A practical approach to end-to-end Solvency II reporting in Databricks

Summary

Solvency II reporting connects data, actuarial reserving, capital calculation, quantitative reporting templates (QRTs), the ORSA, approvals, and disclosure, but fragmented systems make readiness and cross-process questions difficult to manage. The described Databricks implementation provides a governed workflow and control tower around existing actuarial and capital suites, automating ingestion checks, data-quality review, reconciliation, orchestration, reporting, and audit trails. It uses MLflow-managed reserving models alongside engines such as Prophet, RAFM, and Igloo, while narrow-scope AI agents can investigate QRT mismatches, suggest remediation, route questions, and support human review. An LLM can draft an ORSA from current numbers, while scenario analysis assesses doubling a cyber book over 12 months; reviewers retain decision authority. The workflow is presented as easier to monitor and investigate.

Takeaways

1. 1

   Databricks is positioned as an orchestration and governance layer around existing suites such as Prophet, RAFM, and Igloo, rather than as a replacement for them.

2. 2

   The workflow supports freshness, completeness, ownership, customizable data-quality rules, row disposition decisions, and reconciliation checks between QRTs.

3. 3

   AI agents can investigate mismatches, suggest remediation, draft ORSA content, route questions, and provide alternative review perspectives, but reviewers retain decision and approval authority.