Adoption of AI to Support 2nd Line Functions

This is Risky Women Radio, a show that connects, celebrates and champions women in risk regulation and compliance. We’re here to share the insights on the biggest issues in our industry and hear inspiring journeys from our global members. Sign up to our newsletter at riskywomen.org. I’m Kimberley Cole, your Chief Risky Woman.

Thank you everybody for joining us today. I have the great pleasure of speaking with a couple of guests on a very hot topic, artificial intelligence in the in the business, and in particular in the second line and how the the respective ladies organizations are embracing AI as part of the business. So let me introduce our two guests. I'll start with

Naomi. Naomi, who is Senior Vice President, Chief Compliance Officer, Chief Privacy Officer and Chief AML Officer for IGM Financial Inc is responsible for the overall compliance program across the wealth management, asset management and enterprise compliance functions for the IGM Group of Companies. Naomi does bring extensive legal and compliance experience, both in

private practice and in the financial services industry. She previously held the position of Vice President Compliance Canadian Wealth Distribution with Scotiabank, where she was responsible for the development and oversight of the compliance risk management programs for several businesses within the Canadian Wealth Distribution, including responsibilities as Chief Compliance Officer for investment dealer, full service and order execution only, trust insurance agency, investment

fund manager and portfolio management business. Welcome, Naomi. Now my Jackie, My other guest, Natasha, who is the Director of Legal, Privacy and Data Products at Wealthsimple. She advises the company on clearly, privacy and data regulatory matters. She's based in Toronto, and leads the legal privacy function, providing expertise related to data use and ownership, privacy compliance, anti spam and

artificial intelligence. She works closely with Wealthsimple's leadership to advise on privacy and policy issues and the developments that impact the firm's business. Prior to Wealthsimple, Natasha was an associate at a full service firm in Toronto, in the firm's privacy and data management group. Welcome Natasha. Thanks so much, Jackie.

So ladies, maybe we'll hop right into it. My first question for both of you is, really, what are your top three challenges in managing risk in your respective areas? Maybe I'll start with you, Natasha.

Yeah, absolutely so as a woman working in risk at Wealthsimple, Wealthsimple is kind of growing out of that tech startup phase, but has definitely been part of the culture for the last 10 years in which Wealthsimple has been around, and so speed is really always a struggle and a challenge, because we definitely have that startup move very quickly, do things very quickly,

mentality that's been really ingrained in the culture. So with new ideas, new products, cut projects and products coming down the line, it's always make making sure that you're part of the conversation from the get go. You're providing really fast, practical guidance, advice, risk management is really a key to success. And building strong relationships with stakeholders across the organization, I think has been

really important for me to ensure that I'm trusted. I'm brought in early, and if I do need to, you know, let's pump the brakes on this. We you have that goodwill and that trust to really fall back on. Second thing, I would say is probably just the constantly changing goal posts. Our priorities are changing. Our product offerings are changing. How we're doing things operationally is constantly changing. And on the A front, for example, on the AI front, for example, use cases

are evolving constantly. Every one of our partners and vendors is leveraging AI, and so trying to be really nimble and adaptable to our in our approaches to risk is something that I am working on constantly. And then I think the third challenge is balancing both being proactive and reactive. You know, we're, you're trying to handle all these changes, building out while also building out and maintaining proper risk and governance frameworks. You know, when I was brought in,

there was no real privacy program. And so balancing out that build with managing those day to day asks and those these day to day changes, especially as it pertains to AI, which moves so quickly, is definitely a challenge that I'm working through all the time. Those are excellent challenges, and I think that probably resonates with all of our listeners today. Naomi, what Well, given I would echo a lot of what Natasha just said, I about you?

think what you just noted is likely the case. I I would flavor them maybe a little bit differently than Natasha, just because of the nature of our business, but really fundamentally, they're the same core issues. So one, the pace of reg change, we have a lot of different businesses trying to keep up with that, while, at the same time keeping up with all the movement in the tech space, I think is is interesting, and it touches on the priority piece that that Natasha spoke to. So

how do you balance all of that? How do you make sure you have a team that's able to pivot and and really a group from a governance perspective, that are able to pivot as well with that. So that would be one two is almost a downstream impact, or maybe an upstream impact, depending on how you think about it, and that that's really the team component, is what I would

say. So it's interesting with the tech, because we have team members who have been in compliance for a very long time, or risk, managing programs that I would say it's not new, that you really haven't been able to develop them and park them now for some time, you've had to be nimble and always thinking about these changes. But what I'm finding is really different, is

the way we're doing it. So trying to get, you know you're trying to develop teams at the same time that all of this is going around you and making sure you've got the development and the skill set that you need, and you're changing your processes, that's very hard, but again, still touching on the balancing piece that Natasha said, you're just adding another element into

it, which is your own team members. So on the one hand, is the balancing with your seasoned team members, and then it's been a very interesting exercise to think about what your new team members bring. And I'm not just I'm talking about new team members who might be coming from a different firm, but also new team members who maybe are new to the industry as a whole, and their lens and how they've grown up with tech is very different. So I think it's actually causing a lot of need for strategic

thinking on how to construct your teams. And it's no longer the case of having, you know, those triangles that that we often see for org structure, in terms of the way you build, which then probably goes into the third piece, and that's touching on the overarching tech. The tech part is really changing noticeably in the last year, certainly for us, how

we're thinking about doing our activities. We don't have, necessarily, everybody who's equally conversant, comfortable, curious, you know, able to really move with that pace of change separately, and I find that it's been exacerbated and really noticeable in the last three months. Never mind what's happened, you know, in two years since we first heard about it,

it's, it's exponential. And so thinking about what your team needs to be, what your team needs and how it needs to be structured in order to accommodate all of that is a challenge I don't think I've seen to this extent in the last 20 years.

Wow. You know that's, that's a really good point, and I'm going to throw out there just anecdotally, I think post pandemic, or coming out of the pandemic, is where we've just seen the craziness. Right for everybody had to pivot right away, in a short period of time, right, embracing technology and enabling people to work differently, using technology, and now we're all on that bandwagon, if you will, right?

We're all just on the tech highway because it's not like you're going to undo what was just invested, right for enabling the business to work through through the pandemic? No, those are excellent, excellent points / realities that I think all of our listeners are probably right now

nodding, going, Yes, exactly. And to your point, Naomi, and I'm sure you've already seen this very recently, the Canadian the securities regulators published concept paper and some guidance related to the use of AI right in in the wealth

management asset management business. So. Yeah, yeah. Reg. I mean, there has always been rapid regulatory change, but now, with the technology that everyone's adopting even more so the regulators are probably resonating with the exact same challenges that that both of you have raised, because they, too, need to change, perhaps, how they regulate and even monitor right and engage in examinations, etc, because of the rapid adoption of of the tech and AI in particular.

This episode is brought to you by Protiviti. Protiviti is a global consulting firm with deep expertise in transformation, risk management and compliance, partner with Protiviti. Well, that brings me to my next question. And Natasha, I'll direct this one to you. So being that you work, you know, essentially for a FinTech, which is an industry that's known right for innovation, sort of very, you know, disruptive, differentiated and customer centric, you know, value

propositions. Can you maybe share with our listeners the types of AI development your firm is working on, and, you know the sort of governance frameworks around what's happening in your organization?

Yeah, definitely. I think, like a lot of companies right now, there's a lot of work being done on just the customer facing side, with customer support, for example, helping respond directly to our customers through chat bots help, using AI to help triage requests and even just empower our customer service agents to with better tools to help our customers quicker, get them responses faster. That's been a pain point for our organization for a while, and so there's been a lot

of investment in that area. There's, of course, definitely, we're working on some more traditional AI modeling, which I think sometimes gets a little bit left out of the conversation in terms of, you know, our personalized financial advising, credit decisioning that we're making about our customers, for example, on the compliance side, AML has a lot of different use

cases for generative AI right now that we're exploring. So helping us identify risky behaviors, helping support our analysts and their investigations, for example, is a key use case. Tooling for our employees. You know, making difficult tasks, either low code or no code, compliance, tooling, automations, all of those kinds of things, really, it's a little bit of a question of creativity right now. I think there's so

many areas in which you can kind of slot AI in. And so really, it depends on the group, what their needs are, and can we find some sort of solution that can help them in a way that's not super

that is a little bit more risk averse. And our comms and marketing team actually has a couple interesting use cases recently, using AI to do some sentiment analysis on content that we're thinking about putting out there, trying to get a sense of how understandable a value proposition is, how receptive people would be to certain communications, for example. So that's been an interesting case that's come up

recently. And then in terms of frameworks, I think really our focus has been on having a very principles based approach that sets out kind of very high level but clear guardrails, taking cues from, you know, the proposed AI legislation that we've seen in Canada, codes of practice, that we've seen the EU AI act, for example, and really the strategy there is to focus

on being nimble. A lot of what we're doing is assessing things on a case by case basis, kind of within that framework, as opposed to having a very like, very rigid and specific risk management framework to work within. And that is that framework also really needs to keep and to take, really takes into account our build versus buy strategy, which is something that I think we haven't fully articulated yet. You know, what are we going to work on developing in house? Where does

that give us a potential competitive advantage? Where is, and then where is it just makes more sense, from a business perspective, to outsource and use a tool to help us to XYZ, and also, then what is, what are those two, how do those two options reflect on, you know, our security, our security, standing like our privacy, standing, for example. So all of these factors kind of factor into this, like overarching

framework. We have a group that meets fairly regularly to talk about all of these issues, and to be constantly reassessing where where we are and where we're going.

Excellent. Sounds like there's a lot going on. Wow. Naomi, so, and I just mentioned this too about the Canadian the securities regulators very recently publishing sort of a position piece and a consultation, which, I think that consultation, the response date or the closing is March 31, of 2025, so I'm not sure if you you are going to respond to it. But the reality is, as much as there's a lot written about, you know, the use of AI in reshaping kind of the future of of asset

management. Already, for quite some time now in the industry, machine learning, bots and other types of technology have been used. You know, transaction monitoring comes to mind.

Obviously, AML, the alerts, et cetera, comes to mind. But I guess my question for you is similar to Natasha, what types of use cases for AI, including generative AI, that Natasha had even mentioned, do you foresee for your firm, or even for the asset management industry, sort of more broadly, and what risks, if any, do you see, as you know, needing to be managed around those use cases?

So we have similar use cases to what Natasha mentioned. Let me maybe start by saying how we've chosen to help govern this. I'll do the reverse, Jackie. What we've done is we've created a an AI working group, and the working group consists of leaders from all of our units, including frontline business,

including support functions. And that working group was instrumental in developing, really what we're calling our starting point, Gen AI policy and the policies principles based much like what Natasha described meant to say, here are your starting guard rails, and it has what you would typically expect to see in it human in the loop being our biggest guard

rail for anything that anyone's using. Now, what I think is working really well for us is everyone's been given tools where we're trying to bring tools in house as quickly as we can to make sure that we're controlling for any privacy issues and any data that may or may not get out there because of the differences in creativity, we have some team members that are very much on top of this space, wanting to use tools very quickly. We are trying to keep up bring things in house so

people can actually play in a sandbox. And our model is that you go play, but we have a centralized way of collecting all of those creative mechanisms and saying, You know what, here's a good use case. So the bubbling up of the use cases brings me to really, I think it was your second question, which is, what's kind of the difference, and what's the risk

component here? And what I would say it really is, is, you know, keeping your line of sight into all of these use cases, and it's democratized things, because now it's available on people's desktops, whereas before, I think you're right, we had AI. We've been using, you know, LLMs for some time, but I think they were very specific and very behind the scenes, and that's no

longer the case. Now we can access, you know, very quickly, once you get your licensing up and running, and all of a sudden people are going, Wait a second, this is how we can work in my space. So that's where in the last three months, and I'll get into specific tools between Copilot and say, Gemini, the teams that that have been playing with it have made, I would say, monumental advancements in some use cases

that that just wouldn't have happened before. So it that is truly exponential, because you've given it to people who really know their work and really know their processes, and everybody's working separately on it. So that, to me, is the biggest change. Even though we've had it, it's now across the board. And I'll give you an example of one use case that is really quite astounding. This is in the compliance space specifically. We were are taking it to use it for developing

sampling. So whereas once upon a time, you needed a human to look at very large Excel spreadsheets and data points to figure out how we're going to sample. As part of our oversight, we were able to put data into one of our tools and it spit out the sampling and what would have taken probably about 50 hours worth of work to 15 minutes. And I think that's astounding when I just think about that the number, and it leads to the

first point, like, what's the first question? What are the challenges we no longer have that admin burden on someone, someone taking that work and really, you know, doing, you know, searching and filtering things like that. And that's tremendous, at least from the compliance perspective. From the business perspective, we're seeing research teams use it, really, using it to pull together vast amounts of data. The summarizing that it's doing is is really facilitating a lot

of faster, quicker absorption kind of work. I think that's important. We are using chat bots and exploring similar to Natasha in order to really address questions more quickly. But again, it comes down to the vast amount of policies we might have that people can or cannot find. It's client facing chat bots as well. It's queuing people differently for a better client experience whenever they need to call in for an issue like it's all of that that's really coming together frontline

as well. And the big one I would, I would say, from a risk perspective and a compliance oversight perspective, is the thematic help. So it's almost, I would say the two posts, one is the front work, and I gave the example of the sampling, but the back end is the thematic work saying, I've got all this info.

Here it is, because now you can upload all these documents, you can upload all these sources of info, links, all this stuff that people would have had to have gone through manually before, yes, and it kind of gives you context and kind of says, you know, and picks up on things that maybe we wouldn't have ourselves. So lots of different components coming together. It's

exponential. Our model of bringing those use cases centrally so we actually have the working group that's maintaining kind of oversight on how it gets used, not just from a risk and control perspective, but for a broadening aperture perspective, because if someone's able to think of a really good use case, we want it actually to be practiced more

widely. So that's our way of also making sure it's being disseminated, and to really pull on that creativity concept that Natasha spoke about, and make sure that it just works in that in and is broadened across the entire firm. Wow, that's a great my head is spinning with ideas and thoughts

from both of you, your your insights. Let me then direct this question to both of you, and you know each of you have touched on it a little bit, but sort of consciously, what are your sort of perspectives, and in this case, whether it's your personal perspectives or just sort of the position of your respective firms on building a very sort of risk disciplined strategy around its adoption, its being AI, the technology especially, and Naomi, you said this well, the rapid, rapid

exponential change, right, and and the changing nature of the regulatory pronouncements And the sophistication of your people right in terms of wishing and wanting and willing to play right and learning rapidly. So maybe, maybe Naomi, I'll start with you, and then I'll turn to you after Natasha for any additional insights.

So I think the easiest way for me to respond to that is really by walking through in a little bit more detail our governance structure. We've tried to think about it in a flexible way and in a way that allows us to pivot as needed. So my starting point is, everybody accepts that it's always interim, which is a bit Excellent. That was great. Natasha, what about you? of a strange concept, I think, for you know, when you're thinking governance, people want things drilled down a little

bit. But what we've done with this AI working group that I've mentioned, we have representation across the board, and that's our starting point. We therefore, have really formed a cohort of subject matter experts. They are the people within each group that are viewed to be the AI contacts. And so it's not central, in the sense of having it in your data team, which or whichever tech team might have had that

previously, we have done a hub and spoke type model. That's the closest analogy in that sense, which means, and then I'll take it to my team specifically. We have a role at a senior level that's dedicated to AI and tech across the entire compliance function. And what that allows us to do is, and the concept is as so that the team, in its entirety, doesn't need to feel overwhelmed with, what do I need to change? What do I need to

learn? What we have our internal subject matter experts that serve as the conduit to the tech and data folks as well as it's coming in. They then diffuse and allow for that experimentation with us. So. So for the people who might not be as creative, or who might not see its use in its day to day, they help facilitate that. And what my personal hope is is you kind of dovetail it.

You have this overarching, you know, role and function, and that individual is also supported by another dedicated role that really gets to play in the details of all of this, and the two of them get to work with the remaining the rest of the team and help them come up the learning curve. So we have hopefully reduced some of that anxiety to say, look, we have that we recognize its focus and its its importance on the team,

but don't worry about it right now. We'll bring everybody up the learning curve, so it gives us time, and it gives people the ability to say, Yes, this is learning space, and I'm not required to all of a sudden be a subject matter expert. So that's

working very well for our team. I do think, I, you know, in I do think in very short period of time that centralized group will likely be bigger, with more folks thinking about it in terms of their day to day, with specialist type roles, but at the same time, I do see overall, all team members, myself included, needing to make sure we keep up to date with technology more than we've ever had to before, just to understand how data might be structured, how it gets fed, the

different ways you need to think about architecture. And I don't mean as a subject matter expert, but I mean enough facility because of how important it is in our day to day function. So those are kind of my my the two concepts that I think are working very well for governance. It also means that we have a central point. And Jackie, you've noted the recent legislation, but as a firm, we've received reach outs formally in terms of questionnaires from various

regulators, saying, Can you answer these questions? We're just doing a survey to understand how you're using AI, so we have a central point in compliance that's able to respond to that they are the face now with the regulators to be able to respond, to have that holistic firm view as well. So it's, it's built into the structure where we're not Honestly, a lot of what we're doing is going to sound similar

saying, Okay, well, who actually knows this? To be able to answer, you know, this type of questionnaire, and we're trying to make sure that we are engaging and responding to regulators. We will be commenting on the recent legislation as well, and we will be drawing on this working group and their experiences in order to be able to have comments that really span the entire firm, which so far means at least I could say for the next month to three months, weekly, I feel

like we've got the right governance structure. We'll see what you know, we keep on top of that for how it needs to change. And at the same time, the governance allows us to move to the tools that I mentioned earlier quickly, because I think we need to, we have people who are experimenting or saying this tool is better than that tool, or I need this kind of accessibility. And you can't really wait, you know, a year,

or whatever that used to look like. You're doing your risk assessments very quickly, and you need to bring teams together to say, really, what is my risk, what has changed? Because I think you have a cohort of people who are thinking, wow, to Naomi. I think you know having we have a similar group

that's really quite different. When you think about, you know, of individuals that are kind of embedded within the organization discrete elements, you really has it changed, compared to the that feed in and, you know, meet and discuss AI and what's going risk that we've always had, especially with publicly accessible forms of AI, etc. So it's balancing that, and I think this governance structure allows us to bring all of that to the table, because we've identified accountable folks for it.

on. I, we have a very like lean team right now, and so I have a very good picture, kind of what everything that's going on in terms of AI, all of the risks, and I think that's really key. I think, as Naomi pointed out, to have really that line of sight, because all of these risks are, you know, build on top of each

other over time, right? So staying on top of what different teams are trying to do, what they're doing, and also trying to stay on top of what's coming in the space technology, technology wise, regulatory wise, that's a full time job in itself, maybe it has several full time jobs, so it's something that I'm constantly trying to, you know, figure out better ways to address using AI tools to help me digest information on AI is like something that's very meta,

that's part of my life now so and just at the digesting all of the information I think, like, from a practical standpoint, what we're really trying to focus on is ensuring that we're documenting everything properly. Like this sounds like very basic stuff, but it's so important because things move so quickly, teams want to get up and going so fast that it can kind of be a bit of an oversight sometimes. So making sure that we're we're really being diligent understanding everything that

we're doing. Also part of, I think something that's really important is we're trying to make sure that we're not overly reliant on one single system at any given point in time, in case that we need to pivot, in case some regulation comes out and says that we can't do it XYZ way, building in a way that is not system dependent, or LLM dependent is very key to us right now. And so that's been really kind of part of the risk strategy as well, from just like our business continuity

standpoint. And then really just making sure that when we do have guardrails in place, that we're enforcing those making decisions, essentially, that we that we don't want to make decisions that can't be undone later down the line, right?

Like, I think, especially when we're talking about sensitive data, financial data, once it's out there, you can't put you can't put the toothpaste back in the tube, which is a terrible analogy that I like to use, but so being really diligent, especially right now, where there are so many things in flux, and our strategy is constantly changing. I think taking that more risk averse approach has been the way that I think our strategy has gone, and I think has worked well for us so far.

Fair enough, I think, and I know Naomi, you had given some flavor and examples to this, but I put this out to you both, and really as sort of a takeaway for our listeners, when you think about the second line functions that you know both of you are in, where do you see, and this is sort of looking, not that we have crystal balls, but future looking, where do you see, or can you envisage the most value for second line practices, such as the ones that you're both involved in in embracing AI,

including generative AI, and whatever potential future iterations of of the technology that could come and and how does that help you, not just sort of, as Naomi said, make it more efficient, right for the team, but actually help you manage risk in your organization. So maybe Natasha, I'll start with Yeah, for sure. Um, so I think one that I just mentioned is, you. you know, compiling and staying up to date with information is something that I'm currently doing quite a bit in my legal

practice. For example, AI has been so helpful in just streamlining the legal research that I'm doing sometimes to answer a question, you know, I can, I've built a small like bot that essentially just has all privacy guidance, all privacy investigations, all privacy breach information in there. And so just being able to, like, pinpoint, okay, this is what I'm doing. It really kind of doing the work of like a student for

me, in a way. And so I want to try to try to figure out ways that I can kind of keep building that into especially like my legal practice. One of the things that I've been exploring, actually, just in my personal life, like outside of my work

context, is using AI agents. And so I would love to get AI an AI agent, kind of into more of a professional use case for me, I think I haven't completely thought through exactly what I'm going to get an agent to do. It's a bit of that, like creativity element, that I'm still really working through and but I think, you know, to the sense that they can automate

tasks. For me, one of the big things that I'm struggling with, right, not struggling with, sorry, one of the big challenges I would say at in my job right now is scaling, you know, we have very small teams, and so figuring out ways to leverage AI in a way that can help me better manage risk, help me better stay on top of like, our compliance needs is something that's very

top of mind for me. So any type of automation taking first cuts at risk assessments, risk scoring, prioritization from a risk perspective, is something that I'm very, very excited to see. And I was actually thinking about this last night, and I thought that was very ironic, that one of the things I'm really excited about is just when I think we have a little bit more guidance and we have a little more structure, and there's a little bit more room to experiment, especially with

our internal data. We're taking a very risk averse approach, which is usually the approach that I want to take, but here, I'm just very excited to see a world in which we can leverage a little bit more data to use Gen AI, you know, I work in the privacy space, so thinking about ways that we can use our

customer data. And, you know, understand how our customer data is being used internally, doing our privacy impact assessments, for example, and automating that through Gen AI, is something I'm very much looking forward to, and something that we're very much not doing right now. So I think there are ways in which, and again, like I think sometimes it's hard to just envision what those use cases are going to be. It's every other day I'm getting an email from a salesperson with some new

tool. It's like, oh, I hadn't even thought about using AI to do that for me. So it's a bit of a it's, yeah, it's a bit of a kind of choose your own adventure right now. And I think figuring that out is something I'm looking forward to. Great, Naomi? If I had to pick one, Jackie, the word I would use, would be moving towards greater use on a thematic basis. And what I mean

by that is, again, you noted not new. We've had alert based review tools for some time, haven't had, maybe the line of sight, and really team members who are now very cognizant of about how it works and why it works that way, and that's a little bit new. But what I'm finding is with what where we could really use technological help is moving towards, what do all these different data points mean together? Is there a way to look at things thematically? Now that's not a new concept. We

have had it in different realms, I would say. And I'll use the comms monitoring for those businesses out there who have to monitor comms. My second big word is the triangulation. So

it's thematic, but it's triangulating all our data. And comms has been able to, certainly, in my last decade, I've seen tools that can triangulate your voice, your email, your texting, etc, bring it all together and say what's going on here, you can get bubbles that show you know this person's talking to that person, etc, and maybe pick up thematically on things that you really can't as a human, pick up

on all of that. It's too big. I think that would be our biggest advantage when I think of overall compliance and risk taking all these disparate or what can be disparate data points because of these alert based mechanisms or trending or whatever data point you're gathering, it could be some of what Natasha spoke about, where are breaches happening, where are fraud events happening, bigger than just my alerts, And what does it maybe look like altogether so that we can get

deeper understanding and smarter understanding about that risk and any action that might be required.

Makes total sense. I mean, the whole theme of our chat today has really been around taking, you know, piles and piles of data, and being able to process them in a way that, not to say a human could never do it, but not fast enough, not effective enough for us to deal with the risk, because the risk is long come and gone by the time somebody pieces it, you know, all together with the volumes of of data that you know these businesses deal with regularly, and I agree, and I think you

know, as much as there's this sort of fear that technology might replace people, my personal view, and I'm sure you might both agree, is that, really, it can empower us to to Natasha, you talked about capacity, not just give us capacity, but Naomi, what I heard was that it can give us, instead of data, information, and we can use that information right to to determine in our respective organizations, what

risk is within our appetites and what can we live with? Whereas before it might have, you know, taken two weeks, three weeks, to understand a particular risk point, but that's already old. You know, who knows what happened in those two weeks that it took us to figure out something from from the past. So, no, I think, I think people should not be, I mean, you need to be risk averse and, you know, controlling, as you both have

very well articulated. But I think AI really does have an important role to play, and if anything, it will make us be more effective in what we do as risk professionals. So having said that, thank you very much, ladies for for joining us today. It was very informative, and I'm sure many of our listeners will, you know, be thinking Wow, we got to do this. Those are great ideas. Start seeing how they can implement it in their respective organizations. Because while you're both in similar industry,

really, this transcends all industries. I just think the asset management industry, because of the reams of data we deal with on any given day, is a really good sort of place to think about AI, just like maybe banks would would be in the same position. Well, thank you very much, ladies, and have to our listeners. Have a great day and enjoy thinking through everything you just heard. Thank you Jackie. Thanks Jackie.

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