CAS Monograph No. 9: Data Quality Management in the P&C Insurance Sector
Reliable data has always been integral to P&C insurer operations, but the importance of data quality has increased significantly as new data sources and analytical methods, such as machine learning and artificial intelligence, have become available. The phrase “garbage in, garbage out” has never been more relevant, and actuaries increasingly must understand and quantify the impact that the quality of the data has on their work.
This monograph begins in Section 1 with an introduction to the concept of data quality management, including a discussion of what is meant by data quality. Section 2 then discusses the impact of data quality on different actuarial processes and product lines, followed by a presentation of the current state of data quality within the P&C insurance market as informed by a survey of CAS members in Section 3. Section 4 then analyzes the treatment of data quality by the most significant global insurance regulatory regimes.
In Section 5, the authors describe key considerations when designing a data quality management framework, including data architecture and technology/systems design; common data models, including the relational and NoSQL data models; and data governance. Building from the relational data model, the authors define a series of data anomaly types and use these to formally define data quality measures in Section 6. Finally, in Section 7, data quality improvement/imputation techniques are discussed and demonstrated on a sample insurance dataset. The sections are ordered with the reader’s intentions in mind. A focus on Sections 1 through 4 is recommended for the reader who is interested in an overview of data quality management and its importance. The reader who desires a more technical and practical overview of building a database and working with anomalous data should begin with Section 1 and then place more emphasis on Sections 5 through 7.
Appendix Files [ZIP]
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