Reducing information asymmetry in automotive insurance through advanced technology strategy: A study of emerging markets

The information asymmetry, which is a critical challenge in the ‍insurance market, primarily manifests as adverse selection, moral hazard, and fraud. This paper provides a conceptual clarification and categorization of these phenomena between the ‍insurer and the insured. The study also revisits existing statistical modeling tools to formalize insurance fraud risk within a ‍deterministic audit framework, drawing inspiration from the ‍foundational work of Picard (1996, 2001) and Bond and Crocker (1997). The major contribution of this research lies in the ‍modeling of the optimal insurance contract designed to incentivize policyholders to declare the actual amount of their loss under deterministic audit conditions. This formalization allows us to derive an optimal indemnity that maximizes utility while effectively controlling moral hazard. Furthermore, this research highlights numerous advanced techniques (including artificial intelligence [AI]) for overcoming these asymmetry problems. It ‍provides concrete examples of the successful implementation of these strategies by insurance companies in specific emerging markets, such as Saudi Arabia and Tunisia. Finally, many avenues are proposed to find solutions to address the challenges of personal data collection and protection posed by the integration of ‍AI in the insurance sector.