The Scenario
Your fraud team at an e-commerce company has a Google Sheet with 1,000 recent transactions. Column A has the BIN — the first six to eight digits of the card number. You need to know, for each transaction, the card brand, card type (credit or debit), and the issuing country. That combination is a key input into your fraud scoring model.
Your fraud analyst is waiting for the enriched sheet before she can run the risk model. The transaction data has been sitting in the sheet since this morning.
The bad version:
- Find a BIN lookup tool with a batch option, export the BIN column to CSV, upload
- Wait for results, download the results file, discover it came back with the card brand in a column called "scheme" — not what you mapped in your model
- Rename the column, align the results file against the original sheet rows, find that 47 BINs returned no results because they're newer cards not in the tool's database
The analyst is still waiting. It is 4 PM.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent that lives inside your Google Sheet. Through its built-in Neutrino integration, it runs BIN lookups on every row in column A and writes the card brand, card type, and issuer country into the columns your fraud model expects.
For each BIN in column A, use Neutrino to look up the card brand, card type, and issuer country and write them into columns B, C, and D respectively.
What You Get
- Column B: card brand (Visa, Mastercard, Amex, Discover, etc.)
- Column C: card type (credit, debit, prepaid)
- Column D: issuer country — the two-letter country code of the bank that issued the card
- All 1,000 rows processed in one pass — ready to pipe into the fraud model without re-cleaning
What If the Data Is Not Quite Ready
You want to flag transactions where the issuer country doesn't match the customer country
Run Neutrino BIN lookups on all 1,000 BINs in column A. Write card brand in column B, card type in column C, and issuer country in column D. Compare the issuer country in column D against the customer country in column E. Flag any mismatch with COUNTRY MISMATCH in column F.
Some BINs are 6 digits and some are 8 — you want to standardize
For each BIN in column A, note the digit count in column B. Then run Neutrino BIN lookups and write card brand in column C, card type in column D, and issuer country in column E.
You want to score prepaid cards separately in the fraud model
Run Neutrino BIN lookups on all BINs in column A. Write card brand in column B, card type in column C, and issuer country in column D. Flag any row where column C is prepaid with PREPAID RISK in column E.
Full risk enrichment in one shot
Run Neutrino BIN lookups on all 1,000 BINs in column A. Write card brand in column B, card type in column C, and issuer country in column D. Flag mismatches between issuer country and customer country (column E) with COUNTRY MISMATCH in column F. Flag prepaid cards with PREPAID in column G. Then add a summary row at the bottom showing the count by card brand and the count of flagged rows.
Try It
Get the 7-day free trial of SheetXAI and open your transaction BIN sheet, then ask SheetXAI to run Neutrino BIN lookups on column A and write the enriched fields into the columns your fraud model is waiting for. See also the IP blocklist screening spoke if you're also screening customer IP addresses as part of the same fraud review.