The tracebloc Playbook: How to Achieve Top Performance in Automotive Warranty Claims Classification 

Tracebloc is a tool for benchmarking AI models on private data. This Playbook breaks down how a team used tracebloc to benchmark AI models on their claims dataset and discovered which model truly delivered the best results. Find out more on our website or schedule a call with the founder directly.

Why Model Performance Matters

Processing thousands of claims is costly and identifying critical cateories timely is crucial. Using tracebloc, an OEM supplier uncovered which UAV object detection model truly performs under pressure, saving over €1.5 million a year.

Step 1: The Challenge 

Karim Soliman, Lead Data Scientist at an OEM supplier, leads initiatives to enhance operational analytics in the automotive supply chain. One of his main challenges: improving the classification of warranty claims into actionable root cause categories. While many claims relate to wear and tear, others stem from fraud or hidden quality issues in manufacturing or logistics.

The company receives thousands of warranty claims per year for components like steering systems, transmission modules, or electronic sensors. Currently, claim analysts manually inspect each case and label it into one of five categories. The data is a mix of embedded warranty claims, vehicle master data, failure catalogs, and production metadata, capturing both the reported issue and technical context of each vehicle.
It is noisy and highly imbalanced—only ~1% of cases relate to critical hidden manufacturing issues, which are the most expensive to miss. Misclassification causes slow containment and high related costs. Karim's goal is to deploy a robust machine learning model that can pre-classify claims, especially helping identify rare but critical categories.

Key Requirements:

• The model must run securely within the company’s internal infrastructure
• It must reach ≥50% recall on minority classes (1–5% prevalence)
• It must integrate into the existing claims platform 
•  Explainability is required for audit and supplier negotiation

The company has accumulated a proprietary dataset of over 500,000 warranty claims with around 50 features per case. Internal efforts have plateaued at ~85% overall accuracy, with poor precision on rare classes. Karim now decides to benchmark several vendors using tracebloc to understand if external models can offer a performance lift.

Step 2: What the Vendors Claimed

Karim cares most about identifying critical cases. Overall accuracy is nice, but not sufficient. Performance on minority classes is key.

Step 3: Secure Evaluation and Fine-Tuning

Using tracebloc, Karim sets up secure model evaluation workflows. Vendors fine-tune their models on-premises, without ever accessing the company’s sensitive warranty data directly. Each model is trained on 400,000 samples and tested on 100,000 previously unseen claims.

Class definitions include:

• Wear & Tear (60%)
• Misuse (25%)
• Assembly Error (10%)
• Manufacturing Defect (4%)
• Config Flaw (1%)

Benchmark Metrics:

• Recall on rare classes (~5%)
• Overall Accuracy 85%
• Top-3 feature importance via SHAP

Surprise Outcome: Vendor B outperformed its own claims after secure fine-tuning. The model showed strong sensitivity on rare failure types while remaining computationally lightweight and keeping overall accuracy high.

Step 4: Business Casec

Assumptions:

• Annual volume: 100,000 claims
• Misclassification cost of classes 0-2 are negligible
• Estimated misclassification cost of rare cases: €1000 per missed case
•  AI deployed for full classification pipeline (with manual review for flagged high-risk cases)

Vendor B demonstrates the best performance, reducing misclassified critical cases to 1950, resulting in the lowest total costs of €1,950,000, a substantial improvement over the internal baseline with 3500 missed cases and €3,500,000 in losses. Vendor C offers a competitive alternative, but at a slightly higher total cost. Vendor A, while cheaper, misses significantly more critical cases. The business case supports prioritizing models with strong sensitivity on rare classes, even if overall accuracy or cost per prediction is slightly lower.

Step 5: Vendor Selection and Strategy

After secure on-prem fine-tuning, Vendor B is selected as it delivered the best balance between overall accuracy and rare-class sensitivity, reaching a recall of 0.61 on critical failure cases. The preferred strategy is a full-pipeline deployment where the AI model automatically classifies all warranty claims and flags uncertain or high-risk predictions for manual review by experts.

Estimated annual savings: €1,550,000

The rollout plan includes:

• Containerized deployment into the existing analytics stack
• Confidence-based routing for human-in-the-loop review for edge cases
• Monthly model retraining on fresh claims
• Regular vendor review with performance and recall targets

Disclaimer:

The persona, figures, performance metrics, and cost calculations in this case study are illustrative and based on fictionalized inputs designed to mimic real-world scenarios. They are intentionally kept at a high level to make the concepts easier to understand and communicate. These do not represent actual clinical results, vendor performance, or contractual terms, and are intended solely for strategic discussion and conceptual exploration.