2011-05-13-奥纬咨询-Data_Quality_for_Insurance_7页_64kb

The report argues that data quality issues in the insurance industry are severe and impact decision-making, risk management, and regulatory compliance, particularly for Solvency II. It emphasizes that poor data leads to inaccuracies in exposures, customer information, and risk assessments, hindering strategic goals and operational efficiency. Key reasons for this problem include misaligned incentives: executives prioritize other uses of capital due to short time horizons, regulators struggle with third-party assessments and rely on poor-quality data, CIOs see data quality as purely technical plumbing, risk managers face unobservable performance metrics, investors and analysts focus on external alpha-generation without scrutinizing internal data, and the public overlooks data issues in favor of more dramatic causes like greed. Proposed solutions include gaining executive support, implementing comprehensive data frameworks, integrating data quality into incentive systems and job descriptions, defining clear standards for information quality, eliminating proprietary data versions, and focusing on high-priority initiatives to maintain momentum. Insurance firms with committed leadership can improve data quality, transforming it into a strategic asset.