Severity prediction launch
Severity prediction launch studio
Move a severity model from notebook to monitored production with claims-specific guardrails.
- Duration
- 14 weeks
- Format
- Blended squad with embedded weekly demos
- Indicative fee
- ₩31,200,000
Overview
We pair data science leads with MLOps engineers to harden training pipelines, package inference services, and wire observability that matches how adjusters actually consume scores.
What is included
- Feature store alignment (or interim feature manifest)
- Inference SLOs tied to adjuster screen load budgets
- Backtesting harness with leakage checks tuned to claims timelines
- Champion/challenger design with ethical review prompts
- Runbooks for rollback and manual fallback
- Integration tests across batch and interactive paths
- Executive summary that translates metrics to operational risk
Outcomes you can inspect
- Production checklist signed by IT and actuarial counterparts
- Live dashboards for score distribution and stability
- Documented limitations on geographic segments with thin history
Noa Kim
ML deployment architect with experience in property severity models across APAC markets.
FAQ
Usually not. Most clients arrive with candidate models; we focus on packaging, deployment, and monitoring. Fresh modelling can be scoped separately.
Only what your privacy and employment agreements allow. We default to aggregated inference metadata without storing free-text claim narratives in monitoring stores.
Incomplete historical loss triangles or sudden definition changes for closed claims can pause validation until sources stabilise.
Experience notes
“Their leakage checks caught a subtle calendar shift we had missed. Launch felt less like a big bang and more like turning a dial.”