Genworth Financial, Inc.

General Electric Company

Anindya Chakraborty

Piero Patrone Bonissone

Weizhong Yan

Rajesh Venkat Subbu

Naresh Sundaram Iyer

Kareem Sherif Aggour

Richard Paul Messmer

Kete Chalermkraivuth

Srinivas Bollapragada

Michael Craig Clark

Carol Lynn Kiaer

A method and system for fusing a collection of classifiers used for an automated insurance underwriting system and/or its quality assurance is described. Specifically, the outputs of a collection of classifiers are fused. The fusion of the data will typically result in some amount of consensus and some amount of conflict among the classifiers. The consensus will be measured and used to estimate a degree of confidence in the fused decisions. Based on the decision and degree of confidence of the fusion and the decision and degree of confidence of the production decision engine, a comparison module may then be used to identify cases for audit, cases for augmenting the training/test sets for re-tuning production decision engine, cases for review, or may simply trigger a record of its occurrence for tracking purposes. The fusion can compensate for the potential correlation among the classifiers. The reliability of each classifier can be represented by a static or dynamic discounting factor, which will reflect the expected accuracy of the classifier. A static discounting factor is used to represent a prior expectation about the classifier's reliability, e.g., it might be based on the average past accuracy of the model, while a dynamic discounting is used to represent a conditional assessment of the classifier's reliability, e.g., whenever a classifier bases its output on an insufficient number of points it is not reliable.

System and process for a fusion classification for insurance underwriting suitable for use by an automated system

A method and system for automating the decision-making process used in underwriting of insurance applications is described. While this approach is demonstrated for insurance underwriting, it is broadly applicable to diverse decision-making applications in business, commercial, and manufacturing processes. A structured methodology is used based on a multi-model parallel network of multivariate adaptive regression splines ('MARS') models to identify the relevant set of variables and their parameters, and build a framework capable of providing automated decisions. The parameters of the MARS-based decision system are estimated from a database consisting of a set of applications with reference decisions against each. Cross-validation and development/hold-out combined with re-sampling techniques are used to build a robust set of models that minimize the error between the automated system's decision and the expert human underwriter. Furthermore, this model building methodology can be used periodically to update and maintain the family of models if required to assure currency.

System and process for multivariate adaptive regression splines classification for insurance underwriting suitable for use by an automated system

The invention provides systems and methods for determining the allocation of securities in a portfolio. The method includes providing a collection of securities in a portfolio, each security being associated with associated attributes; providing risk factor data related to the portfolio; pooling the securities into a plurality of security clusters based on the attributes associated with each security and the risk factor data, each security being assigned to an security cluster, the pooling being performed using multivariate decision tree processing; processing the security clusters using a nonlinear programming optimizer to generate optimization results; and presenting the optimization results in a risk-return space for determination of a security allocation.

Methods and systems for analytical-based multifactor Multiobjective portfolio risk optimization

The invention provides systems and methods for determining an efficient frontier, which comprises a collection of security allocations in a portfolio, with multiple, conflicting objectives in a multi-factor portfolio problem. The method includes providing a mathematical model of a relaxation of a problem; generating a sequence of additional constraints; and sequentially applying respective nonlinear risk functions to generate respective adjusted maximum return solutions to obtain an efficient frontier.

Methods and systems for analytical-based multifactor multiobjective portfolio risk optimization

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Schenectady, NY, United States of America

Anindya Chakraborty