You know, the exact feeling. It is midnight.
The office is completely dead. Oh, yeah. We have
all been there. You sit there rubbing your eyes.
You are physically exhausted. You are literally
copy pasting numbers from a blurry PDF report.
Right into Excel. That's right into Excel. And
you're doing this just to fix a single slide
for tomorrow's pitch. It is a painful reality.
We have all stared at that glowing screen. It
feels like endless grunt work. Welcome, learner.
to today's Deep Dive. We are looking at something
incredibly practical today. We're exploring AI
tools for finance professionals. And, you know,
these tools exist specifically to kill that midnight
grunt work. But there is a massive trap here.
The biggest mistake people make is expecting
one single tool to do everything. They want one
AI to handle research, modeling, and presentation
slides. Which is impossible. Right. That approach
always collapses under pressure. It really does.
You can't just throw a Swiss army knife at a
construction project. You need the exact right
tool for the specific job to sex silence. So
we are going to trace the exact sequence of a
real finance workflow today. From start to finish.
Right. We will start with the initial due diligence
and research. Then we will pull the trapped data
out. Next, we will model that data properly.
And finally, we will turn it into a pitch -winning
presentation. It is all about building a highly
reliable sequential system. You really cannot
skip any steps here. So let's start at the very
beginning of that workflow. Before you can build
a fancy valuation model, you need the raw facts.
You have to start with the correct data source.
Absolutely. And I see so many analysts trip up
immediately on this very first step. Oh, all
the time. They immediately open up a general
chat bot. They type a prompt into chat GPT or
Gemini. They ask for specific financial metrics
on a public company. Which, you know, seems logical
at first glance. It feels intuitive. But general
chat bots fail miserably here. They are just
pattern matching engines. They guess the next
word based on language patterns. Which is dangerous.
incredibly dangerous in finance. They are highly
prone to hallucinating. And just to clarify that,
hallucination is when the AI confidently invents
fake numbers. Yes. It invents them because they
look statistically probable. Right. Using a general
AI for finance research is like asking a poet
to do your taxes. It sounds beautiful, but the
underlying math is entirely fictional. That is
a brilliant way to put it. You need highly specialized
research tools instead. Like what? Well, let's
talk about Fintool. Fintool is built specifically
for researching U .S. listed companies. It doesn't
guess the next word. It pulls data directly from
actual SEC filings, and it reads verbatim earnings
call transcripts. But wait, let me push back
on that a bit. SEC filings are notorious for
heavy management speak. Oh, yeah. They bury the
real story in massive footnotes. Does Fintool
actually cut through that corporate spin? That
is the beauty of it. It doesn't just summarize
the spin. Because it indexes the actual transcripts
and the filings together, you can cross -reference
them. Oh, I see. You can ask Fintool to generate
a structured investment thesis. It will explicitly
separate the company's stated growth drivers
from the buried risk factors. So it maps the
narrative against the required disclosures. Exactly.
You can even screen for specific insider buying
behavior. Really? Yeah. You ask it to show recent
executive stock purchases. It returns the exact
purchase amounts and shows you the specific buyer
roles. That is a massive time saver for initial
screening. But Fintool does have a strict limitation.
It focuses heavily on U .S. stocks. Beat what
if your mandate covers European markets or emerging
economies? Then you absolutely need AlphaSense.
AlphaSense is the global enterprise -grade alternative.
It doesn't just look at public filings. What
else does it pull? It accesses proprietary broker
research worldwide. It pulls in global news feeds
and complex regulatory filings across different
jurisdictions. So it handles the heavy international
due diligence. Yeah. It is widely used by major
investment banks. It is absolutely perfect for
preparing complex pitch books. It uses natural
language processing to scan massive volumes of
multilingual documents in seconds. I have a probing
question about that, though. Do these specialized
tools actually eliminate human blind spots entirely?
Not entirely, no. They are amazing filters. They
narrow down a massive mountain of documents into
a highly focused pile. But they do not replace
your human judgment. Fintool cannot tell you
which specific risk factor actually matters most.
They hand you the right puzzle pieces, but you
build the picture. Exactly. You still have to
do the thinking. So you've got these pristine
summaries and broker reports. You know what you
want to analyze. But now you hit the classic
analyst's wall. The data you need is trapped.
It is always trapped inside locked, unselectable
PDFs. Right. You need to get those numbers out.
You definitely cannot afford to manually retype
them at midnight. Never. This brings us to the
extraction phase. This is where a specialized
tool called Quadratic really shines. Quadratic
looks exactly like a traditional spreadsheet
interface, but it has a powerful spatial AI layer
built directly into it. Wait, spatial AI? How
does that actually work in practice? Well, spatial
AI is an AI that reads the visual layout of a
document. Traditional extraction tools just read
text from left to right. Which gets messy. Right.
They get confused by weird spacing. Quadratic
actually looks at the visual bounding boxes of
the document. Imagine you have a dense 80 page
annual report. You only need the income statement.
Normally you scroll forever trying to copy and
format it. And the formatting always breaks when
you paste it. Always. But with quadratic, you
simply upload the PDF. Then you give it a natural
language prompt. You say, extract the income
statement into a table with years as columns.
And it just spatially maps and pulls it. It pulls
the table perfectly. It places it directly into
your spreadsheet view. No manual copying, zero
formatting repairs. Whoa. Imagine scaling to
comparing three years of reports from two different
companies instantly. It handles that kind of
scaling easily. You can bring multiple disparate
files into one single working view. That is incredible.
It even has a live connection feature built in.
You can connect an external transaction feed
via API. It updates your spreadsheet automatically
on a set schedule. That completely eliminates
so much manual updating work. Okay. I feel like
we have to issue a vital warning here. Yes, a
massive non -negotiable warning. A strict human
check is absolutely mandatory. Because the AI
is reading visual structure, right? Yeah. Sometimes
those column headers get completely mislabeled.
I have seen extra dates suddenly appear from
nowhere. Just because a page number looked like
a year. Right, you get errors. The spatial mapping
isn't flawless. No, it is not. You always have
to verify the output against the original source
document. So what is the biggest trap when that
extracted data looks perfectly formatted? The
biggest trap is consolidation logic. A company
might group line items differently year over
year. The extracted table looks pristine. But
the underlying components of something like operating
expenses might have completely changed. The AI
just blindly copies the top level label. Beautiful
tables can still hide dangerously grouped numbers.
Spot on. You always have to read the footnotes
yourself. So extraction gets the data out of
the PDF and onto a grid. But the actual heavy
lifting has to happen where finance actually
lives. It has to happen inside Excel. Excel is
not going anywhere. It is the absolute bedrock.
But AI can significantly supercharge your workflow
inside it. Let's start with Microsoft's Excel
Copilot. Copilot is fantastic for messy logic
-based data cleanup. Give us a concrete example
of that messiness. Think about categorizing highly
varied credit card transactions. You have a massive
list of raw expenses, thousands of rows. The
descriptions say coffee shop, ride hailing app,
or software subscription. You need to strictly
categorize them into food, transport, or office
costs. A standard VLO cup formula completely
fails there. It completely fails. The exact text
strings just do not match. Flash Fill also fails
because there isn't a strict syntactic pattern.
But Copilot uses semantic vector mapping. mathematically
interprets the actual meaning of words. Exactly.
It understands the context. It knows a coffee
shop is conceptually related to food. It interprets
the underlying context. It doesn't just strictly
match characters' beat. But what about actually
building out financial models from scratch? For
generating first draft models, Claude directly
inside Excel is fantastic. You just feed it a
highly specific prompt. Like what? For example,
you type, build a loan schedule, 20 year mortgage
at 6 .5 % on a $500 ,000 principle. And it just
builds the whole architectural table. It does.
It sets up the entire grid. It even automatically
color codes your blue cells for hard inputs and
your black cells for formulas. I have a vulnerable
admission to make here. Oh. I still wrestle with
trusting AI to build full schedules. I find myself
checking row by row, just... painstakingly slow
math checks. You absolutely should be checking.
Claude has severe context window limits. Meaning
how much text the AI remembers at once. Right.
Because of those limits, it sometimes just randomly
stops processing. A 240 -month amortization schedule
might abruptly just end at row 10 for no reason.
It just completely loses the thread. Exactly.
It forgets what it was doing. That is why, for
actual heavy modeling, you need to step up to
TraceLite. TraceLite is an AI built purely for
rigorous finance work. How does TraceLite handle
Dynamic, complex scenarios. Let's say you are
analyzing a startup. Their starting monthly recurring
revenue is $50 ,000. You need to model three
distinct scenarios. OK. The best case is 15 %
monthly growth. The base case is 8%. The worst
case is 3%. That is very standard forecasting
stuff. You prompt TraceLite to build a dynamic
12 -month profit and loss statement based on
those parameters. It instantly builds it on a
clean new sheet. Wow. adds a functional scenario
toggle at the top. That is incredibly powerful,
Bede, but there's a massive structural risk there,
isn't there? A huge risk. TraceLite usually generates
that scenario toggle as a simple free text cell.
If a junior analyst types worst instead of worst
case, the entire model breaks instantly. The
formulas completely lose their specific string
reference. Yeah, the reference errors cascade
everywhere. A pro tip here is to immediately
replace that free text AI cell. Use standard
Excel data validation drop downs instead. It
takes five seconds to fix and it completely saves
your model from typos. I have noticed something
though. Why do these AI tools completely fall
apart on multi -sheet three -statement models.
It comes down to AI working memory constraints.
A proper three -statement model requires holding
hundreds of highly interdependent variables in
mind simultaneously. Right. It is like trying
to play a complex game of chess while looking
through a tiny straw. The AI loses track of the
grand architecture across the different tabs.
It forgets exactly how the balance sheet links
back to the cash flow statement. AI is great
at single tasks. bad at holding the whole building's
blueprint. Exactly right. Keep the AI strictly
focused on modular, single -sheet tasks. Mid
-roll sponsor, Reed. We are back. We have thoroughly
researched our initial data. We have safely extracted
it from those stubborn PDFs. We have rigorously
modeled it inside Excel. But raw spreadsheets
do not actually win client pitches. No, they
do not. You have to communicate those findings
visually. Presentations are everything. And formatting
slides manually is mind -numbingly tedious. It
is the absolute worst part of the job. Which
is precisely why Claude integrated directly into
PowerPoint is so highly useful. It actively pulls
your live Excel data directly into your presentation
slides. How aggressively specific do you need
to be with your prompts? You have to be incredibly
specific. You give it a highly structured prompt
like... Create a single page company profile
for Apple using the ticker AAPL. Include a business
overview, key financial metrics, and major risk
factors. Does it actually handle the visual design
layout itself? It does, but you absolutely must
guide it. You need to explicitly specify a white
background in your text prompt. You must demand
a clean, minimalist layout. What happens if you
don't? If you do not dictate the aesthetics,
you will spend three hours manually fixing the
hideous formatting it guesses. What if you don't
have hard data yet? What if you need to build
a high -level concept deck from scratch? Then
you pivot and use Gamma. Gamma is an AI platform
built purely for sheer visual speed. So you wouldn't
use it for heavy data integration? Right. It
is striply for communicating ideas and concepts.
You tell the prompt engine to create a six -slide
executive deck. Okay. Let's say comparing equity
versus debt financing for a small logistics business,
Gamma's engine instantly handles all the visual
layouts automatically. It is absolutely perfect
for rapid internal strategy decks. It provides
that foundational visual structure instantly.
Yeah. What about reporting decks that we have
to painfully update every single month? For recurring
monthly reviews, you really want to use Bricks.
Bricks is designed to build interactive live
updating dashboards. How does that work? The
visual structure of the presentation stays exactly
the same month over month, but the underlying
numbers change dynamically as new data flows
in. It securely connects your raw spreadsheet
directly to the final visual output. Exactly.
It is absolutely perfect for tedious monthly
budget tracking or variance analysis. Is the
hallucination risk higher when an AI is trying
to make something look pretty? Absolutely it
is. Design -focused AI models are explicitly
optimized for visual layout, not mathematical
accuracy. Wow. They will happily invent a plausible
-looking bar chart or perfectly round a crewful
number simply because it makes the slide look
more symmetrical. Pretty slides can easily distract
from completely fabricated data points. That
is the ultimate danger. You cannot ever let your
guard down just because a chart looks professionally
designed. Let's move to our big idea recap. We
have covered a massive amount of ground today.
The overarching philosophy here is remarkably
simple. You must match the specific tool to the
specific step. Never, ever use one single tool
for everything. Use Fintool or AlphaSense for
your foundational research. Use Quadratic for
your structured document extraction. Use Copilot,
Claude or Tracelite, Inside Excel for modeling.
And use Claude, Gamma or Bricks for your final
presentation slides. And the absolute, undeniable
golden rule. AI speed must always be paired with
rigorous human review. Those review habits are
completely non -negotiable. Always check three
to five random line items on your extracted data
directly against the source PDF. Yes. Always
test your AI generated models with a deeply familiar
known scenario to check the math. And, critically,
read every single number on a presentation slide
out loud before sharing it. Reading out loud
catches so many hidden errors. It physically
forces your brain to slow down. You actually
process the math instead of just skimming the
visual shapes. It really does. It breaks the
visual hypnosis. Two -sex silence. So, learner,
here's our specific call to action for you today.
Take a hard look at your current weekly workflow.
Identify the single most painfully time -consuming
step. Just pick the absolute worst bottleneck.
And try integrating just one of these specialized
tools this week. See how it materially changes
your workday. Start very small. Build your trust
gradually. Yeah. Beat. I want to leave you with
a final slightly philosophical thought. Let's
hear it. If specialized AI eventually handles
all the tedious grunt work of extracting PDF
data and building those initial baseline models,
does the future finance professional become less
of a mathematical builder and more of an editor
or a philosopher of risk? That is definitely
something to think about as the industry shifts.
Thanks for joining this deep dive.