While the fairness and accountability in machine learning tasks have attracted attention from practitioners, regulators, and academicians for many applications, their consequence in terms of stakeholders' welfare is under-explored, especially via empirical studies and in the context of insurance pricing. In this paper, we develop a framework to evaluate the impact of various fairness and accountability regulations on both consumer welfare and firm profit. General insurance pricing is a complicated process that may involve cost modeling, demand modeling and price optimization, depending on the line of business and jurisdiction. The scope of this paper covers the entire insurance pricing process and associated existing and potential regulations on both cost modeling and pricing, which extends the existing work that mainly focuses on cost modeling (technical prices). We apply our approach to the data of auto insurance market and show that the accountability requirement incurs significant cost on the insurer and consumers. Fairness-aware ML algorithms on cost modeling cannot achieve fairness in the market price or welfare, while they significantly harm the insurer's profit and consumer welfare, particularly of females. Our results also demonstrate that the fairness and accountability constrains considered on the cost modeling or pricing alone cannot achieve individual-level fairness (such as the EU gender-neutral insurance pricing) unless we combine the price optimization ban with particular individual fairness notions in the cost prediction.