保险公司数据失真

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险企数字化转型:"数据失真"顽疾待解
Zhong Guo Jing Ying Bao· 2025-08-12 03:39
Core Viewpoint - The issue of data distortion in insurance companies has become a key focus for regulatory authorities, with multiple companies facing penalties for inaccuracies in their data reporting [1][2][11]. Group 1: Regulatory Actions - In August alone, several insurance companies and their branches were penalized for data distortion issues, with a total of 11 companies facing fines this year [2][3]. - The penalties stem from various violations, including inaccurate financial and business data, failure to properly reserve for claims, and submission of false financial documents [2][3]. Group 2: Specific Cases - Notable companies penalized include Xinjiang Qianhai United Property Insurance, ICBC-AXA Life Insurance, and China Pacific Insurance, among others, for issues such as "unrealistic financial data" and "inaccurate reporting" [3][4]. - China Pacific Life Insurance received a fine of 4.23 million yuan for violations related to data inaccuracies and improper use of approved insurance terms [4]. Group 3: Underlying Issues - The persistent problem of data distortion in the insurance industry is attributed to a lack of familiarity with reporting procedures, misunderstanding of rules, and insufficient staff qualifications [6][7]. - Motivations for data falsification differ between headquarters and branches, with headquarters aiming to meet regulatory requirements and beautify performance, while branches often seek personal benefits such as bonuses [8]. Group 4: Regulatory Enhancements - The regulatory body has issued a notification to enhance the standardization of data reporting for life insurance companies, emphasizing the need for improved data governance and quality management [9][10]. - Companies are required to correct historical data reporting issues by August 20, 2025, to support the non-site supervision approach increasingly relied upon by regulators [10]. Group 5: Industry Outlook - The frequency and severity of penalties indicate a decreasing tolerance for data falsification by regulatory authorities, with a focus on critical data affecting solvency [11].
险企数字化转型:“数据失真”顽疾待解
Zhong Guo Jing Ying Bao· 2025-08-08 18:52
Core Viewpoint - The issue of data distortion in insurance companies has become a key focus for regulatory authorities, with multiple companies facing penalties for inaccuracies in their financial and operational data [1][2][6]. Group 1: Regulatory Actions - In August alone, 11 insurance companies and numerous branches have been penalized for data distortion issues, including inaccurate financial and operational data, failure to properly reserve for claims, and falsifying financial documents [2][4]. - The China Banking and Insurance Regulatory Commission (CBIRC) has intensified its scrutiny, conducting on-site inspections that have led to significant penalties for companies like China Pacific Insurance and Guoyuan Agricultural Insurance [4][5]. - The recent notification issued by the Financial Regulatory Authority outlines comprehensive requirements for standardized data reporting by life insurance companies, aiming to enhance data quality and regulatory compliance [1][8]. Group 2: Underlying Issues - Data distortion has been a long-standing issue within the insurance industry, often stemming from a lack of understanding of reporting procedures, inadequate staff qualifications, and in some cases, intentional fraud [6][7]. - The motivations for data falsification differ between insurance headquarters and branches, with headquarters often aiming to meet regulatory requirements and beautify performance, while branches may be driven by personal incentives such as bonuses and promotions [7][8]. Group 3: Future Directions - The regulatory framework is set to become more stringent, with ongoing on-site inspections and a focus on solidifying the data foundation for non-site supervision in the digital age [8][9]. - Companies are required to correct historical data reporting issues by August 20, 2025, and enhance their internal controls and data governance to improve compliance and operational integrity [9].