Fehlende Datenqualität und Risikoclassifizierung in der Policen-Verwaltung (Risk Classification & Underwriting Data Gaps)

Definition

German insurers' profitability challenges (2022–2024) stem partly from claims inflation, but also from inadequate risk selection and pricing discipline. Manual policy administration creates data quality gaps: (1) Incomplete risk profiles at inception (missing driver information, vehicle specs, property condition); (2) Delayed risk classification updates (driver age changes, claims history updates); (3) Missing preference data (e.g., telematics opt-in, covered use assumptions); (4) No feedback loop from claims back to underwriting. Moody's reports that motor insurers raised premiums 30% (2022–2025) to restore profitability, but these rate increases lag actual loss trend by 6–12 months due to slow data refresh cycles. Underwriters using stale risk data make suboptimal decisions: approving policies that should be declined, pricing below-risk rates, or missing upsell opportunities. Industry loss ratios remain elevated (combined ratio >105%) partly due to underwriting data deficiencies.

Why This Matters

This pain point represents a significant opportunity for B2B solutions targeting Insurance Carriers.

Affected Stakeholders

Underwriting & Risk Assessment, Actuarial & Pricing, Claims & Loss Management, Policy Administration, Data Analytics

Related Business Risks