آتوسا رستمی

Unlike other industries where their interaction with risk involves risk transfer, the insurance industry, considering its existential nature, embraces risk. The nuanced role of risk management within the insurance industry underscores the pivotal importance of insurance companies' corporate operations. Indeed, these companies serve as the cornerstone of the entire insurance sector, owing to their central function in managing and mitigating risks effectively.

Therefore, while insurance companies operate with the primary objective of maximizing profits, it is equally imperative for them to prioritize robust risk management within their operations.

Based on this, the accuracy and predictability of tariff determination, pricing strategies, and risk management for insurance customers directly influence the attainability of profitability and resilience for insurance companies.

In this research, we delve into the impact of liability insurance risk factor fluctuations on the financial losses experienced by insurance companies in Iran. Specifically, Liability insurance for doctors and paramedics. Our objective is to identify locally relevant benchmarks for tariff determination and portfolio pricing. Leveraging machine learning and deep learning algorithms, we aim to introduce innovative approaches to liability insurance within Iran. The central challenge lies in enhancing risk management practices among insurance companies, enabling precise loss prediction, and making informed portfolio selections while strategically implementing these novel techniques for tariff strategies.