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About this use case: A Tier 1 automotive supplier processes 100,000 warranty claims per year with an internal model that's achieving 85% overall accuracy and under 35% recall on the Manufacturing Defect class — the category that triggers containment actions and supplier charge-backs. tracebloc benchmarks competing AI vendors on 400,000 real claims inside the supplier's infrastructure, with no VINs, customer records, or supplier codes leaving the company. Explore the data, submit your own model, and see how your approach compares.
A Tier 1 automotive supplier processes tens of thousands of warranty claims per year. Every claim must be classified into one of five root cause categories — and getting it wrong is expensive. A Manufacturing Defect misclassified as Wear & Tear means no containment action, no supplier charge-back initiated, and the same defect continues accumulating across the field. Karim Soliman, Head of After-Sales Analytics at a steering systems supplier in Stuttgart, knows his internal model hits 85% overall accuracy — and he knows that number is hiding a problem. His rare class recall, the metric that actually drives warranty cost reduction, is under 35%.
Karim deploys a tracebloc workspace loaded with 500,000 anonymised warranty claims — 400,000 for vendor fine-tuning, 100,000 held out for evaluation. Each vendor submits their classification model to the workspace. Inside tracebloc's containerised training environment, vendors train their model on the 400,000-record dataset — fine-tuning the model weights to Karim's specific claims mix, failure distribution, and feature patterns — without the data ever leaving the supplier's infrastructure. tracebloc handles orchestration, scores each adapted model against the holdout set, and publishes results to a live leaderboard ranked by F1 score. This is a federated learning application of vendor acceptance testing: the claims data stays on Karim's infrastructure from start to finish.
In this example evaluation, Vendor B exceeded its claimed recall on rare defect classes after fine-tuning — the only vendor to do so. The highest-claimed vendor (95% overall accuracy) collapsed on rare classes, hitting just 54% recall on Manufacturing Defects after fine-tuning. The leaderboard made that gap impossible to ignore. The tracebloc workspace stays in place for continuous re-evaluation as the claims mix evolves and new models enter the market.
Karim's team manages warranty analytics for a supplier that builds steering systems for multiple OEM platforms. Across a portfolio of roughly 100,000 claims per year, five failure categories drive very different business responses — and very different costs.
Wear & Tear accounts for 60% of claims and is relatively cheap to handle. The dangerous category is Manufacturing Defect (4.2%) and Config Flaw (1.2%): claims that, when classified correctly, trigger a containment action and a supplier charge-back. When they are classified incorrectly as Wear & Tear, nothing happens. The defect keeps accumulating. The warranty reserve gets hit without recovery. And if the field return rate crosses a threshold, the OEM flags a potential recall.
The internal baseline model achieves 85% overall accuracy — a number that sounds reasonable until you break it down by class. On rare categories, recall is under 35%. That means more than six in ten Manufacturing Defects are being quietly misclassified. The warranty cost reduction opportunity is hiding in those six claims.
Karim's challenge is structural: he needs an external AI vendor to beat the internal baseline on rare classes. But three vendors are competing for the contract, all claiming 91-95% overall accuracy. Overall accuracy is the wrong metric. And he cannot give vendors a slice of production claims data to prove themselves — claims records contain VINs, customer identifiers, part numbers, and supplier codes. Sharing that data with an external vendor during evaluation requires GDPR clearance, legal review, and supplier consent that takes months to organise. Without real data, vendor benchmarks are worthless. With real data, the procurement process stalls.
The evaluation dataset contains 500,000 anonymised warranty claims split across a training set of 400,000 records and a holdout set of 100,000 records. Full dataset statistics, class distributions, and feature analysis are available in the Exploratory Data Analysis tab.
This dataset is augmented. It was constructed to reflect the statistical structure of real-world automotive warranty claims — the class distribution, feature correlations, and failure pattern mix — without containing any vehicle identification numbers, customer records, supplier codes, or other proprietary data.
| Property | Value |
|---|---|
| Total records | 500,000 |
| Training set | 400,000 records |
| Holdout set | 100,000 records |
| Features | 50 (continuous, anonymised) |
| Classes | 5 |
| Evaluation metric | F1 Score |
| Missing values | None |
Class distribution (training set):
| Class | Label | Count | Share |
|---|---|---|---|
| 0 | Wear & Tear | 238,364 | 59.6% |
| 1 | Misuse | 99,806 | 25.0% |
| 2 | Assembly Error | 40,401 | 10.1% |
| 3 | Manufacturing Defect | 16,643 | 4.2% |
| 4 | Config Flaw | 4,786 | 1.2% |
The class imbalance is preserved exactly as observed in real-world warranty portfolios. A model that classifies every claim as Wear & Tear achieves 59.6% accuracy — which is why F1 score across all classes is the evaluation metric, not overall accuracy.
Each vendor submitted their classification model to the tracebloc workspace. The evaluation ran in two phases.
Phase 1 — Out-of-the-box performance. Each vendor's model was benchmarked as submitted, with no adaptation to Karim's claims data. This reveals what the system actually delivers on a new customer's data without customisation — typically the number no vendor publishes in their proposal.
Phase 2 — Fine-tuning. Vendors were given access to the training environment inside the tracebloc workspace. Each vendor transferred their model into tracebloc and ran training on the 400,000-record dataset. The training process fine-tuned the model weights to Karim's specific claims distribution — the 4.2% Manufacturing Defect rate, the 1.2% Config Flaw tail, and the feature patterns in his supplier's data. After training, the adapted model was evaluated automatically against the 100,000-record holdout. Vendors received only their own results; no vendor had visibility into another's runs or scores before the leaderboard published.
→ View the full model leaderboard — complete vendor rankings, per-class recall breakdown, and F1 scores across all submissions.
| Vendor | Claimed Accuracy | Out-of-the-Box | After Fine-tuning | Rare Class Recall |
|---|---|---|---|---|
| Vendor A | 91% | 84% | 86% | 46% |
| Vendor B ✅ | 93% | 86% | 90% | 61% |
| Vendor C ⚠️ | 95% | 88% | 89% | 54% |
What the numbers reveal:
Vendor B did what most vendor evaluations never surface: it improved on its own claimed accuracy after fine-tuning on real claims data — the only vendor to do so — while delivering the strongest rare class recall in the evaluation at 61%. It started at 86% out-of-the-box and reached 90% after training its model weights on 400,000 real-distribution warranty records inside the tracebloc workspace.
Vendor C had the highest claimed accuracy at 95%. Out-of-the-box it delivered 88% — the strongest baseline. After fine-tuning it reached 89%, a marginal gain that hints at a model already near its ceiling on Karim's data. More critically, its rare class recall of 54% trails Vendor B's by seven percentage points. On 100,000 claims per year, that gap is 700 additional Manufacturing Defects and Config Flaws missed — 700 missed charge-backs, 700 uncontained field failures.
Vendor A never got close to its claimed 91% accuracy on real data. At 86% post-fine-tuning and 46% rare class recall, it fails on the metric that matters most for warranty cost reduction.
Illustrative assumptions: 100,000 claims per year / rare class volume: 5,800 cases (Classes 3 + 4) / €1,000 average cost per misclassified rare case (missed charge-back + uncontained field failure exposure)
| Strategy | Rare Class Recall | Missed Critical Cases | Misclassification Cost | AI Cost (p.a.) | Total Annual Cost |
|---|---|---|---|---|---|
| Internal baseline | 30% | 4,060 | €4,060,000 | — | €4,060,000 |
| Vendor A | 46% | 3,132 | €3,132,000 | €100,000 | €3,232,000 |
| Vendor B ✅ | 61% | 2,262 | €2,262,000 | €250,000 | €2,512,000 |
| Vendor C | 54% | 2,668 | €2,668,000 | €150,000 | €2,818,000 |
Vendor B reduces total annual warranty misclassification cost from €4,060,000 (internal baseline) to €2,512,000 — a saving of €1,548,000 per year. Despite having the highest licence cost in the evaluation, it delivers the lowest total cost because rare class recall is where the money is.
Vendor C's higher claimed accuracy and lower price point look attractive until you run the numbers by class. The additional 406 missed Manufacturing Defects and Config Flaws per year more than offset the licence saving.
Karim selects Vendor B for a full-pipeline deployment with confidence-based routing. Claims where the model's confidence on a rare class exceeds a defined threshold are auto-classified and flagged for containment review. Edge cases — low-confidence predictions on Classes 3 and 4 — are routed to a human analyst with the model's SHAP feature importance pre-populated, cutting manual review time while maintaining audit trail quality for supplier charge-back documentation.
The tracebloc workspace stays active after the initial evaluation. As the claims mix evolves — new platforms, new component generations, new failure modes — Vendor B can retrain inside the workspace on updated claims data without the procurement cycle repeating. If a new vendor enters the market claiming better rare class recall, the same infrastructure benchmarks them on the same holdout set. The leaderboard becomes a live record of which systems are performing and which are degrading — turning a one-off vendor selection into ongoing warranty analytics governance.
Explore this use case further:
Related use cases: See how the same secure evaluation approach applies to credit card fraud detection and AI weld inspection in manufacturing. For a broader view of what federated learning applications look like across industries, see our federated learning applications guide.
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Disclaimer: The dataset used in this use case is augmented — designed to closely reflect the statistical structure of real-world automotive warranty claims, including class distribution and feature relationships, without containing any vehicle identification numbers, customer records, supplier codes, or other proprietary data. The persona, vendor names, claimed performance figures, business impact assumptions, and procurement scenario are illustrative and based on patterns observed across Tier 1 automotive supplier environments. They do not represent any specific company, product, or contractual outcome.
