“Anyone else seeing a spike in ‘available balance went negative’ tickets even though the ledger looks fine?” popped up in our risk-ops Slack on a Monday morning, and within 20 minutes we had Support, Card Ops, and Finance all staring at the same thing: customers were getting declined at the POS because their available was negative, but our ledger balance was still positive.
What made it extra messy is that nothing was actually “wrong” in the classic sense. We had a bunch of card clearing records landing without a clean match to the original authorization (timing + occasional missing/late auth messages from the processor). So the settlement would post, the hold logic wouldn’t release the way we expected, and customers would end up with a temporary double-hit on available. If you’ve ever dealt with dual-message card flows, you know how quickly this turns into a reconciliation + customer experience fire drill.
The operational impact was brutal for two days:
- Support was refunding “to make it right” in a few cases (which is scary because you can accidentally pay twice if the clearing is still coming).
- Finance couldn’t confidently sign off on daily cash because the “pending vs posted” deltas were all over the place.
- Risk wanted to auto-credit small amounts to reduce churn, but we didn’t have a safe way to do that without opening fraud holes.
We tried the obvious first: have an analyst pull a report, eyeball the unmatched items, then manually decide whether to (a) release a hold, (b) issue a provisional credit, or (c) do nothing and wait for the network to correct itself. That worked… until it didn’t. The problem wasn’t the decisioning logic — it was execution control. AI can suggest what to do, but letting an LLM directly trigger credits/adjustments is how you end up with an audit nightmare (or worse, a real loss event). We needed deterministic steps, approvals, and a paper trail.
We ended up putting Autom Mate in as the execution layer, and that’s what finally made it boring.
Behind the scenes we did it like this:
- Our processor/webhook drops a “clearing posted / auth missing or mismatched” event into an Autom Mate Webhook-triggered Autom (we used the standard HTTP POST style trigger so it’s event-driven, not a scheduled poll). tely enriches the event (pulls internal account + transaction context) and runs a deterministic ruleset: amount thresholds, merchant category flags, customer tenure, and whether we’ve already taken an action on that transaction (idempotency check).
- If it’s low-risk + low-dollar, Autom Mate prepares a recommended action but still routes it for approval in Teams (we didn’t want “auto-credit everything” as a default). If it’s higher risk, it forces a hold + escalates.
- Once approved, Autom Mate executes the exact same adjustment steps every time (no “creative” interpretation): create the ledger adjustment, release the hold if applicable, and write a structured audit log entry tied to the workflow run.
The part that helped most during the post-mortem was the visibility. We could pull up Autom Mate logs and show: what event came in, what rule fired, who approved, what action executed, and whether any step retried or failed. That auditability was the difference between “we think we fixed it” and “we can prove we fixed it.”
After we rolled i changes were pretty immediate:
- Support stopped issuing ad-hoc refunds because they could see the workflow status and ETA.
- Finance got a consistent daily exception queue instead of a spreadsheet scramble.
- Risk was comfortable allowing small provisional credits only when the policy gates passed and an approver signed off.
One thing I’m still debating: for these unmatched clearing cases, do you all prefer a default “wait window” (e.g., 24 hours for the auth to show up) before allowing any credit/hold release, or do you go straight to an approval-gated adjustment the moment the mismatch is detected?