream for large volumes of traffic data. That's what we're building. Any persistence is essentially pulled off those same topics and then we persist on the side. And then we can replay it if someone wants by just taking it and putting it back on a topic. That little bit of Postgres there, we use that. So if Spark needed some of the information like road network configuration, if we're doing flow balancing of traffic between say three points, two inputs and an output, we'll need the road network information so that's available to our Spark processing engine as well. So some of the opportunities is starts to open up for people who are interested in data. Looking at traffic and travel and mobility impacts and control and how these things interact over a large urban area. This is a unique data set, I think. There are a few places that have one similar to this. San Diego has done some of this sort of thing. But I think this is gonna be the most comprehensive probably around. You can start to, once you have this data, start to look at how, as transportation is evolving, how is the data changing and what does that growth of data you're going to get? You're gonna start to look at connected vehicles, vehicle to infrastructure communications, the autonomous vehicles, all that data starts to become available and this becomes a platform for that sort of data. Looking at human decision making, Anthony mentioned what routes do people take? You can also look at when they choose to travel, what mode choices they make under different situations. The impacts of different incentives that you give people to travel can now be looked at at this sort of scale. You can do the predictive studies we mentioned. The idea is to put the platform in place to allow this sort of thing to happen. This is going to be a production system. So everything goes through here. All that data is gonna go through. So there's a lot of challenges of how much the scale that we have to put in place and the real time of this much information. And really, we're actually looking for people right now that'll help, who want to help build this platform and eventually start to look at the data. So it's an opportunity to get a whole lot of nuts and bolts experience of how do you put something in a production scale, running real time analytics on what is potentially a lot of data and a lot of interaction between a lot of different types of data. So that's my last slide. So I don't know, I've never been to one of these. I don't know if you wanna open it up for questions or where do we go from here? Okay. And I'll direct.


We got touch base on the mortgage needs, you know, what are the users, who are the participants, who are the actors, who are the clients and how we manage to deliver user experience that could transform the business of these guys, you know, on this side, these guys. I categorically have taken these three pins, actually there are five clients who are using this application, but these three are working in, you know, different areas of mortgages. So, have you guys any time taken any loan, typically home loan? Yeah, mostly yes, right? So, we are typically called borrowers.  Who are the actors? What are the different, you know, segments of industry? And, you know, what are the load? Also, if you see the loan origination is the first part where people like you and me would, you know, go to bank to apply loan, then, you know, bank would process our loan, then it would fund and, you know, then it gets into servicing where we start paying the EMI's. But any idea, you know, how big is this mortgage industry in US is? It's actually $10 trillion industry, which is 10 folds more than a credit industry that, you know, you buy credit card, you buy other loans, it's 10 folds more in, you know, in US. But this loan origination where people like you and me are doing most of the work are being burdened with these many tasks, if you look at it, whereas banks will have streamlined process. Why? Because every time a borrower is new, buyer is new, there is a new property, there is a new valuation and everything happens differently. Everybody has a different income, everybody has a different, you know, appetite for, you know, the loan or property choices are different. Now, when you look at the journey, you know, from here, typically what happens is you start thinking about buying a property that, you know, somebody else is buying property, my friend is buying my parents have asked me to buy property.  So let's apply for loan. Then you apply, then it gets into the status, you know, that, you know, I applied 10 days back, is bank really processing it? Do they need any documents from me? Are you guys doing it well? Is there anything that I need to do? These kind of things would come here. Then your loan is finally approved. You move into a new house, you plan renovations, and then, you know, that's by and large the end of the journey. Now, most of the banks would actually focus in this area from let's apply to loan approval and the status part. They won't go above and beyond this, they won't do any augmentation of services. By augmentation, what I mean is, you know, when you provide a service to an extent, but you increase your touch point, increase and reach your touch points to an extent where, you know, you involve more actors into it. For example, when you are thinking about buying a loan, can bank do something here? Or when you are thinking this, yes, I want to buy a loan, I want to buy property, can bank help you here? Or in your new renovations, in your new house, can buy do something here? So when let's say, our guy Steve, you know, he is thinking to buy a loan, what he would do typically?  These problems, these are the activities that he would do. These are the people that he would interact with. And these are the channels. So when you think that, yes, I want to buy home, I want to buy property, you would typically interact with your colleagues, with your family, with a broker and with your friends, maybe over the phone, over the chat, over in person meetings. Then you see, yeah, then, you know, what's my affordability?  And then you figure out the affordable locations in consultation with the broker. But while doing so, right, so when you talk to family, you get the information, but is it really appropriate? What is the real reliability of that information? When you talk to broker, you always have a fear of being misguided. And when you go to the sites, there's a lot of cognitive load, because there are a lot of information that is not personalized to your needs.  So we are, by and large, I'm trying to, you know, get you into a context of a buyer. And I'm sure you must most of you have gone through it. So where we typically have problem is, you know, am I really doing it right? Am I really, you know, is this the right process? Is the guy guiding me properly? Because if you notice, and if you look at it in this perspective that any mortgage or any home loan is the longest financial commitment that you would make in your life. Apart from people who are investors, they would buy multiple properties, but people like, you know, generalized people, they would buy, you know, one or two properties, one for the second home and one is like, you know, are like primary living home. So that is something, you know, ban law is the case in the US mortgage industry also. So here, when you're doing the first time and probably the last time in life, you need a lot of hand holding. That doesn't happen. That doesn't happen. Neither here, when you go to bank and tell them that, you know, I want to apply for loan. How would you do? So you go to a loan officer, which in India we typically call an agent, right? He'll tell you that, okay, bank needs these documents. For him, it's a monotonous process. For him, he would, you know, handles many such clients every day. But for you, it is new. So you would ask 10 questions and he might get irritated with that. Then he would give you some loan application form in typical US, you know, industry, this loan form is standardized, unlike India. But it is very lengthy. It is around 300 field form. And it'll ask you for assets. It'll ask you for liabilities. It'll ask you for your income, your expenses, everything. And that has to be documented and correct to the last digit. So once you go to bank, once you specify your need, apply for the loan. And then, you know, you move on to check the status. Currently, banks don't disclose the status on their own unless you call them up. And you don't have number of a bank. You have what you have is only a number of this guy, this agent. So you would call up this guy or meet him personally or send an SMS to him. This guy would connect with the bank, underwriters, you would check the status, he would give it to you back. This is too much of a lengthy process. You take the status, then your loan is approved.  So this is a typical journey. Now, we designed a product that could transform this business. This is something that we saw when Steve is actually looking at this, what he is doing is job.  But a simplest way to put this thing together is to come up with a system. You know, when Steve can interact with the system, the other actors can interact with the system and everything is available on the system itself. So he doesn't have to go back and take, you know, the other channels likes of telephone calls, which are not documented or which are not recorded. So this is something that we did. This was typically simple to achieve and to conceptualize. And what we got out of that is this of a business input, business impact. What we are talking about is 50% reduction that we could found in our clients cycle time.  So if they could reduce 50% time in that and mind you, the loan origination cost only is $9,000 on an average when you borrow $300,000 of mortgage property. So $8,000 for every customer that bank is spending and you end up reducing the time on that. Then the loan officer, the agent became 40% more productive by not doing mundane tasks. By that is, you know, not giving you status updates every day, right? Not being in touch with you all the time over the phone, but because you can do it on the system on your own. And then the pull through rate, that is where, you know, the onboarding rate comes in, where, you know, your service augmentation comes in, you get more involved, you have more touch points into the system.  Now, these numbers with previous slides sounds really interesting and some sounds really simple, but that was really not simple thing. We took a lot of process, we took a lot of steps, we took a lot of, you know, back and forth transitions, we did something, we tested with the people and then we realized, oh, this is not working. So let's come up with some different approach. So the approach final that you see is, you know, by and large, pretty similar that you would, you do anyways in any other, you know, a project of yours. You first find the scope, then you do the secondary analysis, then you do conceptualization, then you do design, do some usability analysis and finally release the product. Typical, you know, MVP structure, but activities that we did were slightly different here. We didn't do routine things in most of the stuff, wherever it is routine, I'll just skip this thing. So secondary research, no great deal, you will find a lot of authentic material on web, you will find a lot of, you know, SMAs around, you will find a lot of literature in terms of research papers and everything around. We did that. We did that completely. But we did competitor analysis also here. By that, what did we do is we took 13 usability parameters. So by the way, this is, you know, a TCS kind of copyrighted Excel wherein we kind of or a model statistical model, where we do statistical analysis on the competitors to figure out how much usable is a particular application is. So we have around 13 parameters, starts from ease of use, speed of use, learnability, consistency, content, accessibility, etc. On these 13 parameters, we evaluated six major lending banks. So if you can see BB&T, Wells Fargo, Bank of America, Chase, Quicken Loans and City, we evaluate it. What we got or what we understood is none of them actually fits into all these criteria.  You know, it is easy to use, but it is really efficient. It is really not efficient. It is really time consuming that you do not suddenly want. So none of them would impact this.  Then we did touch point analysis. If you remember the previous diagram, the previous mind map, there were at least 50 touch points that we could figure out from the mind map, which we then put it in this format. And we try to analyze them further. There are six actors in this. So your loan officer is there, your borrower is there, your, you know, underwriter, processor, and a lot of people are involved into this. And they keep communicating over the phone, SMS, WhatsApp, email and every possible channel. Now, interesting thing, I'll just take a couple of minutes to explain this Excel because this is something different. So how did we do actually or go about the touch point analysis? This is not readable actually, but you know, I'll just read out. So the first column is I want to. So what I want to do? Second is frequency. How frequently I would do that on the system? Third is the drivers, you know, what would make me do that? Fourth is what is the context of information? And then the patterns and some notes, which is loosely, you know, kind of arranged. What I want to, let's say I want to make a payment. Why do you want to make payment? One, because you got a notification from the bank or you realized, oh, today it's night, I need to make payment of my homeworld.  I should have paid that. Or, you know, it's a good day. It's an auspicious day. I just want to pay my mortgage on this day. So these are the drivers.  They at times do not give that consent. They want to make that payment, you know, every month because that payment, you know, there could be a lot of needy gritties in multiple user set. Now, in this context of information, what are things that you would need while making payment?  What is your payment amount? What is the, you know, status? Can I make additional payment? Can I make, you know, future payment also? Such things can come here. And this by and large is pretty loose kind of a Excel, I would say, or a format or model.  Then we looked at the demographic data, you know, what is the typical age of a first time buyer in US?  And the same is reflecting in the in these statistics. This statistics is from one of our customers who, you know, have this demographic of their users or their current customers who are being in services sector. So if you see the largest part or the largest area is, you know, here, where you are typically in the range of 32 years, not no need to talk about the persona specifically, because, you know, we all know what persona is, but we a novel and a different thing in our persona is that our persona had not only goals, but it also had emotions. Persona feels happy when the payment is paid. Persona, you know, the suggestion is happy when you know, he doesn't have to care about the status of his loan. He doesn't have to care about the documents that he needs to, you know, give the bank when because the application can give you. We set thereafter creating the persona, we set the experience goals. What are the experience goals is like, you know, if we want to design the application, what is it that that application should serve to the user? So it should have smoother onboarding so that, you know, your new buyers, your new, you know, potential customers of your bank should not have what you say any hassle while onboarding. You don't ask a lot of documents. You don't ask the documents which you already have or you can figure out from different sources. You can just eliminate that. Then you need the retention because typically people would go for refinancing, that is, they would transfer the loan. So you need to retain through a good service augmentation. You need to support your first time buyers, you know, maybe through learning material, maybe through contextual help, maybe through intelligent messages and good notifications. It has to be, of course, easy and efficient. Technology should take up the human, you know, aspects. If there is some mundane activity, then maybe technology can aid that, maybe technology can use that and a consistent quality. What happens is typically, you know, when you are interacting with the bank or loan offices, you would, you know, often you will feel that, you know, or you will get an experience that, you know, I'm not getting a good feel of this customer.  So that a system would certainly not do. It will have a consistent quality to deliver. So with these experience goals, so what we saw so far is we identified the problems, we identified the, you know, user groups, and then we set the experience goal thereafter. Then we got into the, you know, system design style. So we did a lot of, you know, card sorting activities.  And we asked people to kind of the target audience, you know, what we had is to come in the morning, look at this activity, look at these cards and reshuffle this based on their expectations, their needs and their, you know, kind of likes and dislikes.  So we did this activity for two days. People used to come in the morning, they used to take coffee breaks here, they used to look at these maps and they used to change these sheets and, you know, these post-its. And we used to take, you know, pictures of the photographs or after every 30 minutes so that, you know, we could record what is happening, what is changing and what is, you know, resulting into a good system. So this resulted into a, you know, high level information architecture that we could, you know, design for a system. While we do that, we cannot, you know, again go back in the system and neglect the users that, you know, previously what other systems we often realize that. Now let us look at the first time borrower as a persona. He would always be concerned about, am I doing it right? He would like to know what is my contribution to the bank and what is my, what is bank share in the property or the loan that I am buying. Then what is my monthly payment? Can I make it a low payment? What is going to be my tenure? Then he would also be interested in knowing, you know, how should I actually approach this? Is my approach really right? Am I, is my property really legal? Are my all documents really clear on this property or will I get into any litigation after once I move into this property? So all these questions, we framed it for one persona and then we designed a page or, you know, started designing the system or the navigation or the screens for one particular user. So if you look at this, so these is a subtle guidance to the users, to the steps that he is actually carrying out. For smoother onboarding, we don't want, you know, we did not want to go for, I think I just, I'll have to, you know, run you through.  What is the good for you? You also have financial calculators here that you can go back and check. This guy is your, you know, loan officer whom you can connect with, you get the messages within the system. So that was the part. Then we moved to the other users, the loan officers, this guy, if you see this guy, the loan officer, his objectives to look at the system are different. What he is looking at is what is my loan pipeline? How many are my customers in what stage? How many have actually applied for the loan? How many documents, you know, am I supposed to receive? How many are defaulters that he needs to know? Then whether my applications are being processed properly by the bank or not, that is another thing that he would want to know. What is my priority for the day? You know, how my day looks like?  How should I go about it? Can system really help me? And I don't want to miss my SLS because my increment, my salary and my incentives based on the SLS. With these in mind, you'll find it really overwhelming. Initially, we also thought of that. But then when we interacted with a lot of loan officers, they felt, you know, I'm getting everything that, you know, I need on this one single page. If you see, this is the loan pipeline. This is the applications that are being submitted. These are the SLA marks. And this is the day. And these are 100 messages that this guy is supposed to respond to. Then I'll just quickly skip this. Another thing wherein we, you know, in the, this is the loan application form around 300 fields, we tried to put it on mobile phone and it worked really fantastic. We gave hidden accelerators wherein we took, you know, a couple of forms which we could read using OCR techniques and then it can give you a head start of, let's say you upload your W2 is like form 16 in US. It give you head start of 40% in the form. If you upload the tax return form, it'll give you head start of 30%. And this was one of the trials. This was, you know, another one for the tablet. But then this was another one wherein we kind of played a jigsaw puzzle as you start completing one of the, one of the other, you know, segments of your application, you know, the, your home will start unveiling from this. So that's what the objective of, you know, kind of keeping it interesting. So that, that this is something that really appealed to a lot of people that we saw.  It'll help you what is the banks, the best rate that bank can give you and at what percent and am I really eligible for that loan? So then what did we do? How did we analyze this? We conducted seven cognitive workflows. We conducted seven semi-structured usability tests and more importantly and differently what we did is we had around 10 plus business viability check.  And from then there, we could learn, you know, what the users are facing difficulty. In quickly in one minute, I'll just want to cover. So this is what is done now. Okay. So this has been running in, you know, in US markets with five customers getting a lot of attention. But what's the future? Now, we can't only give mobile application or tablet application and stay back. What we can do perhaps with artificial intelligence or maybe with augmented reality or with connected world is if I'm looking at a property, if I point out my finger or point out my camera or my phone, can system tell me that this property is actually approved by Bank of America and I know your income and I know all your documents that you already have in the system. So, you know, you are actually eligible for this loan and this could be your interest rate.  Or you look at one property, you point out the finger, the point of the camera, the other property, you can compare the properties, you can see the best fit, you can also see whether the property has clear tax or not. All these things we can perhaps do with, you know, additional this thing, you know, advent of technologies. Moreover, what you can also do is reduce the burden of loan officer. When I'm talking to loan officer over the phone, can system, you know, recognize and can system learn about my conversation if I'm talking about certain documents, can system intelligently suggest the loan officer that, you know, you are you should ask for these documents to this guy. So on this note, I think I'm overrunning during the time. So thank you for attending this. And thank you for being part of this.


piracy, a real issue in the Gulf of Guinea. Another one is illegal, unregulated, and unauthorized fishing. So we see a variety of different countries fishing in here and really pulling material resources, fish and protein from the coastal West Africa shores without permission. I think that one is a significant one that we're really trying to work with our partners to get our arms around and to slow down. And then there's a variety of other threats, criminal threats, human trafficking, drugs, other things that are coming into the maritime environment. So our partners here in coastal West Africa really face a variety of threats in the maritime and we're really happy. This is the first time we've been able to do a maritime special operations training group here during Flintlock and it's something we want to sustain. This is the output, the desired output is to build our capacity to be able to address the emerging security problems in our waters. Sometimes when they are going to board ships, we have what they call oppose boarding. They don't comply, so we should be able to either drop people from helicopter or do insert our personnel on board to ensure that whatever missions that we have been assigned, we are able to achieve them. Number one, it's criminal activity, right? Number two, it actually pulls resources, food from the mouths of our partners in coastal West Africa. So it's not just an environmental issue, it's also a security issue. So we really are concerned about it and looking at it from both perspectives. So when you have the fishing taking place, it takes the fish and that commerce away from the Gulf of Guinea nations and then the ramifications are multiple. We also see other activities taking place on top of the IU fishing or legal unauthorized and unregulated fishing fleets like human trafficking, like drug movement. And so it's like many smuggling networks or illegal activities, you see multiple layers of illegal activity happening under the guise of IU fishing. They know, I think they know about our regulations, but they somehow try to flout those regulations because of our enforcement capabilities. That is one of the biggest problems. If we had enough ships that are out there and they knew that we are monitoring the place, we'll be able to keep the situation.