Automating Refund Request Triage
Every refund request currently gets the same manual review regardless of size or risk — an agent checks order history, refund policy eligibility and payment status by hand for a €12 request exactly the way they would for a €900 one, which means low-risk, clearly-within-policy refunds wait in the same queue as genuinely ambiguous or suspicious ones. Meanwhile refund abuse patterns (repeat requesters, chargebacks combined with refund requests on the same order, accounts requesting refunds right after a promotional bonus in iGaming) are hard to spot manually because no single agent sees enough volume to notice the pattern across different tickets.
STARTING PRICE
From €299
Standard tier · Multi-step workflow with AI extraction/decisioning and 2-3 integrations.
Get a quote →Saves roughly 4-7 hrs/week and reduced refund-abuse exposure.
How the automation works
We build a triage layer that checks each refund request against your policy rules and payment/order data automatically — eligibility window, order status, prior refund history on the account — and auto-approves clearly within-policy, low-value requests directly through your payment processor, while routing anything above a value threshold, outside policy, or matching a risk pattern (repeat requester, recent chargeback, unusual timing) to a human reviewer with the relevant history already compiled. This keeps low-risk refunds fast for genuine customers while making sure the requests that actually need judgment get a reviewer's full attention instead of being rubber-stamped under queue pressure.
Process flow
- 01
Refund request submitted trigger
A refund request arrives via ticket, in-app form, or support chat and triggers the triage workflow before any manual review begins.
- 02
Check policy eligibility ai
The request is checked against your refund policy — time window, product/service type, prior usage — using order and account data, not just what the customer states in the ticket.
- 03
Scan for risk patterns integration
Payment and account history is checked for risk signals: repeat refund requests, recent chargebacks, unusual timing relative to a promotion or bonus, or a pattern across linked accounts.
- 04
Auto-approve or route to reviewer ai
Clearly within-policy, low-value, low-risk requests auto-approve and process through your payment system; everything else routes to a human reviewer with the policy check and risk signals already compiled.
- 05
Process refund or notify reviewer output
Approved refunds are processed directly through your payment processor with a confirmation sent to the customer; flagged requests notify a reviewer with full context, not just the raw request.
Inputs
- Refund request details
- Order/payment history
- Refund policy rules
- Prior refund and chargeback history per account
Outputs
- Auto-processed low-risk refunds
- Risk-flagged requests with compiled context for review
- Refund policy compliance log
- Refund abuse pattern reporting
Works with
Prefer a fully custom build instead of an off-the-shelf integration? We scope both options during your free consultation — most jobs like this one work fine on standard connectors, but higher-volume or non-standard systems sometimes need bespoke API work, reflected in the complex tier.
Where this goes wrong if you get it wrong
- Auto-approving based on order value alone misses pattern-based abuse — a customer requesting many small refunds just under the auto-approve threshold across multiple orders is a bigger risk than one large legitimate request, so risk scoring needs to look at account-level patterns, not just single-transaction value.
- In iGaming specifically, refund requests clustered right after a bonus or promotional credit is a well-known abuse pattern that a naive within-policy check won't catch — timing relative to promotional activity needs to be an explicit risk signal, not an afterthought.
- Auto-approval thresholds set without regard to actual fraud loss data either block too many genuine customers (set too low) or expose real financial risk (set too high) — the threshold needs to be calibrated against your own chargeback and refund-abuse history, not a generic industry default.
- Refund requests tied to a live chargeback dispute need to route to review even if they'd otherwise qualify for auto-approval, since processing a refund and losing a chargeback dispute on the same transaction is a double loss that a policy-only check won't catch.
Frequently asked questions
What counts as 'low risk' for auto-approval?
We set thresholds with you based on your actual policy, typical order value, and historical abuse patterns — there's no universal default, since what's low-risk for a retail order is different from what's low-risk for an iGaming withdrawal-adjacent refund.
Does this connect directly to Stripe to process refunds?
Yes, auto-approved refunds process directly through your connected payment processor, with a full log kept of every automated decision for reconciliation and audit.
Can we adjust the risk rules as abuse patterns change?
Yes — risk rules are configurable and should be reviewed periodically, since refund abuse patterns shift over time as customers and bad actors learn what triggers review.