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Often, limited data is available to determine appropriate charges for high limits of insurance. In order to price policies with high limits of insurance adequately, actuaries may first determine a "basic limit" premium and then apply increased limits factors. The basic limit is a lower limit of liability under which there is a more credible ...
In machine learning, reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent with human preferences. It involves training a reward model to represent preferences, which can then be used to train other models through reinforcement learning .
In the insurance context an actuarial reserve is the present value of the future cash flows of an insurance policy and the total liability of the insurer is the sum of the actuarial reserves for every individual policy. Regulated insurers are required to keep offsetting assets to pay off this future liability.
By definition, the advantage function is an estimate of the relative value for a selected action. If the output of this function is positive, it means that the action in question is better than the average return, so the possibilities of selecting that specific action will increase. The opposite is true for a negative advantage output. [1]
For AI alignment, reinforcement learning with human feedback (RLHF) was used with a combination of 1,418,091 Meta examples and seven smaller datasets. The average dialog depth was 3.9 in the Meta examples, 3.0 for Anthropic Helpful and Anthropic Harmless sets, and 1.0 for five other sets, including OpenAI Summarize, StackExchange, etc.
For example, the California Department of Insurance imposed a rate increase moratorium for over two years on auto insurers during the pandemic. If you are unsure if your auto insurance policy is ...
California Insurance Code Section 676 requires insurers to provide a specific reason for non-renewal at least 75 days before the policy expires, allowing homeowners time to address issues or find ...
Ultimate loss amounts are necessary for determining an insurance company's carried reserves. They are also useful for determining adequate insurance premiums, when loss experience is used as a rating factor [4] [5] [6] Loss development factors are used in all triangular methods of loss reserving, [7] such as the chain-ladder method.