Dutch Insurance Provider (unnamed)

Motor claims processing in high-volume auto insurance operations is inherently labor-intensive. For this major Dutch insurer, each incoming claim required manual adjuster review — intake, coverage verification, liability assessment, and decision documentation — regardless of complexity. With thousands of routine motor claims arriving each month, the workforce was stretched across cases that varied widely in nuance. The result was a bottleneck: straightforward two-party incidents sat in the same queue as complex fraud investigations, driving up average processing times, increasing operational costs, and leaving customers waiting longer than necessary for resolution. The operational ceiling was hard to scale without proportional headcount growth.

Beam built a custom AI agent that integrated directly into the insurer's existing claims management system, replicating the decision logic used by experienced human adjusters. Rather than replacing the workflow, the agent operated within it — using the same criteria and tooling adjusters relied on. The intake layer classified incoming claims for automated eligibility, filtering for two-party incidents with clear coverage, no fraud indicators, and claim values below a defined monetary threshold. Eligible claims were then routed through automated assessment using predefined rules to evaluate coverage and liability, with the agent either approving, denying, or escalating to human review. The initial deployment focused on this well-scoped subset to establish accuracy and trust before any broader rollout.

The AI agent automated 91% of eligible motor claims, handling thousands of routine cases monthly without adjuster involvement. Average claim processing time dropped by 46%, directly reducing the resolution window customers experienced. Customer satisfaction, tracked via Net Promoter Score, rose by 9% — attributable to faster turnaround and more consistent decision outcomes across similar claim types. Qualitatively, the shift freed claims adjusters to concentrate on complex, high-judgment cases — fraud suspicion, multi-party disputes, legal considerations — where human expertise meaningfully affects outcomes. The automation also introduced decision consistency that manual review at scale cannot reliably guarantee.

• 91% of eligible motor claims automated
• 46% reduction in average processing time per claim
• 9% increase in Net Promoter Score

• Scope the initial automation target tightly: two-party claims with clear coverage and no fraud signals are the highest-confidence starting point, maximizing early success before expanding.
• AI agents gain organizational trust faster when they mirror existing adjuster criteria rather than introducing new decision logic.
• Faster, consistent decisions have a measurable customer satisfaction impact — NPS improvement is achievable alongside operational efficiency gains.
• Human escalation paths are not a fallback — they are a core part of the design, ensuring edge cases receive appropriate review without degrading automation rates on routine claims.
• Monetary thresholds are an effective risk control for early-stage automation deployments in claims environments.