Latest / Bloomberg Intelligence / BNY Mellon's Pandiri on Real Time Data For Asset Flows (Audio)

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BNY Mellon's Pandiri on Real Time Data For Asset Flows (Audio) · Bloomberg Intelligence

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  Speaker 1: Flash is Taking Stock with Kathleen Hayes and Prim Fox
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  Speaker 1: on Bloomberg Radio, continuing a very special live broadcast today.
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  Speaker 1: We are at Dove Mountain near Tucson, Arizona, the Ritz
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  Speaker 1: Carlton here in the midst of the Tortalita Mountain range.
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  Speaker 1: We are broadcasting live it invested sixteen. It's the power
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  Speaker 1: of Big Ideas of bn Y Melon client conference. Joining
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  Speaker 1: us now is the person who kicked off the conference
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  Speaker 1: last night down by the pool with little burning crucibles
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  Speaker 1: in the water and a beautiful Arizona evening. And I'm
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  Speaker 1: referring to Samir Pandery. He's executive vice president and global
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  Speaker 1: CEO of Assets Servicing at b in why Non Semir,
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  Speaker 1: Welcome to Taking Stock and thank you for inviting us
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  Speaker 1: to the confidence. Thank you for having us on your program.
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  Speaker 1: Uh the uh the challenges that your client's face and
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  Speaker 1: of course you goose is an intimate group about two
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  Speaker 1: people from across the country and around the world. The
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  Speaker 1: big questions what what? What is the biggest one at
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  Speaker 1: the top of the list. When you're looking particularly at
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  Speaker 1: UH some of the regulatory changes that have occurred lately,
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  Speaker 1: it seems that that no matter whether you're a servicer
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  Speaker 1: or a money manager, everybody is still grappling with some
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  Speaker 1: of the big changes. Yeah, you know, I think fundamentally,
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  Speaker 1: for example, if you're a money manager or if you're
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  Speaker 1: a money investor, what you really want to do is
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  Speaker 1: focus on managing money. So that means all of the
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  Speaker 1: other things, the operations, the technology, those are things that
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  Speaker 1: are not a creative to your core function. So I
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  Speaker 1: think what regulation does. It adds responsibilities to your non
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  Speaker 1: core activities, and I think that is a huge burden.
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  Speaker 1: You know, there's non core activity. Like my job if
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  Speaker 1: you're my client, is to make sure that you're invested
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  Speaker 1: in the right securities, the right assets, just to save
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  Speaker 1: money and make money exactly. And so if you have
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  Speaker 1: to distract or you know, spend part of your time
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  Speaker 1: doing other things like worrying about cybersecurity or worrying about
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  Speaker 1: your technology architecture or how you're actually um robust. From
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  Speaker 1: an operating infrastructure perspective, it's very difficult UM and it's
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  Speaker 1: very expensive. So again, I think one of the things
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  Speaker 1: that people are looking for or solutions where they can
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  Speaker 1: take advantage of scale, they can take advantage of people
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  Speaker 1: that are that can do this in a professional way
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  Speaker 1: so that they can really focus on their core business activities.
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  Speaker 1: How can the customer also take advantage of all the
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  Speaker 1: data that you have? Uh, the idea being that it's
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  Speaker 1: no longer playing vanilla investing stocks, bonds, cash, it's everything alternative,
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  Speaker 1: non cord less on. You built a model of yes.
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  Speaker 1: So you know, we have a digital ecosystem called Nexon,
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  Speaker 1: which is really, i think technologically ten years ahead of
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  Speaker 1: where the industry is and really where we're driving the
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  Speaker 1: transformation of this company. And the digital ecosystem is based
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  Speaker 1: on a couple of fundamental principles. We collect large amounts
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  Speaker 1: of data, so we for example, collect about one point
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  Speaker 1: four billion transactions on a daily basis, because the cost
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  Speaker 1: of story that data is significantly cheaper now compared to
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  Speaker 1: a decade ago. But then the second part, which is
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  Speaker 1: really the harder part, is taking the data that is
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  Speaker 1: important and relevant and making it consumable in a way
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  Speaker 1: that's useful to the client. So again, if I can't
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  Speaker 1: take that and translated into a form that's usable, it's
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  Speaker 1: um you know. So that's really the trick and that's
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  Speaker 1: the challenge. So give us a specific example of a
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  Speaker 1: batch of data compiled over the years by B and
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  Speaker 1: Y melon that relates to something that be very specific
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  Speaker 1: that before you created these technologies like next in and
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  Speaker 1: digital polls, that it was just kind of a lot
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  Speaker 1: of data out there, and now you can say, look specifically,
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  Speaker 1: here's something someone can learn. What's one example of that.
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  Speaker 1: So one example is if you look at our pension universe.
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  Speaker 1: We have about three and a half trillion dollars worth
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  Speaker 1: of pension assets that we administer. About a decade ago,
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  Speaker 1: three percent of those assets were invested in alternatives. Today
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  Speaker 1: that number is two so hedge funds, private equity, real estate.
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  Speaker 1: So as these flows happen into these real assets, you know,
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  Speaker 1: clients would like to get that information on real time basis,
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  Speaker 1: so we provide them the data on the huge asset flows,
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  Speaker 1: the transformations that are happening in real time so that
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  Speaker 1: they can actually look at where they are invested relative
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  Speaker 1: to their peer group and really decide if that's really
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  Speaker 1: what they want to be in terms of the risk spectrum. Uh.
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  Speaker 1: And by the way, this was not information that we
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  Speaker 1: were able to provide. Let's say even five to ten
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  Speaker 1: years ago. We had the data, but was not consumable.
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  Speaker 1: Now I think with the technology you can actually consume
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  Speaker 1: that data in real time. You also get the issue
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  Speaker 1: of many investors are paying for alpha, but they're really
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  Speaker 1: just getting beta. It isn't that kind of also going
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  Speaker 1: to be a look into what you're paying for and
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  Speaker 1: what you're getting in terms of the holistic universe of investments. Yes.
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  Speaker 1: So one of the things that our data allows. So
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  Speaker 1: if you have, for example, as an investor our clients
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  Speaker 1: they might be using multiple managers, we can actually show
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  Speaker 1: them not only what their manager is delivering in terms
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  Speaker 1: of alpha, but what are they delivering relative to their
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  Speaker 1: peer group. So it's very clear, very transparent in terms
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  Speaker 1: of what the performances. And again we have a g
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  Speaker 1: r S which is called a Global Risk services um
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  Speaker 1: business which is really predicated on giving that comparisons so
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  Speaker 1: that as an investor you can have a really good,
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  Speaker 1: solid foundation to make these comparisons the fintech firms, disruptors, innovators.
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  Speaker 1: Is that a threat to be in White Milon and
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  Speaker 1: its model or is it an opportunity In some sense,
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  Speaker 1: I think it's a threat if we did nothing about it.
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  Speaker 1: We are obviously very very engaged UM and we're also
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  Speaker 1: trying to think about how we should perhaps disrupt our
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  Speaker 1: own models. So, you know, as an example, we are
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  Speaker 1: looking at the use of technology. We're using it the
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  Speaker 1: use of robotics, UH in terms of our operational processes
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  Speaker 1: to really see if there is a better way to
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  Speaker 1: actually um, you know, create the end product you know
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  Speaker 1: that the clients. So robots can really do what I
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  Speaker 1: would call the mind numbing things that are you know,
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  Speaker 1: task oriented but rule based that you can actually program
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  Speaker 1: that you don't need to have a person do, so
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  Speaker 1: you can actually have the person focus on the exceptions
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  Speaker 1: and the higher value added things that require some kind
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  Speaker 1: of a cognitive ability to make a decision and using
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  Speaker 1: all of this data. One example I was given this
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  Speaker 1: morning had to do with emerging markets. Investing in a
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  Speaker 1: non market correlated fund that specializes in emerging markets looking
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  Speaker 1: at capital flows into and out of emerging markets and
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  Speaker 1: trying to make a decision to see whether there's a relationship, right,
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  Speaker 1: because you've got the emerging market fund, but then you've
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  Speaker 1: got the capital flows. What they found was almost counterintuitive.
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  Speaker 1: As the flows went into emerging markets, this fund actually
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  Speaker 1: did poorly. Why because everyone was benchmarking against the m
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  Speaker 1: s c I and yet the investments were not part
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  Speaker 1: of the MSCI index. So this is going to allow
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  Speaker 1: not only the investor, but also the people that manage
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  Speaker 1: the money to differentiate themselves in a new way. Yeah,
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  Speaker 1: I think, you know, the the data is the issue here, right,
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  Speaker 1: because you know, when you have metadata that can go
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  Speaker 1: at a much deeper level, you can drive analytics that
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  Speaker 1: you actually couldn't do before. So and again I think
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  Speaker 1: this is an example where if you actually have data
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  Speaker 1: at the most underlying, basic transactional level, where you see
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  Speaker 1: the flows going in and out, and if you can
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  Speaker 1: digitize that and make that consumable, you will have information
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  Speaker 1: that's not aggregated, that's general and non specific. But you
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  Speaker 1: know the contrary. So I think that's really the power
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  Speaker 1: of technology and data which is really I think, you know,
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  Speaker 1: driving decision making now very differently than even just five
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  Speaker 1: ten years ago. What's the challenge for individuals who worked
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  Speaker 1: in this industry? You have a very interesting background. You
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  Speaker 1: you start out in chemical engineering and got your NBA
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  Speaker 1: and finance, and you worked at JP Morgan and you've
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  Speaker 1: worked on the trust side at being my Melon. You're
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  Speaker 1: involved with some of the emerging market issues. What is it?
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  Speaker 1: What is it? What? What? What do I need to know? What?
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  Speaker 1: What is the challenge for for the professional in this industry? Now?
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  Speaker 1: I think in UM in my business and I have
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  Speaker 1: some of this background. UM. I think there's three things.
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  Speaker 1: You know, we are a very global organization. I lived
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  Speaker 1: abroad for over a decade UH in Asia and Europe.
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  Speaker 1: I think, you know, having that global perspective is absolutely
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  Speaker 1: critical if you want a problem solving this industry. I
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  Speaker 1: think the second thing you have to have the ability
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  Speaker 1: to deal with change and drive the change in the
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  Speaker 1: industry as opposed to letting that happen to you. Having
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  Speaker 1: great change management skills I think are really important. And
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  Speaker 1: I think the last thing is you have to be
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  Speaker 1: able to disrupt you know, traditional mental models and business
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  Speaker 1: models and really come up collaboratively working in different ways
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  Speaker 1: with your clients, with your regulators, to solve problems that
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  Speaker 1: you couldn't solve before. So again, I think having that
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  Speaker 1: sort of open environment in terms of how you solve
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  Speaker 1: problems is going to be really important. So I would
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  Speaker 1: say those things are probably really important globality, change management
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  Speaker 1: and being able to work with different business models. Thank
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  Speaker 1: you very much for spending time with us. Samir pent Deer,
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  Speaker 1: he's the executive vice president, chief executive Asset Service. Thing
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  Speaker 1: coming up we're gonna be taking a look at the
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  Speaker 1: markets with Stavilson our stock Sedator