1 00:00:04,050 --> 00:00:06,420 Sean Aylmer: Welcome to the Fear and Greed Daily Interview. I'm Sean 2 00:00:06,420 --> 00:00:10,619 Sean Aylmer: Aylmer. In the digital age, there's more data than ever before. It's 3 00:00:10,619 --> 00:00:14,580 Sean Aylmer: collected by companies and government departments in every interaction and 4 00:00:14,580 --> 00:00:18,180 Sean Aylmer: every transaction. But how many companies are actually using that 5 00:00:18,210 --> 00:00:21,509 Sean Aylmer: data to its full potential? Today we're doing something a 6 00:00:21,509 --> 00:00:24,509 Sean Aylmer: little bit different. Fear and Greed is supported by Do 7 00:00:24,509 --> 00:00:28,230 Sean Aylmer: Data Better, a campaign to demonstrate the difference an actuary 8 00:00:28,230 --> 00:00:31,679 Sean Aylmer: can make to the way your business operates. I'm joined 9 00:00:31,679 --> 00:00:34,769 Sean Aylmer: by three leading actuaries who work in major Australian data 10 00:00:34,769 --> 00:00:38,129 Sean Aylmer: science businesses. Adam Driussi is the co- founder and CEO 11 00:00:38,130 --> 00:00:40,080 Sean Aylmer: of Quantium. Welcome to the podcast. 12 00:00:40,680 --> 00:00:41,220 Adam Driussi: Thank you. It's good to be here. 13 00:00:42,540 --> 00:00:45,210 Sean Aylmer: Jonathan Cohen, principal at Taylor Fry. Good morning. 14 00:00:45,659 --> 00:00:46,049 Jonathan Cohen: Hi, Sean. 15 00:00:46,530 --> 00:00:48,690 Sean Aylmer: And Deloitte partner Rick Shaw. Thanks for your time. 16 00:00:49,229 --> 00:00:50,040 Rick Shaw: Yeah, no worries. 17 00:00:50,700 --> 00:00:54,299 Sean Aylmer: Righto. Let's jump into it. Big data. Even the word 18 00:00:54,510 --> 00:00:59,040 Sean Aylmer: data can be overwhelming to many people, to businesses that 19 00:00:59,040 --> 00:01:03,480 Sean Aylmer: don't really understand it. In a sentence or two, you 20 00:01:03,480 --> 00:01:06,509 Sean Aylmer: got to dumb it right down, can each of you 21 00:01:06,509 --> 00:01:09,900 Sean Aylmer: tell me what we're talking about in this podcast today? 22 00:01:10,289 --> 00:01:12,869 Sean Aylmer: What is it that data can do for business? I'm 23 00:01:12,869 --> 00:01:14,339 Sean Aylmer: going to start with you, Adam. You got the first 24 00:01:14,340 --> 00:01:15,630 Sean Aylmer: intro, you get the first question. 25 00:01:16,350 --> 00:01:19,200 Adam Driussi: Sure. I mean, I guess in a nutshell, if you 26 00:01:19,200 --> 00:01:22,679 Adam Driussi: think back to the early days of a shopkeeper running 27 00:01:22,679 --> 00:01:24,750 Adam Driussi: a retail store, you'd walk into a local shop and 28 00:01:24,750 --> 00:01:26,279 Adam Driussi: the person would know your name and they'd know what 29 00:01:26,280 --> 00:01:27,929 Adam Driussi: you're into and they'd know the sorts of things you 30 00:01:27,929 --> 00:01:31,650 Adam Driussi: want to buy. And data is the modern way of 31 00:01:31,650 --> 00:01:34,050 Adam Driussi: doing that, where large companies can deal with lots of 32 00:01:34,050 --> 00:01:37,350 Adam Driussi: customers but understand them in a detailed way. So they can actually 33 00:01:37,350 --> 00:01:42,300 Adam Driussi: give customers a personalized experience by understanding their preferences and 34 00:01:42,300 --> 00:01:45,269 Adam Driussi: who they are as individuals. And so when we talk 35 00:01:45,270 --> 00:01:46,830 Adam Driussi: about big data, I really think we're talking about an 36 00:01:46,830 --> 00:01:49,620 Adam Driussi: age where there's lots and lots of data being collected 37 00:01:49,620 --> 00:01:51,900 Adam Driussi: now compared to in the past. So with all of 38 00:01:51,900 --> 00:01:55,110 Adam Driussi: that data comes great opportunities to do that better for customers. 39 00:01:55,650 --> 00:01:59,190 Sean Aylmer: Okay. You had first- mover advantage there, Adam. Jonathan, have 40 00:01:59,190 --> 00:02:00,600 Sean Aylmer: you got something to add to that? 41 00:02:02,219 --> 00:02:06,420 Jonathan Cohen: I'd say Adam did a pretty good description there. The 42 00:02:06,780 --> 00:02:10,710 Jonathan Cohen: one comment I'd add is that it certainly extends beyond retail 43 00:02:10,710 --> 00:02:13,740 Jonathan Cohen: and customer- facing businesses through to areas like government and 44 00:02:13,740 --> 00:02:16,410 Jonathan Cohen: health record (inaudible) lots of opportunity there also. 45 00:02:16,950 --> 00:02:17,460 Sean Aylmer: And Rick? 46 00:02:17,460 --> 00:02:23,159 Rick Shaw: I like what my friends said, I would just challenge 47 00:02:23,190 --> 00:02:25,980 Rick Shaw: the framing of the question a little bit, in the 48 00:02:25,980 --> 00:02:28,470 Rick Shaw: sense that I think it's what we want to do 49 00:02:28,470 --> 00:02:32,430 Rick Shaw: and it's part of the actuarial role, is ensure that... 50 00:02:32,820 --> 00:02:36,330 Rick Shaw: We have these wonderful, great tools. And as Adam says, 51 00:02:36,330 --> 00:02:40,469 Rick Shaw: we can personalize the way we interact with people. I 52 00:02:40,469 --> 00:02:43,048 Rick Shaw: would just frame it as not see business as something 53 00:02:43,260 --> 00:02:46,380 Rick Shaw: separate from society. How can we use these great tools 54 00:02:46,470 --> 00:02:48,510 Rick Shaw: for the betterment of society generally? 55 00:02:49,350 --> 00:02:51,690 Sean Aylmer: Okay. What I want to do is ask each of 56 00:02:51,690 --> 00:02:54,360 Sean Aylmer: you to now give me an example, maybe you've worked 57 00:02:54,360 --> 00:02:56,910 Sean Aylmer: on the strategy, maybe you've implemented it, maybe you've just 58 00:02:56,910 --> 00:03:01,799 Sean Aylmer: heard about it, where data strategy or data has really 59 00:03:01,800 --> 00:03:04,470 Sean Aylmer: helped a business or disrupted a market or something like 60 00:03:04,470 --> 00:03:07,410 Sean Aylmer: that. I'm looking for real- life examples so I can, 61 00:03:07,860 --> 00:03:10,830 Sean Aylmer: as a non- expert in this area, can identify with 62 00:03:10,830 --> 00:03:12,090 Sean Aylmer: what you're talking about. 63 00:03:12,810 --> 00:03:17,279 Rick Shaw: I'll start off. We're doing extensive work with a number 64 00:03:17,279 --> 00:03:26,460 Rick Shaw: of financial services organizations, which are automating decisions that impact 65 00:03:26,550 --> 00:03:32,370 Rick Shaw: people, say, around the mortgage approval process or the premium 66 00:03:32,370 --> 00:03:37,770 Rick Shaw: rating with an insurer. And our approach to this work is 67 00:03:37,830 --> 00:03:42,000 Rick Shaw: we set the framework for the actual technical analysis and 68 00:03:42,000 --> 00:03:45,270 Rick Shaw: the governance reporting and all that. What we're tending to 69 00:03:45,270 --> 00:03:50,069 Rick Shaw: do more and more is to ensure that the algorithmic 70 00:03:50,130 --> 00:03:54,900 Rick Shaw: outputs are consistent with corporate and social objectives. So what 71 00:03:54,900 --> 00:03:57,600 Rick Shaw: we're doing is making the black box of algorithms a 72 00:03:57,600 --> 00:04:02,040 Rick Shaw: glass box. And so we develop governance systems with this 73 00:04:02,040 --> 00:04:06,749 Rick Shaw: human ownership of the algorithmic output, because the framing here 74 00:04:06,750 --> 00:04:11,010 Rick Shaw: is that the algorithm is only ever an input into 75 00:04:11,010 --> 00:04:14,369 Rick Shaw: a decision- making process. A human always makes a decision. 76 00:04:14,370 --> 00:04:17,339 Rick Shaw: And we are doing a lot of work in that area of bringing 77 00:04:17,339 --> 00:04:20,370 Rick Shaw: transparency to the algorithmic processes. 78 00:04:21,210 --> 00:04:24,330 Sean Aylmer: Okay. Adam, I mean, you work with a lot of 79 00:04:24,330 --> 00:04:29,279 Sean Aylmer: very large Australian corporates. Do you have an example where data 80 00:04:29,279 --> 00:04:31,680 Sean Aylmer: has really made a difference in one way or another? 81 00:04:32,970 --> 00:04:35,880 Adam Driussi: If you think about something like a supermarket, like a 82 00:04:35,880 --> 00:04:38,970 Adam Driussi: Woolworths or any supermarket, frankly, when you walk down a 83 00:04:38,970 --> 00:04:43,678 Adam Driussi: supermarket aisle, there are all sorts of decisions that humans 84 00:04:43,678 --> 00:04:46,379 Adam Driussi: have had to make over the years around what things 85 00:04:46,379 --> 00:04:48,900 Adam Driussi: they put in the aisle. As an example, how much 86 00:04:48,900 --> 00:04:51,510 Adam Driussi: of a store do you dedicate to soft drink? And 87 00:04:51,510 --> 00:04:53,428 Adam Driussi: then within that soft drink, where do you place the 88 00:04:53,430 --> 00:04:55,979 Adam Driussi: soft drink, and do you put Coke Zero next to 89 00:04:55,980 --> 00:04:57,960 Adam Driussi: Diet Coke next to Diet Pepsi, or do you put 90 00:04:57,960 --> 00:04:59,520 Adam Driussi: all of Coke next to each other, all of Pepsi 91 00:04:59,520 --> 00:05:01,438 Adam Driussi: next to each other and so on? So you've got 92 00:05:01,440 --> 00:05:03,299 Adam Driussi: decisions around how much of a store you allocate to 93 00:05:03,299 --> 00:05:06,330 Adam Driussi: certain products. You've got decisions around how you price those 94 00:05:06,330 --> 00:05:09,480 Adam Driussi: products. So do you make a product 50% off? Do 95 00:05:09,480 --> 00:05:11,670 Adam Driussi: you make it two for one? What sort of promotions 96 00:05:11,670 --> 00:05:14,670 Adam Driussi: do you have? And they're massive decisions for retailers and 97 00:05:14,670 --> 00:05:18,479 Adam Driussi: suppliers. So just a simple thing like those promotions, I 98 00:05:18,480 --> 00:05:23,068 Adam Driussi: think retail and suppliers collectively invest about, I think it's about $ 6 billion a 99 00:05:23,070 --> 00:05:25,979 Adam Driussi: year just in a Woolworths in promotions. So there's really 100 00:05:25,980 --> 00:05:28,800 Adam Driussi: big decisions that humans make where, if you think about 101 00:05:28,800 --> 00:05:31,170 Adam Driussi: it, you've got a lot of data understanding how customers 102 00:05:31,170 --> 00:05:34,379 Adam Driussi: are actually interacting in individual stores and how they're buying 103 00:05:34,380 --> 00:05:37,139 Adam Driussi: those products, where you can use that data to better 104 00:05:37,139 --> 00:05:39,900 Adam Driussi: tailor the experience for customers, to make sure you've got 105 00:05:39,900 --> 00:05:43,770 Adam Driussi: the right products on the shelf so when they walk into a store, there's 106 00:05:43,770 --> 00:05:46,920 Adam Driussi: not their favorite item missing and so on. So you 107 00:05:46,920 --> 00:05:50,099 Adam Driussi: need forecasting algorithms to actually predict how much of every 108 00:05:50,099 --> 00:05:52,080 Adam Driussi: item you're going to sell every day, depending on what 109 00:05:52,080 --> 00:05:54,539 Adam Driussi: price you've set, so that you can deliver the right 110 00:05:54,540 --> 00:05:57,599 Adam Driussi: amount of stock to stores to get on the shelf 111 00:05:57,599 --> 00:06:00,059 Adam Driussi: and so on. And all of those things can be 112 00:06:00,059 --> 00:06:02,580 Adam Driussi: done better with data and analytics. And I would say 113 00:06:03,480 --> 00:06:05,640 Adam Driussi: that all of those things I just mentioned are problems 114 00:06:05,640 --> 00:06:07,919 Adam Driussi: that we work on with retailers around the world to 115 00:06:07,920 --> 00:06:11,339 Adam Driussi: help transform the way that they make sure they're getting 116 00:06:11,339 --> 00:06:13,770 Adam Driussi: the right products on shelves to customers when they need it. 117 00:06:14,549 --> 00:06:18,209 Sean Aylmer: Jonathan, examples where data has made a difference. 118 00:06:18,779 --> 00:06:22,260 Jonathan Cohen: During COVID, we did work with the semi- local health 119 00:06:22,260 --> 00:06:25,440 Jonathan Cohen: district in New South Wales Health who built the vaccination 120 00:06:25,440 --> 00:06:28,529 Jonathan Cohen: hub at Olympic Park, which administered something like half a 121 00:06:28,529 --> 00:06:31,379 Jonathan Cohen: million vaccines over a three- month period at a peak 122 00:06:31,380 --> 00:06:35,789 Jonathan Cohen: operating rate of about 10, 000 vaccines a day. So the challenge 123 00:06:35,789 --> 00:06:39,180 Jonathan Cohen: there was to set up some sort of data analytics 124 00:06:39,180 --> 00:06:42,810 Jonathan Cohen: framework so they can know what's going on. And we also developed 125 00:06:43,260 --> 00:06:46,140 Jonathan Cohen: what's called a digital twin simulation. So we had a 126 00:06:46,469 --> 00:06:50,129 Jonathan Cohen: simulation model of the center itself so that the operational 127 00:06:50,130 --> 00:06:53,639 Jonathan Cohen: team could look at questions like what happens if we 128 00:06:53,639 --> 00:06:56,220 Jonathan Cohen: change the number of pharmacists or the number of nurses 129 00:06:56,220 --> 00:06:59,460 Jonathan Cohen: or change the queue structure, and how can we improve 130 00:06:59,490 --> 00:07:03,210 Jonathan Cohen: throughput and reduce the length of queues and make the 131 00:07:03,210 --> 00:07:06,059 Jonathan Cohen: whole process more resilient? So that was really cool because 132 00:07:06,059 --> 00:07:08,549 Jonathan Cohen: it was very concrete. You can kind of see the 133 00:07:08,549 --> 00:07:12,090 Jonathan Cohen: changes. You're talking to operational people and having to do 134 00:07:12,090 --> 00:07:15,210 Jonathan Cohen: things very quickly. So it also highlighted how within a 135 00:07:15,210 --> 00:07:19,410 Jonathan Cohen: matter of weeks you can establish an analytics framework that 136 00:07:19,410 --> 00:07:23,040 Jonathan Cohen: delivers actual value to an issue, because you've got sort 137 00:07:23,040 --> 00:07:25,290 Jonathan Cohen: of a razor focus on the thing you need to 138 00:07:25,290 --> 00:07:27,630 Jonathan Cohen: do and a very limited timeframe to do it in. 139 00:07:28,020 --> 00:07:31,739 Jonathan Cohen: I've got another example, somewhat more abstract, but we do 140 00:07:31,740 --> 00:07:34,440 Jonathan Cohen: a lot of work in the social services sector, so 141 00:07:34,440 --> 00:07:38,970 Jonathan Cohen: government welfare systems. And sort of the first global example 142 00:07:38,970 --> 00:07:42,270 Jonathan Cohen: of that was 10 or 15 years ago, we worked with the 143 00:07:42,270 --> 00:07:45,090 Jonathan Cohen: New Zealand government to restructure the way in which they 144 00:07:45,540 --> 00:07:48,870 Jonathan Cohen: administer social welfare. So historically, it was based on annual 145 00:07:48,870 --> 00:07:51,960 Jonathan Cohen: appropriations. And the question was, well, can we change that 146 00:07:51,960 --> 00:07:54,090 Jonathan Cohen: to say, well, let's work out who has the greatest 147 00:07:54,090 --> 00:07:57,179 Jonathan Cohen: need over their lifetime and where we can put the 148 00:07:57,179 --> 00:08:00,599 Jonathan Cohen: money to improve outcomes the most over a long period? 149 00:08:00,599 --> 00:08:03,000 Jonathan Cohen: So running things much more in a sense how an 150 00:08:03,000 --> 00:08:06,960 Jonathan Cohen: insurer would run their business. And so we've worked with 151 00:08:06,960 --> 00:08:11,670 Jonathan Cohen: the New Zealand government for the last 10 or 15 years on 152 00:08:12,240 --> 00:08:15,690 Jonathan Cohen: building lots of statistical models that fit within an actuarial 153 00:08:15,690 --> 00:08:19,740 Jonathan Cohen: framework that allow them to project out 30, 40 years of 154 00:08:19,830 --> 00:08:23,459 Jonathan Cohen: welfare payments and welfare structures and work out how to 155 00:08:23,459 --> 00:08:27,870 Jonathan Cohen: redistribute and structure those benefits. And that concept and work 156 00:08:28,440 --> 00:08:31,260 Jonathan Cohen: has spread from there and it's been implemented more broadly. 157 00:08:31,260 --> 00:08:33,988 Jonathan Cohen: So Australia is going down a similar path. Issues such 158 00:08:33,990 --> 00:08:37,350 Jonathan Cohen: as social welfare, but also broader social issues, for example, 159 00:08:37,590 --> 00:08:41,759 Jonathan Cohen: how to improve outcomes and lives of vulnerable children who 160 00:08:41,760 --> 00:08:42,810 Jonathan Cohen: go through foster care. 161 00:08:43,530 --> 00:08:52,410 Sean Aylmer: Okay, we'll be back with more in a minute. My 162 00:08:52,410 --> 00:08:56,370 Sean Aylmer: guests this morning ar Quantium's Adam Driussi, Jonathan Cohen from 163 00:08:56,370 --> 00:09:00,779 Sean Aylmer: Taylor Fry and Deloitte's Rick Shaw. I'm now going to ask 164 00:09:00,779 --> 00:09:03,270 Sean Aylmer: each of you, this is our final question, each of 165 00:09:03,270 --> 00:09:06,660 Sean Aylmer: you, what you think is next in data and analytics. 166 00:09:06,660 --> 00:09:09,540 Sean Aylmer: Where will we be in five years or 10 years 167 00:09:10,050 --> 00:09:15,690 Sean Aylmer: as a society because of how data and analytics have been 168 00:09:15,719 --> 00:09:19,828 Sean Aylmer: used in society? Perhaps not the easiest question to answer 169 00:09:19,950 --> 00:09:22,110 Sean Aylmer: in a sentence, I do get that. But I'm going 170 00:09:22,110 --> 00:09:23,338 Sean Aylmer: to go to you first, Jonathan. 171 00:09:23,910 --> 00:09:25,828 Jonathan Cohen: I think there's obviously a lot of buzz around what's 172 00:09:25,830 --> 00:09:29,098 Jonathan Cohen: called generative AI, of which ChatGPT is an example. I 173 00:09:29,099 --> 00:09:32,400 Jonathan Cohen: think we'll see a lot of growth in systems that 174 00:09:32,400 --> 00:09:35,129 Jonathan Cohen: help people in the creative process. These have existed for 175 00:09:35,129 --> 00:09:38,429 Jonathan Cohen: quite a while, but haven't been that known about. That'll 176 00:09:38,429 --> 00:09:41,610 Jonathan Cohen: expand. I think there'll be a lot of technical advances, but 177 00:09:41,610 --> 00:09:44,040 Jonathan Cohen: I think one trend that we'll see, particularly over the 178 00:09:44,040 --> 00:09:47,519 Jonathan Cohen: next three to five years, is an increase in regulation 179 00:09:47,520 --> 00:09:49,469 Jonathan Cohen: and guidance on the usage of AI. So there's a number 180 00:09:49,469 --> 00:09:55,050 Jonathan Cohen: of international developments around setting standards and guidance around the 181 00:09:55,050 --> 00:09:59,250 Jonathan Cohen: use of AI. There's local developments on several legislative and 182 00:09:59,250 --> 00:10:03,000 Jonathan Cohen: regulatory fronts to start applying standards there. So I think 183 00:10:03,000 --> 00:10:06,870 Jonathan Cohen: that'll be an increasing trend, particularly as consumers become more 184 00:10:06,870 --> 00:10:10,859 Jonathan Cohen: aware of the way in which data and algorithms are 185 00:10:10,859 --> 00:10:12,510 Jonathan Cohen: affecting decisions that impact their life. 186 00:10:13,320 --> 00:10:13,650 Sean Aylmer: Adam? 187 00:10:14,309 --> 00:10:17,340 Adam Driussi: Yeah, I agree with Jonathan there in terms of the 188 00:10:17,340 --> 00:10:21,030 Adam Driussi: increase in consumer expectations and governance around the space. I 189 00:10:21,030 --> 00:10:24,059 Adam Driussi: think that's only going to increase over the next, say, 190 00:10:24,059 --> 00:10:26,700 Adam Driussi: five years. I think if I add to what Jonathan 191 00:10:26,700 --> 00:10:29,850 Adam Driussi: said, because again, I agree broadly with what he was saying there, I 192 00:10:30,570 --> 00:10:32,640 Adam Driussi: also just think you're going to see a more mainstream 193 00:10:32,640 --> 00:10:35,850 Adam Driussi: adoption of analytics and data. So like I said, I 194 00:10:35,850 --> 00:10:38,939 Adam Driussi: think today you're talking about some of the bigger players 195 00:10:38,970 --> 00:10:41,639 Adam Driussi: who are really using analytics at scale to make decisions. 196 00:10:41,639 --> 00:10:43,290 Adam Driussi: So it's a little bit of a case of the 197 00:10:43,290 --> 00:10:45,689 Adam Driussi: haves and the have- nots today, I think. And I 198 00:10:45,690 --> 00:10:49,410 Adam Driussi: think over the next five years, you're going to see increasing almost all 199 00:10:49,410 --> 00:10:53,790 Adam Driussi: companies starting to make much more decisions using data and 200 00:10:53,790 --> 00:10:57,809 Adam Driussi: analytics. Now, obviously, the technology, the range of data that's 201 00:10:57,809 --> 00:11:00,059 Adam Driussi: being captured and the technologies that were being used are 202 00:11:00,059 --> 00:11:03,599 Adam Driussi: only going to explode. They're going to continue to explode 203 00:11:03,599 --> 00:11:07,228 Adam Driussi: this. We could talk for ages in terms of whether 204 00:11:07,230 --> 00:11:10,050 Adam Driussi: it's computer vision, whether it's a smart trolley in a 205 00:11:10,380 --> 00:11:13,290 Adam Driussi: supermarket or whatever it might be in different industries. I 206 00:11:13,290 --> 00:11:16,980 Adam Driussi: think we're going to continue to see explosions in the amount of data, 207 00:11:16,980 --> 00:11:21,119 Adam Driussi: the technology to use that data. But I'm really interested 208 00:11:21,120 --> 00:11:25,949 Adam Driussi: to see the broader adoption of data and analytics across companies. 209 00:11:26,700 --> 00:11:27,030 Sean Aylmer: Rick. 210 00:11:27,900 --> 00:11:31,439 Rick Shaw: I think that if we as a society play our 211 00:11:31,440 --> 00:11:37,078 Rick Shaw: cards right, the data and computable systems will empower the 212 00:11:37,080 --> 00:11:41,519 Rick Shaw: consumer and the individual, and we could start sharing wealth 213 00:11:41,520 --> 00:11:45,179 Rick Shaw: in a lot more equitable ways. I think these are 214 00:11:45,179 --> 00:11:47,400 Rick Shaw: great tools that can be used for the benefit of 215 00:11:47,400 --> 00:11:50,580 Rick Shaw: society, and I think we've got a bit of imbalance 216 00:11:50,580 --> 00:11:54,689 Rick Shaw: in society at the moment around the beneficiaries being the 217 00:11:54,690 --> 00:11:59,369 Rick Shaw: large institutions and the governments not acting appropriately and misusing 218 00:11:59,369 --> 00:12:02,730 Rick Shaw: tools. And I think as a society, the power of 219 00:12:02,730 --> 00:12:08,429 Rick Shaw: these tools, they enable individuals to collectivize and not rely 220 00:12:08,429 --> 00:12:12,420 Rick Shaw: on the state so much. And I'm quite excited about 221 00:12:12,420 --> 00:12:16,500 Rick Shaw: that sort of rising up of moral agents, the facilitation 222 00:12:16,500 --> 00:12:21,030 Rick Shaw: by the web and the internet of collective structures to 223 00:12:21,030 --> 00:12:25,170 Rick Shaw: create a demand ownership of your own data, for example, 224 00:12:25,530 --> 00:12:27,478 Rick Shaw: in a way that is not abusive, as I think 225 00:12:27,480 --> 00:12:28,649 Rick Shaw: it has been in the past. 226 00:12:29,219 --> 00:12:30,809 Sean Aylmer: Look, I said that was my last question but I 227 00:12:30,809 --> 00:12:33,930 Sean Aylmer: do have one more. What are your last thoughts about 228 00:12:34,260 --> 00:12:40,530 Sean Aylmer: how actuaries and data scientists play a role in what we are 229 00:12:40,650 --> 00:12:43,860 Sean Aylmer: talking about going forward? Rick, let's start with you. 230 00:12:44,790 --> 00:12:48,719 Rick Shaw: I think that as I talked about the two cultures 231 00:12:48,719 --> 00:12:52,020 Rick Shaw: problem that we have, we have these great people who 232 00:12:52,020 --> 00:12:55,410 Rick Shaw: are building algorithms which appear to be black boxes to 233 00:12:55,410 --> 00:12:59,189 Rick Shaw: the boards' executives and the ethicists, who are often academical 234 00:12:59,190 --> 00:13:03,001 Rick Shaw: lawyers or something. I think the role of the professional (inaudible) 235 00:13:03,001 --> 00:13:06,029 Rick Shaw: , the engineers and the actuaries, is to have a 236 00:13:06,030 --> 00:13:10,170 Rick Shaw: foot in both the technical camp and the governance and 237 00:13:10,170 --> 00:13:14,670 Rick Shaw: ethics camp, and for society to rely on professions such 238 00:13:14,670 --> 00:13:21,540 Rick Shaw: as ours to make sure that the algorithmic output are 239 00:13:21,540 --> 00:13:27,389 Rick Shaw: consistent with corporate and social objectives. And that's the sort 240 00:13:27,389 --> 00:13:31,770 Rick Shaw: of business offering that we've developed. And I think it's that 241 00:13:31,770 --> 00:13:36,540 Rick Shaw: professional responsibility is our role as actuaries, subject to harsh 242 00:13:36,540 --> 00:13:40,889 Rick Shaw: professional standards, to stand somewhat separate from the outcomes of our 243 00:13:40,889 --> 00:13:43,619 Rick Shaw: work, is how I think society should handle this issue. 244 00:13:43,620 --> 00:13:45,838 Rick Shaw: It's too complex for governments and regulations. 245 00:13:46,769 --> 00:13:47,939 Sean Aylmer: Jonathan, what do you think? 246 00:13:48,449 --> 00:13:52,410 Jonathan Cohen: Yeah, I'd say actuaries have an inherent focus on becoming trusted business 247 00:13:52,410 --> 00:13:56,099 Jonathan Cohen: advisors. And so well- placed to help navigate both the 248 00:13:56,099 --> 00:13:59,519 Jonathan Cohen: huge potential benefits of AI, but also identify and manage 249 00:13:59,520 --> 00:14:00,929 Jonathan Cohen: the risks associated with that. 250 00:14:02,160 --> 00:14:03,270 Sean Aylmer: And Adam, the last word. 251 00:14:04,619 --> 00:14:06,420 Adam Driussi: A little bit of a funny situation in that I'm an 252 00:14:06,420 --> 00:14:08,280 Adam Driussi: actuary by background, but I run what I think of 253 00:14:08,280 --> 00:14:11,490 Adam Driussi: as a data science company. And so we think about 254 00:14:11,610 --> 00:14:15,000 Adam Driussi: this as a data science problem that, in my opinion, 255 00:14:15,000 --> 00:14:18,389 Adam Driussi: actuaries just make very good data scientists. So when we 256 00:14:18,389 --> 00:14:20,670 Adam Driussi: go about hiring data scientists every year, to give you an 257 00:14:20,670 --> 00:14:24,059 Adam Driussi: idea, we just hired about 150 graduates who start in 258 00:14:24,059 --> 00:14:27,600 Adam Driussi: the next few weeks. A large proportion of those are actuaries, probably 259 00:14:27,600 --> 00:14:30,600 Adam Driussi: about a third of them are actuarial graduates. And that's 260 00:14:30,600 --> 00:14:33,929 Adam Driussi: because we think they bring the right combination of technical 261 00:14:33,929 --> 00:14:38,250 Adam Driussi: skillset, strategic mindset, financial acumen, and so on. And so 262 00:14:38,250 --> 00:14:42,629 Adam Driussi: there's something about the actuarial course that attracts the right 263 00:14:42,629 --> 00:14:45,360 Adam Driussi: people who have got that combination of skills and also the 264 00:14:45,360 --> 00:14:49,560 Adam Driussi: education that they get. So I actually think it's a data science challenge 265 00:14:49,560 --> 00:14:51,990 Adam Driussi: where actuaries have a role to play. Now, whether in 266 00:14:51,990 --> 00:14:55,170 Adam Driussi: the future, the Actuaries Institute can play a broader role 267 00:14:55,170 --> 00:14:57,750 Adam Driussi: to think about how do you, from a governance point of 268 00:14:57,750 --> 00:15:00,659 Adam Driussi: view, introduce more protections and so on remains to be 269 00:15:00,660 --> 00:15:03,929 Adam Driussi: seen. But today, that's how I see actuaries fitting into 270 00:15:04,019 --> 00:15:04,619 Adam Driussi: the piece. 271 00:15:05,549 --> 00:15:09,570 Sean Aylmer: Whatever, it's a very exciting future for actuaries, I suspect. Adam 272 00:15:09,570 --> 00:15:12,000 Sean Aylmer: Driussi, co- founder and CEO of Quantium, thank you for 273 00:15:12,000 --> 00:15:12,779 Sean Aylmer: your time today. 274 00:15:13,349 --> 00:15:14,190 Adam Driussi: No problem. Thanks, Sean. 275 00:15:14,520 --> 00:15:17,699 Sean Aylmer: Jonathan Cohen, principal at Taylor Fry. Thank you for joining us. 276 00:15:18,090 --> 00:15:18,840 Jonathan Cohen: Thanks very much, Sean. 277 00:15:19,260 --> 00:15:22,170 Sean Aylmer: And Deloitte partner Rick Shaw. Thanks for your time this morning. 278 00:15:22,830 --> 00:15:23,400 Rick Shaw: Thanks a lot. 279 00:15:23,850 --> 00:15:26,610 Sean Aylmer: Adam, Jonathan and Rick joined me today as part of 280 00:15:26,610 --> 00:15:29,400 Sean Aylmer: the Do Data Better Campaign, a supporter of this podcast. 281 00:15:29,580 --> 00:15:34,889 Sean Aylmer: For more information, head to dodatabetter, all one word, dodatabetter. com. au. 282 00:15:35,129 --> 00:15:37,140 Sean Aylmer: This is The Fear and Greed Daily Interview. Join us 283 00:15:37,140 --> 00:15:39,450 Sean Aylmer: every morning for the full episode of Fear and Greed, 284 00:15:39,450 --> 00:15:43,320 Sean Aylmer: Australia's most popular business podcast. I'm Sean Aylmer. Enjoy your day.