The Effectiveness of Actuarial Models in Predicting Insurance Claims in Germany

Purpose: The aim of the study was to analyze the effectiveness of actuarial models in predicting insurance claims in Germany. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Actuarial models in Germany effectively predict insurance claims by leveraging extensive historical data and advanced statistical techniques. They assess risks like mortality, morbidity, and catastrophic events crucial for pricing and underwriting decisions. Challenges include continuous recalibration for changing conditions and regulatory shifts. Integration of technologies like machine learning enhances their predictive power against complex risk scenarios. Unique Contribution to Theory, Practice and Policy: Theory of Bayesian statistics, theory of generalized linear models (GLMs) & theory of machine learning may be used to anchor future studies on analyze the effectiveness of actuarial models in predicting insurance claims in Germany. Insurers should prioritize investments in data governance frameworks to ensure data accuracy, completeness, and timeliness. Robust data quality assurance practices are essential for optimizing actuarial model effectiveness and decision-making processes. Policymakers should collaborate with industry stakeholders to develop regulatory frameworks that support the adoption of advanced modeling techniques.