AI Governance Workflow for IFRS 17 / LDTI Accounting Standard Compliance

Manual compliance with IFRS 17 and LDTI is a high-cost bottleneck, requiring actuarial teams to manually extract policy data, run models, validate outputs, and format disclosures across fragmented systems like SAP, Oracle, and legacy policy administration platforms. A custom automation workflow eliminates this repetitive labor by orchestrating data pipelines, calculation engines, and approval gates, reducing reporting cycle times by 60-80% and freeing actuarial resources for higher-value analysis while ensuring audit-ready traceability.

Implementation integrates a LangGraph orchestrator to manage state across data extraction from SAP S/4HANA, calculation via specialized actuarial libraries, and validation by rule-based agents. The workflow includes mandatory human review gates for material judgments and exceptions, with all data lineage, model versions, and approval states captured in an immutable ledger. This architecture provides the controlled, defensible automation needed to meet auditor and regulator scrutiny while delivering operational leverage and cost savings.

This workflow automates the high-volume, repetitive data collection and actuarial calculations mandated by IFRS 17 and LDTI, directly targeting the operational bottleneck of manual, spreadsheet-driven close processes. The savings come from reducing cycle times by 60-80%, lowering actuarial labor costs, and minimizing error-driven restatements. Implementation requires orchestrating data extraction from policy admin systems (e.g., Guidewire, Duck Creek), general ledgers (SAP, Oracle), and actuarial models, then routing outputs through validation and approval gates before final reporting.

The architecture is built on a framework like LangGraph, where specialized agents for extraction, calculation, and assembly are connected with explicit governance gates. Each gate enforces business rules, data quality checks, and mandates human-in-the-loop approval for material variances or exceptions. Critical controls include immutable audit logging of all agent decisions, data lineage tracking back to source systems, and integration with GRC platforms for evidence collection. Deployment requires phased rollout, starting with low-risk cohorts, and robust monitoring for calculation drift and pipeline failures.

Phase 1 establishes the core data pipeline, ingesting policy, claims, and actuarial data from sources like SAP, Oracle, and legacy administration systems into a governed data lake. This initial orchestration layer, built with frameworks like LangGraph, validates data quality and lineage, creating the immutable audit foundation. The focus is on automating the initial aggregation and reconciliation steps, delivering immediate ROI by reducing manual data collection effort by 60-80% and eliminating spreadsheet-based errors.

Phase 2 deploys the calculation engine, executing the CSM or VFA models with embedded controls and human review gates for material adjustments. The final phase integrates with the general ledger (e.g., SAP S/4HANA) and reporting tools, enabling parallel runs and controlled cutover. This staged approach de-risks the build, allows for iterative validation with finance and actuarial teams, and ensures the final system meets both accounting standards and internal audit requirements before full production deployment.

This workflow automates the high-cost, error-prone manual processes of closing insurance books under IFRS 17 and LDTI. It ingests policy data from administration systems (e.g., Guidewire, Duck Creek), actuarial models, and the general ledger (SAP, Oracle) to perform the required liability calculations, contract grouping, and profit recognition. The operational upside comes from compressing month-end close from weeks to days, reducing actuarial and accounting labor by 60-70%, and creating a fully auditable, granular data trail for external auditors and regulators.

Implementation requires a central orchestrator (LangGraph, Apache Airflow) to manage stateful data flows between source systems, calculation microservices, and validation checkpoints. Key controls include automated reconciliation checks against source totals, outlier detection in liability movements, and mandatory actuarial sign-off gates before report generation. The system must log every data transformation, assumption, and approval to support the rigorous audit requirements of these standards, deploying changes via controlled release cycles to avoid quarter-end disruption.

An automated IFRS 17/LDTI workflow eliminates the manual, error-prone cycles of gathering policy data, running actuarial models, and mapping results to ledger accounts. It directly addresses the operational bottleneck of quarterly and annual close, where finance teams struggle with fragmented data from policy admin systems (e.g., Guidewire, Duck Creek), actuarial platforms, and the general ledger (SAP S/4HANA, Oracle). The savings come from compressing closing timelines by 40-60%, reducing reconciliation labor, and minimizing the risk of material misstatement and audit findings.

Implementation requires a central orchestrator (using LangGraph or similar) to sequence data ingestion, validation, and calculation agents. Each agent handles a specific domain: data mapping, CSM amortization, or risk adjustment. The workflow integrates via APIs with source systems and posts journal entries to the ERP. Critical controls include pre-calculation data quality checks, post-calculation variance analysis against tolerances, and a mandatory review gate for material entries before report generation. All inputs, calculations, adjustments, and approvals are immutably logged to an evidence locker for audit.