Data-Driven Risk Assessment in Insurance Underwriting: Evaluating the Ethical and Economic Trade-offs of AI-Powered Actuarial Models
Artificial intelligence integration in US insurance underwriting is revolutionizing the way risk is assessed, costs are made efficient and fraud is detected, such use raises many ethical and economic tradeoffs. A key problem of AI powered actuarial models is that speed and accuracy in the underwriting is enhanced, biases within the algorithms, transparency of the algorithms, trust of the consumer and regulatory oversight are issues that can still prevent the advancement of AI in underwriting. this research study uses a quantitative research approach in studying the impact of AI underwriting models through using survey data and data analysis as well as real life case studies in evaluating gains in efficiency, ethical risks and regulatory consideration. Findings indicate that AI can dramatically lower the cost of underwriting and enhance the rate of detecting fraud while consumers remain very skeptical about fully automated underwritten models, looking most positively upon hybrid AI and human models. Important factors that affect adoption of AI in underwriting are regulatory oversight and mitigation of bias. The study argues that the existence of explainable AI frameworks, the presence of the data governance and compliance measures are all necessary to strike a balance between efficiency and fairness. Overcoming these challenges, AI-powered underwriting can contribute to the country’s economic growth, improve consumer trust and be aligned with the country’s changing U.S. regulatory frameworks. These insights can benefit insurers, policymakers and regulatory bodies in responsible development of fair, efficient and transparent AI underwriting models for the U.S. insurance industry.
