Advancing Reinsurance with AI-Driven Data Integration and Compliance

This study has developed a new way to structure the data used in global life/health reinsurance by converting large cedant bordereaux into a scalable, AI-enabled product that will overcome some of the limitations of traditional DB2 (batch ETL) methods. This new Azure Synapse-focused model complies with both Solvency II/IFRS 17, enables schema-agnostic ingestion processes, allows for the application of Apache Spark transformations, and utilizes Python to process reconciliations. As a result, the analysis of this product shows significant outcomes; a reduction in treaty liability calculation time by 75%, a decrease in reconciliation resources used by 70%, an 80% reduction in the costs associated with preparing for audits, and a 400% increase in ingestion capacity. The innovations originally focused on resolving inconsistencies in the cedant formats, eliminated many of the manual processes, and addressed some of the regulatory barriers, utilizing AI-enabled quarantine queues, real-time catastrophe accumulation processes, and proactive fraud alerts. The evaluation metrics show very high success with an AUC of>0.92 (for real-time fraud detection), data quality metrics exceeding 97%, and an average uptime of 99.99%, thus preparing the platform for future evolutions around GenAI, federated learning, and the development of digital twin risk modeling that can adapt to a rapidly changing marketplace.