This course covers Data Reconciliation Controls, which involves assessing the processes and controls used to verify the accuracy, consistency, and completeness of portfolio data across multiple systems, reports, and monitoring platforms within Credit Monitoring & Portfolio Surveillance workflows. It focuses on identifying discrepancies, data mismatches, reporting errors, and control weaknesses that may affect risk assessment, portfolio monitoring, and governance reporting. The course examines how effective reconciliation controls support reliable risk information, strengthen decision-making, and reduce the likelihood of inaccurate exposure reporting or missed risk signals. It evaluates key dimensions such as control lapses, early warning signal identification, risk trend analysis, and proactive portfolio risk management, with each requiring independent validation and documented rationale before any credit action is finalized. Particular emphasis is placed on data validation procedures, exception management, reconciliation accuracy, root-cause analysis of discrepancies, and ongoing monitoring of data quality controls. It is distinct from a compliance monitoring framework, as it focuses specifically on ensuring the integrity and consistency of portfolio data and risk information, rather than the broader monitoring of regulatory, policy, and compliance obligations. Within Portfolio Review & Governance Reporting, the senior credit leader sets portfolio limits, governs exception criteria, and drives strategic alignment across the Credit Monitoring & Portfolio Surveillance function, shaping escalation scope, reporting priorities, and portfolio risk management decisions through accurate and reliable data governance practices.