Patent Publication Number: US-2016239917-A1

Title: Processing Performance Risk Using a Performance Risk Navigator

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present application claims benefit of U.S. Provisional Patent Application No. 62/115,530 filed Feb. 12, 2015 by Theodore A. Goldman, et al., and entitled, “Pension Risk Navigator,” which is incorporated herein by reference as if reproduced in its entirety. 
    
    
     STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT 
     Not applicable. 
     REFERENCE TO A MICROFICHE APPENDIX 
     Not applicable. 
     BACKGROUND 
     Determining the amount of risk and performance associated with a user and user-defined goals is challenging. Existing tools are limited in their ability to aggregate user information for determining the amount of risk and performance associated with the user and the user-defined goals. For example, many financial tools are not web enabled and cannot be easily integrated to work with other financial tools. As a result, financial calculations are performed and analyzed independently using a plurality of financial tools. 
     SUMMARY 
     In one embodiment, the disclosure includes a performance risk analysis method comprising receiving, at a device implemented in hardware, user information and a performance goal, generating, at the device, a portfolio based on the user information and the performance goal, performing, at the device, a stochastic simulation on the portfolio to generate a performance metric, and outputting, from the device, the performance metric. 
     In another embodiment, the disclosure includes an apparatus comprising a receiver configured to receive user information and a performance goal, a memory, and a processor operably coupled to the receiver and the memory, and configured to generate a portfolio based on the user information and the performance goal, perform a stochastic simulation on the portfolio to generate a performance metric, and output the performance metric. 
     These and other features will be more clearly understood from the following detailed description taken in conjunction with the accompanying drawings and claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       For a more complete understanding of this disclosure, reference is now made to the following brief description, taken in connection with the accompanying drawings and detailed description, wherein like reference numerals represent like parts. 
         FIG. 1  is a schematic diagram of an embodiment of a system for implementing a performance risk navigator. 
         FIG. 2  is a schematic diagram of another embodiment of a system for implementing a performance risk navigator. 
         FIG. 3  is a schematic diagram of an embodiment of a network element for implementing a performance risk navigator. 
         FIG. 4  is a flowchart of an embodiment of a performance risk analysis using a performance risk navigator. 
         FIG. 5  is an illustration of an embodiment of a chart your course module user interface. 
         FIG. 6  is an illustration of an embodiment of a scenario builder module user interface. 
         FIG. 7  is an illustration of another embodiment of a scenario builder module user interface. 
         FIG. 8  is an illustration of an embodiment of a dashboard module user interface. 
         FIG. 9  is an illustration of another embodiment of a dashboard module user interface. 
         FIG. 10  is an illustration of an embodiment of a take action module user interface. 
         FIG. 11  is a flowchart of an embodiment of a performance risk analysis method. 
     
    
    
     DETAILED DESCRIPTION 
     It should be understood at the outset that although an illustrative implementation of one or more embodiments are provided below, the disclosed systems and/or methods may be implemented using any number of techniques, whether currently known or in existence. The disclosure should in no way be limited to the illustrative implementations, drawings, and techniques illustrated below, including the exemplary designs and implementations illustrated and described herein, but may be modified within the scope of the appended claims along with their full scope of equivalents. 
     Disclosed herein are various embodiments for implementing a performance risk analyses using a performance risk navigator. In an embodiment, a performance risk navigator may be employed to perform a performance risk analysis for a pension plan by obtaining user information and performance goals (e.g., financial information and pension goals), performing model simulations using the provided user information and performance goals, and determining performance metrics (e.g., likelihood of success) for meeting the performance goals for the pension plan based on the model simulation results. Further, a performance risk navigator is employed to use user information (e.g., historical or on-going financial information for a pension plan) to determine a performance risk factor (e.g., a pension risk factor) that indicates an amount of risk associated with the pension plan in accordance with the user information. As an example, a performance risk navigator may be configured to allow a user (e.g., a pension plan sponsor or client) to access a detailed analysis of a pension plan&#39;s funded position and progress towards achieving funding and investing goals. Further, a performance risk navigator may be configured as a pension risk navigator to establish outcome-based goals for pension plans and to evaluate the impact of potential mitigating actions for reducing the pension plan&#39;s risk. For example, mitigating actions for a pension plan include, but are not limited to, closing a pension plan to new participants, freezing accruals, purchasing lump sums, purchasing annuities, modifying asset allocations, and implementing a de-risking glide path. The performance risk navigator allows users to view (e.g., in about real-time) the potential effects on the likelihood of achieving performance goals using stochastic forecasting methods. A user can make better informed decisions on matters that impact their employees and shareholders using the performance risk navigator. 
     Using a performance risk navigator allows a user to use a single user interface to obtain inputs from a variety of sources such as remote databases and a local user. Existing solutions may require the user to use multiple user interfaces to obtain user inputs from a variety of sources. The user interface is adaptable and accommodates different combinations of inputs. For example, the performance risk navigator may be configured to operate using various combinations of inputs where the number of inputs may vary. The performance risk navigator is robust and supports a broad range of inputs. The performance risk navigator is configurable to generate and process one or more portfolios at a time based on the inputs. For example, multiple portfolios may be generated and processed at once. The performance risk navigator may be configured to simultaneously generate and output the performance metric for multiple portfolios. The performance risk navigator is also configurable to generate action items that are uniquely associated with the one or more portfolios based on the inputs and the performance metrics. Many of these features cannot be performed without the computer system described here. 
     A performance risk navigator is configured to obtain inputs such as user information and performance goals from a plurality of sources. For example, a performance risk navigator may obtain inputs for a pension plan from a user (e.g., a pension sponsor or client), an actuary&#39;s work product, trustee asset information, or a database. The performance risk navigator is configured to use the inputs to generate several performance metrics for measuring and/or displaying performance (e.g., a pension plan&#39;s health). For example, performance metrics for a pension plan may comprise liabilities (e.g., projected benefit obligation (PBO), accumulated benefit obligation (ABO), and funding target liability) computed daily, projected asset levels, projected required contribution levels, projected pension benefit guaranty corporation (PBGC) premiums (e.g., per head or variable), projected funded ratios, and/or any other suitable performance metric as would be appreciated by one of ordinary skill in the art upon viewing this disclosure. 
     As an example, performance metrics for a pension plan may be reported stochastically with a probability distribution computed each year up to ten years. A user (e.g., a pension plan sponsor) can articulate pension goals with the performance risk navigator, for example, by establishing performance metrics and one or more performance goals. Multiple performance goals may also be concatenated. The performance risk navigator is configured to compute a likelihood of success (e.g., a probability score) for the performance goals based on outcomes of prepopulated stochastic paths using a model simulator. A user may compare resulting probability scores with other probability scores based on alternative scenarios to make informed decisions. 
     As another example, a user (e.g., a client) can use the performance risk navigator to perform a lump sum versus annuity analysis. The user may want to reduce their long-term risk by offering lump sums or purchasing annuities for plan participants. The performance risk navigator is populated with financial inputs and projection models for each option, for example by a consultant. The performance risk navigator will process the financial inputs and models to generate one or more performance metrics for the client. The client can use the performance metrics to determine the likelihood of success for their goals based on each scenario and to determine a course of action in accordance with the performance metrics. 
     Further, the performance risk navigator is configured to compute a performance risk factor. A performance risk factor for a pension plan is a value that reflects a pension plan&#39;s funded ratio, hedge ratio, and equity exposure. A user can see the impact on their performance risk factor of various decisions such as lengthening their bond portfolio, making additional contributions, or reducing equity exposure. 
       FIG. 1  is a schematic diagram of an embodiment of a system  100  for implementing a performance risk navigator. System  100  comprises a server device  102 , a user device  104 , and a database  106 . System  100  may be configured as shown or in any other suitable configuration as would be appreciated by one of ordinary skill in the art upon viewing this disclosure. Server device  102  is a network node configured to support the transportation of data traffic through a network  108 . Examples of network  108  include, but are not limited to, Internet Protocol (IP) networks, virtual network, and local area networks. Server device  102  may comprise a computer, a server, a switch, a router, or any other suitable networking device for communicating data packets or supporting the transportation of data packets as would be appreciated by one of ordinary skill in the art upon viewing this disclosure. Server device  102  is operably coupled to and in data communication with user device  104  and database  106 . Examples of connections between server device  102 , user device  104 , and database  106  include, but are not limited to, links, tunnels, an internet connection, wireless network connections, and wired network connections. Links discussed herein may be physical links, such as electrical links, optical links, and/or logical links (e.g., virtual links). A tunnel may include, but is not limited to, an IP security (IPsec) tunnel or a generic routing encapsulation (GRE) tunnel. 
     In an embodiment, the server device  102  has a processor (not shown), a memory  112  and application  110 . Alternatively, the application  110  is stored in the user device  104 . The memory  112  may be a volatile or non-volatile read-only memory (ROM), random-access memory (RAM), ternary content-addressable memory (TCAM), static random-access memory (SRAM), or any other suitable type of memory as would be appreciated by one of ordinary skill in the art upon viewing this disclosure. Memory  112  is configured to store user information and/or instructions for executing application  110 . The application  110  is configured to execute the performance risk navigator. The application  110  may be an application or an application suite configured to receive and to transmit data between the server device  102 , the user device  104 , and the database  106 . For example, the application  110  may be configured to interact with the user device  104  via a user interface  114  on the user device  104 . The application  110  is also configured to store and retrieve data, such as, user information, from memory  112  and/or database  106 . Examples of the user device  104  include, but are not limited to, network computers, tablet computers, desktop computers, mobile telephones, servers, or any other suitable networking device as would be appreciate by one of ordinary skill in the art upon viewing this disclosure. The user device  104  has a user interface  114  that is configured to interact with the application  110  in the server device  102  to exchange (e.g., transmit and receive) data with application  110 . The user interface  114  may be realized as a virtual element, a physical network element, or embedded in a physical element. The user device  104  may be configured to have or to access one or more other applications, an operating system (OS), or a hypervisor. Database  106  is an external memory that may be stored in another device. The database  106  may be located in about the same geographical location or in a different geographical location as the service device  102  or the user device  104 . Database  106  is configured to store user information for application  110 . Database  106  may be prepopulated by the user with user information or may be populated by a third-party with user information. 
       FIG. 2  is a schematic diagram of another embodiment of a system  200  for implementing a performance risk navigator. System  200  comprises a user device  202  and a database  204 . System  200  may be configured as shown or in any other suitable configuration as would be appreciated by one of ordinary skill in the art upon viewing this disclosure. User device  202  is operably coupled to and in data communication with database  204 . Examples of connections between user device  202  and database  204  include, but are not limited to, links, tunnels, an internet connection, wireless network connections, and wired network connections. Links discussed herein may be physical links, such as electrical links, optical links, and/or logical links (e.g., virtual links). The user device comprises a processor (not shown), a memory  206 , user interface  210 , and application  208 . The application  208  is configured to execute a performance risk navigator. The memory  206  may be a volatile or non-volatile ROM, RAM, TCAM, SRAM, or any other suitable type of memory as would be appreciated by one of ordinary skill in the art upon viewing this disclosure. Memory  206  is configured to store user information and/or instructions for executing application  110 . The application  208  may be an application or an application suite configured to interact with the memory  206 , the user interface  210 , and the database  204 . The user interface  210  is configured to interact with application  208  to exchange (e.g., transmit and receive) data with application  208 . The user interface  210  may be realized as a virtual element, a physical network element, or embedded in a physical element. The user device  202  may be configured to have or to access one or more other applications, an OS, or a hypervisor. Examples of the user device  202  include, but are not limited to, network computers, tablet computers, desktop computers, mobile telephones, servers, or any other suitable networking device as would be appreciate by one of ordinary skill in the art upon viewing this disclosure. Database  204  is an external memory that may be stored in another device. The database  204  may be located in about the same geographical location or in a different geographical location as the user device  202 . Database  204  is configured to store user information for application  110 . Database  204  may be prepopulated by the user with user information or may be populated by a third-party with user information. In an embodiment, the database  204  may be omitted. 
       FIG. 3  is a schematic diagram of an embodiment of a network element  300 . The network element  300  may be suitable for implementing the disclosed embodiments. Network element  300  may be any device such as a computer, a laptop, a mobile phone, a smartphone, a tablet, a web-enabled computing device, a client, a server, or any other suitable device as would be appreciated by one of ordinary skill in the art upon viewing this disclosure. For example, network element  300  may be in and/or integrated within a server device  102 , a user device  104 , or database  106  in  FIG. 1  or user device  202  or database  204  in  FIG. 2 . Network element  300  includes ports  310 , transceiver units (Tx/Rx)  320 , a processor  330 , and a memory  340  comprising a performance risk navigator module  350 . Ports  310  are coupled to Tx/Rx  320 , which may be transmitters, receivers, or combinations thereof. The Tx/Rx  320  may transmit and receive data via the ports  310 . Processor  330  is configured to process data. Memory  340  is configured to store data and instructions for implementing embodiments described herein. The network element  300  may also include electrical-to-optical (EO) components and optical-to-electrical (OE) components coupled to the ports  310  and Tx/Rx  320  for receiving and transmitting electrical signals and optical signals. 
     The processor  330  may be implemented by hardware and software. The processor  330  may be implemented as one or more central processing unit (CPU) chips, logic units, cores (e.g., as a multi-core processor), field-programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), and digital signal processors (DSPs). The processor  330  is in communication with the ports  310 , Tx/Rx  320 , and memory  340 . 
     The memory  340  includes one or more of disks, tape drives, and solid-state drives and may be used as an over-flow data storage device, to store programs when such programs are selected for execution, and to store instructions and data that are read during program execution. The memory  340  may be a volatile or non-volatile ROM, RAM, TCAM, SRAM, or any other suitable type of memory as would be appreciated by one of ordinary skill in the art upon viewing this disclosure. The performance risk navigator module  350  is implemented by processor  330  to execute the instructions for implementing various embodiments for carrying out the various example embodiments described herein. The performance risk navigator module  350  performs at least part of the performance risk analysis  400  in  FIG. 4  or method  1100  in  FIG. 11 . In an embodiment, the performance risk navigator module  350  may employed for a pension plan to obtain financial information, pension goals, and/or portfolios, to perform model simulations using the provided financial information and pension goals, and to determine performance metrics (e.g., likelihood of success) for meeting the pension goals based on the model simulation results. Further, a performance risk navigator module  350  is employed to determine a pension risk factor for the pension plan that indicates an amount of risk associated with the pension plan based on the provided financial information. The inclusion of the performance risk navigator module  350  provides an improvement to the functionality of network element  300 . The performance risk navigator module  350  also effects a transformation of network element  300  to a different state. Alternatively, the performance risk navigator module  350  is implemented as instructions stored in the processor  330 . 
       FIG. 4  is a flowchart of an embodiment of a performance risk analysis  400  using a performance risk navigator (e.g., performance risk navigator module  350  in  FIG. 3 ). Performance risk navigator  400  may be implemented in an application such as application  110  in a server device  102  in  FIG. 1  or application  208  in a user device  202  in  FIG. 2 . Performance risk analysis  400  comprises a chart your course module  402 , a scenario builder module  404 , a project module  406 , a take action module  408 , and a dashboard module  410 . The pension risk analysis  400  may be configured as shown or in any other suitable manner. The performance risk analysis  400  may implemented to perform various analysis operations. For example, analysis operations for a pension plan may include, but are not limited to, assessing a current funded position of a pension plan, quantifying risks associated with a pension plan (e.g., interest rate risk, equity risk, and longevity risk), measuring an impact of daily changes in interest rates and assets returns on a pension plan&#39;s funded status, measuring an impact on a probabilistic basis of changes in asset allocation and/or implementation of liability driven investing glide paths, measuring an impact of settlor decisions (e.g., mitigating actions), and generating projections based on a pension plan&#39;s current position and simulation models. 
     Chart your course module  402  is configured to obtain user inputs such as user information (e.g., financial information), performance goals (e.g., pension goals), and performance goal parameters (e.g., pension goal parameters) and to display one or more performance metrics that are generated in accordance with the obtained user inputs. Chart your course module  402  is configured to display a likelihood of success that is generated in accordance with the user defined performance goals. Scenario builder module  404  is configured to create custom portfolio scenarios using the obtained user inputs. The custom portfolio scenarios can be used to generate portfolio projections using an asset/liability modeler (ALM) such as project module  406 . An ALM may generate portfolio projections based on risks due to mismatches between assets and liabilities for the portfolio. For example, the ALM may be configured to maximize assets to meet complex liabilities that are associated with the portfolio. Any suitable ALM may be employed as would be appreciated by one of ordinary skill in the art upon viewing this disclosure. Project module  406  is an asset/liability modeler configured to obtain portfolio scenarios from scenario builder module  404  and to perform stochastic simulations (e.g., Monte Carlo simulations) on the portfolio scenarios. For example, the project module  406  for a pension plan may perform stochastic simulations of pension liabilities and assets performance on the portfolios for a pension plan. Project module  406  is configured to use a plurality of inputs (e.g., project liability cash flows, current assets, liabilities, discount rates, and projection assumptions) and to output performance metrics (e.g., performance metric graphs) in accordance with the simulation results from the stochastic simulations. For example, the project module  406  for a pension plan may output performance metrics for projected liabilities, contributions, plan expense, and assets. Project module  406  may also be configured to store performance metrics into a memory or to output a file, a graph, a table, a summary, a report, or any other suitable output as would be appreciated by one of ordinary skill in the art. Further, project module  406  is configured to implement one or more models (e.g., assets/liability models from a financial consultant) that can be used in conjunction with portfolios and/or performance goals to generate performance metrics. In an embodiment, project module  406  may be implemented using ALM express. Take action module  408  is configured to display one or more actions or action items associated with user plans (e.g., a pension plan) and portfolio scenarios. Actions are generated in accordance with the simulation results from project module  406 . Dashboard module  410  is configured to display one or more performance metrics associated with a user plan and portfolio scenario that are generated in accordance with the results from project module  406 . For example, the performance metrics for a pension plan may comprise a likelihood of success, a pension risk factor, and a funded status for the pension plan. 
       FIG. 5  is an illustration of an embodiment of a chart your course module user interface  500 . Chart your course module user interface  500  may be implemented for a chart your course module (e.g., chart your course module  402  described in  FIG. 4 ) of a performance risk analysis (e.g., performance risk analysis  400  described in  FIG. 4 ) in a performance risk navigator (e.g., performance risk navigator module  350  in  FIG. 3 ) for a pension plan. Chart your course module user interface  500  may be implemented in a user interface for an application such as user interface  114  for the server device  102  in  FIG. 1  or user interface  210  for the user device  202  in  FIG. 2 . Chart your course module user interface  500  comprises a plurality of performance goal parameter modules  502  and  504  and a performance metric module  506 . Chart your course module user interface  500  may be configured as shown or in any other suitable configuration. Performance goal parameter modules  502  and  504  for a pension plan each comprise one or more pension goal settings that are associated with pension performance goals (e.g., cash contribution goals, funded position goals, PBGC premium goals, and pension expense goals). Pension performance goals may comprise any suitable number of performance goals and/or combination of performance goals. For example, a cash contribution goal can be set for next year and another cash contribution goal can cover the total amount contributed over the next ten years. performance goal parameter settings for a pension plan may include, but are not limited to, a funded ratio setting (e.g., assets divided by liabilities), a funded status setting (e.g., assets minus liabilities), a funding liability on a Pension Protection Act (PPA) basis setting, an accounting liability on a PBO setting, an accounting liabilities on an ABO setting, a PPA basis setting, a financial accounting standards basis setting, a termination basis setting, a target funded ratio setting, a target funded status setting, a target end year setting, a target value not to exceed setting, other funded position settings, and other cash contribution settings. Performance metric module  506  is configured to display one or more performance metrics. Performance metric module  506  may comprise any suitable combination or configuration of performance metrics for a performance goal. For example, the performance metric module  506  comprises a likelihood of success indicator that measures or indicates performance goals against a particular projection scenario. The percentage represents the number of successful trials and is determined from the simulation results of the project module for portfolios defined by a scenario builder module (e.g., scenario builder module  404  described in  FIG. 4 ) and performance goals defined by a chart your course module (e.g., chart your course module  402  described in  FIG. 4 ). 
       FIG. 6  is an illustration of an embodiment of a scenario builder module user interface  600 . Scenario builder module user interface  600  may be implemented for a scenario builder module (e.g., scenario builder module  404  described in  FIG. 4 ) of a performance risk analysis (e.g., performance risk analysis  400  described in  FIG. 4 ) in a performance risk navigator (e.g., performance risk navigator module  350  in  FIG. 3 ) for a pension plan. Scenario builder module user interface  600  comprises a portfolio organizer module  602  and a portfolio parameters module  604 . Scenario builder module user interface  600  may be implemented in a user interface for an application such as user interface  114  for the server device  102  in  FIG. 1  or user interface  210  for the user device  202  in  FIG. 2 . Scenario builder module user interface  600  may be configured as shown or in any other suitable configuration. Portfolio organizer module  602  is configured to establish and organize user-defined portfolios that are associated with various user plans. For example, a user may generate and store a plurality of custom portfolios with different portfolio parameter settings. Portfolio parameters module  604  is configured to portfolio parameter settings that are associated with a portfolio. Portfolio parameter settings for a pension plan may include, but are not limited to, cash flow sensitivity settings (e.g., cash flow streams and cash flow information), sensitivity settings (e.g., high sensitivity and low sensitivity for cash flow streams), asset allocation settings, constant asset allocation settings (e.g., constant investment allocation), vary by funded asset allocation settings (e.g., adjusts asset allocation as funded status changes), vary by year change asset allocation settings (e.g., adjusts asset allocation each year), PPA discount rate method settings (e.g., measure liabilities using a full yield curve, PPA unadjusted segment rates, and PPA adjusted segment rates), PPA asset method setting (e.g., market value of assets, actuary value of assets, and smoothed actuary value of assets), investment portfolio settings, liability variables settings, and take action output settings. 
       FIG. 7  is an illustration of another embodiment of a scenario builder module user interface  700 . Scenario module user interface  700  may be implemented for a scenario builder module (e.g., scenario builder module  404  described in  FIG. 4 ) of a performance risk analysis (e.g., performance risk analysis  400  described in  FIG. 4 ) in a performance risk navigator (e.g., performance risk navigator module  350  in  FIG. 3 ) for a pension plan. Scenario builder module user interface  700  may be implemented in a user interface for an application such as user interface  114  for the server device  102  in  FIG. 1  or user interface  210  for the user device  202  in  FIG. 2 . Scenario builder module user interface  700  may be configured as shown or in any other suitable configuration. Scenario builder module user interface  700  comprises one or more performance metric modules  702 . Performance metric module  702  is configured to display one or more performance metrics for user-defined portfolios that are associated with various pension plans. Performance metric module  702  may comprise any suitable combination or configuration of performance metrics for a portfolio and/or a performance goal. For example, performance metric module  702  for a pension plan comprises a PPA target liability graph. 
       FIG. 8  is an illustration of an embodiment of a dashboard module user interface  800 . Dashboard module user interface  800  may be implemented for a dashboard module (e.g., dashboard module  410  described in  FIG. 4 ) of a performance risk analysis (e.g., performance risk analysis  400  described in  FIG. 4 ) in a performance risk navigator (e.g., performance risk navigator module  350  in  FIG. 3 ). Dashboard module user interface  800  comprises a plurality of performance metric modules  802 - 814 . Dashboard module user interface  800  may be implemented in a user interface for an application such as user interface  114  for the server device  102  in  FIG. 1  or user interface  210  for the user device  202  in  FIG. 2 . Dashboard module user interface  800  may be configured as shown or in any other suitable configuration. Performance metric modules  802 - 814  are configured to display one or more performance metrics for user-defined portfolios that are associated with various user plans. Performance metric modules  802 - 814  are generated in accordance with the results from a project module (e.g., project module  406  described in  FIG. 4 ). Performance metric modules  802 - 814  may comprise any suitable combination or configuration of performance metrics for a performance goal. For example, a performance metric module  802  for a pension plan comprises funded status performance metrics such as a liability basis, a current funded value, a target funded value, amount funded change value (e.g., percentage of funding change from beginning of year), and a status indicator. Performance metric module  804  for the pension plan comprises funded ratio at risk performance metrics such as a current funded ratio at risk value, a target funded ratio at risk value, and a status indicator. Performance metric module  806  for the pension plan comprises hedge ratio performance metrics such as a current hedge ratio value, a target hedge ratio value, and a status indicator. Performance metric module  808  for the pension plan comprises asset allocation performance metrics such as a next projected glide path change indicator. Performance metric module  810  for the pension plan comprises pension termination performance metrics such as a target pension termination year and status indicator. Performance metric module  812  for the pension plan comprises a likelihood of success indicator. The likelihood of success can be determined as described in  FIG. 5 . Performance metric module  814  for the pension plan comprises a pension risk factor indicator. A pension risk factor indicator indicates the amount of pension risk associated with a pension plan by examining the amount of investment risk and the current funded position. The pension risk factor may be calculated using historical financial information for portfolios and pension goals and/or financial information from external sources. The pension risk factor may be determined as follows: 
     
       
         
           
             
               
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     where PBO Funded Ratio is the market value of assets divided by PBO liabilities, Risky Asset Exposure is the percentage of total invested in equities, hedge funds, and commodities, and Hedge Ratio is the estimated portion of liabilities that are protected from interest rate risk (e.g., not exposed to the risk of interest rate fluctuations). The pension risk factor may be stored into a memory or output to a file, a graph, a table, a summary, a report, or any other suitable output as would be appreciated by one of ordinary skill in the art. For example, a summary may be a text-based report that indicates user information, a funded status, a funded ratio risk, a hedge ratio, an asset allocation, a pension termination date, a likelihood of success, and/or the pension risk factor. 
       FIG. 9  is an illustration of another embodiment of a dashboard module user interface  900 . Dashboard module user interface  900  may be implemented for a dashboard module (e.g., dashboard module  410  described in  FIG. 4 ) of a performance risk analysis (e.g., performance risk analysis  400  described in  FIG. 4 ) in a performance risk navigator (e.g., performance risk navigator module  350  in  FIG. 3 ). Dashboard module user interface  900  may be implemented in a user interface for an application such as user interface  114  for the server device  102  in  FIG. 1  or user interface  210  for the user device  202  in  FIG. 2 . Dashboard module user interface  900  comprises a performance metric module  902 . Performance metric module  902  is configured similarly to performance metric modules  802 - 814  described in  FIG. 8 . Performance metric module  902  for a pension plan comprises a graph of funding percentages, PBO accounting percentages, ABO accounting percentages, and termination percentages over time. Performance metric module  902  may also include, but is not limited to, performance metric graphs or displays for assets, liabilities, and discount rates. Dashboard module user interface  900  may be configured as shown or in any other suitable configuration. 
       FIG. 10  is an illustration of an embodiment of a take action module user interface  1000 . Take action module user interface  1000  may be implemented for a take action module (e.g., take action module  408  described in  FIG. 4 ) of a performance risk analysis (e.g., performance risk analysis  400  described in  FIG. 4 ) in a performance risk navigator (e.g., performance risk navigator module  350  in  FIG. 3 ). Take action module user interface  1000  may be implemented in a user interface for an application such as user interface  114  for the server device  102  in  FIG. 1  or user interface  210  for the user device  202  in  FIG. 2 . Take action module user interface  1000  comprises action modules  1002 - 1008  for user-defined portfolios that are associated with various user plans. Take action module user interface  1000  may be configured as shown or in any other suitable configuration. Action modules  1002 - 1008  are generated in accordance with the results from a project module (e.g., project module  406  described in  FIG. 4 ). Action modules  1002 - 1008  may comprise any suitable combination or configuration of actions for a portfolio and/or a performance goal. For example, the action module  1002  for a pension plan comprises a contribution schedule. The contribution schedule may indicate action items that are associated with making contributions to the pension plan. The action module  1004  for the pension plan comprises investment policies. The investment policies may indicate policies associated with making investments such as scheduling a meeting with an investment advisor or reviewing an analysis of the pension plan with an advisor. The action module  1006  for the pension plan comprises risk transfers. The risk transfers may indicate action items for mitigating or shifting risks that are associated with the pension plan. The action module  1008  for the pension plan comprises plan designs. The plan designs may comprise action items associated with the design of the pension plan. Alternatively, any other suitable action modules may be employed as would be appreciated by one of ordinary skill in the art upon viewing this disclosure. 
       FIG. 11  is a flowchart of an embodiment of performance risk analysis method  1100 . Method  1100  may be implemented by device such as server device  102  or user device  104  in  FIG. 1 , user device  202  in  FIG. 2 , or network element  300  in  FIG. 3 . In an embodiment, method  1100  may employed for a pension plan to obtain financial information, pension goals, and/or portfolios, to perform model simulations using the provided financial information and pension goals, and to determine performance metrics (e.g., likelihood of success) for meeting the pension goals based on the model simulation results. Further, method  1100  may be employed to determine a pension risk factor for the pension plan that indicates an amount of risk associated with the pension plan based on the provided financial information. 
     At step  1102 , the device receives user information and a performance goal. For example, the user information may be financial information and the performance goal may be a pension performance goal. The device may receive the user information and performance goal via user and a user interface (e.g., user interface  114  in  FIG. 1  or user interface  210  in  FIG. 2 ) or a database (e.g., database  106  in  FIG. 1  or database  204  in  FIG. 2 ). In an embodiment, the device may receive a first portion of the user information from the user interface and a second portion of the user information from the database. At step  1104 , the device generates a portfolio based on the user information and the performance goal. The device uses a scenario builder module (e.g., scenario builder module  404  in  FIG. 4 ) to generate the portfolio based on the user information and the user goal. At step  1106 , the device performs a stochastic simulation on the portfolio to generate a performance metric. The device uses a project module (e.g., project module  406  in  FIG. 4 ) to perform stochastic simulations on the portfolio to generate a performance metric. For example, the device may use an ALM express to perform the stochastic simulation. In an embodiment, the device may output performance metrics for projected liabilities, contributions, plan expense, and/or assets. For example, the device may generate a likelihood of success performance metric or a pension risk factor such as the pension risk factor discussed in  FIG. 8 . At step  1108 , the device outputs the performance metric. For example, the device may display the performance metric via the user interface, send the performance metric to a second device, or output a file, a graph, a table, a summary, a report, or any other suitable output as would be appreciated by one of ordinary skill in the art. The device may also be configured to store performance metrics into a memory. 
     While several embodiments have been provided in the present disclosure, it should be understood that the disclosed systems and methods might be embodied in many other specific forms without departing from the spirit or scope of the present disclosure. The present examples are to be considered as illustrative and not restrictive, and the intention is not to be limited to the details given herein. For example, the various elements or components may be combined or integrated in another system or certain features may be omitted, or not implemented. 
     In addition, techniques, systems, subsystems, and methods described and illustrated in the various embodiments as discrete or separate may be combined or integrated with other systems, modules, techniques, or methods without departing from the scope of the present disclosure. Other items shown or discussed as coupled or directly coupled or communicating with each other may be indirectly coupled or communicating through some interface, device, or intermediate component whether electrically, mechanically, or otherwise. Other examples of changes, substitutions, and alterations are ascertainable by one skilled in the art and could be made without departing from the spirit and scope disclosed herein.