Abstract: A method for assessing differences in value distributions of real property and personal property coverage at an insured's site comprising collecting information on the amount of insurance coverage from various sources to provide a database and using the database information to create a distribution of values for the building and contents for each occupancy. A reference distribution is created for value of the building and contents for an occupancy based upon assessments of similar buildings and contents. The value distributions based upon the customer supplied valuation data are compared with reference distributions for the same occupancy and a report is generated which includes an assessment of the deviation of the customer contents value distribution from the reference distribution.

Abstract: A method is disclosed for determining the prior net benefit of obtaining data relating to an individual risk in an insurance portfolio, via a survey or similar procedure. A risk model is developed at the individual risk level for mathematically estimating the probability of expected loss given a set of information about the risk. The risk model is incorporated into a profitability model. A probability distribution relating to the type of survey information to be obtained is developed, which is used for determining the gross value of obtaining the information. The method produces as an output a quantitative estimation (e.g., dollar value) of the net benefit of obtaining survey data for the risk, calculated as the gross value of the survey less the survey's cost, where the benefit of the survey relates to a quantitative increase in predictive accuracy resulting from incorporating the survey data into the predictive model.

Abstract: A method of predictive modeling is for purposes of estimating frequencies of future loss and loss distributions for individual risks in an insurance portfolio. To forecast future losses for each individual risk, historical data relating to the risk is obtained. Data is also obtained for other risks similar to the individual risk. Expert opinion relating to the risk is also utilized for improving the accuracy of calculations when little or no historical data is available. The historical data, any current data, and expert opinion are combined using a Bayesian procedure. The effect of the Bayesian procedure is to forecast future losses for the individual risk based on the past losses and other historical data for that risk and similar risks. Probability distributions for predicted losses and historical data for use in the Bayesian procedure are obtained using a compound Poisson process model.