Abstract: A system receives asset data indicating assets that have an uncertainty contributing to a financial risk of a portfolio. The system controls a multi-stage financial risk estimate process by determining a first portion and a second portion of the asset data; and computing, for the first portion, a first stage of the process. The system computes a second stage of the process for the first portion in an overlapping time period as computing the first stage for the second portion. The system, responsive to a completion of all computations of the process for the first portion, aggregates a computed risk according to the process for the first portion with any previously computed risks for other portions of the asset data besides the first and second portion. The system outputs an estimate for the financial risk for the portfolio before completing the process for the second portion.

Abstract: A compressed data structure is disclosed for storing collected data and delivering ad-hoc data analysis. The compressed data structure can reduce hardware requirements, such as data storage requirements, and decrease processing requirements during ad-hoc data analysis. Raw data can be pre-aggregated to a base level of aggregation (base cell level) that is determined by the unique combination of attributes available for aggregation. Rather than storing the raw data, the entire distribution is stored at the base cell level along with a selection of pre-computed statistics of the raw data. At the time of ad-hoc analysis, pre-computed results based on the raw data can be retrieved or computed from the pre-computed statistics, or analysis of higher-level data can be computed on-demand based on the pre-aggregated base level data.

Abstract: A compressed data structure is disclosed for storing collected data and delivering ad-hoc data analysis. The compressed data structure can reduce hardware requirements, such as data storage requirements, and decrease processing requirements during ad-hoc data analysis. Raw data can be pre-aggregated to a base level of aggregation (base cell level) that is determined by the unique combination of attributes available for aggregation. Rather than storing the raw data, the entire distribution is stored at the base cell level along with a selection of pre-computed statistics of the raw data. At the time of ad-hoc analysis, pre-computed results based on the raw data can be retrieved or computed from the pre-computed statistics, or analysis of higher-level data can be computed on-demand based on the pre-aggregated base level data.

Abstract: Possible outcomes can be determined by combining simulation methods on a pool of input variables. Certain members of the pool are identified as members of a first set of variables (e.g., priority set), and certain other members of the pool of input variables are identified as members of a second set of variables (e.g., non-priority set). A first set of possible values for the first set of variables can be generated by applying a first simulation method. A second set of possible values for the second set of variables can be generated by applying a second simulation method that differs from the first simulation method in various ways, such as accuracy, completion time, and computational expense. A copula data structure can be used to maintain correlations between the variables of the pool of input variables when generating a hybrid set of simulated values based on the first and second simulation.

Abstract: A method of performing a query on a cube of data is provided. An access key associated with a user is created at a computing device. The access key defines the user's access to a cube of data distributed onto a plurality of computing devices with each computing device of the plurality of computing devices storing a different portion of the cube of data. A plurality of access masks is stored in association with the portion of the cube of data stored on the computing device. A process space associated with the user is created. A query on the cube of data is received by the computing device. The query is associated with the user. The query is processed while masking the created access key with the stored plurality of access masks, wherein the masking controls access to the stored portion of the cube of data. A result of the processed query is sent to a requesting computing device.

Abstract: A method of performing a query on a cube of data is provided. A cube of data is distributed onto a plurality of computing devices with each computing device of the plurality of computing devices storing a different portion of the cube of data. A perturbation rule configured for application to the cube of data and associated with a user is received. A process space associated with the user is created. The received perturbation rule is compiled in association with the created process space. A query on the portion of the cube of data stored at the computing device is received. The received query is associated with the created process space. The query is processed while applying the compiled perturbation rule to data extracted from the portion of the cube of data stored at the computing device. A result of the processed query is sent to a requesting computing device.

Abstract: A method of performing a query on a cube of data is provided. An access key associated with a user is created at a computing device. The access key defines the user's access to a cube of data distributed onto a plurality of computing devices with each computing device of the plurality of computing devices storing a different portion of the cube of data. A plurality of access masks is stored in association with the portion of the cube of data stored on the computing device. A process space associated with the user is created. A query on the cube of data is received by the computing device. The query is associated with the user. The query is processed while masking the created access key with the stored plurality of access masks, wherein the masking controls access to the stored portion of the cube of data. A result of the processed query is sent to a requesting computing device.

Abstract: A method of performing a query on a cube of data is provided. A cube of data is distributed onto a plurality of computing devices with each computing device of the plurality of computing devices storing a different portion of the cube of data. A perturbation rule configured for application to the cube of data and associated with a user is received. A process space associated with the user is created. The received perturbation rule is compiled in association with the created process space. A query on the portion of the cube of data stored at the computing device is received. The received query is associated with the created process space. The query is processed while applying the compiled perturbation rule to data extracted from the portion of the cube of data stored at the computing device. A result of the processed query is sent to a requesting computing device.

Abstract: Systems and methods are provided for determining a loss mitigation reserve requirement based on a risk measure estimation and a confidence interval associated with the risk measure estimation. Distribution parameters of a frequency model and distribution parameters of a severity model are determined, and a covariance matrix representing the determined parameters of the distribution of the frequency model and the determined parameters of distribution of the severity model is generated. One or more analytical derivatives of a cumulative distribution function of the frequency model, one or more analytical derivatives of a cumulative distribution function of the severity model, and a parameter covariance matrix are calculated. A confidence interval is computed for the risk measure estimation based on a vector of derivatives of a cumulative distribution function.

Abstract: Systems and methods are provided for providing secure transmission of software code, which includes a mathematical function, from a first computer to a second computer so that the mathematical function's content cannot be determined at the second computer. A method includes generating a secure container, where the secure container includes an encrypted representation of the mathematical function and metadata identifying the mathematical function encrypted in the secure container. The method further includes providing the secure container from the first computer to the second computer over a communication transmission medium, where the secure container is accessed at the second computer using the metadata to identify the mathematical function, and where the mathematical function contained within the secure container is decrypted and incorporated into program code in a compiled form so that the mathematical function can be used but the mathematical function's content cannot be determined at the second computer.

Abstract: A method is provided for analyzing operational risk associated with one or more organizations, comprising receiving operational loss data from a plurality of organizations at a third-party risk management entity that is a separate entity from the plurality of organizations. The operational loss data includes confidential information regarding one or more of the plurality of organizations. The data received from the plurality of organizations, including the confidential information, is pooled. The pooled data is used to generate an operational risk model for one of the plurality of organizations, and this operational risk model is transmitted to the organization. A system for modeling operational risk for a plurality of organizations is provided, comprising a first data store configured to collect data regarding operational losses from the plurality of organizations, whose data includes confidential information regarding one or more of the organizations.

Abstract: Systems and methods are provided for simulating a portfolio risk of a portfolio managed according to one or more portfolio management rules. An initial holding amount of an investment instrument is received, and a portfolio management rule is received. One or more risk factors are simulated a first time period into the future. An adjustment amount is determined based on the portfolio management rule and the one or more risk factors simulated a first time period into the future and the holding amount of the investment instrument is adjusted based on adjustment amount. The one or more risk factors are simulated a second time period into the future, and a portfolio risk value is calculated based on the adjusted holding amount and the one or more risk factors simulated a second time period into the future.

Abstract: Systems and methods are provided for determining a loss mitigation reserve requirement based on a risk measure estimation and a confidence interval associated with the risk measure estimation. Distribution parameters of a frequency model and distribution parameters of a severity model are determined, and a covariance matrix representing the determined parameters of the distribution of the frequency model and the determined parameters of distribution of the severity model is generated. One or more analytical derivatives of a cumulative distribution function of the frequency model, one or more analytical derivatives of a cumulative distribution function of the severity model, and a parameter covariance matrix are calculated. A confidence interval is computed for the risk measure estimation based on a vector of derivatives of a cumulative distribution function.

Abstract: Computer-implemented systems and methods are provided for generating a simulated forecast based on members of a pool of input risk factor variables. Certain members of the pool of input risk factor variables are identified as members of a first set of variables, and certain other members of the pool of input risk factor variables are identified as members of a second set of variables. A first simulation is generated via a first simulation method using the first set of variables, and a second simulation is generated via a second simulation method that differs from the first simulation method using the second set of variables. The first simulation and the second simulation are generated using correlations among variables in the first set of variables and variables in the second set of variables.

Abstract: Computer-implemented systems and methods are provided for implementing a dynamic model switching simulator that generates a plurality of simulations. A system and method generates a simulation comprising predictions over a plurality of time periods. Generating a simulation includes generating a first time period prediction using a first model of a first model type. Generating the plurality of subsequent time period predictions includes evaluating the model switching rule to identify whether to switch models for a subsequent time period prediction, generating the subsequent time period prediction using the first model if a switch of models is not identified, and generating the subsequent time period prediction using a second model of a second model type otherwise.

Abstract: Systems and methods are provided for providing secure transmission of software code, which includes a mathematical function, from a first computer to a second computer so that the mathematical function's content cannot be determined at the second computer. A method includes generating a secure container, where the secure container includes an encrypted representation of the mathematical function and metadata identifying the mathematical function encrypted in the secure container. The method further includes providing the secure container from the first computer to the second computer over a communication transmission medium, where the secure container is accessed at the second computer using the metadata to identify the mathematical function, and where the mathematical function contained within the secure container is decrypted and incorporated into program code in a compiled form so that the mathematical function can be used but the mathematical function's content cannot be determined at the second computer.