Data Quality
Detect data problems before calculation. Configurable data-quality controls before IFRS 17 processing.
Quality Controls
Comprehensive validation before IFRS 17 processing.
Data Quality Framework
Ensuring reliable inputs for accurate IFRS 17 results.
Pre-calculation validation rules screen incoming data for completeness, validity, and consistency before any IFRS 17 processing begins. Rules are configurable by portfolio and data type, allowing organizations to enforce their specific business requirements alongside standard data quality checks. Cross-system reconciliation compares data across policy administration, claims, and actuarial systems to identify discrepancies that could affect measurement accuracy.
Exception management provides a structured workflow for investigating and resolving data quality issues, with routing to responsible data owners and escalation paths for critical items. Quality score tracking monitors data health trends over time, highlighting improving or deteriorating data sources and supporting targeted remediation efforts. This proactive approach to data quality reduces calculation errors, minimizes rework during period close, and builds confidence in the reliability of IFRS 17 financial statements.