Abstract: The disclosure may include an exemplary method comprising receiving risk metric data from a plurality of data sources; determining if the risk metric data passes quality control requirements; factoring the risk metric data; weighting the risk metric data by multiplying the weights at a sub-metric level; standardizing the risk metric data by scaling each risk value in the risk metric data to a range of values for each advisor to obtain standardized risk values; prioritizing the risk metric data by assigning a metric weight to the standardized risk values; further prioritizing the risk metric data by assigning a category weight to the standardized risk values; aggregating the risk metric data for an advisor to create advisor risk metric data; scoring a risk associated with the advisor based on the advisor risk metric data; and transferring the advisor risk data to a dashboard.

Abstract: The method may comprise scanning the configuration of cloud resources and identifying where the cloud resources may have drifted from a desired state. The method may comprise identifying a creation of a cloud resource or a change to a configuration of the cloud resource; scanning, in real-time, the configuration of the cloud resource, in response to the identifying; analyzing the configuration of the cloud resource for deviations from a desired state; determining a type of the cloud resource; determining a deployment of the cloud resource; obtaining a desired state for the configuration of the cloud resource, based on the type of cloud resource and the deployment of the cloud resource; obtaining rule sets for the desired state; identifying the deviations of the configuration of the cloud resource from the desired state and rule sets; and automatically remediating the deviations based on remediation policies.

Abstract: The present application relates to a method of receiving a selection of a particular client (or advisor) and determining likelihoods of the selected client (or clients associated with the selected advisor) purchasing products based on one or more factors correlating to previous purchasers of the products. The present application also relates to generating a prioritized list of sales opportunities associated with the selected client (or selected advisor) based on the likelihoods.

Abstract: An automated funds management system and in particular a system and a method to at least ensure the payment of guaranteed minimum income benefit (“GMIB”) based on the automated sector based allocation of funds in a variable annuity. A contract holder's profile data is evaluated and a profile score is computed based on the evaluated profile data. The GMIB payment stream value is computed based on the available profile data and the assigned profile score. An appropriate asset class percentage allocation of contract holder funds is recommended based on the assigned profile score and the computed GMIB payment stream value. Appropriate subaccounts are identified based on the defined asset class percentage allocation of contract holder funds, GMIB payment stream value, and/or profile score.

Abstract: A method includes determining a plurality of complexity scores. Each of the plurality of complexity scores corresponds to a particular underlying component of a plurality of underlying components of a portfolio. The complexity score of the particular underlying component is based on a complexity rating of the particular underlying component and a contribution level of the particular underlying component to the portfolio. The method also includes determining a complexity score of the portfolio based on the plurality of complexity scores of the plurality of underlying components.

Abstract: A method includes receiving a selection of a particular client (or advisor) and determining likelihoods of the selected client (or clients associated with the selected advisor) purchasing products based on the one or more factors correlating previous purchasers of the products. The method includes generating a prioritized list of sales opportunities associated with the selected client (or selected advisor) based on the likelihoods.

Abstract: A method includes receiving first user input indicating selection of a guaranteed income option to fund essential retirement expenses. The method further includes receiving second user input indicating selection of an investment option to fund lifestyle retirement expenses. The method also includes receiving third user input indicating selection of an unexpected liability coverage option, wherein the unexpected liability coverage option includes a long-term care funding option, an unexpected legal expense funding option, or any combination thereof. The method includes receiving fourth user input indicating selection of one or more legacy planning options. The method also includes generating a retirement plan based on the first user input, the second user input, the third user input, and the fourth user input.

Abstract: A particular method includes creating a financial planning goal for a financial planning client based at least in part on a financial planning objective. A financial planning recommendation is associated with the financial planning goal, and data identifying the financial planning goal and the financial planning recommendation is stored in a data store. The method also includes monitoring activity of the financial planning client and at least one other financial planning client to detect a financial planning trend. In response to the detected trend, a financial planning product is selected for potential incorporation into subsequent recommendation(s).

Abstract: The system includes automated methods for managing risk associated with investment products. A logic engine executes portfolio realignment and contract benefit calculations according to timing rules or event-based triggers. The investment product may provide a guaranteed withdrawal benefit option that allows for a variety of investment, payment, withdrawal, fee and termination options.

Abstract: Automated methods for managing risk associated with investment products is disclosed. A logic engine executes portfolio realignment and contract benefit calculations according to timing rules or event-based triggers. The investment product may provide a guaranteed withdrawal benefit option that allows for a variety of investment, payment, withdrawal, fee and termination options.

Abstract: An active overlay strategy based on rule-based risk management is implemented by a computer implemented method. The method includes receiving a data value representing implied volatility of a market. The method also includes automatically generating an order to sell call options. The order includes a quantity of call options to sell, where the quantity is determined at least in part based on an evaluation of the data value.

Abstract: A method includes generating a first interactive graphical user interface (GUI) that identifies one or more guaranteed income options to fund essential retirement expenses and receiving a selection of a guaranteed income option. The method includes generating a second interactive GUI that identifies one or more investment options to fund lifestyle retirement expenses and receiving a selection of an investment option. The method includes generating a third interactive GUI that identifies one or more unexpected liability coverage options and receiving a selection of an unexpected liability coverage option. The method includes generating a fourth interactive GUI that identifies one or more legacy planning options and receiving a selection of a legacy planning option. The method includes generating a retirement plan that identifies the selected guaranteed income option, the selected investment option, the selected unexpected liability coverage option, and the selected legacy planning option.

Abstract: A method includes receiving first user input indicating selection of a guaranteed income option to fund essential retirement expenses. The method further includes receiving second user input indicating selection of an investment option to fund lifestyle retirement expenses. The method also includes receiving third user input indicating selection of an unexpected liability coverage option, wherein the unexpected liability coverage option includes a long-term care funding option, an unexpected legal expense funding option, or any combination thereof. The method includes receiving fourth user input indicating selection of one or more legacy planning options. The method also includes generating a retirement plan based on the first user input, the second user input, the third user input, and the fourth user input.

Abstract: A particular method includes identifying a financial planning objective for a financial planning client and creating a financial planning goal for the financial planning client based at least in part on the financial planning objective. A financial planning recommendation is associated with the financial planning goal, and data identifying the financial planning goal and the financial planning recommendation is stored in a data store. The method also includes monitoring activity of the financial planning client and at least one other financial planning client to detect a financial planning trend. In response to the detected trend, a financial planning product is selected for potential incorporation into subsequent recommendation(s).

Abstract: A particular method includes detecting a context switch to a first interface. In response to the context switch, data values from a plurality of data fields of the first interface may automatically be copied to a shared storage location.

Abstract: A method includes generating a first interactive graphical user interface (GUI) that identifies one or more guaranteed income options to fund essential retirement expenses and receiving a selection of a guaranteed income option. The method includes generating a second interactive GUI that identifies one or more investment options to fund lifestyle retirement expenses and receiving a selection of an investment option. The method includes generating a third interactive GUI that identifies one or more unexpected liability coverage options and receiving a selection of an unexpected liability coverage option. The method includes generating a fourth interactive GUI that identifies one or more legacy planning options and receiving a selection of a legacy planning option. The method includes generating a retirement plan that identifies the selected guaranteed income option, the selected investment option, the selected unexpected liability coverage option, and the selected legacy planning option.

Abstract: There is provided a rebalancing tool and method that enables an advisor to monitor client account performance against a client account model, receive alerts out-of-balance client accounts, and preview rebalancing hypothetical results. The rebalancing tool enables the advisor to perform the steps of creating a client model via a web interface and compare the client account against the client model at the asset class level. The rebalancing tool can display out-of-balance client account alerts and out-of-balance client accounts on a rebalancing tool page, and enable a user to test a rebalancing hypothetical that includes modifications to the percentage asset allocation or percentage tolerance threshold at the asset class level, and planned trades to the client account. The rebalancing tools enables a user to prepare rebalancing hypothetical results that may then be presented to the client for approval.

Abstract: A method includes receiving a selection of a particular client (or advisor) and determining likelihoods of the selected client (or clients associated with the selected advisor) purchasing products based on the one or more factors correlating previous purchasers of the products. The method includes generating a prioritized list of sales opportunities associated with the selected client (or selected advisor) based on the likelihoods.

Abstract: The present invention provides systems and methods for facilitating financial advising and planning for a user. The system includes a portfolio integration module that integrates goals, assets, savings, and risk tolerance to develop a customized strategy for financial portfolio planning. A portfolio reconciler module facilitates comparison of the customized strategy to other strategies and projected user financial decisions in order to further facilitate the financial portfolio planning. A stochastic modeling module uses data from the portfolio integration module and the portfolio reconciler module in a stochastic modeling analysis using a synchronous stationary bootstrap sampling method to construct a proposed situation portfolio. A simulator module forecasts the effects of the proposed situation portfolio on the user's portfolio, and monitoring, simulating, designing, and testing the portfolio integration module, the portfolio reconciler module, and the stochastic modeling module.

Abstract: An automated funds management system and in particular a system and a method to at least ensure the payment of guaranteed minimum income benefit (“GMIB”) based on the automated sector based allocation of funds in a variable annuity. A contract holder's profile data is evaluated and a profile score is computed based on the evaluated profile data. The GMIB payment stream value is computed based on the available profile data and the assigned profile score. An appropriate asset class percentage allocation of contract holder funds is recommended based on the assigned profile score and the computed GMIB payment stream value. Appropriate subaccounts are identified based on the defined asset class percentage allocation of contract holder funds, GMIB payment stream value, and/or profile score.