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RHA models combine effective analytical techniques with sound principles of spreadsheet design to assure a reliable base for decision support.
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The key to effective decision making is understanding the interaction of the various factors influencing your organization. RHA models are designed to test and manage numerous scenarios.
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A good model will not only provide a quantitative answer, but will also provide an understanding of the underlying qualitative issues. For a discussion of this, see:
Structural Inferences from Actuarial Models
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Local, specific data are more relevant to your decisions than national, generic data. RHA models use sophisticated actuarial techniques to make the best use of available client data, while incorporating broader industry data where needed due to credibility considerations. An example is given in the paper: Stop Loss Method
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RHA models will help you make better decisions by demonstrating the impact of assumptions on reaching modeled results. Model assumptions become management objectives.
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To respond to the unique needs of the problem at hand, RHA models incorporate creative ideas and approaches, with the single goal in mind:
To provide you with the best information possible.
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