Finance

Cleaning and standardizing finance data for models and underwriting

Use AI to clean and standardize messy imported data—from ERP/bank exports, FP&A imports, or real estate financial spreadsheets and rent rolls—into model-ready formats, reducing manual prep time and downstream errors.

Why the human is still essential here

A human still defines the underwriting requirements, validates the transformed numbers/line items, checks for omissions or misclassifications, and makes the final investment judgment; AI primarily accelerates the formatting and consolidation work.

How people use this

T12 consolidation into a standardized income statement

AI maps inconsistent chart-of-accounts line items across multiple historical spreadsheets into one monthly income statement with variance notes for analyst review.

Claude / Microsoft Excel (Copilot)

Rent roll cleanup and charge breakout

AI reshapes a Yardi rent roll export into one row per unit/tenant and splits base rent and additional charges into dedicated columns for underwriting.

Microsoft Excel (Power Query) / Microsoft Copilot

Populate underwriting model from OM/T12/rent roll PDFs

Document extraction pulls key values from offering memo, T12, and rent roll PDFs and fills a standard Excel underwriting template with traceable sources.

PropRise Primer / Microsoft Excel

Bank/ERP CSV normalization

AI helps standardize inconsistent delimiters, dates, and numeric formats from bank or ERP exports so they load cleanly into FP&A models.

Power Query / Microsoft Copilot

GL description-to-account mapping

AI suggests account/category mappings from messy transaction descriptions to accelerate reclassification and reduce manual tagging.

ChatGPT

Duplicate and outlier cleanup workflow

AI-assisted data prep flags duplicates, missing fields, and outliers in imported actuals and outputs a cleaned table with an audit trail of changes.

Alteryx AiDIN

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Community stories (2)

I watched a $3.5M acquisition almost fall apart during a board review.

I watched a $3.5M acquisition almost fall apart during a board review.

Not because the strategy was wrong.
Not because the market changed.

Because of a single circular reference error in the debt schedule.

One hashtag#REF! error appeared. Then another.

Suddenly, the NPV didn't make sense. The IRR was jumping. The Board lost confidence in 10 minutes.

I spent 14 hours that weekend rebuilding the entire model from scratch. At that point, I had been in finance for 15 years.

And there I was, tracing cell dependencies like an intern at 2 AM.

That’s when it hit me: the manual way is a massive risk.

We accept "messy data" and "manual linking" as normal. It isn't. It’s a liability.

Today, that same error takes me 60 seconds to fix.

I use AI to debug formulas, clean messy imports, and review models.

I don't need to be a Excel advanced. I just need to know how to direct the AI agent inside my sheet.

What took 14 hours now takes 14 minutes.

You can either keep grinding manually, or you can learn the new way to work. Ready to level up? Join our Corporate Finance Hub® and master AI in finance. Stop fixing formulas. Start making decisions.

Join here:

https://t.co/SU0kSRB1i5

Also, do not forget to join my Webinar : AI in Action: 10 Real Use Cases for FP&A:

luma.com/xjj56tuc

Bojan RadojicicCEO at Finance & Tax Advisory Firm

Mar 6, 2026

Two ways Iam using AI to streamline real estate underwriting and operations

Two ways I'm using AI to streamline real estate investment underwriting and operations:

1) Analyst-level underwriting work
As an emerging GP, Im handling analyst work like spreading financials and formatting rent rolls. I tested Claude on a new deal: it consolidated three separate historical financial spreadsheets with inconsistent line items into a single 36-month income statement with metrics, variance highlights, and self-checks. It also reformatted a ~600-row Yardi rent roll into a clean single-row format with additional charges separated into columnswork that would have taken all morning done in ~20 minutes.

2) Auditing monthly property management reports
AI is serving as a second set of eyes on month-end financials. I had Claude convert Januarys invoice binder into a detailed spreadsheet, cross-check each invoice against its GL entry, and flag discrepancies. Im not fully automating review, but this preliminary audit is a strong starting point before deeper review.

Brian SobieckiManaging Partner

Feb 23, 2026