Frameworks for Data Quality Enhancement in Life Reinsurance

The master's thesis introduces an improved framework to enhance data quality in the field of life reinsurance, where precise data is essential for effective decision-making and risk management. The thesis acknowledges the challenges posed by inconsistencies in client-submitted data and proposes a comprehensive solution that can be seamlessly integrated into existing data processing infrastructure. The primary goal is to enhance the quality of data in life reinsurance through the development and implementation of strategic improvements. This involves establishing an advanced data profiling process to understand the structure, content, relationships, and patterns within life reinsurance data, achieved through a data assessment phase to evaluate and ensure data quality. Additionally, the thesis outlines the design of data quality checks tailored to the specific business needs and challenges in the reinsurance domain. It also proposes mechanisms for effective data correction to accurately and efficiently address identified issues. The thesis emphasizes the development of an improved reporting system for data quality problems, providing transparent insights into existing data quality discrepancies and enabling stakeholders to take appropriate actions. Beyond practical applications, the thesis delves into a theoretical exploration of data quality and management principles, examining broader concepts and methods beyond the specific context of reinsurance. This theoretical foundation not only supports the proposed practical framework but also contributes to the broader academic discourse on data quality management, with significant implications for operational efficiency and decision-making accuracy across various industries.