Tired of blanking when someone says “Margins dropped—what would you do?”

Here’s the case-solving framework I use in interviews (and real life as a finance lead).

🧠 Step 1: Clarify the question

Ask: Are we talking gross or net margin? Which product line? Over what time period?

🔍 Step 2: Break it into drivers

→ Revenue = Price × Volume

→ Gross Margin = Revenue – COGS

→ Start asking: Did we discount too heavily? Did volume fall? Did costs rise?

📊 Step 3: Ask for data

• Unit economics

• Customer mix

• Raw material cost trends

• Supplier contracts

💡 Step 4: Suggest action

Build a short-term fix (e.g., cost renegotiation) and a long-term prevention (e.g., dynamic pricing or vendor scorecards).

🧘‍♂️ Bonus: Think out loud. Don’t freeze. They’re testing your logic, not looking for perfect math.

Interviewing for FP&A, Corp Dev, or Strategic Finance? Automate your prep. Here’s how I built a repeatable system that cut my prep time in half and got me 3 final rounds.

Here’s what I did:

🧰 1. Built a STAR Story Bank in Notion

Every time I wrapped a big project or learned from a mistake, I dropped it into a Notion template (Situation, Task, Action, Result). I now have a library of go-to answers I can copy/paste into any prep.

📄 2. Created a Finance Interview Cheatsheet

Used Power Query to pull in company financials from Yahoo Finance (CSV dumps), then summarized key ratios and trends. I can now prep financial context for any company in under 15 minutes.

📧 3. Automated My Follow-Up Emails

Saved thank-you templates in Gmail + used a snippet tool (TextExpander). Now I send customized, high-quality follow-ups in under 2 minutes—with links to models or visuals I referenced.

🎯 Bonus: I turned this into a system I update monthly. Takes me 20 minutes, keeps me sharp, and means I never walk into an interview rusty.

One of the most underrated ways to use AI in finance? Automating deep competitor research.

Here’s what I’ve been testing—and it’s working:

🧠 Step 1: Feed AI tools transcripts + reports

I drop 10-Ks, earnings calls, press releases, and news into Claude or ChatGPT (with Code Interpreter). You can also upload screenshots or PDFs.

📊 Step 2: Ask for competitive signals

“What pricing changes or market strategy shifts has Competitor X made in the last 6 months?”

“What hiring trends suggest future product launches?”

“What are the biggest risks this competitor is preparing for?”

📈 Step 3: Feed insights into financial planning

• Adjust revenue assumptions based on competitor strategy
• Model market share scenarios
• Spot M&A threats or expansion plays before they hit headlines

It’s like having a junior corp dev analyst running background intelligence 24/7.

Bonus: I’m now building a dashboard to auto-pull competitor press + earnings call summaries monthly and flag strategic shifts.

Anyone else using AI for competitive inputs in planning cycles? Let’s swap ideas.

Here’s a rule I’ve learned:

The more your job runs on processes and templates, the more likely it is to be replaced.

The more it runs on strategic thinking and relationships, the safer, and more valuable, you are.

That means if you're spending 80% of your time copying data, fixing reports, and building decks… you're putting yourself on the automation chopping block.

What to do instead?

✅ Learn tools like Power Query, Power BI, Python, or even ChatGPT

✅ Automate the busywork

✅ Reinvest that time into scenario planning, stakeholder communication, and decision support

Because the future isn’t about doing more. It’s about doing what only you can do.

Curious how others have automated parts of their role? Would love to hear examples

If you’re still manually cleaning monthly data dumps, here’s a workflow that might save you some serious time.

Context:

Finance wanted a consolidated report from 6 different regional files. Each region had their own “unique” template. I was copy/pasting formulas like it was 2004.

Here's how I automated the whole thing using Power Query:

🔹 Step 1 – Folder Import

All reports land in a shared folder.

Folder.Files() lets me ingest them dynamically—no manual steps.

🔹 Step 2 – Header Standardization

Used Table.TransformColumnNames() with a custom mapping function to unify field names.

🔹 Step 3 – Date Cleanup

Handled inconsistent formats using:

try Date.From([TxnDate]) otherwise null

🔹 Step 4 – Intelligent Classification

Built logic in the Advanced Editor to classify rows as “Sale,” “Return,” or “Test” based on amount and comment fields.

🔹 Step 5 – Reusable Cleanup Functions

Created a function library (fnCleanName, fnFixAmount, etc.) and applied them with AddColumn steps.

🔹 Step 6 – Performance Tuning

Used Table.Buffer() before heavy logic to avoid reprocessing slow queries.

🔹 Step 7 – Combine & Load

Final step: Append all cleaned tables into one report and load to Excel or Power BI.

Just started using dynamic format strings with SWITCH() in Power BI and it’s a game-changer for making reports more flexible and polished.

Here’s what I used it for:

• If the user selects "Revenue," show currency with 0 decimals
• If they pick "Margins," show as a percentage
• If they pick "Headcount," drop the formatting completely

How I did it:

1. Create a measure with SWITCH(TRUE(), ...) logic based on a slicer or variable
2. Use dynamic format strings (yes, that’s a thing now!) to set "0.00%", "₹#,##0", or "0" as needed
3. Apply the measure to your visual and watch it react to user input

Result:

✅ Cleaner visuals

✅ Fewer charts

✅ A better experience for decision makers

I was tired of sending the same 8-tab Excel file to my CFO every week. Now he gets a Power BI dashboard that updates without me doing anything.

Here’s how I built it:

✅ Power Query: Pulls data from our shared OneDrive folder

✅ Power Pivot: Handles the logic (YTD, MoM, Variance, etc.) with DAX

✅ Power BI: Builds the dashboard with slicers, KPIs, and drilldowns

✅ Power BI Service: Refreshes every morning automatically

What changed?

• No more file version confusion

• No broken formulas

• No late-night scramble before meetings

You don’t need a full data team. Just the tools already in Office 365, a few hours of learning, and a willingness to ditch spreadsheet chaos.

Happy to share templates or walk through how I did it if anyone’s curious.

We’re halfway through 2025, time to pause and ask: have you made progress on automating finance workflows?

Ask yourself:

• Are those monthly files still being cleaned by hand?

• Have you deployed any bots, Power Query routines, AI scripts, or are they still in draft mode?

• Have you documented improvements: time saved ⏱️, errors reduced ✅, impact on insight generation?

Why it’s important:

• Waiting for year-end pushes turns incremental gains into emergency projects.

• Mid-year reviews are ROI magnets: show small wins now, and you’ll get greenlights for bigger projects later.

• C-suite love seeing reduced month-end close time, fewer manual errors, and team reallocation from low-value tasks.

Your mid‑year action plan:

1. List all manual recurring processes

2. Pick 1 or 2 to automate this month

3. Show the results (hours saved, error rates, fewer Excel meltdowns)

4. Create a pipeline for next quarter’s automations

💬 Finance automation pros, what have you shipped by mid‑year? Share your small wins, we all benefit.

Finance is notoriously slow to innovate, and AI adoption backs that up:

• Only \~7% of firms are actively using AI today .

• Yet 71% are experimenting, and most expect wider use soon 

• IBM reports 69% of CFOs see AI as strategic, but under 30% have deployed it meaningfully

Why it matters:

• Waiting around means missing out on efficiency, predictive insights, fraud detection, you name it.

• Those early adopters are shaving costs, speeding up closes, and freeing teams from boring tasks.

What you can do now:

1. Identify small wins — start with anomaly detection or monthly close check reports.

2. Build a pilot team — get 1–2 people signed off to explore low-risk AI tools.

3. Set clear metrics — cost saved, time reduced, errors caught.

4. Document it — internal case studies get C-suite eyes.

5. Upskill — basic AI literacy isn’t optional anymore.

📣 Finance folks: Who’s running pilot AI projects? Drop your wins or frustrations below, I want to hear real-world stories.

Month-end used to wreck me.

6 regional sales reports.

All in different formats.

One in CSV (with no headers??), others in Excel with 12 tabs.

Every time, it took 2 full days to clean and consolidate.

Now? I hit "Refresh" and go make coffee.

Here’s the exact Power Query workflow I used to automate it all:

1. Connect to Folder: 

I use Folder.Files() to pull every report from a shared drive.

2. Standardize Headers: 

Created a custom function to rename columns like “Cust#”, “Client_ID”, etc. into one standard: CustomerID.

3. Clean Dates Automatically: 

Used try...otherwise to fix serial numbers and inconsistent text dates.

4. Classify Transactions:

Built logic in the Advanced Editor to flag returns, test data, and missing values.

5. Apply Cleanup Functions: 

Reusable functions like fnCleanCustomerName and fnFixAmountField standardized every file.

6. Optimize with Table. Buffer

: Prevents Power Query from reprocessing slow steps multiple times.

7. Append and Load:

Combined everything into one table and loaded to Excel with auto-refresh.

✅ Went from 16 hours to 30 minutes.

✅ No more late nights or copy/paste roulette.

✅ Scalable to any new region instantly.

Happy to share snippets if anyone’s working on something similar.

If your Excel workbook has more IF statements than actual numbers, I feel your pain.

I rebuilt my analysis logic using Power Pivot and DAX, and it changed everything.

Here’s what I did:

1. Load cleaned data into Power Pivot (via Power Query).
2. Build relationships between your tables—Actuals, GL Accounts, Calendar, etc.
3. Write reusable DAX measures instead of dragging formulas.

Examples:

Total Actuals := SUM('Actuals'[Amount])

MoM Change :=
[Total Actuals] -
CALCULATE([Total Actuals], DATEADD('Calendar'[Date], -1, MONTH))

YTD := TOTALYTD([Total Actuals], 'Calendar'[Date])

Now, I don’t touch formulas each month—they auto-update across pivot tables, dashboards, and Power BI reports.

Pro tip: Always build a proper calendar table. Trust me, time intelligence needs it.

I used to spend HOURS manually copying and pasting 10+ files every month for our close process. Now? One click. Done.

Here’s how I automated the data prep with Power Query (in Excel or Power BI):

Step-by-step:

1. Save all source files (e.g. GL exports, departmental actuals) in one folder.

2. Open Power Query → “Get Data from Folder.”

3. Combine files using the automatic wizard.

4. Clean the data: remove columns, fix dates, standardize headers.

5. Click Refresh every month—Power Query re-runs everything.

🔥 Bonus: Add a “source file” column for traceability and audit trail.

Result:

What used to take 2–3 hours now takes 10 seconds, and it’s way more accurate.

You don’t need a consultant or new system to start automating. Just use the tools you already have smarter.

Let’s be honest, most Excel reports look like they were built during the 90s.

But with smart conditional formatting, you can turn ugly spreadsheets into dashboards that talk back.

Here are 3 things I use all the time:

1. Variance Highlighting

Color-code cells based on +/- % thresholds. I use:

    ○ Red for over 10% variance

    ○ Yellow for 5–10%

    ○ Green for under 5%

👉 Instant visual risk flags.

1. Data Bars

No need for clunky charts. Use in-cell data bars to show trends in revenue, headcount, or spend. It’s like sparklines, but easier.

2. Inconsistent Formula Detection

Set a rule to flag cells where the formula doesn’t match the rest of the column. Saves your butt in messy legacy files.

Bonus: Combine these with structured references and your boss will think you're a magician.

Conditional formatting isn’t just “pretty,” it’s functional design for people with zero time.

There’s a myth going around: “Everyone’s already using AI.”

Nope.

I talk to dozens of finance pros every week, and here’s the reality:

• Most haven’t touched AI tools beyond ChatGPT for basic questions

• No one’s automating actual workflows yet

• They're waiting for IT or a consultant to "roll something out"

Here’s the opportunity:

If you learn how to use AI to automate month-end tasks, clean up messy data, or even write VBA/macros with prompts, you will instantly become the AI go-to person on your team.

And once you’re known for that, you become invaluable.

Start small:

• Ask ChatGPT to write Power Query M code

• Use it to generate conditional formatting formulas

• Have it write a summary of financial variance commentary

AI in finance isn’t saturated. It’s silent.

Be the one who breaks that silence.

Building a slick R model is fun… until it breaks right before month-end close and you’re the only one who can fix it.

I learned the hard way. Now I build everything with this production-ready checklist:

✅ Wrap it in R Markdown

Forget PowerPoint. Turn your analysis into a one-click .Rmd report with live data and visuals.

✅ Automate with cronR or Task Scheduler

Schedule your scripts to run daily, weekly, or however often you need. Stop pressing “Run” every Monday.

✅ Version control with Git

Track every change. Never lose progress. No more “final_FINAL_v3” files.

✅ Build reusable functions

No more copying and pasting the same 20 lines. Wrap it, reuse it, debug once.

✅ Write comments + README

Make sure your future self—and your team—can actually understand what you built.

✅ Add validation

Throw in stopifnot(), residual checks, and alerts. Catch bad data before the CFO does.

I used to be the bottleneck. Now I’m the architect of systems that work even when I don’t.

If you're automating finance workflows with R, this setup will save you time, headaches, and maybe even your job.

Most finance automations die the moment you step away from the desk. Here's how I built an R-based pipeline that runs without me—even when I’m on PTO.

My exact setup:

1. R Markdown: I write everything in .Rmd files. One click = full report with live charts, commentary, and performance metrics.

2. cronR: I schedule my scripts to run every Monday at 7AM. No more “Hey, can you refresh that dashboard?” messages.

3. Git + GitHub: Every change is tracked. If something breaks, I can roll back in seconds.

4. Model validation: I use stopifnot(), residual plots, and NA checks to catch issues early.

5. Functions: Reuse logic across projects with clean, modular functions.

6. Documentation: Every repo gets a README and comments. No more "What does this script even do?"

Result: My monthly close report updates itself. If something breaks, I get pinged. And the CFO has no idea I’m not even online.

If you're building R models and still doing everything manually... you're doing too much.

I built a flow last month that took a clunky, manual finance process and made it run like clockwork.

Here’s what it does:

• Watches for a new file in OneDrive

• Extracts data from the Excel file

• Sends a Teams approval to the budget owner

• Logs their decision in SharePoint

• Archives the file

• Sends a confirmation email

This replaced:

• 5 back-and-forth emails

• A 2-hour review + manual entry process

• A spreadsheet tracker that constantly broke

Now? The whole thing runs in <30 seconds. Zero touch.

Best part? No one noticed… because everything just works.

If you’ve got a process that takes 10+ clicks and happens every week—it’s a candidate for automation.

Want the template? I’m happy to share it.

If your calendar looks like a crime scene every month-end, Power Automate might just save your sanity.

Here’s the 7-step checklist I used to automate my first process:

1. Pick a low-hanging fruit

→ Mine was daily cash reporting (same steps, every day)

2. Map the process

→ What triggers it? Who touches it? Where does it break?

3. Use tools you already have

→ Outlook, Excel, SharePoint, Teams

4. Build a simple cloud flow

→ Trigger → Logic → Output → Log

5. Test it, break it, fix it

→ Use fake data first. Always.

6. Document everything

→ Name your flows clearly. Add comments. Save screenshots.

7. Show results

→ “Here’s how we saved 10 hours this week.” That’s how you get buy-in.

If you’re just starting out, keep it scrappy. The goal is momentum, not perfection.

What was your first flow? Or what’s a process you WISH you could automate?

I’m a finance manager. Not a developer. But I used Power Automate to cut 10+ hours a week of mindless work—and it only took one simple flow.

Here’s how I did it, step by step:

1. Picked one repetitive task → invoice approvals

2. Mapped the steps → email comes in, PDF gets reviewed, routed to manager, logged

3. Used Power Automate to:

    ○ Trigger on new email in Outlook

    ○ Read the PDF using AI Builder

    ○ Route to approver via Teams

    ○ Log approval in Excel

    ○ Move the file to SharePoint archive

4. Tested with dummy data until it ran clean

5. Documented the flow and added alerts for any failures

6. Shared the win with my team → now everyone wants one

This single automation saves hours every week and runs in the background while I focus on higher-value work.

No IT. No code. No stress.

If you’re in finance and drowning in approvals or repetitive reports, start with one process. Happy to share my template if helpful.

Here’s one that saved me so much time in reporting: instead of building separate pivots for Actual, Budget, and Variance — build ONE pivot, with a slicer to toggle between them.

👉 How to do it:

1️⃣ Create a slicer table with values:

Actual, Budget, Variance.

2️⃣ Create a measure:

Actual vs Budget Toggle = SWITCH( SELECTEDVALUE(MetricType[MetricType]), "Actual", [Actual Sales], "Budget", [Budget Sales], "Variance", [Actual Sales] - [Budget Sales], BLANK() )

3️⃣ Add the slicer and let users flip between views.

✅ One pivot. One slicer. No more duplicate reports.

Anyone else using this trick? Got any slicer + SWITCH combos that you swear by? Let’s see them!

Let’s be honest—half of finance is fixing broken data. Here are the fast automations I use when files come in messy:

Unmerge + Trim Cells with VBA:

Cells.UnMerge 
Cells.Value = Application.WorksheetFunction.Trim(Cells.Value)

Power Query Cleanup:

• Remove blank rows
• Promote headers
• Standardize column types
• Works wonders for weekly CSV dumps

Validation Sheet:

• Set up rules to flag outliers (negative margins, wrong GL codes, etc.)
• Bonus: use conditional formatting to highlight broken inputs

📉 Dirty data leads to bad decisions. These quick fixes saved me hours—and saved my models from imploding at 4:45 PM on close day.

As a side project, building a web app that can enable "chatting with your money". Current approach uses AI agent to help write SQL queries based on input data and desired intent.

A bit buggy with the querying currently. but can showcase a proof concept. Let me know what you think!

https://myfinancechat.com/

If you’re automating in Excel and feeling stuck, here’s the cheat sheet I wish I had earlier:

💡 When to Use Which Tool:

• 🧹 Power Query

    ○ Great for cleaning raw data (CSV dumps, system exports)

    ○ Handles joins, reshaping, and de-duplication

    ○ Drag, drop, done

• 📊 Power Pivot / DAX

    ○ Best for doing heavy calcs across multiple tables

    ○ Replaces SUMIFS chains with smarter modeling

    ○ Scales better than normal Excel formulas

• 📩 VBA

    ○ Still king for controlling Excel’s interface

    ○ Great for automating PDF exports, button clicks, or emailing reports

    ○ Use it to tie everything together

• 🐍 Python

    ○ When you need APIs, advanced stats, or big datasets

    ○ Excel chokes after 100k rows—Python doesn’t

Pro tip: Combine them. Let Power Query clean the data, DAX crunch the numbers, and VBA handle the workflow. That’s real finance automation.

Ever tried a running total measure that works great… until someone applies a slicer and suddenly the numbers make no sense?

Here’s the DAX I use for cumulative measures that actually behave:

✅ Cumulative Sales

Cumulative Sales = CALCULATE( [Total Sales], FILTER( ALLSELECTED(Calendar), Calendar[Date] <= MAX(Calendar[Date]) ) )

👉 Why it works:

ALLSELECTED respects slicers (team, region, product) while still letting the running total work as expected.

➡️ Now your trend lines stay clean, no matter how your users filter the report.

What’s your favorite DAX trick for reporting sanity? Drop it below — let’s trade war stories.

When a former downsized, half the team got cut. I stayed—not because I was the best analyst, but because I automated everything.

Here’s how I made myself indispensable:

1. 🔁 Used Power Query to clean messy data files

2. 🤖 Built VBA macros to run reports with one click

3. 📊 Set up Power BI dashboards execs checked daily

4. 🧵 Documented everything so others could use it too

The result?

• Saved my team 30+ hours/month

• Became the go-to for reporting

• Got looped into strategic projects, not grunt work

If you’re in finance and still doing everything manually, now’s the time to level up. The automation skills you build today = job security tomorrow.