ESG Data Reconciliation

ESG Data Reconciliation uses AI to perform reconciliation across ESG metric submissions, prior-year restated values, source system exports, and disclosure drafts, enabling higher data integrity before reporting close, by comparing and matching values across sources, periods, entities, and calculation methods to surface inconsistencies, across ESG data management and assurance workflows.

ESG reporting teams collect metrics from multiple source systems, templates, and site submissions that often conflict in value, unit, boundary, or methodology. Manual cross-checks are time-consuming and error-prone, and discrepancies persist into disclosure drafts until assurance review.

The AI performs reconciliation on ESG submissions, source exports, prior-year values, and methodology notes and produces matched or flagged records with conflict type and source context. The output is reviewed inside ESG data management and pre-assurance workflows.

The primary metric is ESG data exception rate at reporting close; the target direction is fewer unresolved exceptions and lower time to reconcile across sources.

• •ESG metric submissions, source system exports, prior-year values, and boundary definitions are available for the reporting period.
• •ESG data management platforms can store reconciliation results, exception flags, and resolution history.
• •Reconciliation rules, materiality thresholds, and exception ownership are defined for each metric family.