00:00Are we all looking at this new thing in the right way?
00:03Is it creating compliance risk?
00:04Is it creating regulatory risk?
00:07Is it creating performance issues?
00:14My name is Chris McEntee.
00:16I've been working for about 16 years in various capacities.
00:19I currently have the title of Vice President of Corporate and Product Development.
00:23And what that means is I'm looking still at integrations or potential deals
00:28where we do some of the M&A work,
00:30but more importantly, more in a product strategy role
00:32to see how the pieces fit together.
00:35And today, of course, we're going to be talking about the data business
00:38that I'm responsible for.
00:45Business intelligence because of AI is going through a pretty major evolution.
00:49If you think about historically,
00:51business intelligence was tracking metrics, getting data, cleaning data,
00:55and then trying to create report engines, right?
00:58Whether they were crystal reports, you know, they've evolved over time or Tableau.
01:03Business visualizations, so complex data being able to show,
01:07you know, clearly delineate decision-making.
01:10So that's where a lot of the underlying data, enterprise data,
01:14really in its more recent movement was, you know,
01:17going through very sophisticated visualization tooling.
01:21Now you bring in AI and you have data as well as reporting
01:26that is going to be acting on some of those reports
01:29and some of them will be sequenced.
01:31And I think the complexity of being able to say,
01:33I have data, the data is telling me something about the business.
01:38Can I create an automated tool that would react to that signal,
01:42right, in the data?
01:43And so really what a lot of people are doing nowadays
01:46is just trying to architect a future space
01:49for what they want to see, especially with AI.
01:52AI's got immense amount of promise.
01:54We all know that.
01:55And a lot of the POCs are working on, you know,
01:58trying to delineate where can we automate certain tasks,
02:01where are there certain compliance-driven tasks
02:03that we need to rope in.
02:05But most importantly,
02:06it's going to be driven by business intelligence, right?
02:09I'm going to see a signal.
02:10I'm going to see an update on a borrower or a consumer.
02:13I'm going to see some counterparty risk.
02:15I would like my automated tools,
02:17if they're driven by AI,
02:18to notify me as soon as that emerges.
02:20And that's going to require very direct connection
02:23into business intelligence and business data.
02:29Because lending is so diverse within the US
02:33and there's different business models,
02:35really a lot of the drivers of how they use the data
02:39and how they want to consume the data
02:41is very driven by, you know, again,
02:44counterparty risk, product offering,
02:47consumer direction, multi-channel.
02:49So when you look across each of the businesses
02:53and what their business model is,
02:54whether direct to consumer
02:55or what channel they're working on
02:57or what they want to grow,
02:58or the campaigns, for instance,
03:01being a marketing campaign
03:02and trying to automate that
03:03or in the midst of a refi boom,
03:06all those are driven by real-time data
03:08to the extent you can have it.
03:10A lot of the industrial consumers of our data
03:14are going out to third parties independent of us.
03:17So they have their enterprise data,
03:18call it their proprietary data,
03:20generated by their own volume and transactions.
03:23They're going to us
03:24because we might have proprietary market data
03:26that influences a decision.
03:28So it has to get combined in there
03:29and then go to a third party.
03:32So depending upon
03:33what the data infrastructures look like
03:35and how sophisticated they are,
03:36they can have probably a sophisticated role
03:38as a JP Morgan does,
03:40which, you know, the data teams are,
03:42you know, driven by data scientists,
03:44super complex environments.
03:45They can bring in all sorts of data real-time
03:47or just somebody saying,
03:49look, I want to look at a pipeline
03:50and I want to see what my customer,
03:52you know, click through
03:53and I get some reports
03:54and I want to see that
03:55through my marketing engine.
03:57So it really runs the gamut.
03:58The key thing is,
04:00is that getting the data accurate,
04:03which involves some internal governance, right?
04:05What is the source of truth
04:06within your enterprise?
04:08And then if I go out to third parties,
04:10am I going to get some conflict
04:12between how these data sets get put together,
04:14especially if they're going to be
04:15put into something that's automated?
04:17So a lot of decisions,
04:19we see the customers come to us collaboratively
04:21and ask us about what that looks like.
04:24Have you seen an implementation like this?
04:26We'd like to do this.
04:27We have this firewall issue.
04:28So we want to be collaborators
04:30as well as helping them get the best solution.
04:38Data governance,
04:40the initial perception would be,
04:42oh, that's putting a wet blanket
04:44on this novel innovation
04:45and what have you.
04:46And it's actually the opposite
04:49because a governance structure,
04:51and what I mean by that
04:52is explicit charters,
04:54what you're going to do with data,
04:55where are you going to store data,
04:56where are you going to update data?
04:57All those questions get answered
04:59in a more collaborative fashion.
05:01When I think about governance
05:02and I have a number of governance roles,
05:04you're bringing all the stakeholders
05:06initially and saying,
05:07what do you see here?
05:08So that's cyber risk,
05:10it's infrastructure,
05:11it's the systems engineering.
05:13So, you know,
05:15the word governance,
05:16kind of an abstraction,
05:17but from a practical standpoint
05:18is are we prepared
05:20to move this thing
05:21through a product design
05:23or what have you
05:23through a proof of concept
05:26into a limited release
05:28and then into production?
05:30The power of the tools
05:31that we're now encountering
05:33are so sophisticated,
05:34so complex.
05:35There's our concerns
05:36that people think,
05:37oh, well,
05:37either one,
05:38I get a false positive
05:40or I put out incorrect information
05:42to a bar,
05:43you know,
05:43to the extent
05:44that's a compliance issue
05:45or if I'm doing
05:47a business-to-business,
05:48talk about the reporting
05:49we did earlier,
05:50I'm getting inaccurate signals
05:52because, I don't know,
05:54somebody didn't update
05:55their model accurately,
05:56okay,
05:56some dependency
05:57on a third party.
05:59So I think that
06:00what's happening
06:01is a combination
06:02of available data,
06:05you know,
06:05real-time almost compute
06:06capability and automation
06:08as well as
06:09these kind of evolving
06:11things called LLMs
06:13and these large language models
06:14and these things,
06:15you know,
06:15the dynamic as to
06:16what is the output,
06:18right?
06:19I want an answer,
06:20which, you know,
06:20we're seeing through
06:21some of the query,
06:22but more importantly,
06:23I want to set up
06:24a series of tasks.
06:26So think about it.
06:27If I get the first task wrong,
06:29the following five tasks
06:30are going to be off.
06:31And so I think people are very,
06:34want to be as precise
06:34as they can be
06:35engineering-wise,
06:36but more importantly saying,
06:38are we all looking
06:39at this new thing
06:40in the right way?
06:41Is it creating compliance risk?
06:43Is it creating
06:43regulatory risk?
06:46Is it creating
06:47performance issues?
06:49So go back
06:49to the core of governance.
06:50I think it starts there,
06:52your entitlements,
06:53your controls,
06:55and then you think about
06:56what's the use case,
06:57distribution,
06:59are people ingesting
07:00this data today
07:01in a different fashion?
07:03And then specifically,
07:04go back to the point
07:05I brought up earlier,
07:06you probably have
07:08some nested activities there.
07:09What I mean by that is
07:10you're asking for data,
07:12then you're replying,
07:14and if that initial data
07:15response is called dirty,
07:17it's just going to
07:18kind of cascade through.
07:19So people are doing
07:20a lot of testing in QC
07:21to make sure these things
07:22work properly,
07:23and that's all governance.
07:24It's governance 101,
07:25so it's good hygiene.
07:33Primarily because
07:33we are a system of record
07:35for two large,
07:36you know,
07:37infrastructure systems.
07:38So obviously,
07:38your servicing platform
07:40as well as
07:40your origination platform.
07:42So you start there
07:43and you say,
07:44what is my system of record?
07:45What's my source of truth?
07:46And we have the applications
07:47that actually have that data.
07:49So once you have that data
07:51and you can say,
07:51look, I can manage it,
07:53I can give transparency to it,
07:54and then I can also produce reports
07:57that people can consume.
07:58The other benefit that we have
08:00is we also have
08:01a lot of data businesses.
08:03So to the extent
08:03that data gets presented
08:06to one of the users
08:07of the application,
08:08we can also put data
08:09into the workflow.
08:10So go back to my analogy
08:12either on the,
08:13or earlier on,
08:14the automation piece.
08:15I'm getting that real-time data.
08:17It doesn't necessarily
08:18have to be my data.
08:19It could be with some
08:20of the data businesses
08:21that are other parts of ice.
08:23Rates,
08:24product offerings.
08:26So again, rate sheets,
08:27but to the extent
08:27even market data.
08:29So I think some of the benefits
08:31that you see
08:31is our ability
08:32to be able to say,
08:33you have your enterprise data lender.
08:36We've kept good hygiene on it
08:39for that and availability.
08:41You have workflows
08:42that are in the application.
08:43We can inject data into that,
08:45whether it's your data
08:47or third-party data
08:48just to make the efficiencies go.
08:50And to the extent
08:51that we have a network
08:52where other third parties
08:53can provision data
08:54and when you see this
08:55in fees and fraud
08:56and some of the other products,
08:58there's a very robust market
08:59out there for it.
09:00So I think the idea
09:01that the data
09:03just gets sole-sourced,
09:05we think one of the benefits
09:06is we give you options
09:08and you're going to have
09:09multiple options.
09:10I want to go proprietary
09:11with some of my own data.
09:13I want to use this third party.
09:14We can accommodate it.
09:15So I think it's our flexibility,
09:17scalability,
09:18and just the extent
09:19of the amount of data
09:21that we do have.
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