Operational Loss Database

An operational loss database systematically captures information about realized operational risk events including event descriptions, dates, root causes, business lines affected, loss amounts, recovery amounts, and lessons learned. Organizations use loss data for trend analysis, risk identification, control effectiveness evaluation, scenario calibration, and capital modeling. Financial institutions often categorize losses using Basel event types such as internal fraud, external fraud, employment practices, and system failures. A retail bank might analyze its loss database to identify that branch transaction errors represent the highest frequency loss category, prompting investment in automated reconciliation controls. Databases may include internal losses, near-misses, external industry loss data, and scenario-based hypothetical events.