Ask most OEM warranty teams how they cover their dealer network and the honest answer is some version of sampling: a percentage of claims reviewed each month, weighted maybe toward higher-value repairs, rotated across dealerships over time. At a small network, that’s a reasonable trade-off. At 100+ dealerships, it stops being a trade-off and starts being a blind spot with a percentage attached to it.
\n\n\n\n\nThe math is the problem. If a warranty team reviews 10% of claims and a dealership knows, even informally, that review rates sit somewhere in that range, the incentive to keep problems inside the unreviewed 90% doesn’t require anyone to be malicious — it just requires the sampling gap to exist.
\n\n\n\nSampling isn’t a bad idea on its own. Reviewing every claim manually, in detail, was never realistic for a warranty team of any reasonable size once claim volume passed a certain point. Sampling was the practical answer to a resourcing problem: limited reviewers, limited hours, a claim volume that outpaces both.
\n\n\n\nThe issue isn’t that sampling was chosen. It’s that sampling was chosen as a permanent strategy rather than a stopgap — and it stayed in place well past the point where the network size it was designed for had changed.
\n\n\n\nA few things happen once a network crosses into this range that a smaller network doesn’t have to deal with:
\n\n\n\nClaim volume outpaces reviewer capacity by an order of magnitude. The gap between what should be reviewed and what can be reviewed manually widens every quarter the network grows, without the review team growing at the same rate.
\n\n\n\nPatterns get diluted across more locations. A recurring issue that would stand out clearly across 10 dealerships gets statistically buried across 150 — the same underlying pattern, but harder to see because it’s spread thinner.
\n\n\n\nDealer-to-dealer comparison becomes unmanageable by hand. Knowing which dealerships are consistently flagged versus which are clean requires cross-referencing hundreds of evaluation histories — not something a review team can hold in their heads or a spreadsheet at that scale.
\n\n\n\nRegional and OEM-level reporting gets harder to trust. Aggregate warranty health numbers built from a sample start to reflect the sample’s blind spots more than the network’s actual condition.
\n\n\n\nThe alternative to sampling isn’t necessarily “review every claim in full manual detail” — that’s not realistic either. It’s building a model where every claim gets checked against a consistent set of criteria automatically, and only the ones that trigger a flag get pulled for deeper manual review. A few components make that possible:
\n\n\n\nIt’s tempting to think scaling warranty evaluation just means hiring more reviewers to sample more claims. That doesn’t solve the underlying issue — it just makes the sample larger while the gap, proportionally, stays the same. The actual shift needed is structural: moving from “a person decides which claims to check” to “every claim gets checked automatically, and a person decides what to do with the ones that get flagged.” That’s a different model, not a scaled-up version of the old one.
\n\n\n\nSampling doesn’t just risk missing fraud — it distorts the OEM’s own picture of warranty health. A network’s reported warranty cost trends, dealer scorecards, and budget forecasts are all built on whatever data got reviewed. If that data is a 10% sample, the OEM isn’t managing warranty cost based on reality — it’s managing it based on a slice of reality, and hoping the slice is representative. At 100+ dealerships, that’s a large assumption to be resting a budget line on.
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