Ep#028 - Julia Lessing - Mathematical Magic with Guardian Actuarial - NOT The Rob Bell Podcast | Limeworks

Episode 028 - Julia Lessing

Mathematical Magic with Guardian Actuarial

Julia Lessing is an Actuary. You may not know what that is, but Actuaries are behind some of the biggest forecasting and financial modelling decisions made in business. But even if you know what an Actuary does, you may not realise how it's used to make people-focused decisions.

We explore just how Actuaries perform what seems to be Mathematical Magic, how their data informs intelligent decision making for Complex People Oriented problems, and so much more on Episide #028 of NOT The Rob Bell Podcast.

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Rob: So Julia Lessing from Guardian Actuarial, thanks so much for joining us on the podcast today.

Julia: Thanks for having me, Rob, really excited to be here.

Rob: I think there's some really interesting things and they might have picked up from the company name that you are an actuary and I think we'll dive into what that actually means a little bit later. But first up, can you give us a little bit about Julia Lessing and your professional history and take us through that journey of your professional life?

Julia: Sure. Thanks, Rob. So I guess my professional history starts back in the high school days when I was making decisions about what to do with my life. I was really good at maths. And I guess the obvious choice was to be a maths teacher. And, you know, my mom thought that that was a really great thing to be a teacher because then you have school holidays off with your children. But I'm not really a fan of, like, other people's children too much. So,

Rob: Fair call.

Julia: You know, I love my own. But the idea of teaching, I just thought, you know, I enjoy teaching, but teaching other people's children the money didn't sound that great. So I sort of started exploring what else could I do with those math skills? And my Voynich maths teacher said to me, you know, if you thought about doing actuarial and I had no idea I had no idea what an actuary was

Rob: Like most people currently.

Julia: Like most people. But, you know, you don't have to read too far through the guide because actuary starts with age. So, you know, you get past accountant, you think that looks a bit boring and you get to actuary. Anything that could be that could be an option. So I liked the idea of being able to put numbers to things that haven't happened yet, you know, being able to quantify stuff that happened in the future. And so that's where it started.

Rob: So that's where it started, and so you're fortunate Mass obviously had a talent for mathematics out of the gate if you had a math teacher. And so what was that that clinched point of, you know, really affirming that this is what you wanted to do? I think you've probably got to explain that it is quite a journey to become an actuary. What really set the tone for taking on that challenge?

Julia: Look, I think, you know, I went to a selective high school, and when we're in year 12, it was just all about maximizing the TCR or the eight hours the kids call it these days, you know, choosing the subjects that were going to scare you. Well, it was it was all about what will you do at uni, not what are you doing next year? And I guess, you know, it seemed like there was some prestige that came with being an actuary. Not many people knew what an actuary was. And there was a bit of a around that. I sometimes kick myself around that these days because it's very frustrating when people ask you what, what, what you do when you tell them and they just kind of you blankly like, "what is that?".

Rob: Move on to the next question. Yep.

Julia: But I probably just got to be honest, I probably just got swept up in the wave. You know, we were headed towards uni. We were we knew what subjects we were good at. We knew what opportunities we had available to us based on those subjects that we were good at and just followed the path. We just followed the path to university and got stuck into the actuarial studies.

Rob: I mean, it's been quite a windy path or at least a long path, from what I understand. And I mean, your professional credits include work at Iwai, Munich Re Zurich and then ultimately founding Guardian in 2015. That's that's some really big companies. You know, what's what's it like transitioning from, you know, aspiring to be something to going through that that degree process and ultimately landing where you intended?

Julia: Yeah, well, it's a really good question, Rob, because I think aspiring to to where I was going, I wasn't really sure where that was. And in my first year of university as a first year, actually, still not quite sure what actuaries did at that point. But working through my exams, I actually got married and shortly after I fell pregnant with my first baby. So that then sort of took life on a bit of a different bit of a detour, I suppose.

Rob: Sure.

Julia: And actuarial kind of became part of the background that was, you know, this is this is what's going to ultimately support my family. This is what's ultimately going to give me that long career path. And, you know, I've always wanted that sort of suit and high heels kind of job. So knowing that I would have one of those corporate careers and work for those big companies and help clients in the big end of town, that was quite appealing. So that was that was sort of where it started. And I guess in parallel, raising a small family at the same time that just I was always making decisions about how could I continue that career and continue to climb the ladder while also balancing that with my young family.

Rob: Certainly a balance, no doubt.

Julia: Indeed.

Rob: And so for anyone who's listening or watching this, just as you were trying to figure out when you were entering university, can you give us the the cliff note version of what is an actuary and what do you actually do?

Julia: The cliff note version, well, the short answer is rays do different things. We use data, we're good at understanding the context around a problem and being able to look at the data and do some cool maths around how to solve that problem. We'll get some insight into that problem. Based on the data that's available, sometimes the data is incomplete or relates to things that haven't happened yet. So we use probability and statistics and our understanding of economics and finance to be able to bring together a whole lot of different angles when we're looking at data to be able to solve problem.

Rob: So when we think of say EY, Z├╝rich, things like that is the actuarial explanation that you've just given us kind of does fit into that pattern

Julia: Hmm.

Rob: There. But does it extend beyond just financial modelling and and what you would sort of assume that leads to?

Julia: Absolutely, and I think that's one of the things that really differentiates actuary's from other quantitative professionals, you know, we are kind of the jack of all trades when it comes to different mathematical techniques. Some actually see themselves as data scientists or getting to some really heavy coding and different analytical techniques and machine learning. And certainly that's an area where actuaries are getting more into as time goes on and as data sets get bigger and data analysis techniques become more complex. But really, I think what distinguishes us is actuary's is our ability to look at the context around a problem and to look at different scenarios or the way things might play out, different options in terms of how things might play out in the future, not just what's happening right in front of us, what happened last year, but being able to set assumptions and make predictions and, you know, really bring some insight from the data that's available, but also being able to supplement that with our understanding of the context and a professional judgment around what else is important in that context.

Rob: It really opens the eyes a little bit into just how broad, you know, you've given us a rough explanation of what an actuary is, but it sounds like it's a little bit like saying someone's a doctor, they may be an ear surgeon, they may be a, you know, a general practitioner. What what areas of actuary do you focus on and how does that kind of

Julia: Yeah,

Rob: fit into what you enjoy?

Julia: Yeah, good question. So the path to becoming an actuary is very long, and as you get towards the end of that path, you specialize in to different kind of actuarial services. So, you know, typically actuaries come out and become qualified as either a life insurance specialist. So they know everything about life insurance and the maths that goes behind their in the mathematical techniques in terms of pricing, serving around life insurance, how insurance works in the industry and in the location and jurisdiction that they're working on. Same goes for general insurance actuaries or health insurance actuaries. Typically, actuaries work in financial services or banking, wealth management, superannuation. So those sort of financial based type streets. So by the time you get qualified, usually fall into one of those specialty type areas. And that's where you are. That your. There are a number of us, though, who, whilst we have gone through those traditional paths to become qualified, we're using those actuarial skills, different areas. So aguardiente actuarial, we help senior leaders use data to help solve complex, people oriented problems. And by that, I mean, you know, we might be looking at workforce's large workforces, the well being or the staff retention around that. And what data is available to derive insights about why people leaving or what do we need to attract good staff or how do we need to improve staff wellbeing? But we also do work for government agencies as well, looking at their workforces, who might deliver services to community groups, whether that's child protection services, whether it's health, the other clinical services, whether it's NGO community services as a whole lot of different applications where we can use as actuarial techniques outside of financial services and superannuation and wealth management to be able to make a difference.

Rob: So it really sounds like even though it may be applied in different ways, I know from my experience where we're good at, say, taking a marketing campaigns data and looking at, you know, the success of that in a probably a very rudimentary way compared to what you would do. But it sounds like you guys are really forecasting and looking further into the future with more of a predictive modelling sort of lens than just looking at what's at hand that.

Julia: Yeah, that's absolutely right. So, you know, if we were looking at, for example, you know, what's so in your marketing example, if we're wanting to look at some data about what's sold in the past and how things have sold or what sort of factors drive different sales, you know, that's that's helpful to the extent of what's available in that data set. But if there's certain factors that apply, let's say that data comes from America in a completely different context and completely different culture, completely different behaviours. It's a it's a real data set, but it might not necessarily translate well when you're looking at sites as to how things might occur here. So I think it's really important to be not just looking at what's the data analysis technique or what's the data set or are we using machine learning or artificial intelligence or any of those really cool? Well, I think they're cool.

Rob: That's my jam.

Julia: You know, all these really cool techniques. At the end of the day, if you've got data that doesn't make sense or it doesn't reflect what you're looking at or it doesn't make sense in the context of a problem, doesn't matter hackle technique, data analytics techniques that you've got available at your disposal, at your disposal, you're just going to get rubbish coming out. So, you know, again, I think that's where actuaries can add value because we are really good at the maths, but we're also really good at understanding the problem in the context of the problem to make sure that we're not just coming up. We're not just crunching some numbers and coming up with an answer that makes sense in a certain context that doesn't actually apply to the problem.

Rob: So I understand that you have a real strong people focus around people based problems in in data. How does that throw up new challenges that say actuary's working against stock markets or something that is a little bit more predictable? How does that human element factor into sort of that wildcard factor?

Julia: Yeah, you know, I think that wildcard factor actually applies to more industries than we realize. I think even when, you know, sometimes you can have a false sense of security in an industry where you have numbers, you have things that you can measure and you start to think, oh, this is all very unscientific. And there's always a risk when you start putting numbers to that, people think, oh, that must be real. You know, you've called that five. It must be five.

Rob: Confirmation bias,

Julia: Exactly.

Rob: Yeah.

Julia: Exactly. So, you know, understanding psychology, understanding the, you know, human behavior, I think that's a really important to being able to understand problems, but also make sure that you've got a solution that makes sense and fits. And one of the things that actually is a really good at is understanding complexity. So, you know, if you're looking at a data set, we've got a project we're working on at the moment and we've got times of certain activities. Right. And we're trying to understand what's the relationship between different factors that can tell us whether or not a certain activity takes like longer or shorter. So we're looking at different factors. But, you know, you can you cannot work out what those factors might be by understanding context, understanding industry, talking to people and experts in the space to really understand the context. But, you know, sometimes those factors are connected. So what if you've got two factors that both increase time, but actually when one factor applies, the other factor also applies? You know, you need to make sure that you're not double counting. So, you know, statisticians would know that that's around correlation of those different factors. But, you know, understanding the nuances around those factors, understanding what would be correlated as well. It's not just about crunching the numbers and applying statistical tests. It's about understanding the context of the problem. What is it trying to solve and and what's the best process to actually refine your analysis, to get a model that best predicts the outcome.

Rob: Sands are extraordinarily complex enough to make my brain hurt and and probably quite a few brains, but I mean, you've obviously got quite a passion for it. And I know history, historically speaking, you've got some humanitarian aspects to what you've done professionally and personally. You were a volunteer breastfeeding counsellor with the ABA Lifeline, telephone crisis supporter, and even a foster carer did to those interests and and skills that you develop there, then help blend into making you a better actuary when it comes to human services.

Julia: Well, I hope so, Rob. I really hope so. I've always had a passion for the people and anything related to people. I have four little people, four children of my own, and my husband and I basically have a life motto, which is people before things. So, you know, you need to put people before things. Things are just stuff. Now we all like our toys, but prioritizing people and making decisions about what's right for the people is very important. So I guess, you know, winding back to high school days, what I actually thought I would do when I grew up was I wanted to become a psychologist. I've always been really fascinated by how the brain works, what makes people behave differently, what you know, what sort of things distinguish us from each other and and make us different. What do we think differently and why do we behave differently? And I've always found that really, really interesting. However, I was much better at math than I was at English at high school. And so I just thought, you know what, I'm I'm just never going to make it through the essays. I might take psychology at uni. So I stuck to the maths, did really well with that, but in parallel have always loved anything related to people, demographics, communities, and in particular justice and equity.

Julia: So, you know, I was a teenage mother and and at the time I met other teenage mothers as well. And, you know, we're in our forties now looking at our different trajectories through life and just realizing how, you know, sometimes having a little bit of extra support can really make a difference to where you go and how your life plays out and that some people, just through sheer bad luck around the lottery of birth, end up in situations that no one would ever ask to be in. And, you know, through no fault of their own, have a lot of challenges, a lot of difficulties. And we as a community have a responsibility, I believe, to look after those people. And when people find themselves in situations where they need a hand, we should be able to rally together and help each other. So I guess governments play a role in being providers of last resort in terms of certain services. We've got a whole lot of NGOs that also deliver really important services. People need help. And I think even people in in privileged positions, especially people in privileged positions, should be using that privilege to some extent to be able to make things better for other people who aren't as fortunate.

Julia: So I really do feel passionate about that just as a community. I really just think that we all should be doing what we can to make sure that we've all got a minimum level of care and and standard of living. So I suppose through that passion I have in parallel with my professional work, in parallel with raising my family of little people, um, I've always had volunteer roles on the side that give me an opportunity to learn more and to give back to the community in a way that I can with what's available to me. And probably counselling has been one of those areas where I've done most of my work in that space. I know I take my hat off to the front line workers, especially everything we've been through over the last year with a pandemic. You know, I think that would be a really, really tough job having those kinds of frontline frontline roles through such difficult times, you know, mental health workers, community service caseworkers. I just think it's incredible what they do. And I know personally I wouldn't be cut out to do that work full time. So I like to be able to do my professional work and supplement that with the volunteer work that I can do a difference.

Rob: I mean, it's emotional intelligence is is a real kind of buzzword professionally right now and has been for several years, I suppose. But I'm curious to know when you are dealing with data, financial modelling and all that emotional intelligence and life experience can give you particular insight into particular sectors. How do you determine what's giving you insight and what may be giving you your own confirmation bias when you're dealing with sort of loose data?

Julia: It's a really it's a really good question, and I think it's one thing that you need to be really careful of. So, you know, I do a lot of work in the analytical space of child protection. So looking at data gathering, new data, helping to understand that data and to interpret that data, to derive insights or help make decisions about what needs to happen around child protection, how we can improve outcomes for vulnerable children. Um, I am also an authorized foster carer and have direct experience being a foster carer. And it's it can be a double edged sword, having that personal experience and having to translate that into your professional life. Because, you know, as a as a foster carer, my experience and as a mother, you know, my experience is around those children that I've cared for and the dynamics that we've had. But that's not necessarily representative, representative of all children or all foster children. So you do have to be careful. I think, when you're bringing your own personal experience to work, I think it can definitely enhance your ability to understand the context of the problem. It can enhance your ability to identify important nuances that need to be considered when you're making, um, you know, when you make assumptions or when you're giving advice.

Julia: But it's really important to make sure that you realize that your experience is still your experience. And it's not everyone's experience. And I think I learnt that when I was a breastfeeding counsellor in my early 20s, when my children were small and to be a breastfeeding counsellor for ABA, you need to have breastfed your own babies, which is great because it means that mothers can go to another they can go to a counsellor who has breastfed their own babies and they understand. But the very first thing they teach you in your counsellor training is. It's about the person that you're counselling. It's not about you. Don't assume that what the mother's come to you about, don't just think, oh, I've had that problem before. I know what the answer is because that was the answer to your problem. It's not necessarily the answer to their problem. So I think it's really good to have that lived experience when you're helping professionally. But you do really need to make sure that you're taking that where it belongs and keeping that in context and remembering that it's not necessarily the same as everybody else's experience.

Rob: I mean, I suppose I don't know the answer to this, but I'm assuming just like, say, scientific peer reviewed studies, you have processes and mechanics for basically cross validating data and and trying to rule out that personal bias.

Julia: Yeah, definitely, definitely, and I think, you know, as actuary's, often we are having to set assumptions around things, and one of the ways that we can get around that personal bias is by having several different data points to help corroborate or justify the assumption setting. So at the end of the day, we're all we're all human with our own professional experience. And in the same way that, you know, you might say, you know, once like three psychiatrist might say the same person and give three different diagnoses,

Rob: Sure.

Julia: You know, sometimes the same thing. Actuaries, too. But I do think that part of our training is around the rigor of working out how do we come up with our analysis and how do we set our assumptions and how can we make sure that they're robust and justify?

Rob: It's interesting and I think when you talk about gathering new data, when you're sort of working with maybe a real bare bones, you know, I think the worst dataset you've ever been given to to derive an outcome. What are some of the processes used in actuarial processes to sort of derive new data?

Julia: Yeah, good question. So some of my favourite ways of deriving data or collecting data are through surveys and focus groups, which typically can be more sort of qualitative data capture. So qualitative researchers would be very familiar with these techniques. But it's interesting when it comes to survey design, because sometimes you can actually you can collect new data and derive numerical or quantitative results out of that. So one example is we had a I had a new manager come to me and say, Julia, I've just inherited this large workforce. I've got 60 staff here. And there's a couple of there's a handful of managers and there's a lot of staff reporting to these managers. But I'm losing people and I don't really know why. I can't understand what's happening here. I don't know why people aren't happy. They all look like they're very capable, very professional. But I can't get to the bottom of it. And so I met with the managers and they weren't really able to shed that much light. But by having some focus groups with the staff, I was able to flush out some of the key issues and challenges that those staff just didn't feel comfortable escalating to their managers. And then once we had those themes, I was able to run a survey to test those things right across the whole workforce. And out of that, we were able to identify quantitatively and validate some of that qualitative data capture focus groups. We were able to identify what some of those issues were and who they affected, you know, whether it was affecting everybody or whether it was just affecting one person that had told me that in the focus group. So that's that's a really useful way of identifying and collecting additional data that can help solve a problem. So and very specific to the issue at hand, you know, it wasn't just picking up some data off the shelf or some data from another team was actually understanding what's going on, understanding that problem, collecting the right data to understand how to solve that problem and to identify the issues.

Rob: And so the terms, qualitative and quantitative data are probably very, very familiar to yourself. But can you give us a bit of a rundown of what the difference is there between the two?

Julia: So quantitative data is basically anything that's numeric, so it might be a data set from the Australian Bureau of Statistics that's counting the number of people in different areas, in different age groups. It's quantitative data, it's numbers. Um, qualitative data is data that you capture that's descriptive. It might not be numeric. It might be about people. It might be understanding things about how people feel about things or people's attitudes towards things. And of course, if you collect that in a certain way, you can turn that into quantitative data. And that's one of the things that we do. We're collecting the human element and translating that into actionable insights by being able to fire some of those qualitative insights.

Rob: So thinking about our own experiences as

Julia: Hmm.

Rob: People in the world, we've probably all filled out surveys at some point, is it fair to say that quantitative data is essentially the yes or no answers in that survey and the qualitative data is the please add free comments?

Julia: The free text, yeah, yeah, that's a really good way to describe it, and I probably take it one step further and say that all that qualitative protects information. We can sometimes mine that information and turn to tidy science.

Rob: Interesting, I'd ask how exactly you do that, but I guess that's the secret sauce of what it is that you do.

Julia: That's the secret sauce, Rob.

Rob: I'm curious to know we've talked or spoken about how actuaries are used in big business and we're talking about massive programs for government, massive service undertakings, building new hospitals in the right areas, anything like that. Is there a role or actuarial service in a smaller context or is it is it that you need so much data and you need to be able to derive big outcomes that it doesn't necessarily you know, there's there's not a place for it. It's a small business.

Julia: So it's a good question and I think it depends on the nature of small business and exactly how large it is, but also what the problem is that needs to be solved. So I wouldn't say that actuaries are only helpful when it comes to large businesses because actuaries often help in individual situations. So let's say there's a family law settlement, a divorce settlement, and husband and wife are separating up their assets. But there's some sort of assets in there that's contingent on somebody being well or somebody dying. And you need somebody to look at life expectancy type metrics to be able to work out how to divide that asset. That's an example of where an actuary might get involved. So at the very most individual level, that's that's one application of where we can help. But even the maths behind that, it's based on large numbers. So actuaries are making assumptions based on the law of large numbers, big, you know, pooling risk across many people, big populations, big groups of people. Um, because that's that's the way that we can robustly apply our statistical techniques if we're making assumptions about how things might play out or what might happen in the future. It's very hard to do that for an individual person. And to you know, if you were to say to me now, what's my life expectancy? I can give you an average. You know, I can even do probably a little bit better than that. But on the whole, life expectancy and statistics are now some of the numerical tools that we actuaries are designed to be applied to big groups of people. So when you start to drill down and want to apply that to small populations of small numbers, those techniques are not always as reliable or robust as they be. And that's why we typically at large populations, large workforces, large groups when we're doing our analysis.

Rob: Interesting, so I want to say just a little bit now, but maybe for selfish reasons, I'm a real fan of machine learning and I love messing around with machine learning. And you touched on it. That actuary's sometimes end up pushing into machine learning themselves. I'm curious to know if the ability that an actress such as yourself has to sort of make sense of really noisy data, where it's human data, where it's you know, it's I don't know the the Life funness index or something like that. Do you find yourselves sort of working to sanitize data to ultimately end up in a machine learning style context, or do you always sort of see it through to the end to deliver a report or, you know, some some statistical analysis?

Julia: So I always think with any kind of analysis project, the very first step is understanding exactly the problem. So, um, so we're trying to build a big model that simulates everything in the world and everybody's fun factor. Um, you know, that's going to be a total challenge, right. Because to be able to get that and fit a robust model that gives you really sensible is going to be. So being able to really be clear about the problem you're trying to solve and the parameters that are important to that problem is really critical in terms of no matter what technique is, whether it's just some, you know, projection modelling in Excel or whether it's some whiz bang machine learning or artificial intelligence, it comes down to what is the problem in trying to solve. So I'll give you an example. Sometimes you can really overcook these models and it really did come and it comes down to the context of the problem. So if we're looking at the number of children moving in and out of out of home every year, you know, we don't need a very highly detailed, very refined model that's going to predict where children go each day. We need a high level model with some high level assumptions if we try to overengineer that we're not necessarily going to get any better outcomes or any better results around that. Um, but we still need to step back and think, OK, what is it that we're actually trying to model? What are we trying to predict? What are we trying to understand? What insight is at or from the data? Um, contrast that to something like, you know, something that's very high volume, high turn over, you know, some of your marketing examples where you're trying to sell products, you're trying to understand what drivers are behind those products and what's what's helping excel and what's not.

Julia: If you've got high turnover, high volume, you know, that's in it. And things are changing over time in the context and the drivers and the factors behind that change over time. That's a great example of where machine learning, because you can set things up, you understand what the important things are, but machine learning allows you to be able to adapt those assumptions over time, in real time, so you can have that set up so that instead of having to just. Statically look at what the assumptions could be model, and for your projection and effects, you can set that up so that that can be evolving as time goes on and it can be adjusting and evolving to allow for different changes, in fact. So what changes in demographics or changes in spending? We've got changes in other other key factors that are affecting the result that you or if those things are changing over time and need to be resetting those assumptions, then that's an example where very.

Rob: It's interesting, I'm curious to know as well, obviously, I mean, machine learning has been around since computing much to people. Surprise when I. Discuss that, you know, my favourite dinner party conversation, but I'm curious to know over your career, which, you know, spans a little bit of time now, have you seen a change in the way people are sort of applying machine learning, which may have traditionally been an actuarial role or the two still very distinct.

Julia: I think the two operate in parallel, I think, you know, if you go back to traditional actuarial work, there'd be no pens and paper and calculators and slide rules maybe.

Rob: Sure.

Julia: So certainly as computing power has improved and technology has improved as well, that's giving us more options to be able to take on some of these bigger datasets, but also more processing start ups. It's too um. I don't think actuaries have the monopoly on this, though. And I think the what we bring is slightly different to just the pure computing power behind what's possible, because certainly data analytics and data scientists are doing a lot of work in space, not qualified as actuaries, but or not always qualified as actuaries, but have a different role and and are able to evolve those techniques as well.

Rob: It's interesting, it's certainly sounds like it's, you know, one is benefiting the other by directionally, it's for mutual gain of us to make sense of all these crazy numbers. It's good. So I want to throw to you a little bit and just see if there's anything around actuarial as a is it actuary or actuary when it's a what does it have to be an actuary or.

Julia: So an actuary is a person and things that an actuary might do would be considered to be actuarial.

Rob: Actuarial, all right,

Julia: So

Rob: We might

Julia: So

Rob: Cut that bit out, will say.

Julia: Actuaries study actuarial studies. Actuaries do actuarial work. But yes, I am an actuary and.

Rob: And the work be considered actuarial, it's like

Julia: Yes,

Rob: Sort of.

Julia: Work where?

Rob: Ok, makes sense. Glad we clarified that, I'm curious if there's anything, maybe misconceptions around actuarial work that you sort of come across when you if you say I'm an actuary and people make assumptions about what that means, maybe to save face or maybe just because they have the wrong assumption there, anything that comes to mind.

Julia: So typically, when someone asks me what I do and I tell them that I'm an actuary, one of two things happens. They either look at me very blankly or they go, oh, you must be really smart and good at numbers. So some people know what actuaries do. But, you know, there's only about 2000 qualified actuaries in Australia. So and and we don't typically support individuals. We typically support big business and big end of town. So unless you work in a company that needs actuaries or something, most people don't know what actuaries do. So I'm not sure that there's necessarily assumptions that people make. Other than that, we're probably good with numbers, maybe smart, often quite introvert, maybe a bit nerdy.

Rob: Always a bit nerdy, it's a good good thing that it's fashionable these days, so it's good.

Julia: Can be.

Rob: So you said there's only 2000 qualified actuaries in Australia. How rigorous is the process to being formally recognised as an.

Julia: It's really tough, it's really tough, so, you know, I started my actuarial studies straight out of school, which is the pathway that most people take these days, you can do a Bachelor of Actuarial Studies or actuarial science or most agree with major studies at number of Australian universities. And if you get good enough marks in that, then that gives you exemptions from your first set of exams. If you don't, you have to go out and do some more exams to make up for that. But then after that, you do a lot of postgraduate work, a lot of postgraduate study. While you're working typically and most actuarial lawyers, the big ones support their students because even after you graduate, you still known as a student because you're still not qualified.

Rob: Lovely.

Julia: You can't call yourself an actuary until you've met certain criteria. Those criteria are often very character building as we go through many years of exams where the past rates often less than half. So the odds are very low of passing each of those actuarial exams. But, you know, the standard needs to be quite high. Actuaries are trusted within our business community to be able to give important advice around key issues. So we we need to ensure that we're preparing our students well for those roles and that they're they ready to take on those roles and those responsibilities when they.

Rob: So it sounds pretty tough to get to make it through, but I suppose once you are qualified, actually you can be informing, informing decisions that affect billions of dollars

Julia: Yes,

Rob: In spend.

Julia: Absolutely.

Rob: So what support exists for actuaries who are coming up, obviously, you want to bring as many to the top as you can of the actuarial journey

Julia: Yeah.

Rob: Is there's support for, you know, upcoming actuaries who are going through what you went through.

Julia: So, look, the big the big companies often offer in-house training for their actuaries coming through the process, often in those early years, the first year or two, the actuarial students are very busy completing their postgraduate actuarial exams. So there's not often a lot of time for additional training, but often once they get qualified, they're about to step into their first management role. So some of the big companies run management training for new managers. In my experience, actually a little bit different to a lot of other professionals in terms of stepping up into those managerial roles just because of the nature of the work that we do. You know, it's not pure management. There's a there's a real technical roll around the review and the responsibility that you have over the delegating to junior staff. So I also I run in-house training for companies, but I also run across company group coaching program for actuary managers. And this is really useful because not only are these budding managers learning the important skills around management, but they're having an opportunity to share different experiences with other people at the same level, but in different companies. So they were able to build that camaraderie and that professional network of other managers at the same level. So where is in a small actuarial team? They might only have people more senior to them and maybe one or two people who are junior to them. But they might not have a lot of years at the same level working. So being able to work across different companies and share experiences with different people at the same level, it's really valuable for their development. But it's also a really great way to learn those skills and to learn each other. So

Rob: You've always got to keep learning.

Julia: Absolutely.

Rob: We're about out of time here, but I just had a realization that probably every person listening to this has actually encountered actuarial one way or another, whether they've walked into a hospital, taken a Centrelink payment, maybe gone into a post office. But you guys really are behind massive, massive projects and you're sort of the silent engine informing some of this government policy and infrastructure spending and everything else. Am I off base there?

Julia: I don't think you're off base, Rob, but I actually think that you're applying an actuarial view to where actuaries might be in the future. You know, I would love for actuaries to be playing more of that behind the scenes role when it comes to government planning and government spending. At the moment, we are very limited in our application in that area. I got an actuarial we do a lot of that work, but there's a lot of government out there and a lot of actuaries are still working in financial services, wealth management, superannuation and and insurance. But I think if there were more actuaries that were helping governments make decisions about how might things go if we don't do anything differently now

Rob: Sure.

Julia: Or what might happen for people, then I think we might be able to get some more better bang for our buck, for our people.

Rob: It's good. Well, we may not fully understand everything that it is that you do, but Julia Lessing from Guardian Actuarial, it's been a fantastic chat. Where can people find out more about you?

Julia: Thanks, Rob. So head over to our website, www.gardianactuarial.com.au

Rob: All right, and hopefully they can learn a little bit more about what an actuary actually does. Thank you so much for your time.

Julia: Thanks, Rob.

Rob: There you have it, I hope you really enjoyed this episode, and if you did, please like it, share it or leave us a review on your favorite platform, it helps us show more of this content to people just like you.

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