Some patterns are invisible in a spreadsheet — not because the data isn't there, but because no human can eyeball 40,000 rows of transaction history and spot a repeating anomaly. That's where Python and SQL take over: API pulls, fraud pattern detection, and reconciliation at a scale Excel was never built for.
A ₹37 lakh returns-fraud pattern was found not by staring harder at a spreadsheet, but by pulling three years of marketplace transaction data through an API with Python and letting a script do what no human has the patience for: check every single row against every other row, consistently, without getting tired on row 30,000. SQL is where that same data gets stored, queried and joined once it's too large and too relational for any spreadsheet to hold cleanly.
Python and SQL work here falls into three buckets — pulling data in, finding what's wrong with it, and moving it where it needs to go.
Scripted margin-leakage and returns-fraud pattern detection across marketplace/ERP transaction history — the same approach behind the Last Layer Approach™.
GST/GSTR-2A matching, inter-company reconciliation, bank-to-book matching — built once, run every cycle without a human re-doing the matching by hand.
Moving transaction data out of a system of record and into a queryable warehouse, so a dashboard or a model never waits on a manual export again.
₹37L surfaced in one cycle via a Python + API script pulling marketplace data into Power BI — the manager later admitted ₹1.25Cr diverted over three years.
SAR 1.4M (≈₹3.08Cr) cash crunch identified 8 weeks early by scripting overlapping project cash flows that were invisible when viewed one at a time.
21-day consolidation cut to 5 days once currency and format differences across entities were normalized through a scripted data pipeline.
The ₹37L returns-fraud pattern below was found by pulling three years of marketplace data through an API with Python. That same API-integration instinct goes back to 2020, when Zerodha's live NSE data feed was wired directly into Excel via VBA for Tradesimply — a working trading app, not a demo. Different language, same underlying skill: connect to a live data source, trust nothing until it's verified, and let the data update itself.
These are written for finance people who need one specific job done, not a computer-science course.
Match GSTR-2B against your purchase register automatically and flag mismatches before the filing deadline, not after.
Ageing buckets, duplicate payment detection, and month-over-month variance — written once as reusable queries against a standard chart of accounts.
The exact pattern used to pull three years of transaction history for pattern-detection work, explained without assuming you're a developer.
Where it starts — 22+ years of Excel, MIS and financial modeling, now with Claude for Excel.
When a spreadsheet needs to become a live dashboard your whole team can see.
When the numbers outgrow a spreadsheet and need to live in a proper database. You're here.
Step 3 of 4 — Python & SQL. Once the checks are scripted, the last step is putting them on a schedule so nobody has to run them.
Next Step: Automation →Once checks live in Python and SQL, the next question is what to build first. This report ranks your top 5 financial gaps from your own numbers.
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