1 00:00:01,680 --> 00:00:05,000 Speaker 1: Global business news twenty four hours a day at Bloomberg 2 00:00:05,040 --> 00:00:08,119 Speaker 1: dot Com, the radio, plus Globolo and on your radio. 3 00:00:08,440 --> 00:00:12,400 Speaker 1: This is a Bloomberg Business Flash from Bloomberg World Headquarters. 4 00:00:12,400 --> 00:00:15,000 Speaker 1: I'm Katherine Cowdery and Bloomberg. Taking Stock is brought to 5 00:00:15,000 --> 00:00:18,560 Speaker 1: you by Bentley University. What to rebooting America's oldest ski 6 00:00:18,600 --> 00:00:21,480 Speaker 1: shop and crunching numbers at visciperent Have in Common and 7 00:00:21,720 --> 00:00:25,279 Speaker 1: MBA from Bentley University that prepares graduates to innovate and 8 00:00:25,400 --> 00:00:30,040 Speaker 1: lead Because business is everywhere, prepare here. Gains and technology 9 00:00:30,040 --> 00:00:33,040 Speaker 1: shares are being offset by losses and energy producers today 10 00:00:33,280 --> 00:00:36,600 Speaker 1: as the stock market fluctuates between gains and losses. Crude 11 00:00:36,640 --> 00:00:39,880 Speaker 1: oil is declining, Canadian producers are working to resume operations 12 00:00:39,920 --> 00:00:43,480 Speaker 1: and around continued to raise exports. Apple is advancing leading 13 00:00:43,520 --> 00:00:46,680 Speaker 1: technology stocks higher, which like the markets every fifteen minutes 14 00:00:46,680 --> 00:00:49,640 Speaker 1: throughout the trading day. DAL Industrial lverage is currently up 15 00:00:49,680 --> 00:00:52,280 Speaker 1: thirty seven points to tens of a percent at seventeen 16 00:00:52,320 --> 00:00:55,400 Speaker 1: thousand five D thirty seven, SMP five founded up at 17 00:00:55,440 --> 00:00:58,200 Speaker 1: one point to two thousand fifty three. The NASTAC is 18 00:00:58,280 --> 00:01:02,639 Speaker 1: up eleven points a quarter percent. Rating last Texas Intermediate 19 00:01:02,640 --> 00:01:05,080 Speaker 1: crude oil down twenty seven cents of barrel six tenths 20 00:01:05,120 --> 00:01:08,040 Speaker 1: of a percent to forty fourteen s about gold is 21 00:01:08,080 --> 00:01:11,679 Speaker 1: down a dollar twenty announced at seventy and the tenure 22 00:01:11,720 --> 00:01:13,760 Speaker 1: treasury is up one thirty second with the yield of 23 00:01:13,800 --> 00:01:17,479 Speaker 1: one point eight three percent. And that's a Bloomberg Business 24 00:01:17,480 --> 00:01:22,880 Speaker 1: Flash is Taking Stock with Kathleen Hayes and Prim Fox 25 00:01:23,000 --> 00:01:28,720 Speaker 1: on Bloomberg Radio, continuing a very special live broadcast today. 26 00:01:28,880 --> 00:01:32,920 Speaker 1: We are at Dove Mountain near Tucson, Arizona, the Ritz 27 00:01:33,040 --> 00:01:36,400 Speaker 1: Carlton here in the midst of the Tortalita Mountain range. 28 00:01:36,560 --> 00:01:39,839 Speaker 1: We are broadcasting live it invested sixteen. It's the power 29 00:01:39,840 --> 00:01:43,160 Speaker 1: of Big Ideas of bn Y Melon client conference. Joining 30 00:01:43,200 --> 00:01:46,160 Speaker 1: us now is the person who kicked off the conference 31 00:01:46,240 --> 00:01:51,320 Speaker 1: last night down by the pool with little burning crucibles 32 00:01:51,360 --> 00:01:54,760 Speaker 1: in the water and a beautiful Arizona evening. And I'm 33 00:01:54,760 --> 00:01:57,919 Speaker 1: referring to Samir Pandery. He's executive vice president and global 34 00:01:58,000 --> 00:02:01,360 Speaker 1: CEO of Assets Servicing at b in why Non Semir, 35 00:02:01,920 --> 00:02:04,640 Speaker 1: Welcome to Taking Stock and thank you for inviting us 36 00:02:04,640 --> 00:02:06,720 Speaker 1: to the confidence. Thank you for having us on your program. 37 00:02:07,400 --> 00:02:11,160 Speaker 1: Uh the uh the challenges that your client's face and 38 00:02:11,240 --> 00:02:12,960 Speaker 1: of course you goose is an intimate group about two 39 00:02:13,400 --> 00:02:16,880 Speaker 1: people from across the country and around the world. The 40 00:02:17,000 --> 00:02:20,800 Speaker 1: big questions what what? What is the biggest one at 41 00:02:20,800 --> 00:02:23,799 Speaker 1: the top of the list. When you're looking particularly at 42 00:02:24,520 --> 00:02:27,600 Speaker 1: UH some of the regulatory changes that have occurred lately, 43 00:02:27,639 --> 00:02:30,280 Speaker 1: it seems that that no matter whether you're a servicer 44 00:02:30,600 --> 00:02:34,919 Speaker 1: or a money manager, everybody is still grappling with some 45 00:02:35,000 --> 00:02:37,840 Speaker 1: of the big changes. Yeah, you know, I think fundamentally, 46 00:02:38,040 --> 00:02:40,600 Speaker 1: for example, if you're a money manager or if you're 47 00:02:40,600 --> 00:02:43,239 Speaker 1: a money investor, what you really want to do is 48 00:02:43,320 --> 00:02:46,440 Speaker 1: focus on managing money. So that means all of the 49 00:02:46,560 --> 00:02:50,480 Speaker 1: other things, the operations, the technology, those are things that 50 00:02:50,560 --> 00:02:53,679 Speaker 1: are not a creative to your core function. So I 51 00:02:53,760 --> 00:02:58,440 Speaker 1: think what regulation does. It adds responsibilities to your non 52 00:02:58,560 --> 00:03:01,520 Speaker 1: core activities, and I think that is a huge burden. 53 00:03:01,639 --> 00:03:03,560 Speaker 1: You know, there's non core activity. Like my job if 54 00:03:03,600 --> 00:03:05,720 Speaker 1: you're my client, is to make sure that you're invested 55 00:03:05,720 --> 00:03:09,000 Speaker 1: in the right securities, the right assets, just to save 56 00:03:09,080 --> 00:03:11,840 Speaker 1: money and make money exactly. And so if you have 57 00:03:12,080 --> 00:03:14,680 Speaker 1: to distract or you know, spend part of your time 58 00:03:15,040 --> 00:03:18,920 Speaker 1: doing other things like worrying about cybersecurity or worrying about 59 00:03:19,000 --> 00:03:23,520 Speaker 1: your technology architecture or how you're actually um robust. From 60 00:03:23,520 --> 00:03:27,919 Speaker 1: an operating infrastructure perspective, it's very difficult UM and it's 61 00:03:28,040 --> 00:03:30,280 Speaker 1: very expensive. So again, I think one of the things 62 00:03:30,360 --> 00:03:33,320 Speaker 1: that people are looking for or solutions where they can 63 00:03:33,400 --> 00:03:36,520 Speaker 1: take advantage of scale, they can take advantage of people 64 00:03:36,640 --> 00:03:39,119 Speaker 1: that are that can do this in a professional way 65 00:03:39,920 --> 00:03:42,920 Speaker 1: so that they can really focus on their core business activities. 66 00:03:43,720 --> 00:03:46,560 Speaker 1: How can the customer also take advantage of all the 67 00:03:46,720 --> 00:03:51,640 Speaker 1: data that you have? Uh, the idea being that it's 68 00:03:51,680 --> 00:03:57,400 Speaker 1: no longer playing vanilla investing stocks, bonds, cash, it's everything alternative, 69 00:03:57,480 --> 00:04:00,720 Speaker 1: non cord less on. You built a model of yes. 70 00:04:00,840 --> 00:04:04,200 Speaker 1: So you know, we have a digital ecosystem called Nexon, 71 00:04:04,600 --> 00:04:08,360 Speaker 1: which is really, i think technologically ten years ahead of 72 00:04:08,560 --> 00:04:11,640 Speaker 1: where the industry is and really where we're driving the 73 00:04:11,720 --> 00:04:15,920 Speaker 1: transformation of this company. And the digital ecosystem is based 74 00:04:16,000 --> 00:04:20,280 Speaker 1: on a couple of fundamental principles. We collect large amounts 75 00:04:20,320 --> 00:04:23,280 Speaker 1: of data, so we for example, collect about one point 76 00:04:23,360 --> 00:04:27,400 Speaker 1: four billion transactions on a daily basis, because the cost 77 00:04:27,480 --> 00:04:30,920 Speaker 1: of story that data is significantly cheaper now compared to 78 00:04:31,000 --> 00:04:33,560 Speaker 1: a decade ago. But then the second part, which is 79 00:04:33,600 --> 00:04:36,800 Speaker 1: really the harder part, is taking the data that is 80 00:04:36,920 --> 00:04:41,400 Speaker 1: important and relevant and making it consumable in a way 81 00:04:41,760 --> 00:04:44,960 Speaker 1: that's useful to the client. So again, if I can't 82 00:04:45,480 --> 00:04:49,320 Speaker 1: take that and translated into a form that's usable, it's 83 00:04:49,520 --> 00:04:51,680 Speaker 1: um you know. So that's really the trick and that's 84 00:04:51,720 --> 00:04:54,880 Speaker 1: the challenge. So give us a specific example of a 85 00:04:55,000 --> 00:04:57,920 Speaker 1: batch of data compiled over the years by B and 86 00:04:58,040 --> 00:05:00,800 Speaker 1: Y melon that relates to something that be very specific 87 00:05:01,120 --> 00:05:06,360 Speaker 1: that before you created these technologies like next in and 88 00:05:06,440 --> 00:05:08,560 Speaker 1: digital polls, that it was just kind of a lot 89 00:05:08,600 --> 00:05:10,680 Speaker 1: of data out there, and now you can say, look specifically, 90 00:05:10,720 --> 00:05:12,600 Speaker 1: here's something someone can learn. What's one example of that. 91 00:05:12,720 --> 00:05:15,600 Speaker 1: So one example is if you look at our pension universe. 92 00:05:15,960 --> 00:05:18,400 Speaker 1: We have about three and a half trillion dollars worth 93 00:05:18,440 --> 00:05:22,520 Speaker 1: of pension assets that we administer. About a decade ago, 94 00:05:22,800 --> 00:05:26,800 Speaker 1: three percent of those assets were invested in alternatives. Today 95 00:05:26,920 --> 00:05:30,880 Speaker 1: that number is two so hedge funds, private equity, real estate. 96 00:05:31,320 --> 00:05:35,320 Speaker 1: So as these flows happen into these real assets, you know, 97 00:05:35,520 --> 00:05:38,839 Speaker 1: clients would like to get that information on real time basis, 98 00:05:39,279 --> 00:05:43,040 Speaker 1: so we provide them the data on the huge asset flows, 99 00:05:43,080 --> 00:05:46,640 Speaker 1: the transformations that are happening in real time so that 100 00:05:46,760 --> 00:05:49,680 Speaker 1: they can actually look at where they are invested relative 101 00:05:49,720 --> 00:05:52,320 Speaker 1: to their peer group and really decide if that's really 102 00:05:52,400 --> 00:05:55,000 Speaker 1: what they want to be in terms of the risk spectrum. Uh. 103 00:05:55,160 --> 00:05:57,880 Speaker 1: And by the way, this was not information that we 104 00:05:58,000 --> 00:06:00,320 Speaker 1: were able to provide. Let's say even five to ten 105 00:06:00,400 --> 00:06:03,640 Speaker 1: years ago. We had the data, but was not consumable. 106 00:06:04,040 --> 00:06:06,600 Speaker 1: Now I think with the technology you can actually consume 107 00:06:06,680 --> 00:06:09,279 Speaker 1: that data in real time. You also get the issue 108 00:06:09,480 --> 00:06:15,320 Speaker 1: of many investors are paying for alpha, but they're really 109 00:06:15,520 --> 00:06:19,240 Speaker 1: just getting beta. It isn't that kind of also going 110 00:06:19,279 --> 00:06:22,040 Speaker 1: to be a look into what you're paying for and 111 00:06:22,120 --> 00:06:26,320 Speaker 1: what you're getting in terms of the holistic universe of investments. Yes. 112 00:06:26,440 --> 00:06:28,640 Speaker 1: So one of the things that our data allows. So 113 00:06:28,760 --> 00:06:31,400 Speaker 1: if you have, for example, as an investor our clients 114 00:06:31,800 --> 00:06:35,600 Speaker 1: they might be using multiple managers, we can actually show 115 00:06:35,680 --> 00:06:39,200 Speaker 1: them not only what their manager is delivering in terms 116 00:06:39,240 --> 00:06:42,480 Speaker 1: of alpha, but what are they delivering relative to their 117 00:06:42,600 --> 00:06:46,360 Speaker 1: peer group. So it's very clear, very transparent in terms 118 00:06:46,400 --> 00:06:49,040 Speaker 1: of what the performances. And again we have a g 119 00:06:49,240 --> 00:06:51,840 Speaker 1: r S which is called a Global Risk services um 120 00:06:52,160 --> 00:06:56,480 Speaker 1: business which is really predicated on giving that comparisons so 121 00:06:56,680 --> 00:06:59,520 Speaker 1: that as an investor you can have a really good, 122 00:06:59,720 --> 00:07:05,280 Speaker 1: solid foundation to make these comparisons the fintech firms, disruptors, innovators. 123 00:07:05,400 --> 00:07:08,200 Speaker 1: Is that a threat to be in White Milon and 124 00:07:08,279 --> 00:07:10,240 Speaker 1: its model or is it an opportunity In some sense, 125 00:07:10,280 --> 00:07:12,240 Speaker 1: I think it's a threat if we did nothing about it. 126 00:07:12,600 --> 00:07:16,160 Speaker 1: We are obviously very very engaged UM and we're also 127 00:07:16,320 --> 00:07:19,440 Speaker 1: trying to think about how we should perhaps disrupt our 128 00:07:19,520 --> 00:07:22,480 Speaker 1: own models. So, you know, as an example, we are 129 00:07:22,600 --> 00:07:25,600 Speaker 1: looking at the use of technology. We're using it the 130 00:07:26,160 --> 00:07:30,600 Speaker 1: use of robotics, UH in terms of our operational processes 131 00:07:30,920 --> 00:07:33,320 Speaker 1: to really see if there is a better way to 132 00:07:33,480 --> 00:07:36,440 Speaker 1: actually um, you know, create the end product you know 133 00:07:36,520 --> 00:07:41,040 Speaker 1: that the clients. So robots can really do what I 134 00:07:41,080 --> 00:07:43,640 Speaker 1: would call the mind numbing things that are you know, 135 00:07:43,760 --> 00:07:47,320 Speaker 1: task oriented but rule based that you can actually program 136 00:07:47,560 --> 00:07:49,680 Speaker 1: that you don't need to have a person do, so 137 00:07:49,760 --> 00:07:52,360 Speaker 1: you can actually have the person focus on the exceptions 138 00:07:52,640 --> 00:07:55,880 Speaker 1: and the higher value added things that require some kind 139 00:07:55,880 --> 00:07:59,920 Speaker 1: of a cognitive ability to make a decision and using 140 00:08:00,760 --> 00:08:03,920 Speaker 1: all of this data. One example I was given this 141 00:08:04,160 --> 00:08:07,680 Speaker 1: morning had to do with emerging markets. Investing in a 142 00:08:08,040 --> 00:08:12,640 Speaker 1: non market correlated fund that specializes in emerging markets looking 143 00:08:12,720 --> 00:08:15,920 Speaker 1: at capital flows into and out of emerging markets and 144 00:08:16,000 --> 00:08:19,200 Speaker 1: trying to make a decision to see whether there's a relationship, right, 145 00:08:19,200 --> 00:08:21,800 Speaker 1: because you've got the emerging market fund, but then you've 146 00:08:21,800 --> 00:08:25,440 Speaker 1: got the capital flows. What they found was almost counterintuitive. 147 00:08:25,880 --> 00:08:28,640 Speaker 1: As the flows went into emerging markets, this fund actually 148 00:08:28,680 --> 00:08:33,240 Speaker 1: did poorly. Why because everyone was benchmarking against the m 149 00:08:33,400 --> 00:08:36,960 Speaker 1: s c I and yet the investments were not part 150 00:08:37,000 --> 00:08:39,680 Speaker 1: of the MSCI index. So this is going to allow 151 00:08:39,800 --> 00:08:42,920 Speaker 1: not only the investor, but also the people that manage 152 00:08:42,960 --> 00:08:46,200 Speaker 1: the money to differentiate themselves in a new way. Yeah, 153 00:08:46,240 --> 00:08:49,920 Speaker 1: I think, you know, the the data is the issue here, right, 154 00:08:50,040 --> 00:08:53,199 Speaker 1: because you know, when you have metadata that can go 155 00:08:53,559 --> 00:08:57,079 Speaker 1: at a much deeper level, you can drive analytics that 156 00:08:57,200 --> 00:08:59,960 Speaker 1: you actually couldn't do before. So and again I think 157 00:09:00,080 --> 00:09:02,439 Speaker 1: this is an example where if you actually have data 158 00:09:02,520 --> 00:09:07,040 Speaker 1: at the most underlying, basic transactional level, where you see 159 00:09:07,160 --> 00:09:09,520 Speaker 1: the flows going in and out, and if you can 160 00:09:09,600 --> 00:09:13,800 Speaker 1: digitize that and make that consumable, you will have information 161 00:09:14,240 --> 00:09:18,160 Speaker 1: that's not aggregated, that's general and non specific. But you 162 00:09:18,200 --> 00:09:20,720 Speaker 1: know the contrary. So I think that's really the power 163 00:09:20,760 --> 00:09:23,319 Speaker 1: of technology and data which is really I think, you know, 164 00:09:23,480 --> 00:09:27,160 Speaker 1: driving decision making now very differently than even just five 165 00:09:27,240 --> 00:09:30,880 Speaker 1: ten years ago. What's the challenge for individuals who worked 166 00:09:30,880 --> 00:09:33,199 Speaker 1: in this industry? You have a very interesting background. You 167 00:09:33,720 --> 00:09:36,440 Speaker 1: you start out in chemical engineering and got your NBA 168 00:09:36,480 --> 00:09:38,559 Speaker 1: and finance, and you worked at JP Morgan and you've 169 00:09:38,600 --> 00:09:41,760 Speaker 1: worked on the trust side at being my Melon. You're 170 00:09:41,840 --> 00:09:44,920 Speaker 1: involved with some of the emerging market issues. What is it? 171 00:09:45,000 --> 00:09:47,120 Speaker 1: What is it? What? What? What do I need to know? What? 172 00:09:47,360 --> 00:09:50,240 Speaker 1: What is the challenge for for the professional in this industry? Now? 173 00:09:51,040 --> 00:09:53,679 Speaker 1: I think in UM in my business and I have 174 00:09:54,160 --> 00:09:56,760 Speaker 1: some of this background. UM. I think there's three things. 175 00:09:56,880 --> 00:09:59,440 Speaker 1: You know, we are a very global organization. I lived 176 00:09:59,480 --> 00:10:02,760 Speaker 1: abroad for over a decade UH in Asia and Europe. 177 00:10:03,000 --> 00:10:06,080 Speaker 1: I think, you know, having that global perspective is absolutely 178 00:10:06,160 --> 00:10:09,319 Speaker 1: critical if you want a problem solving this industry. I 179 00:10:09,400 --> 00:10:11,840 Speaker 1: think the second thing you have to have the ability 180 00:10:11,920 --> 00:10:14,800 Speaker 1: to deal with change and drive the change in the 181 00:10:14,960 --> 00:10:18,080 Speaker 1: industry as opposed to letting that happen to you. Having 182 00:10:18,160 --> 00:10:21,599 Speaker 1: great change management skills I think are really important. And 183 00:10:21,679 --> 00:10:23,439 Speaker 1: I think the last thing is you have to be 184 00:10:23,559 --> 00:10:27,680 Speaker 1: able to disrupt you know, traditional mental models and business 185 00:10:27,760 --> 00:10:32,720 Speaker 1: models and really come up collaboratively working in different ways 186 00:10:33,000 --> 00:10:37,480 Speaker 1: with your clients, with your regulators, to solve problems that 187 00:10:37,640 --> 00:10:40,160 Speaker 1: you couldn't solve before. So again, I think having that 188 00:10:40,280 --> 00:10:42,480 Speaker 1: sort of open environment in terms of how you solve 189 00:10:42,559 --> 00:10:45,200 Speaker 1: problems is going to be really important. So I would 190 00:10:45,200 --> 00:10:49,359 Speaker 1: say those things are probably really important globality, change management 191 00:10:49,640 --> 00:10:52,640 Speaker 1: and being able to work with different business models. Thank 192 00:10:52,720 --> 00:10:55,960 Speaker 1: you very much for spending time with us. Samir pent Deer, 193 00:10:56,520 --> 00:10:59,560 Speaker 1: he's the executive vice president, chief executive Asset Service. Thing 194 00:11:04,880 --> 00:11:08,000 Speaker 1: coming up we're gonna be taking a look at the 195 00:11:08,120 --> 00:11:10,400 Speaker 1: markets with Stavilson our stock Sedator