Automotive
·
Lookback
A 6% return rate looked unremarkable until identity clustering exposed $1.5M+ in claims fraud and serial returns.
A 12-month lookback across 1.2M customers. Lost and damaged claims that looked independent turned out to be coordinated networks.
in claims fraud
from high-risk returners
ops savings from automated claim checks
The challenge
Every lost-package claim was paid at face value
This automotive aftermarket retailer sells parts at an average order of about $325 and a 30% gross margin. Returns ran at about 6% of sales, roughly $36M a year, which did not stand out. The exposure sat in claims. When a customer reported a package lost or damaged, there was no disposition data to check it against, so the claim was accepted and the order written off in full. At these margins a single fraudulent claim takes about $1,100 in new sales to recover. Repeat claimants were never challenged, because nothing connected one claim to the next, and each one was validated by hand. Expensive parts and lenient policies made the store a reliable target.
What Pinch found
1.7% of customers filed repeat lost and damaged claims
Claims that looked independent were not. Identity clustering tied many repeat claimants into coordinated networks that shared addresses and devices across separate accounts, all aimed at high-value orders. A second, smaller group returned at 5 to 7x the store rate and focused on expensive parts where policies were most lenient. None of this showed up in the return rate, because claims and serial returns were judged one at a time. Together the abuse came to $1.5M+, with about $1.1M of it flowing directly to margin. At 30% margin, it would take $5M in new sales to offset.
Claims fraud. 1.7% of customers filed repeat lost or damaged claims on high-value orders, many tied together into coordinated networks.
High-risk returners. 0.4% of customers returned at 5 to 7x the store rate, targeting lenient policies on expensive parts.
Ops savings from validating claims automatically instead of by hand.
Repeat lost and damaged claims looked independent. Linked by address, IP, phone and device, many came from the same networks.
The response
Deny the rings, check the rest automatically
Claims from identified clusters would be denied and their future orders cancelled. Every other claim would need photo evidence: a small step for a genuine customer, and a real barrier for a ring. High-risk returners would face return fees, required descriptions and closer review before refund. Automating claim checks would free about $120K a year in operations time. Good customers would move the other way, with free return labels, instant refunds and priority handling.
Claims fraud
Block
Deny claims from identified clusters and cancel their future orders. Require photos on the remaining claims.
High-risk returners
Friction
Return fees, required descriptions and closer review before refund.
Claims ops
Recover
Automate claim checks to free about $120K a year in ops time.
Best customers
Reward
Free return labels, instant refunds and priority handling.
How Pinch worked
Linking accounts by device, address and IP
Email alone could not connect the claimants, so Pinch ID linked accounts through shipping addresses, IP, phone and device fingerprints across 1.2M customers. Claims models looked for repeat filing, coordinated filing and a preference for high-value orders. Network analysis then grouped separate accounts into rings wherever addresses and devices overlapped. High-risk scoring flagged habitual returners for policy enforcement. Every order and return was scored on 100 risk signals, with no disposition data required.
Pinch ID
Links accounts by address, IP, phone and device, not just email.
Claims models
Spot repeat claims, coordinated filing and high-value targeting.
Network analysis
Clusters separate accounts into rings through shared addresses and devices.
High-risk scoring
Flags habitual returners for policy enforcement.
The takeaway
Brand details are anonymized. Figures come from the brand’s own order and return data.
Industry
Automotive
Size
$500M+ GMV
Platform
Custom platform
Solutions
Claims Automation · Return Abuse
Lookback
12 months
At a glance
~6%
Return rate by value
~$325
Average order value
~$36M
Annual returns
1.2M
Customers analyzed
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Free lookback · for qualified brands
See what you lost last year. Free.
01
Share 12 months of data.
Install the Shopify app or SFCC cartridge, or send order, return and claim exports.
02
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What you lost to return and claims abuse, and who took it.
03
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