The Challenge: Hidden Liability in Safe Segments
The client, a high-volume insurance provider, was bleeding margin in a segment historically considered low risk. Their existing underwriting process relied on static rule-sets which had grown over many years. They were hard to maintain and incapable of detecting complex, non-linear risk factors.
The real economic loss came from a small percentage of high-severity claims slipping through standard filters. They didn’t need a generative chatbot; they needed a precise, interpretable risk compass.
Our Approach: Benchmarks and Interpretability
Instead, we applied White Box models:
We established a Golden Dataset of historical claims to define a quantitative baseline. We found that standard accuracy metrics were misleading due to class imbalance—a model could be 99% accurate by simply rejecting every risk. We shifted the “North Star” metric to Precision at Top Decile (Lift).
We selected Gradient Boosted Decision Trees (XGBoost) because they offer state-of-the-art performance on structured data while remaining mathematically deterministic and auditable.
We deployed an evaluation pipeline to detect concept drift, ensuring the model’s risk definitions evolved with changing market conditions.
Technical Implementation
Interpretable “Glass-Box” Architecture
In regulated industries like insurance, a “black box” prediction is a liability. We utilized SHAP (SHapley Additive exPlanations) values to break down every risk score into human-readable components.
- Constraint-Based Engineering: We purposely limited the feature set to ensure robustness, adhering to the principle that “Constraints force creativity”.
- Human-in-the-Loop Safeguards: Thhe model does not auto-reject. Instead, it acts as a triage layer, routing high-risk applications to senior underwriters with a detailed “risk context” dossier. This raises the ceiling for experts rather than attempting to replace them.
Business Impact
We achieved an improvement in important business metrics:
- Loss Ratio Improvement: The 28% reduction in high-severity claims was achieved not by pricing higher, but by selecting better risks (avoidance).
- Auditability: Unlike vibe-based LLM systems, every decision is backed by a deterministic log of feature contributions, satisfying strict regulatory compliance.
- Operational Efficiency: The system filtered noise, allowing underwriters to focus their “attention budget” on complex cases where human judgment is irreplaceable.
Technology Stack
- Model: XGBoost (Gradient Boosted Trees) - Chosen for performance/auditability balance.
- Explainability: SHAP & ELI5 - For per-prediction transparency.
- Infrastructure: On-premise secure deployment for Data Sovereignty.
- Orchestration: Python/SQL pipeline with automated drift detection.