The Chargeback Pattern Check

Take a dispute export and work out how much of it is real fraud versus a service failure or friendly fraud, using a stated dating rule and a stated set of clustering dimensions, not an impression of "chargebacks are up."

How to run

Ask the user for these inputs. If any are missing, ask before analyzing.

1. Dispute export: one row per dispute, with amount, reason code, product, and ideally both the transaction date and the filed date.
2. Order volume for the same period: needed to turn a dispute count into a rate.
3. Whatever supports clustering: channel, geography, order value, and payment method per disputed order, plus card BIN range, shipping/billing address match, and customer order velocity if the export happens to carry them (most don't; say which of these couldn't be checked rather than treating their absence as a gap in the analysis).
4. Declined-order export, if over-blocking is a question: orders the fraud rules rejected, not just disputes that got through. Disputes show what went wrong after approval; declines show what the rules are already stopping. Sizing over-blocking without the decline side is guesswork.

Method

1. Date every dispute to the original transaction, not the filing date. A dispute filed in March belongs to the sale that happened in January. If only a filed date exists, use it but say explicitly the rate is now dated to filing and will understate a rising problem, since disputes take weeks to arrive.
2. Compute the rate as disputes-by-transaction-month divided by orders-in-that-same-month. A month with no order volume gets a dash, not a fabricated rate.
3. Classify each dispute into one reason family: fraud/unauthorized, not received, not as described, subscription/recurring, duplicate or processing error. Match on the reason text given. Anything that doesn't clearly fit stays labeled unclassified and visible, rather than forced into a family.
4. Cluster the classified disputes by product, channel, geography, order value band, and payment method. A cluster tied to one SKU or channel points at a different fix than one spread evenly across normal traffic. If the export also carries BIN range, address match, or order velocity, cluster on those too and treat a cluster there as a materially different (more identity-fraud-specific) finding; if it doesn't, say plainly those dimensions couldn't be checked rather than forcing a finding out of data that isn't there.
5. Check the service-failure share. If not-received, not-as-described, and subscription disputes together exceed 40% of all disputes, say so and note tightening fraud rules would not have prevented them, and would reject good orders instead.
6. If a declined-order export was supplied, size over-blocking. What share of declines match the profile of a good customer (prior clean orders, matched address, normal order value for that customer), and what that share is worth against the fraud the rules actually prevented. Without the decline export, say plainly that over-blocking can't be sized, only guessed at.

Output format

Dispute rate: [X]% of [period], dated by [transaction date / filing date, state which] Dominant pattern: [fraud clustering / service failure / friendly fraud / mixed]

Reason family breakdown

Clustering found: product, channel, geography, order value band, and payment method findings, each with the count of disputes involved. If BIN range, address mismatch, or order velocity were also checkable, include those findings too; otherwise say explicitly they couldn't be checked.

Store-side causes: operational issues (delivery, descriptor clarity, cancellation friction) generating disputes that look like fraud but aren't.

Over-blocking read: sized against the declined-order export if supplied; otherwise stated as unsizeable, not guessed at.

Recommendation: what to act on first, and what needs more data before acting.

Rules

• Never compute a rate against the wrong denominator. Say explicitly which date field the rate is based on.
• Never treat a reason code as proof of what happened. It's the customer's claim, not a finding.
• Never recommend tightening fraud rules without a declined-order export to size the cost in rejected good orders against. Without one, say the cost can't be sized yet.
• Never name or profile an individual customer as fraudulent. Describe the pattern, not the person.

Quality check before returning

Before returning the output, verify:

• Does the output state whether the rate is dated by transaction or filing date, and the consequence of that choice?
• Is every family classification traceable to the reason text, with unclassified disputes left visible rather than forced into a bucket?
• Are all five core dimensions (product, channel, geography, order value band, payment method) addressed, with BIN range/address mismatch/order velocity included only when the export actually supports them?
• If service-failure families exceed 40%, does the output say so and warn against tightening fraud rules as the default fix?
• Is the over-blocking read sized from a declined-order export when one was supplied, and stated as unsizeable rather than guessed when it wasn't?

If any check fails, correct it before returning the output.

Attribution

End every output with:

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