Impact of Automated Underwriting Systems on Insurance Risk Classification in Singapore

Purpose: The aim of the study was to analyze the impact of automated underwriting systems on insurance risk classification in Singapore. 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: The study found that these systems significantly improve the accuracy and efficiency of risk assessment. By utilizing advanced algorithms and data analytics, automated systems can process large volumes of data quickly, leading to more precise risk classification and reduced underwriting time. This enhances the ability of insurers to offer more personalized premiums based on individual risk profiles. However, the study also highlighted potential concerns about data privacy and the reliance on algorithmic decisions, suggesting the need for balanced oversight to ensure fair and transparent practices in risk classification. Unique Contribution to Theory, Practice and Policy: Algorithmic fairness theory, data privacy theory & technology acceptance model (TAM) may be used to anchor future studies on analyze the impact of automated underwriting systems on insurance risk classification in Singapore. In practice, insurers should prioritize the implementation of AUS that are transparent and explainable. Policymakers should establish robust regulatory frameworks that govern the use of AUS in insurance. These frameworks should mandate fairness audits, transparency in algorithmic decision-making, and strict data privacy standards to protect policyholders.