Welcome to this deep dive. We are really glad
you're here with us today. Yeah, definitely great
to have you listening If you are looking to just
bypass all that dense corporate jargon and get
straight to the actual mechanics of where technology
is taking high stakes finance, you are in the
exact right place. It is a totally fascinating
landscape to explore right now. I mean, we are
moving way past the theoretical stuff. Right.
And we're looking at the actual plumbing of how
these major institutions operate on a daily basis.
Exactly. So our mission today is to explore how
artificial intelligence is just completely rewiring
set management. Down to the studs. Down to the
studs. We're pulling insights from a really comprehensive
panel discussion today. It features digital finance
and security leaders from MetLife, Capgemini
Invent, PNC Bank, and PGM Global Services. Heavy
hitters. Yeah, very. And the sheer scale of what
they are discussing is honestly, it's staggering.
We are not talking about using a chat bot to
summarize an article for you. No. We are looking
at a fundamental shift in how trillions of dollars
in global wealth are managed, secured, and optimized.
And that scale, honestly, that is precisely why
this matters to you, even if you don't manage
a massive institutional portfolio. Right. Even
if you're just interested in how enterprise tech
is evolving. Exactly. What we are really examining
today is the ultimate modern tightrope walk for
these massive firms. They're trying to achieve
this this hyper -efficiency through AI. Which
everyone wants. Which everyone wants, but while
maintaining the absolute non -negotiable necessity
of security. Yeah. One of the panelists from
PNC Bank framed this perfectly, I think. They
said the guiding principle for their AI adoption
is to have foresight, not fear. Foresight, not
fear. I love that. I think that's a great anchor
for our discussion, especially when it is just
so easy to either panic about security or get
completely swept away by the vendor hype. Oh,
the hype is everywhere. It is. OK. Let's unpack
this, starting with the actual productivity leap
we are seeing on the ground. The panel sources
cited that generative AI is currently being used
to reduce manual processes by as much as 80%.
80%. Yeah, and now, I have to admit, whenever
I hear a perfectly round number like 80%, I get
a little skeptical. Sure. It sounds like marketing
math. What does that actually look like in the
trenches? That's a healthy skepticism to have.
But when you look at the specific use cases they
highlighted, that number starts to make a lot
of sense. OK. Take the traditional KYC process,
for example. Know your customer. Right. We all
know how notoriously bottlenecked that can get.
Historically, you have analysts just bogged down
in the mechanics of verification. Pulling disparate
records. Right. Cross -referencing complex corporate
hierarchies. and digging through massive unstructured
PDF documents? just to validate an entity. Oh,
the dreaded unstructured PDF. Anyone who has
ever had to manually pull specific data points
out of a messy 200 -page legal document and put
it into a spreadsheet knows the sheer physical
eye strain involved. You're just living with
dual monitors, using keyboard shortcuts, and
praying you didn't misread a decimal point. Exactly.
And what the AI is doing is sweeping that mechanical
friction away. It effortlessly parses those unstructured
documents, pulls the relevant entities out, and
formats the data for the human analysts to review.
Wow. But here is the critical pivot in the technology.
We are evolving from simple AI into what the
industry is calling agentic AI. Agentic AI. Okay,
that feels like a massive turning point. Break
down agentic AI for us because that is a term
a lot of people are hearing but might not fully
grasp in a corporate context. Sure. Up until
recently, enterprise AI has mostly been a prompt
and response tool. You ask a system a question,
it generates text or code in return. Like a really
smart search engine. Exactly. Agentic AI is fundamentally
different. It acts as a proactive assistant that
has actually granted permission to manage end
-to -end processes across different software
platforms. Oh, wow. Yeah. It isn't just waiting
for a prompt. It is anticipating the next logical
step. making API calls, and queuing up actions
for a human to approve. So it shifts the user's
role from being a fast data gatherer into an
actual strategic decision maker. Spot on. That
makes a lot of sense. To make this tangible for
you, the listener, let's look at a case study
the Capgemini panelists shared. They applied
this to the client contact center. A great example.
Yeah. And instead of a generic retail banking
example, let's put this in the context of high
stakes asset management. Imagine you are an institutional
investor, the market has just taken a massive
unexpected dip, and you are calling your asset
manager to figure out your exposure. Right. And
in a high stress scenario like that, the last
thing the client wants is to navigate an automated
phone tree. Press one for panic. Exactly. Or
sit on hold while an agent frantically tries
to pull up their specific portfolio. Right. So
with agentic AI, the system actually listens
to the voice of the caller. It recognizes the
client. analyzes the urgency in their voice or
the keywords they are using, and instantly routes
them to the appropriate senior desk. Seamlessly.
Seamlessly. But it's what happens on the agent's
side that is truly impressive. Before the human
agent even answers the line, The agentic AI has
generated a comprehensive pre -call dashboard.
It connects all the dots before the conversation
even begins. Exactly. The agent instantly sees
the client's total exposure to the current market
volatility, their recent trade history, and even
automated suggestions for mitigation strategies
based on the firm's current macroeconomic stance.
That's huge. The AI has already done the 20 minutes
of frantic background research and the two seconds
the phone was ringing. And we shouldn't overlook
what happens after the client hangs up either.
Right. The agentic AI performs automated proactive
post call follow ups. It drafts the summary of
the agreed upon actions. It queues up the necessary
trade tickets for the agent to approve and address
an email to the client outlining the next steps.
It's just a seamless end to end orchestration.
Completely. That automated follow up is incredible
for the client experience. But I'm actually more
interested in what this means for the person
sitting on the other side of the desk. How is
this changing the internal worker's day -to -day
reality? That's the real question. Because the
goal here shouldn't just be replacing people.
It should be giving them better tools. And one
of those tools the panel discussed is the ability
to essentially chat with your own proprietary
data. That capability alone. represents a massive
paradigm shift in corporate environments. Think
about the traditional latency involved in getting
an answer to a complex business question. It
takes forever. Right. If a portfolio manager
needed to understand a subtle trend buried in
three years of transaction data, they couldn't
just find that themselves. They'd have to submit
a JIRA ticket to a data engineering team. Oh,
yeah. Wait a week? and hope the SQL query the
engineer wrote actually captured the nuance of
the original request. And half the time it doesn't,
and you have to start the process all over again.
But now, that same portfolio manager can use
natural language to directly ask their data a
question. Under the hood, the AI translates that
plain English into a complex query, runs it against
the database, and instantly generates a custom
bashboard on the fly. You are completely removing
the latency between having a question and getting
an actionable insight. Yeah. And we are seeing
this acceleration outside of the data science
realm too. The panel brought up a great example
regarding sales teams and wholesalers out in
the field. Yes, the wholesaler example was brilliant.
If you've ever worked in that side of the industry,
you know the pain of competitor research. It's
brutal. You're traveling, meeting with institutional
clients, and you have to be prepared to explain
exactly why your firm's product is better than
a rival's. Historically, that meant digging through
hundreds, sometimes thousands of pages of incredibly
dense compa - I always joke that this specific
kind of tedious manual research used to be a
rite of passage for junior analysts, fueled entirely
by bad office coffee and sheer willpower. It
is incredibly grueling work. You are looking
for very specific details, subtle differences
in risk profiles, fee structures, or investment
restrictions, all buried deep within legal text.
But armed with this new AI, those teams in the
field can just query the system directly from
their phone or tablet. They can ask a highly
specific complex question about a competitor's
newly updated policy, and the AI synthesizes
the answer for them right there, in real time,
citing the exact page of the prospectus it pulled
from. It gives them the answer when they actually
need it, right before they walk into the client
meeting. And it isn't just data retrieval, it
is also content creation. The panelists from
PGM Global Services shared how their marketing
and comms teams are utilizing this. They're taking
their past thought leadership materials, white
papers, event copy, and feeding it into an AI
so the model learns their highly specific proprietary
brand voice. They mentioned it took their standard
campaign creation time from 14 days down to six
days. Which is a massive jump in efficiency.
Massive. But my immediate question when I read
that was, does it actually capture the nuance
of a premier financial firm, or does it sound
like a generic robot wrote it? That is the crucial
distinction. Because they are training it specifically
on their own curated historical data, the output
isn't generic. It incorporates their specific
terminology, their required compliance disclaimers,
and their house style. OK. The human editors
are then spending their time refining a highly
competent first draft rather than staring at
a blank page. What's fascinating here is how
this completely reframes the mainstream narrative
we often hear about AI coming to eliminate everyone's
jobs. When you look at these enterprise use cases,
it isn't about human replacement. It is about
human augmentation. It removed the absolute drudgery
of the day -to -day workflow. It allows people
to actually perform the strategic, creative,
and interpersonal relationship building they
were originally hired to do. But of course, to
do any of this effectively, whether we're talking
about call center dashboards, querying databases,
or generating thought leadership, we have to
talk about the underlying engine. And that is
the data itself. There is a very old, very true
saying in computer science, garbage in, garbage
out. Always true. And when you are dealing with
high stakes finance, you absolutely cannot afford
for your AI to be making decisions based on unverified,
noisy public information. It is the single biggest
risk factor in enterprise AI adoption. An AI
model is only as accurate and unbiased as the
data it is trained on. This is why the panelists
from PGM heavily emphasize a strategy they call
the walled garden approach. I love that term
walled garden. Explain the mechanics of that
for us because it sounds like they are basically
fencing the AI off from the rest of the world.
That is essentially what it is. Instead of allowing
their AI to roam free across the open Internet,
which is terrifying, which is filled with unverified
claims, outdated regulations and inherent biases.
Instead of that, these firms deploy their LLMs
within a strict internal perimeter. The AI is
grounded only in curated proprietary data sets.
It only reads the firm's vetted internal knowledge
bases, their secure data lakes, and their approved
policy documents. So it doesn't know what people
are saying on Reddit. It only knows what the
firm's chief investment officer has officially
published. Precisely. And establishing that Waldgarten
matters immensely. First and foremost, it drastically
reduces the risk of AI hallucinations. If the
model doesn't know the answer, It is constrained
to say, I don't know, rather than confidently
inventing a false statistic. Which we've all
seen happen. Exactly. Furthermore, this walled
garden creates a secure sandbox for the employees.
It gives them a protected environment to experiment,
to learn how to properly prompt the system and
to build their digital literacy without any risk
of accidentally leaking sensitive internal data
to a public model. But setting up that walled
garden isn't just a matter of buying a software
license. No. The panel really stressed that this
requires a massive culture shift. There has to
be an incredibly tight ongoing partnership between
the business teams and the technology teams.
If tech operates in a silo, you run into that
classic pitfall where engineers build a highly
sophisticated, shiny new tool, they hand it over
to the portfolio managers, and no one uses it
because it doesn't actually solve a real business
problem. We see that happen constantly. The technology
must be driven by actual business use cases,
not just the desire to deploy AI for the sake
of deploying AI. And that requires comprehensive
enterprise -wide training from the executive
board all the way down to the junior analysts.
Everyone needs a baseline understanding of how
these generative models function, how to evaluate
their outputs, and most importantly, what their
limitations are. Which brings us to perhaps the
most critical part of this entire deep dive,
the security aspect. Here we go. Because everyone
in this industry is moving fast. The security
leader from TNC Bank used a really memorable
analogy for this. They said that in the current
market, everyone wants to drive a fast car. Everyone
wants to adopt AI quickly to stay competitive.
But to drive a fast car safely, you need really,
really good brakes. The guardrails have to be
established before you step on the gas. Here's
where it gets really interesting. Because the
consequences of driving this fast car without
breaks are not theoretical, they are already
playing out. The sources detailed some severe
incidents from 2024, where companies, suffering
from a fear of missing out, allowed employees
to use publicly accessible Gen. AI chatbots for
financial analysis without implementing proper
data loss prevention controls. It is a nightmare
scenario for any chief information security officer.
It truly is. You had well -meaning employees
taking sensitive client portfolios, including
massive amounts of personally identifiable information,
and pasting that raw data directly into public
chatbots just to format a report. Just trying
to save time. Yeah. They didn't realize that
by doing so, they were feeding their clients
private data into the public models training
set. The result was massive data exposures and
millions of dollars in regulatory fines, all
because they bypassed basic internal guardrails.
And accidental data leakage is only one side
of the coin. The threat landscape has fundamentally
changed because malicious actors are also weaponizing
this technology. Oh, the panel did not hold back
on this. They highlighted the terrifying reality
of AI generated phishing and social engineering.
We are no longer talking about poorly worded
scam emails full of typos. Right. Attackers are
using AI to generate hyper realistic deep fakes
of executives. They will clone a CFO's voice
and call a junior employee to authorize an urgent
wire transfer. Or they will execute incredibly
sophisticated business email compromises that
perfectly mimic the writing style of internal
leadership. This is what we refer to as the cybersecurity
arms race. The adversaries are using AI to scale
their attacks, automate their vulnerability scanning,
and generate highly convincing synthetic media.
So you have to fight fire with fire. Exactly.
The defending teams have absolutely no choice
but to utilize AI as well. A security operations
center at a major asset manager simply cannot
rely on human analysts to manually sift through
millions of daily network logs anymore. So how
are the defenders utilizing AI to actually fight
back and gain the upper hand? They're using specialized
internal AI models to process those unimaginable
volumes of event data at machine speed. The AI
establishes a baseline of normal network behavior
and then constantly hunts for anomalies. Like
finding a needle in a haystack? Faster than any
human could. Yeah. It helps defenders spot the
unknown unknowns, the subtle complex attack patterns
that a human analyst would never catch in time.
But the panel also offered a stark reminder.
AI models are, at their core, just software.
Right. And like any software, they have vulnerabilities.
The security leaders emphasized that firms must
continuously stress test these models using established
cybersecurity frameworks. They specifically mentioned
leveraging resources like OWAS, which is essentially
the global standard for application security,
and OpenAI primers to rigorously hunt for flaws
in their own walled gardens. It sounds like a
relentless high stakes battle. But what I found
really encouraging in the sources is that AI
isn't just being used to fight off global hackers.
It is also being aimed at solving one of the
most massive, tedious pain points in the corporate
world. I am talking about compliance. Ugh, yes.
The labyrinth of modern financial regulation.
The panel called this the dream of killing the
questionnaire. If you operate anywhere in asset
management, you know the absolute agony of conflicting
regulations. You have state privacy laws colliding
with federal financial regulations, which then
collide with various industry certifying authorities.
And it never ends. It doesn't. And typically,
a firm handles this by deploying armies of compliance
analysts to do what they call stare and compare
work. They sit there. manually cross -referencing
hundreds of spreadsheet cells, trying to figure
out how to satisfy a 50 -page vendor risk assessment.
It is a massive drain on resources and it is
highly prone to human error. But the experts
on our panel detailed how agentic AI is actively
being used to harmonize these conflicting frameworks.
The AI can ingest a massive new federal regulation,
cross -reference it against the firm's existing
internal controls, and instantly highlight the
gaps. It maps the requirements across different
jurisdictions to create a single rationalized
set of controls. The ultimate goal these firms
are driving toward is automating compliance detection
entirely. They want the AI to continuously monitor
the firm's systems and output the required reporting
automatically, effectively cutting out the middleman
and killing the endless compliance questionnaires
once and for all. That would change the industry
overnight. So what does this all mean? We have
covered incredible ground today. Looking at everything
these leaders from MetLife, Capgemini, PNC, and
PGM have laid out, it is abundantly clear that
AI in asset management is no longer an experimental
toy. Not at all. It is a top to bottom restructuring
of the financial plumbing. We are looking at
a future powered by agentic assistance capable
of eradicating the bulk of manual friction. But
it is a future that completely hinges on pristine
data governance through walled gardens and ironclad,
thoroughly tested security guardrails. If we
step back and view this in a broader historical
context, we are squarely in the middle of the
Fourth Industrial Revolution. And what this deep
dive truly underscores is the pivot in the value
of human capital. For decades, the financial
industry rewarded the manual gathering, sorting,
and verifying of data. Right. That era is ending.
The true value of the human worker is moving
entirely toward interpreting the profound, synthesized
insights that these machines deliver. The machine
does the heavy lifting of data aggregation, while
the human provides the strategic judgment and
the ethical oversight. It's a total shift in
skill sets. It is. But as we look ahead, this
raises a profound question. Something I want
you, the listener, to consider long after this
deep dive ends. Let's fast forward five or ten
years. If agentic AI successfully automates the
vast majority of manual analysis, if it flawlessly
harmonizes global compliance, and if it seamlessly
manages our client interactions, how will firms
actually differentiate themselves in the marketplace?
That's a great point. When every single competitor
has purchased the exact same hyper -efficient
AI capabilities, technological speed is no longer
an advantage. It is just the baseline. In a fully
automated, flawlessly efficient financial world,
will deeply held human intuition, empathy, and
the art of creative relationship building become
the ultimate scarce premium asset? That is the
ultimate question we are all going to have to
answer in the next few years. Thank you so much
for joining us on this deep dive. We hope this
exploration helped you cut through the hype and
grab hold of the real mechanics shaping the future
of finance. Keep learning, keep questioning the
information landscape around you, and we will
catch you on the next one.