Predictive Modeling of Insurance Claims in Rwanda

Abstract

Purpose: The aim of the study was to examine the predictive modeling of insurance claims in Rwanda.

Methodology: The study adopted a desktop methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive's time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library

Findings: Predictive modeling of insurance claims in Rwanda found key predictors such as age, gender, and policy type. Regional variations in claim frequency and severity were evident, emphasizing the importance of localized risk assessment. Historical claims data was instrumental in building effective predictive models for insurers. Data-driven approaches were identified as valuable tools for improving underwriting and pricing strategies in Rwanda's insurance market. Continuous data collection and model refinement were underscored for enhanced accuracy and adaptability in the evolving Rwandan insurance landscape.

Unique Contribution to Theory, Practice and Policy: Actuarial Science Theory, Behavioral Economics Theory & Economic Development Theory may be used to anchor future studies on the examining the predictive modeling of insurance claims in Rwanda. Predictive modeling can help insurers better understand their customers' needs and preferences. Policymakers can establish guidelines and regulations to ensure the responsible use of personal data in the insurance industry.