This is exactly the kind of problem that makes us stop and ask: why are ten people spending eighty hours on a task a spreadsheet should handle in seconds? The insurance company's duplicate-claim issue is real, but the manual approach, matching three columns by eye, marking IDs in red, hoping fatigue doesn't introduce errors, is a recipe for frustration, not resolution. Our take is plain: you don't need more people staring at cells. You need a formula that does the work for you.
The user's instinct is correct: an IF formula that checks whether Column A equals Column B and Column C is the right path. In Excel, that looks like `=IF(AND(A2=B2, B2=C2), "Duplicate", "Unique")`. Apply it to a helper column, then filter or conditionally format the results. The entire dataset can be scanned in the time it takes to write the formula. No highlighting by hand. No cross-referencing grey values. No all-nighters. The same logic works in Google Sheets, with the same clarity and speed.
What this reveals is a broader truth about how we treat data. When a system fails, the reflex is often to throw human hours at the symptom. But that approach costs more than time. It costs accuracy. A tired person misses matches. A quick glance overlooks a row. The product is a list of duplicates with a confidence level far below what a simple conditional check provides. The real fix is not better eyeballs. It is a better process, and that starts with asking the spreadsheet to do what it was designed for.
The solution is already in your hands. You do not need new software. You do not need a data science degree. You need a single formula, applied consistently, and the willingness to trust it over the manual routine. Next time this happens, and it will, skip the eighty hours. Write the formula first. Let the tool do the heavy lifting, and let your team focus on what the duplicates actually mean, not on finding them.