Abstract: Methods and systems for determining the importance of each of the variables, or combinations of variables, that contribute to the overall score generated by a predictive statistical model are presented. In a specialized case, for each variable in the model, an importance is calculated based on the calculated slope and deviance of the predictive variable. In a more general case, for each variable in the model, an importance is calculated based on setting that variable to have the average value for the data set, and then calculating the change in score. The totality of variables (or combinations thereof) is then ranked by the ?score, or a magnitude of it, such as |?score|.

Abstract: A quantitative system and method that leverages data sources external to an insurance company to generate a statistical model that may be used to predict insurance coverage profitability. The system and method can predict profitability on a prospective basis regardless of the internal data and business practices of a particular insurance company.

Abstract: An unsupervised statistical analytics approach to detecting fraud utilizes cluster analysis to identify specific clusters of claims or transactions for additional investigation, or utilizes association rules as tripwires to identify outliers. The clusters or sets of rules define a “normal” profile for the claims or transactions used to filter out normal claims, leaving “not normal” claims for potential investigation. To generate clusters or association rules, data relating to a sample set of claims or transactions may be obtained, and a set of variables used to discover patterns in the data that indicate a normal profile. New claims may be filtered, and not normal claims analyzed further. Alternatively, patterns for both a normal profile and an anomalous profile may be discovered, and a new claim filtered by the normal filter. If the claim is “not normal” it may be further filtered to detect potential fraud.

Abstract: A quantitative system and method that employs data sources external to an insurance company to generate a statistical model that may be used to predict commercial insurance profitability. The system and method are able to predict individual commercial insurance policyholder profitability on a prospective basis regardless of the internal data and business practices of a particular insurance company.

Abstract: A quantitative system and method that utilizes data sources external to a company, and when available, traditional data sources, e.g., internal company information, to (i) provide for matching criteria such as, for example, demographic needs, to a database that can provide a number of potential recruits or customers and that can also be used to screen both current and prospective company employees matching the criteria, and (ii) generate a statistical model that can be used to predict future profitability and productivity of licensed professionals.

Abstract: A method and system for determining the importance of each of the variables that contribute to the overall score of a model for predicting the profitability of an insurance policy. For each variable in the model, an importance is calculated based on the calculated slope and deviance of the predictive variable. Since the score is developed using complex mathematical calculations combining large numbers of parameters with predictive variables, it is often difficult to interpret from the mathematical formula for example, why some policyholders receive low scores while other receive high scores. Such clear communication and interpretation of insurance profitability scores is critical if they are used by the various interested insurance parties including policyholders, agents, underwriters, and regulators.

Abstract: A quantitative system and method that employs data sources external to an insurance company to generate a statistical model that may be used to more accurately and consistently predict commercial insurance profitability (the “predictive statistical model”).

Abstract: A method and system for determining the importance of each of the variables that contribute to the overall score of a model for predicting the profitability of an insurance policy. For each variable in the model, an importance is calculated based on the calculated slope and deviance of the predictive variable. Since the score is developed using complex mathematical calculations combining large numbers of parameters with predictive variables, it is often difficult to interpret from the mathematical formula for example, why some policyholders receive low scores while other receive high scores. Such clear communication and interpretation of insurance profitability scores is critical if they are used by the various interested insurance parties including policyholders, agents, underwriters, and regulators.

Abstract: A quantitative system and method that employs data sources external to an insurance company to generate a statistical model that may be used to more accurately and consistently predict commercial insurance profitability (the “predictive statistical model”). The system and method are able to predict individual commercial insurance policyholder profitability on a prospective basis regardless of the internal data and business practices of a particular insurance company.

Abstract: A quantitative system and method that utilizes data sources external to a company, and when available, traditional data sources, e.g., internal company information, to (i) provide an easily accessible means for matching criteria such as, for example, demographic needs, to a database that can quickly provide a number of potential recruits or customers and that can also be used to screen both current and prospective company employees matching the criteria, and (ii) generate a statistical model that can be used to accurately and consistently predict future profitability and productivity of licensed professionals.

Abstract: A quantitative system and method that utilizes data sources external to a company, and when available, traditional data sources, e.g., internal company information, to (i) provide an easily accessible means for matching criteria such as, for example, demographic needs, to a database that can quickly provide a number of potential recruits or customers and that can also be used to screen both current and prospective company employees matching the criteria, and (ii) generate a statistical model that can be used to accurately and consistently predict future profitability and productivity of licensed professionals.

Abstract: A quantitative system and method that utilizes data sources external to a company, and when available, traditional data sources, e.g., internal company information, to (i) provide an easily accessible means for matching criteria such as, for example, demographic needs, to a database that can quickly provide a number of potential recruits or customers and that can also be used to screen both current and prospective company employees matching the criteria, and (ii) generate a statistical model that can be used to accurately and consistently predict future profitability and productivity of licensed professionals.

Abstract: A method and system for determining the importance of each of the variables that contribute to the overall score of a model for predicting the profitability of an insurance policy. For each variable in the model, an importance is calculated based on the calculated slope and deviance of the predictive variable. Since the score is developed using complex mathematical calculations combining large numbers of parameters with predictive variables, it is often difficult to interpret from the mathematical formula for example, why some policyholders receive low scores while other receive high scores. Such clear communication and interpretation of insurance profitability scores is critical if they are used by the various interested insurance parties including policyholders, agents, underwriters, and regulators.

Abstract: A quantitative system and method that employs data sources external to an insurance company to generate a statistical model that may be used to more accurately and consistently predict commercial insurance profitability (the “predictive statistical model”). The system and method are able to predict individual commercial insurance policyholder profitability on a prospective basis regardless of the internal data and business practices of a particular insurance company.

Abstract: A loss control system for an insurance classification plan has a policy holder database, a predictive apparatus and a derived actual loss ratio generator. The policy holder database generates signals indicative of the premium, actual loss, a one a plurality of classification plan variable values and an actual loss ratio for each policy holder. The predictive apparatus generates a plurality of predicted loss ratio signals indicative of predicted loss ratios of the policy holders. The derived actual loss ratio generator generates signals which are indicative of a difference between the predicted loss ratio and the actual loss ratio of the policy holders.