Optimization and prioritization of account directed distributions in an asset management system

An asset management system is disclosed where a plurality of financial databases may connect with and be in communication with an asset management tool. The asset management tool is able to create an individual spending plan (scheduled distributions) and matching fixed income investment portfolio for a user based on inputted parameters with automatic distributions from the portfolio consistent with the individual spending plan occurring with a high degree of certainty. The asset management tool may communicate with the plurality of financial databases to put the individual spending plan and matching portfolio into action and is able to calculate and communicate an optimized surplus investment as a means of self-insuring so that the predefined schedule of distributions is met with the effective certainty one would expect from an insured/guaranteed income product.

INTRODUCTION

Investment portfolios typically have a return seeking objective consistent with underlying assets. However, when planned distributions from an investment portfolio are considered a mandate or liability, an investment portfolio should be managed to ensure these distributions occur with a high degree of confidence. This fundamentally changes the objective of the investment portfolio from purely a return seeking one to one that necessitates a risk-controlled approach to ensure the portfolio can support planned distributions. This can be accomplished using a risk-controlled investment approach generally referred to as Liability Driven Investing (LDI). LDI is utilized primarily on an institutional basis by Defined Benefit plan sponsors as a means for hedging corporate pension obligations—ensuring these obligations are met with confidence. Liability driven investing techniques can also be valuable to individual investors when considering known personal “liabilities” (i.e., future spending needs) but are rarely utilized given the complexity, cost and inefficiency of building and managing these custom portfolios of fixed income instruments on an individual basis. While certain investing/trading tools are available for investors attempting to build a basic liability driven investment portfolio comprised of “laddered bonds”, these tools require heavy engagement and investment acumen and fail to provide the ability to customize or direct actual distributions. Additionally, ensuring scheduled distributions from an individual investment portfolio occur with high confidence requires a self-insuring mechanism or surplus investment unless the portfolio is perfectly matched and comprised of risk-free assets (i.e., US treasuries). This surplus investment acts as a buffer against known risks within the portfolio (e.g., credit, interest rate). Of course, insurance products such as income annuities can provide payout certainty but come with giving up control of one's money to the life insurance company underwriting the financial obligation. Income annuities also generally do not accommodate custom (non-uniform) payout schedules. And regardless of the type of annuity or annuity rider providing “guaranteed income”, they are ultimately delivered via contract making them inefficient to transact, confusing (even to the agents selling them), and generally inflexible. Financial advisors increasingly recognize the value of predictable income as an important part of an individual financial plan but incorporating annuities is not only challenging, it immediately reduces assets under management (AUM), which is the basis for how most advisors are paid. And with trends in the financial advisory industry moving toward expanded fiduciary responsibilities, incorporating annuities into financial planning will become even more challenging from a compensation perspective given these traditional AUM-based pay structures for fiduciaries.

This traditional advisor revenue model based on AUM, also acts as a natural disincentive for investment management/advisory firms to develop income generating products for individuals that provide scheduled distributions through liquidating portfolios. However, latent demand for a non-insured investment solution designed to simplify income generation that is genuinely predictable continues to increase given demographic aging trends, persistent behavioral attitudes precluding retiree annuity purchases, industry adoption of income planning as necessary and the proliferation of financial technology firms facilitating innovative income product development.

Accordingly, it would be desirable to provide an asset management system that simplifies and expedites the process of constructing and managing an individual investment portfolio of fixed income instruments using enhanced liability driven investing (LDI) techniques incorporating a self-insurance feature with automatic payouts of all capital and interest according to a directed schedule. For purposes of efficiency we will refer to this de novo approach to generating predictable payouts from an investment portfolio for individuals as Income Driven Investing (“IDI”) giving a nod to its Liability Driven Investing roots. Underlying investments within an IDI portfolio may be fixed income instruments meaning they possess characteristics consistent with fixed income (i.e., defined income/maturity). The inventors envision the associated asset management system simplifying the engagement for the investor who is seeking a personalized solution that provides predictable income generation from an investment portfolio. The asset management system is the underlining interconnection of databases and software necessary for the planning and modeling of liabilities of individual investors and delivering an investment portfolio (exclusive of insurance contracts) that extinguishes these liabilities predictably through scheduled/directed distributions of capital and interest while optimizing portfolio yield/return. The asset management system frames the solution as something more akin to guaranteed income annuities in that predictable income generation is the primary objective. Doing so within an investment environment (sans insurance contract) affords absolute liquidity and flexibility in creating a custom portfolio that delivers predictable payouts consistent with an individually directed distribution schedule or Individual Spending Plan (“ISP”).

Further features and advantages of the disclosed embodiments, as well as the structure and operation of various elements of the disclosed embodiments, are described in detail below with reference to the accompanying drawings.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

Referring to the accompanying drawings in which like reference numbers indicate like elements,FIG.1illustrates an example asset management system100. The asset management system100can be employed as a software-as-a-service (SaaS) platform, but may also be a software platform purchasable and licensed for use on an end user's computer. As a SaaS platform, the asset management system100may be implemented as part of computer system105operating as one or more servers. The computer system105may comprise a processor110, a memory115, and a network interface120. The processor110, memory115, and network interface120can interconnected with each other in any of a variety of manners (e.g., via a bus, via a network, etc.)

The network interface120can provide an interface for the asset management system100to connect to a network125. The network125can be any suitable communication network or combination of communication networks, such as the Internet, wired and wireless telecommunication networks, etc. Through the network125, the asset management system100can interact with a plurality of end user computers130and a plurality of financial databases135. The end user computers130and/or financial databases135can take the form of any suitable computer (such as a desktop computer, laptop computer, tablet computer, or smart phone) or larger computer system such as a server or server network. The network interface120may take any suitable form for carrying out network interface functions, examples of which include an Ethernet interface, a serial bus interface (e.g., Firewire, USB 2.0, etc.), a chipset, and antenna adapted to facilitate wireless communication, and/or any other interface that provides for wired and/or wireless communication. The network interface120may also include multiple network interfaces. Other configurations are possible as well.

The processor110may comprise one or more processors such as general-purpose processors (e.g., a single-core or multi-core microprocessor), special-purpose processors (e.g., an application-specific integrated circuit or digital-signal processor), programmable-logic devices (e.g., a field programmable gate array), etc. that are suitable for carrying out the operations described herein.

The memory115may comprise one or more non-transitory computer-readable storage mediums, such as volatile storage mediums (e.g., random access memory, registers, and/or cache) and/or non-volatile storage mediums (e.g., read-only memory, a hard-disk drive, a solid-state drive, flash memory, and/or an optical-storage device). The memory115may also be integrated in whole or in part with other components of the system. Further, the memory115may be local to the processor110, but it should be understood that the memory115(or portions of memory) could be remote from the processor110, in which case the processor110may access such remote memory115through network interface120(or some other network interface).

Memory115may store software programs or instructions that are executed by processor during operation of the system. For example, the memory115may include optimization logic140, which can take the form of a plurality of instructions configured for execution by processor110for executing purchases and sells of identified assets in communication with the asset management system100. The optimization logic140may take into account relevant attributes of the underlying fixed income assets such as, but not limited to, fixed income securities, fixed income funds, cash, certificates of deposit, brokered certificates of deposit, or any financial instrument that specifies a fixed income payout. The memory115may also store user input logic145, which can take the form of a plurality of instructions configured for execution by processor110for receiving, processing, and delivering input data to the optimization logic140to aid in the calculation, timing, and determination of the flow of identified assets. The memory115may also store request-handling logic150, which can take the form of a plurality of instructions configured for execution by the processor110for controlling user access to manage and administer liability profiles. The request handling logic150helps control how end users are granted access to their accounts that in some instances may be known as Individual Spending Accounts (“ISA”s).

The memory115may also be configured as a database or other storage design where a plurality of accounts155for end users is stored. Identifying information, owned assets, and login credentials can be stored in the account for that end user. Other memory addresses in the memory115can store an asset data structure160. The data structure defines the information pulled from outside databases regarding potential assets that may be selected and used as part of the optimization logic140for the asset management system100. It should be understood that the asset data structure160could be a single data structure or a plurality of data structures. It should also be understood that asset data structure160could be fluid and change over time as the content of the memory115changes.

Turning now toFIG.2, and a high-level flow chart of the asset management system100is viewed with its association to outside computer systems. The asset management system100uses the network125to receive and send communication between the computer system105and end user computers130or financial databases135. The end user computers130may be a separate computer system from which an investor200who is an owner of an account on the asset management system100may log into to access the asset management system100and adjust the distribution of selected investments within a selected liability portfolio. These portfolios and their associated distributions are presented to the end user as spending plans. The end user computers130may also include advisor computers where an advisor200who has authorized authority to manage and maintain an account of an investor200can log into and access the asset management system100on behalf of an authorized investor200. Both the investor200computer system and the advisor200computer system may have a display connected to the computer system. The display may be able to visualize instructions communicated by the asset management system100to the end user computers130to produce a graphical representation of account activity initiated by the end users. Additionally, both the investor200computer system and the advisor200computer system may have a user input interface to input commands and information to be evaluated, optimized, executed, and displayed by the asset management system100. The user input interface may consist of many different types of input technology including but not limited to keyboards, mouse use for navigation, or touchscreen technology.

A plurality of financial databases135may also connect to the asset management system100through the network125. The financial databases135may include custodians205who may have control and management authorization over particular assets that may be included as possible investment options within the asset management system100. In some instances, the custodians205may also have control of an individual investor account or a plurality of investor spending accounts stored on the asset management system100. Additionally, the financial databases135may include platforms210that are accessibility by the asset management system100. These platforms210may be investment opportunities in particular sectors of business and different types of investable funds managed and maintained by a third party operation. Data concerning these investment opportunities may be transmitted to the asset management system100through the network125. In other instances, data concerning these investment opportunities may be transmitted to the asset management system100for utilization by the optimization logic140of the asset management system100.

The computer system105of the asset management system100itself is also connected to the network125. To facilitate connections and communication with the end users130and financial databases135, the asset management system100has an input/output interface215from which logic commands are transmitted to either the network interface120and out to the entire network125or to the memory115of the computer system115of the asset management system110. Within the memory115of the asset management system100, storage of applications220may be present. One such application220may be an asset management tool that may take the form of computer software to run the overall operation of the asset management system100. The asset management tool may interpret data stored in each the optimization logic, user input logic, and handling logic to run commands and queries of the asset management system100. It is also possible that additional applications220may be stored within the application partition of the memory115in the asset management system100. These additional applications220may aid in the optimization and execution of the asset management tool or provide additional benefits to the asset management system100. An operating system225may also be stored within the memory115of the asset management system100. The operating system225may be a known or easily accessible operating system, such as but not limited Windows or Linux, which can allow for easy and known operation of the applications220stored in the memory115.

There may also be a database230as a partition of the memory115in the asset management system100. The database230may store and create logic tables from information inputted into the asset management system100through the network125. The database230may store information related to accounts of users that can access the asset management system100as well as tables and data structures created by the optimization logic140, user input logic145, and handling logic150. The database230may also store informational and analytic information related to debt instruments to be used by the asset management system100.

As introduced above, the asset management system100may include an asset management tool to establish a liability profile/Individual Spending Plan (“ISP”) for each account. The asset management tool can also be used to construct and manage the liability portfolio to include asset allocation determinations and trade instructions including redemptions associated with directed payouts.

The asset management tool can be used by account holders such as investors200and financial advisors200to assess an asset investment with payouts/distributions intended to match a given liability profile. The asset management tool has the ability to create multiple different liability profiles. A liability profile of the asset management tool can be described as either uniform or non-uniform. ViewingFIG.3, an example of a created uniform liability profile300can be viewed. In the case of a uniform profile300, an account user305, such as an investor, indicates multiple data points, or parameters, to be entered into the asset management tool for calculation and optimization of the profile300. These data points entered by the account user305can include information related to either the investment amount310, the payout amount315, or the final payout date320. Only two of these three initial inputs are required to be inputted by the account user305to create the desired uniform liability profile300. Once two of the three inputs are entered, the remaining input can be calculated by the asset management system100and can be automatically populated for the account user305.

As seen inFIG.4, an example of a non-uniform liability profile400is viewed as created by the asset management tool. For the creation of a non-uniform liability profile400, an account user405indicates one or more of payout amounts410and payout dates415. After such information is inputted into the asset management system100, the asset management tool can then calculate the initial investment amount420, which can be automatically calculated for the account user405. Alternatively, the account user405can indicate the investment amount420and a single payout date415. In that instance, the calculated payout amount410can be automatically populated for the account user405. This allows an account user405to approach the problem in a way best suited to the situation depending upon whether the account user405has a set investment or wants to know the required investment amount given the desired payout(s).

Customization within the asset management tool includes payments in the form of nominal or real payouts. Real payouts may be adjusted according to an inflation metric disclosed during client engagement with the account user. Alternative inflation metrics may be considered and offered in certain variations. With regards to a uniform liability profile300, an account user of the asset management tool can select the frequency of payouts with monthly being the default setting. An account user may also defer the start date of payouts under a uniform liability profile300. Minimum investment amounts may vary across the financial databases such as certain advisory or defined contribution record keeping platforms where the solution can be made available. Other constraints associated with customization features of the asset management tool may also be imposed including the maximum period over which the directed payouts will occur.

The asset management system100and asset management tool further solves problems of prior graphical user interfaces and their underlying functionality in the context of computerized income modeling relating to individual usability, utility, and execution. The asset management system100and asset management tool is directed to improvements in graphical user interfaces that have no pre-electronic analog comparison and involve the transformation of information inputted into a calculable format for processing to output a strategical asset allocation plan that is more than simple selection and display of general data information. This technological solution requires a specific graphical user interface paired with a prescribed functionality directly related to the graphical user interface's structure that addresses and resolves a specifically identified set of problems in prior analog systems.

Current analog models for income generation through an investment portfolio utilize investment products and associated modeling tools primarily employing asset-only portfolio optimization techniques where effective certainty of planned distributions cannot be assumed with high confidence and require heavy engagement and acumen to implement. Additionally, these planned distributions are not typically scheduled in concert with a modeling exercise leaving the user to independently effect these distributions. Where liability driven income tools exist in the marketplace (for investment portfolios), they are not arranged in a way that allows a user (individual investor, their advisor, or custodian) to present the desired outcome (i.e., desired income). Instead, users are expected to create a “laddered” portfolio with illustration of income occurring subsequent to the selection of securities or funds to construct this portfolio. Furthermore, constructing this “laddered” portfolio using individual corporate debt securities (bonds) presents the daunting task of selecting from a very wide range of individual securities of various terms and credit quality. Generating even a simple uniform distribution of income via this method is very challenging even for experienced advisors and ultimately the risk of concentrated credit exposure to any single issuer cannot be mitigated effectively. One example of an underlying investment type of an income driven portfolio is a defined maturity fund (“DMF”), which facilitates the diversification of fixed income risks associated with individual fixed income securities while maintaining their maturing nature. Traditionally, fixed income (bond) funds are perpetual in nature in terms of their remaining age—replacing maturing bonds with outstanding bonds to maintain the same duration or average remaining age of the fund based upon maturities of all the individual bonds. Defined Maturity Funds on the other hand combine the diversification benefit of pooling individual securities with the characteristics of an individual security in that they liquidate at maturity. Thus, while defined maturity funds (DMFs) can diversify the concentrated credit risk associated with individual debt securities, the income tools associated with these defined maturity funds are rudimentary with the added complication of not being able to actually inform the user as to what future declared dividends could be possible from these funds. In other words, while a defined maturity fund is intended to mimic the individual bond characteristic of a liquidating fixed income asset with a defined maturity, it cannot provide certainty around the level of income an individual can expect prior to maturity as dividends declared by these funds are not precisely aligned with the underlying asset distributions (e.g., coupon payments/maturing bond proceeds).

Thus, no existing digital or technology platform allows an account user to establish a custom liability profile that is intended to be matched using an investment portfolio employing liability driven techniques with execution occurring automatically. The asset management system100and asset management tool provides this capability through a uniquely structured graphical user interface with prescribed functionality directly related to this new interface. This functionality includes portfolio construction and optimization using proprietary liability driven investment techniques, intuitive and de novo visual representation of all requisite information, and automatic scheduling of the corresponding distributions designed to match or extinguish the user defined liability profile.

InFIG.5, the asset management tool500of the asset management system100is view in more detail. The plurality of financial databases505is viewed on the left side. As discussed above, custodians205may be part of these financial databases505as well as platforms such as specific investment platforms510dedicated to a particular asset to be available for calculation by the asset management tool500. Also included as part of the financial databases505may be market data providers515. These market data providers515may provide specific analytics and static information to the asset management tool500regards trends, current status, and value of specific markets in which the assets trade and their current calculated values. Through the network125, these financial databases505communicate with the asset management tool500. The communication signals520between the financial databases505and the asset management tool500may be encrypted at the point of origin and decrypted at the point of reception to further secure the transfer of sensitive information between these system components.

Within the asset management tool500, an application-programming interface (API)525may be present. The application-programming interface525may run call instructions from the asset management tool500to connect to select financial databases505authorized for communication with the asset management tool500. Through these application programming interface connections, data feeds530may flow into the asset management tool500from the connected financial databases505. The data feeds530ingest and egress data into the asset management tool500for later consumption and execution by each the calculation engine545of the asset management tool500and the determination and development of market data analytics540of a specific asset of the data feed530. The data feeds530may be accessed by the asset management tool500in either predefined time intervals, or may update in real time based on the behavior of a specific asset or the reporting procedures of the financial database505communicating with the asset management tool500.

A liability profile535is present within the asset management tool500. The asset management tool500can create a custom liability profile535to the specification of the end user. As discussed above, certain inputs may be inputted into the asset management system100by the end user to be used by the asset management tool500to create the liability profile535. Once created, the asset management tool500selects specific asset holdings that will make up the Individual Spending Account investment portfolio. The asset management tool500can then manage the asset holdings to the specification of the liability profile535.

The asset management tool500is further intended to utilize current and projected future market data and analytics540to produce estimates of income, either through uniform or non-uniform distributions. Once income estimates have been established and an investment has been made, the asset management tool500can allocate assets to and rebalance among several investment options to provide for these defined payouts based on the wishes of the end user of the account. Creation of current and projected future market data and analytics may be accomplished by the passing of information contained in the data feed530to the market data and analytics540for computation by the calculation engine545of the asset management tool500.

The calculation engine545of the asset management tool500both establishes income estimates given sets of parameters inputted into the asset management system100, and establishes a database within the asset management tool500that stores inputs, outputs, market data, and analytics. If/when rebalancing of a liability profile535is needed, a rebalance model will be present and in communication with the calculation engine545. The rebalance model may execute an optimization algorithm or optimization program which confirms that assets currently held within the Individual Spending Account of the end user are on course to meet the desired payment distributions selected by the end user of the account. If these assets are not optimal in being able to make desired payment distributions, the rebalancing model redistributes the assets of the profile through a series of buy and sell transactions to rebalance the profile to meet the desired payment distributions directed by the end user of the account.

The calculation engine545will leverage the inputs and a rebalance model to allocate individual participant assets among various fixed income investments providing a known payout(s) to best match the payouts established as part of an Individual Spending Plan. The calculation engine545and rebalance model will periodically rebalance due to passage of time, analytics, participant holdings and actions, and market data, according to the established rebalance model. Rebalance instructions can be communicated to the financial databases505, such as investment platforms510, through the network125for implementation. Machine learning may be used to test and train the rebalance model used by the calculation engine545.

If applicable, the calculation engine545computes a default risk probability for each available investment component that represents the likelihood of default of one or more underlying holdings of the investment component. These are converted to scaling factors that are applied to the projected cash distributions of each investment component to account for the anticipated aggregated default of the underlying debt holdings of each investment component. Incorporating these scaling factors into the calculation process allows the calculation engine545to cash flow match short and long-term payments with a greater degree of certainty.

The asset management tool500will leverage machine-learning techniques to improve the accuracy of scaling factors over time. The asset management tool500will utilize default risk estimates provided by third-party data providers and apply additional adjustments based on a trained predictive model to improve accuracy. Ex-ante predicted default rates for specific issuers, classifications, categorizations, and other attributes of debt securities will be compared against ex-post actual default rates to train the predictive model. Improvement in the model over time will result in a reduction in the range of outcomes and thus help to minimize the surplus investment necessary to ensure all Individual Spending Plan (ISP) distributions will occur with high confidence.

The asset management tool500will also consist of a graphical user interface (GUI)550that allows a user555to adjust one or more parameters in their liability profile353. These parameters may be either the investment amount, payout amount, or final payout date for the associated liability profile/ISP535. Once the parameters are inputted by the end user555, the calculation engine545establishes income estimates given the sets of parameters input, and the database stores inputs, outputs, market data, and analytics. Interaction with the asset management tool500and asset management system100will be accomplished through a combination of the graphical user interface550and the exposed network application-programming interface to provide data into the system. The plurality of end user computers130from which the Individual Spending Account owner accesses the asset management tool500of the asset management system100may have a display computer to which the asset management tool500transmits electronic signals to view the graphical user interface550.

Turning toFIG.6, operation of the calculation engine545of the asset management tool500is seen in greater detail. When preparing estimates and ingesting user data, the asset management tool500will transform parameters inputted by the user into a liability profile/Individual Spending Plan600or535, which consists of a series of scheduled estimated payments605of invested principal and interest. Surplus investment610will be calculated minimally on a daily basis that represents a cushion or reserve to ensure that expected/implied portfolio volatility will not preclude the portfolio600from generating the scheduled distributions. Furthermore, a surplus utilization rate615of the liability profile600may be present. This surplus utilization rate615represents the expected utilization of the surplus amount calculated at the inception of the plan over time throughout the life of the Individual Spending Plan. The calculation engine545has three main functions within the asset management tool500. The first main function is to calculate a liability profile600of the end user of the account. To accomplish this task, several inputs are required to enter the calculation engine to perform the necessary computations as discussed above.

Once a set number of the required inputs are entered into the asset management tool500, the calculation engine545begins its computational work. The calculation engine545processes the necessary inputs to invoke a simulated path from initial investment to last payment based on the input parameters. The path is created by the calculation engine545by using market data and analytic assumptions to calculate an individual liability profile/spending plan. After the processing of the calculation engine545is complete, the calculation engine545provides outputs to the asset management tool. The liability profile600, which may be visible on the graphical user interface of the end user, shows the set the estimate payment amounts and dates computed by the calculation engine545. The liability profile600consists of a payment schedule605, a surplus investment610, and a surplus utilization rate615. The surplus investment610constitutes the remaining balance estimated to be in the account and incorporates both unused assets and capital appreciation at the end of the payment schedule.

The calculation engine545quantitatively determines the allocation to available underlying investment components by splitting the payment horizon into short-term (e.g., <10 years) and long-term (e.g., >10 years). Short-term payments can be defined maturity funds or other fixed income investments with similar maturity profiles. Long-term cash flows are generated with an allocation to fixed income investments/funds with durations designed to limit yield curve exposure differences versus the desired payouts of an Individual Spending Plan (ISP). Cash flow projections of defined maturity funds and other fixed income investments are adjusted by the previously calculated scaling factors. Allocations are recalculated and rebalanced based upon defined threshold(s) that maintain the effective certainty of ISP payouts in conjunction with established rebalance timeframes (e.g., monthly).

The calculation engine545receives a plurality of inputs to create and compute the desired payouts of an individual spending plan (ISP). The calculation engine545can receive external data sets from third party servers or databases. These external data sets may be specific data compiled and distributable by the third party servers and can access the calculation engine545through the API interface of the asset management tool500. The external data sets may be associated with fund holding information. Thus, these external data sets can be directed to the type of investment vehicle that can be used to create the individual spending plan. In some instances, these external data sets may be information related to defined maturity funds. In other instances, these external data sets may be information related to long-duration funds. Both defined maturity funds and long-duration funds are excellent investment vehicles used and employed by the assets management tool500for creation of the requested individual spending plan. The received external data may also contain information on the default probabilities associated with the external data set. In some instances, these default probabilities can be obtained from a third-party server or database that may or may not be associated with the external data set. The default probabilities help the calculation engine545perform its function in determining which of the external data sets would be most appropriate for the requested individual spending plan.

The calculation engine545can receives a plurality of inputs from the user as discussed both above and below. These user inputs include payment details specified by the user when executing the requested set up of the individual spending plan. For example, the user may input the desired initial investment620, the desired uniform or non-uniform payment amounts625, and/or payment start and end dates630. When the calculation engine545is in operation, the user usually does not input this information for the individual spending plan as such information is already stored within the asset management system100. However, there may be some instances where additional user input at this stage is possible to correct or confirm the requests for the individual spending plan. Overall, the calculation engine's operation is a backend process with few user-facing interactions.

Once the individual spending plan information and external data sets are loaded into the calculation engine545, the calculation engine545can perform the required processing to create the required investments needed to meet the desired payouts of the individual spending plan. Afterwards, the calculation engine545can execute the investment of capital into the selected investments so that the individual spending plan is implemented. The process flow of the calculation algorithm of the calculation engine545can be viewed in greater detail withinFIG.7-11.

As a first step, the calculation algorithm700calculates the cash flows of the component funds (i.e. investments) that are loaded into the calculation engine from the external data sets. The calculation algorithm700, as seen inFIG.7, then projects aggregated flows705for the securities held in each fund based on the holding details obtained from the fund providers. These holding details can be part of the external data set provided to the calculation engine545from the outside servers/databases. To project these aggregated flows705, the calculation engine545can create an aggregated cash flow matrix715associating appropriate data from the external data set in a tabular format for easy evaluation and look up potential. In the aggregated cash flow matrix715, the aggregated cash flow705of the fund holdings normalized to a currency data point is viewed against a period of investment710. Usually, the period of investment710is broken down into yearly segments, but other time based breakdowns are possible.

From there, and as seen inFIG.8, the calculation algorithm700adjusts the flow of funds for expected default losses at the fifth percentile likelihood based on default data from the aggregated cash flow matrix715. If US Government debt securities (treasuries) are utilized as the selected funds, the assumed losses from default are zero. The calculation algorithm700creates an expected default loss matrix800based on this percentage of acceptable loss against a period of investment710. The calculated default loss matrix800is used against the aggregated cash flow matrix715to create an adjusted cash flow matrix805for the calculation engine545. The adjusted cash flow matrix805represents an adjusted aggregated cash flow based on the default loss matrix calculations. The adjusted cash flow matrix805is the aggregated cash flow of the of the fund holdings normalized to a currency data point adjusted with the calculated default loss against a specific period of time. Finally, if dividend policies are needed to be included with the component funds, and required as part of the external data set to create these matrices, the dividend policies are applied to adjusted aggregated cash flows. In these instances, the adjusted aggregated cash flows will become probability-adjusted aggregated cash flows pursuant to the dividend policies required by the respective component fund.

Turning now toFIG.9, step two of the operation allows the calculation algorithm700to compute a cash flow match for the individual spending plan. In the example embodiment provided withinFIG.9, the individual spending plan cash flow match is computed for payments over the period900. It should be understood though, that any possible term may be used. The calculation algorithm700calculates the investment needed in a particular component fund (in this example embodiment a defined maturity fund) to generate the cash flow required by the individual spending plan. As seen in table A905ofFIG.9, the example embodiment represents a user-defined period900. The user in this example provided inputs to the individual spending plan of either a periodic cash draw amount, or a total cash draw amount for the period900. The calculation engine700, then in table B910, determines the amount to invest in a particular underlying fund to generate the periodic cash flow by the user in a particular period. In table C915, coupon payments, bond interest payments, or bond coupons are accounted for by the calculation algorithm700. The coupon payments, bond interest payments, or bond coupons are then applied from the later years of a particular year. Column D920provides a cash flow amount of the coupon and maturity of a current period900. In situations where the expected coupon payments, bond interest payments, or bond coupons exceeds the required cash flow for a particular period, then no investment corresponding to that period is purchased by the asset management system100. Column E925provides for a total amount of cash flow of the underlying fund generated for a particular period. Column F930provides for excess cash flow that is generated in a particular period with respect to the user specified cash flow for that same period.

ViewingFIG.10, the calculation algorithm1000also adjusts for an over-purchase amount of the representative component fund. If the total cash flow for the specific component fund (i.e. defined maturity fund or DMF) generated by the calculation algorithm700in the previous step exceeds the specified individual spending plan cash flows identified by the user, the component fund may be scaled to accommodate this overage. As seen in the example embodiment ofFIG.10, the final component fund investment amounts are scaled by a ratio1005of the present value1000of the individual spending plan cash flows to the total investment amount previously calculated. This presents an adjusted investment amount1010for each year to be allocated to the select component fund for that particular year. The adjusted investment amount1010value and calculation occurs only if coupon payments generated by the component fund exceed the cash flow required in a particular year.

As viewed inFIG.11, step three allows the calculation algorithm700to determine a blend of component funds that most closely matches the interest rate sensitivity of the remaining liability. This can include any residual cash flows from step two discussed above, as well as future term years if they are applicable. For example, the embodiment shown inFIG.11shows a ten-year term for the individual spending plan, but cash flows for years eleven and onward can be part of this calculation. To calculate the residual cash flow1100for the term of the individual spending plan, the calculation algorithm compares user specified individual spending plan cash flows1105from the component accounts against the expected cash flows for the component accounts generated by the final investment amounts1110calculated in step two. The result is the residual cash flow1100for a specific component account during a periodic interval of the individual spending plan. The calculation algorithm700then combines the residual cash flows1100for the period specified by the individual spending plan with subsequent or additional cash flows outside the set period of the individual spending plan. This result gives the calculation algorithm700the remaining liability of the individual spending plan. The remaining liability cash flows are then converted to a dollar sensitivity to a one basis point change in rate “DV01” with the following equation where PV is the present value and D equals duration: DV01(CFt)=PV(CFt)×D(CFt). Next, the calculation algorithm700converts the component funds' cash flows to a contribution to duration (“CTD”). This is accomplished with the following equation:

The cash flows for the component funds are then normalized to a $1 dollar investment. Next, the calculation algorithm700optimizes an objective function of the individual spending plan. The objective function is used to determine fund investment amounts ($Investedf) that minimize duration and yield curve mismatch versus the remaining liability. This is accomplished by the following equation where ($Investedf) is subject to the condition of being greater than or equal to zero:

FIG.12shows step four and the final processing step of the calculation engine545where the calculation engine545outputs the combined component funds to create the target portfolio600requested by the user's desired inputs into the asset management system. As seen inFIG.12, the portfolio total1200for each component fund is viewed based on the calculations from the earlier steps1205of the processing algorithm. From there, the total portfolio1200of the component funds is calculated. After the total portfolio1200is created, the calculation engine545can then address the asset management tool500to execute buy or sell operation to implement the calculated individual spending plan based on the calculation algorithm's computations.

The calculation engine545can also rebalance the individual spending plan as time passes and updated external data sets are available to the calculation engine545. If the duration mismatch (calculated daily) between the individual spending plan and associated portfolio exceeds established thresholds the asset management tool500will run calculations detailed in step four above. The new portfolio will be compared to the existing portfolio and trades will be executed to rebalance to the new target weights.

The calculation engine545can also calculate an expected surplus610of an individual spending plan. To calculate the surplus investment610of the ISP (i.e., the amount by which the current account market value exceeds the expected amount required to generate the ISP payments), the calculation engine545generates cash flows for the underlying investment components based on median/50thpercentile credit default losses (as opposed to 5thpercentile as described in step one above). The investment amount required to meet the ISP based on these assumed underling cash flows is then calculated following the logic from step four. The expected surplus610is the difference between the investment amount required to meet the ISP at the 5thpercentile and the median/50thpercentile.

The second main function of the calculation engine545is to provide account management of a previously created liability profile of the end user.FIG.13shows inputs and outputs of the calculation engine545that are needed to accomplish these management tasks. For each account, the asset management tool500will transform each participant's individual liability profile1300, current holdings1305, and market data and analytics1310into a set of allocation percentages and a set of defined maturity bond funds or any other underlying asset previously described. The calculation engine545of the asset management tool500will then utilize a rebalance model1315to periodically adjust these allocation percentages and submit any necessary rebalance trade orders including sell instructions associated with scheduled distributions associated with the liability profile1300to an outside financial database such as but not limited to a custodial platform. The calculation engine545may also utilize credit risk scale factors1320as part of the inputted data to operate the calculation engine545in creating the optimized liability profile1300and for appropriate rebalancing of the liability profile1300.

To accomplish this second task of account management, several inputs are required to enter the calculation engine545to perform the necessary computations. First, the liability profile1300previously calculated by the calculation engine545is inputted back into the calculation engine545for management purposes. As stated above, the liability profile1300, which may be visible on the graphical user interface of the end user, shows the set the estimated payment amounts and payout dates computed by the calculation engine545. The liability profile1300consists of a payment schedule1330and an expected surplus investment. The surplus investment constitutes any remaining balance estimated to be in the account and incorporates both unused assets and capital appreciation at the end of the payment schedule1330. Holdings1305of the liability profile1300are also inputted into the calculation engine545for liability profile management. The holdings1305represent units held in an individual asset or investment account of one or more defined maturity funds, various debt securities, and/or cash as of a specific date. Market data1310is also electronically received and/or inputted into the calculation engine545for liability profile management. The market data1310represents the price of each underlying asset as of a specific date. Analytics1310are also inputted into the calculation engine545for liability profile management. The analytics1310are calculations processed by the asset management tool500that represent the actual and/or calculated duration of each defined assets, maturity fund, or investment, as well as the liability profile1300, as of a specific date.

The calculation engine545can also account for the liquidity required to meet the liability payments directed by the Individual Spending Plan. The calculation engine545can establish the minimum amount of cash required in the portfolio to make any payments due to the participant as-of the date of calculation.

Once the required inputs are entered into the asset management tool500, the calculation engine545begins its computational work to manage the liability profile1300. The calculation engine545processes the necessary inputs to create a target asset allocation1325of each individual asset of the liability profile1300. The calculation engine545then can utilize a rebalance model1315or algorithm to recommend rebalance trades1335for each individual asset to properly balance the liability profile1300to the wishes of the end user of the account. Afterwards, the calculation engine545outputs two main sources of information: target allocations1325and rebalance trades1335. The output of target allocation1325is the result of the calculations of the calculation engine545that result in the optimal asset allocations in percentages, to defined assets, maturity funds, debt securities, and/or cash for the liability profile1300as of a specific date. The cash component of the target allocations1325is inclusive of any payments or liquidity needs determined by the individual spending plan. The output of rebalance trades1335determines trades between underlying holdings1305for each liability profile1300of the account to move actual asset allocations closer to the target allocations1325. The rebalance trades1335can then be communicated by the asset management tool500to the financial databases to execute the trades determined to optimize the liability profile1300. The calculation engine545can also output the scheduled payments1330of the liability profiles1300created by the calculation engine545, utilizing cash raised as part of the rebalance process. Once complete, the liability profile1300is deemed to be in a correct and optimal condition so that the payment disbursements over the predetermined period are in line to be met.

The third main function of the calculation engine545is to calculate a surplus utilization rate615for the Individual Spending Plan. This surplus utilization rate615represents the expected utilization of the surplus amount calculated at the inception of the plan over time throughout the life of the Individual Spending Plan. The expected utilization of the surplus at any time t is calculated by adding the net surplus expected up to that point to the net present value of future surplus expectations:

St=∑u=1t-1(du-mu)+PVt(∑u=tnPVu(du-mu))
Where Stis the surplus at time t, n is the total number of periods in the term of the spending plan from inception to final payment (termination), duis the surplus at the desired confidence interval at time u, mu is the surplus at the median confidence interval at time u, and PVtand PVuare functions that calculate the present value of future cash flows at times t and u, respectively. To calculate the full surplus utilization rate615, Stis evaluated for each period (for example, monthly) from spending plan inception through the date of the terminal payment (period n).

Once a personalized surplus utilization rate615has been established for an Individual Spending Plan, the calculation engine545will calculate actual surplus amounts each day as time progresses. The calculation engine545will then track deviation of actual surplus amounts from the estimated surplus610in the initial surplus utilization rate615calculation as time progresses. For any deviations in actual surplus amount, the calculation engine545can calculate attribution to determine the relative sources of those deviations. The actual surplus amounts relative to the surplus utilization rate615, along with any relevant attribution data, can be displayed by the user interface.

To aid the calculation engine545in its computational processing, machine-learning software may be used to create an optimal model based on learned trends and successful asset investments. As seen inFIG.14, a flow chart of how information flowing through the asset management tool500for use with machine learning software to refine the data outputted by the calculation engine545. The asset management tool500may use machine-learning software1425to help implement a decision tree as to whether or not to rebalance for a specific liability account. Input offered by the end user of the account will help determine if a specific liability profile is a candidate for rebalancing based on the closeness of the specific liability profile to a representative liability profile created by the machine learning software. The asset management tool500may input certain parameters into the machine learning software to create the representative liability profile used for comparison. These inputs include historical market data and analytics1405to train and improve the machine learning software for rebalance decisions. Inputs also include representative and actual liability profiles1400of the end users of accounts stored within the asset management tool500. Fund holding information1410for third party sources can also be inputted to train and improve the machine learning software for rebalancing decisions. The asset management tool500may also use machine learning to predict the value of dividend payments to be distributed by each defined maturity fund based on an assessment of coupon payments and maturities of underlying debt instruments. This assessment of dividend payments, in combination with the closeness of a dividend payment to a scheduled payment, may also be input to determine the rebalance decision tree. Additionally, predicted results1450based on the representative and actual liability profiles1400and how they should perform over time are inputted into the machine learning software. Before all of these inputs enter the machine learning software, pre-processing of data1415occurs to put all of these inputs into a common data structure for execution and computational processing by the machine learning software1425. From there, the machine learning software1425creates a representative liability profile with the inputted information. That machine learning software1425can be in communication with a credit risk estimation engine1420to help develop and calculate predicted results of a rebalancing module. The rebalancing module may utilize outputs of the credit risk estimation engine1420to create predicted default rates1430and predicted dividends for the liability profile as well as aggregated payments1435. The representative liability profile is compared to the actual liability profile of the end user of the account and the rebalance model may execute the redistribution of investment assets through the investment allocation optimizer1440based on a predetermined threshold of deviation between the actual and representative liability profile. Afterwards, the rebalance engine1445finishes up recommendations in the management of the actual liability profile, the results are outputted to the end user for viewing on the graphical user interface. The predicted results1450from the rebalance engine1445itself can also be used as an input to the machine learning software1425given that the specific sets of parameters used in the lasted iteration may improve the machine learning software1425.

As discussed earlier, the graphical user interface outputted by the asset management tool500is an important advancement of the asset management system100. The graphical user interface presents an easy to understand and comprehend arrangement of data pertaining to a liability profile of an end user of an account. Current systems are analog, extremely complex, and do not offer visual assistance to the end user of any type of liability profiles. The graphical user interface gives the end user an easy to read, easy to access, and easy to control electronic representation of their specific liability profile without extensive backend calculations or information processing.

FIG.15shows and example graphical user interface which may be visible to an end user of an account selecting a uniform distribution of assets1500. In a create plan panel1520, the type of liability profile/ISP can be toggled by the end user between accessible distribution options (e.g. uniform or non-uniform payouts). To access additional information about the selected uniform distribution of assets1500plan, the end user can select an “Explore the Plan” button1545within the create plan panel1520for more detailed plan information and breakdowns. For a uniform distribution of cash flows the end user of the account will use the graphical user interface to input known parameters of their liability profile/ISP. Within a plan parameters panel1525, these inputs may represented to the end user and include a date of first payment, an initial investment amount1505, a date of end payment1515, or an estimated monthly payment1510. Not every input is needed to complete the liability profile by the calculation engine545. Only two of the three inputs of an initial investment amount1505, a date of end payment1515, or an estimated payment1510are needed and the asset management tool500will solve for the unknown parameter. In some example embodiments, the end user can toggle the period of time for the estimated payment1510between selected time period (e.g. monthly, yearly, etc.) with a “Payout Period” button1550. The end user can then select either select a “Submit” button1555or a “Clear” button1560to create the desired uniform distribution plan based on the inputted parameters or to reenter input information necessary for creation of a uniform distribution plan. The graphical user interface can allow the end user to plan for an initial investment, or assess the impact of changing parameters to their estimated series of cash flows. The graphical user interface can also display both the cashflow1535and any surplus investment1540in a cash flow panel1530. The surplus investment1540is subsequent to investment in the liability profile that would be added to the final payment in the cash flow series. The graphical user interface can show schedule payment dates and the uniform amount of those payments to be provided at the determined dates. Finally, the graphical user interface can provide the user with portfolio yield based on the determination of the calculation engine545of the asset management tool500.

FIG.16illustrates another example graphical user interface of a uniform distribution model of the asset management tool500showing a uniform plan inception1600. The graphical user interface viewed in this example embodiment contains the same panels and user activated buttons as seen above inFIG.15. This example uniform plan inception1600illustrations shows a yearly period of distributions as opposed to a monthly period of distribution shown inFIG.15. Thus, the plan payouts panel (also known as the cash flow panel1530) may provide easier visual information to the end user viewing cash flow1535and the surplus investment1540. The plan payouts are viewed in a graphical bar chart form1605wherein the cash flow1535is viewed below the surplus investment1540.

FIG.17illustrates an example graphical user interface showing uniform: allocation1700of the assets in a uniform distribution model of the asset management tool500. This uniform: allocation1700screen may be accessible from the original graphical user interface shown to the user when the uniform distribution of assets1500or1600is created by the end user. The uniform: allocation1700screen contains a plan details panel1705, a portfolio panel1710, and a portfolio composition panel1715. The plan details panel1705can provide the end user with detailed information related to the selected and created uniform distribution of assets plan. Such information may include, but is not limited to inception date of the plan, type of selected plan, payout interval of the plan, total investment amount in the plan, payout amounts of the plan, first payout date for the plan, final payout date for the plan, projected surplus of the plan, current surplus of the plan, duration of the plan, dollar sensitivity to a one basis point change in rate of the plan, and yield percentage of the plan. Of course, other detailed information of the associated plan can also be viewed and displayed to the end user. The portfolio panel1710sets up the multiple different funds used and invested in to execute the desired plan of the end user. The listed funds also include representative amounts currently held and invested in each fund for execution of the plan. The portfolio composition panel1715shows the end user a graphical representation of the multiple funds used to create the desired and executed investment plan. As show in the example embodiment, the portfolio of funds is viewed as a circular pie based chart, but it should be understood that other types of graphical representations are possible for representation.

FIG.18illustrates an example graphical user interface showing uniform: surplus breakdown1800in a uniform distribution model of the asset management tool500. The graphical user interface can display to the user the investment including surplus investment1540that would be required to predictably meet the liabilities of the uniform Spending Plan (i.e., payout according to the uniform distribution schedule). The uniform: surplus breakdown1800shows detailed information related to the surplus investment1540in the uniform model. The surplus investment1540can further be broken down and viewed as a best case1805, average case1810, and poor case1815during the plans execution. The best case1805amount of the surplus investment1540is the required investment assuming ideal portfolio performance. The average case1810amount of the surplus investment1540is the required surplus (buffer) assuming average portfolio performance. The poor case1815amount of the surplus investment1540is additional surplus (buffer) assuming poor portfolio performance. The surplus investment1540itself can further be viewed in a bar chart broken down between the best case1805, average case1810, and poor case1815scenarios.

FIG.19illustrates an example graphical user interface showing a uniform: cashflow profile1900in a uniform distribution model of the asset management tool500. The graphical user interface can display the projected liabilities (scheduled payouts) of the spending plan adjacent to the projected cash flows generated by the generated portfolio so as to compare expected inflows and outflows for a uniform spending plan. Projected cash flows generated by the portfolio and scheduled plan payouts may not align as income-driven investing utilizes a combination approach to mitigate interest rate risk (i.e., cashflow and duration match). The uniform: cashflow profile1900can be panel1905showing the cashflow of the uniform distribution model over a period of time. A cashflow toggle button1910may be present wherein the end user can toggle the period of viewed payments showing cash flow1535over a set period of time (e.g. monthly, yearly, etc.) Furthermore, the cash flow1535can be broken down into each liability metrics1915and portfolio metrics1920. Each the liability metrics1915and the portfolio metrics1920can be visible to the end user in a graphical format over the selected period for payouts defined by the end user.

FIG.20illustrates an example graphical user interface showing a uniform: duration match2000in a uniform distribution model of the asset management tool500. The graphical user interface can display the dollar value of a basis point (DV01) of the liability as compared to the DV01 of the portfolio within a uniform spending plan, along with the amount by which they match or do not match, over time. This comparison can be illustrated by the graphical user interface in both point-in-time and cumulative measurements. The uniform: duration match2000can contain two different panels showing activity of the profile over a set period of time: a DV01 profile panel2005and a cumulative DV01 mismatch panel2030. Within the DV01 profile panel2005, period toggle button2010may be present wherein the end user can toggle the period of viewed payments showing information related to the created plan over a set period of time (e.g. monthly, yearly, etc.) During the selected period of time, liability data points2015, portfolio data points2020, and mismatch data points2025can be charted and interconnected to view a graphical representation of the DV01 profile over the selected period of time. Within the cumulative DV01 mismatch panel2030, a mismatch period toggle button2035may be present wherein the end user can toggle the period of viewed DV01 mismatch information related to the created plan over a set period of time (e.g. monthly, yearly, etc.) During the selected period of time, cumulative mismatch data points2040can be charted and interconnected to view a graphical representation of the cumulative mismatch over the selected period of time.

FIG.21shows and example graphical user interface which may be visible to an end user of an account selecting a non-uniform distribution of assets2100. General information regarding the non-uniform distribution of assets2100plan can be contained within a create plan2120panel with a selectable “Explore this Plan”2125where the end user can access additional information related to the created or general non-uniform distribution of assets2100. To access additional information about the selected non-uniform distribution of assets2100plan, the end user can select an “Explore the Plan” button2125for more detailed plan information and breakdowns. For a non-uniform series of assets and cash flows, the end user of the account may use the graphical user interface to input known cash flow needs along with the dates on which payment is requested. The validation of these requests may be subject to terms and conditions set forth the financial databases in communication with the asset management tool500. Within a plan parameters panel2165, the end user may be able to input a desired investment amount2105, a desired cashflow date2110, and a desired payout amount2115. To verify that such information is allowed, the asset management tool500will communication with the financial databases to confirm that such requests are proper before forming the non-uniform liability profile2100. The asset management tool500through the calculation engine will solve for the estimated required initial investment amount. The end user can then select either select a “Submit” button2140, an “Add” button2135, or a “Clear” button2140to create the desired non-uniform distribution plan based on the inputted parameters or to reenter input information necessary for creation of a non-uniform distribution plan. If the requested payout date is incorrect for whatever reason, the end user has a selectable “Remove” button2170to remove the payout date and payout amount associated with the incorrect information. The graphical user interface can also display both the cashflow2155and any surplus investment2160in a cash flow panel2130. The graphical user interface will also display any surplus investment2160added to the final payment in the cash flow series. In addition to current surplus investment2160, the graphical user interface will display the surplus investment over time in relation to any expected utilization of surplus. Finally, the graphical user interface can provide the user with estimated portfolio yield based on the determination of the calculation engine545of the asset management tool500.

FIG.22illustrates another example graphical user interface of a non-uniform distribution model of the asset management tool500showing a non-uniform plan inception2200. The graphical user interface viewed in this example embodiment contains the same panels and user activated buttons as seen above inFIG.21. This example non-uniform plan inception2200illustration shows a period of distributions selected within the plan parameters panel2165as opposed to only the final payout date viewed inFIG.21. Thus, the plan payouts panel (also known as the cash flow panel2130) may provide easier visual information to the end user viewing cash flow2155and the surplus investment2160. The plan payouts are viewed in a graphical bar chart form2205wherein the cash flow2155is viewed below the surplus investment2160.

FIG.23illustrates an example graphical user interface showing non-uniform: allocation2300of the assets in a non-uniform distribution model of the asset management tool500. This non-uniform: allocation2300screen may be accessible from the original graphical user interface shown to the user when the non-uniform distribution of assets2100or2200is created by the end user. The non-uniform: allocation2300screen contains a plan details panel2305, a portfolio panel2310, and a portfolio composition panel2315. The plan details panel2305can provide the end user with detailed information related to the selected and created non-uniform distribution of assets plan. Such information may include, but is not limited to inception date of the plan, type of selected plan, payout interval of the plan, total investment amount in the plan, payout amounts of the plan, first payout date for the plan, final payout date for the plan, projected surplus of the plan, current surplus of the plan, duration of the plan, dollar sensitivity to a one basis point change in rate of the plan, and yield percentage of the plan. Of course, other detailed information of the associated plan can also be viewed and displayed to the end user. The portfolio panel2310tables the multiple different funds used and invested in to execute the desired plan of the end user. The listed funds also include representative amounts currently held and invested in each fund for execution of the plan. The portfolio composition panel2315shows the end user a graphical representation of the multiple funds used to create the desired and executed investment plan. As show in the example embodiment, the portfolio of funds is viewed as a circular pie based chart, but it should be understood that other types of graphical representations are possible for representation.

FIG.24illustrates an example graphical user interface showing non-uniform: surplus breakdown2400in a non-uniform distribution model of the asset management tool500. The graphical user interface can display to the user the investment including surplus investment2160that would be required to predictably meet the liabilities of the non-uniform Spending Plan (i.e., payout according to the non-uniform distribution schedule). The non-uniform: surplus breakdown2400shows detailed information related to the surplus investment2160in the non-uniform model. The surplus investment2160can further be broken down and viewed as a best case2405, average case2410, and poor case2415during the plans execution. The best case2405amount of the surplus investment2160is the required investment assuming ideal portfolio performance. The average case2410amount of the surplus investment2160is the required surplus (buffer) assuming average portfolio performance. The poor case2415amount of the surplus investment2160is additional surplus (buffer) assuming poor portfolio performance. The surplus investment2160itself can further be viewed in a bar chart broken down between the best case2405, average case2410, and poor case2415scenarios.

FIG.25illustrates an example graphical user interface showing a non-uniform: cashflow profile2500in a non-uniform distribution model of the asset management tool500. The graphical user interface can display the projected liabilities (scheduled payouts) of the spending plan adjacent to the projected cash flows generated by the portfolio so as to compare expected inflows and outflows for a non-uniform spending plan. Projected cash flows generated by the portfolio and scheduled plan payouts may not align as income-driven investing utilizes a combination approach to mitigate interest rate risk (i.e., cashflow and duration match). The non-uniform: cashflow profile2500can be panel2505showing the cashflow of the non-uniform distribution model over a period of time. A cashflow toggle button2510may be present wherein the end user can toggle the period of viewed payments showing cash flow2155over a set period of time (e.g. monthly, yearly, etc.) Furthermore, the cash flow2155can be broken down into each liability metrics2515and portfolio metrics2520. Each the liability metrics2515and the portfolio metrics2520can be visible to the end user in a graphical format over the selected period for payouts defined by the end user.

FIG.26illustrates an example graphical user interface showing a non-uniform: duration match2600in a non-uniform distribution model of the asset management tool500. The graphical user interface can display the dollar value of a basis point (DV01) of the liability as compared to the DV01 of the portfolio within a non-uniform spending plan, along with the amount by which they match or do not match, over time. This comparison can be illustrated by the graphical user interface in both point-in-time and cumulative measurements. The non-uniform: duration match2600can contain two different panels showing activity of the profile over a set period of time: a DV01 profile panel2605and a cumulative DV01 mismatch panel2645. Within the DV01 profile panel2605, period toggle button2610may be present wherein the end user can toggle the period of viewed payments showing information related to the created plan over a set period of time (e.g. monthly, yearly, etc.) During the selected period of time, liability data points2615, portfolio data points2620, and mismatch data points2625can be charted and interconnected to view a graphical representation of the DV01 profile over the selected period of time. Within the cumulative DV01 mismatch panel2645, a mismatch period toggle button2635may be present wherein the end user can toggle the period of viewed DV01 mismatch information related to the created plan over a set period of time (e.g. monthly, yearly, etc.) During the selected period of time, cumulative mismatch data points2640can be charted and interconnected to view a graphical representation of the cumulative mismatch over the selected period of time.

FIG.27shows a flow chart2700of the processes available to an end user of the asset management system100. In block2705, an end user may log into or create an Individual Spending Account on the asset management system100to access the asset management tool500. The log in procedure may be common to any type of software application where a user name and password, or other credentials transmitted from third parties (“single sign-on”) are stored in the memory115of the asset management system100and associated with an account of the asset management system100. A single sign-on system from a parent, roll up, or unified account is also envisioned. From there, the asset management tool500may receive the investor-defined liability profile2710. This liability profile may be previously created by the end user of the account and pulled up for viewing by the end user. In other instances, new users will have the opportunity to create a new liability profile at this point. From there, the asset management tool moves on to a calculation of initial asset allocation targets2715. In this step, the graphical user interface is provided to the end user for input of parameters related to investment amount, payout amount, and final payout date. The end user of the account can also choose whether the payouts will be distributed in a uniform or non-uniform fashion. Once the end user data is inputted into the asset management tool500, the calculation engine545of the asset management tool500provides the necessary computation to create the desired liability profile. Then, the graphical user interface viewed by the end user of the account can illustrate the investment, scheduled payouts, projected yield, and projected surplus data2720. If the information does not fit the desired investment results of the end user of the account, the end user may be able to reenter the necessary inputs to run an additional calculation by the asset management tool500to provide a more desired/accurate liability profile. Once satisfied, the end user of the account may be able to save the liability profile/ISP within the asset management tool500. Saving the liability profile thereby allows the asset management tool500to execute the necessary transactions for the assets detailed in the liability profile with outside financial databases so that the liability profile is in operation to reach the desired payout goals defined by the end user. At this point, the creation and implementation of the liability profile ends2725and the end user may exit the system.

The asset management tool500can also run initial trades and rebalancing trades2730to update a portfolio for a given liability profile. Here, the asset management tool500processes trades with outside financial databases to confirm to the desired wishes of the end user. If a particular asset or investment is not performing to meet the required distributions set forth by the end user, the calculation engine545of the asset management tool500may run a rebalance model to improve the asset distribution to meet the desired payout amounts and schedule identified by the end user. If changes are needed to the assets or investments held by the end user, the asset management tool calculates and submits sell instructions for schedule distributions2735. Once the portfolio is rebalanced, the sell and/or buy instructions are transmitted from the asset management tool500to the outside financial databases for complete rebalancing of the liability profile. Once it is confirmed that the portfolio is rebalanced by the asset management tool500, the new profile may be stored in the memory of the asset management system100and the management process is complete2725.

FIG.28illustrates the high level components of the asset management tool500. The interfaces2800to the asset management tool500include of a set of components that provide access to the tool for user interaction or data exchange. The graphical user interface2805allows a user to select, access, and control one or more Individual Spending Plans or liability portfolios of the asset management system100. The custodian interface2810allows interaction and data exchange with custodial entities, which may include banks or other similar institutions. The investment manager interface2815allows interaction and data exchange with advisory platforms. The calculation engine545of the asset management tool500includes a set of components that allow the asset management tool500to create and manage Individual Spending Plans and their associated investment portfolios. The investment allocation optimizer2820provides a set of target asset allocations to DMFs and other fixed income instruments to achieve the objective set forth in the Individual Spending Plan or portfolio. The analytic engine2825calculates detailed cashflow-level and aggregate analytics for investment vehicle and liability profile cashflow streams. The rebalance engine2830uses a decision tree to determine when to reallocate assets among instruments within an Individual Spending Account to more closely match targets produced by the investment allocation optimizer. The payment engine2835manages cash flow into an Individual Spending Account through contributions, capital or interest payments, or other means, and manages cash flow out of an Individual Spending Account to meet the objectives of an Individual Spending Plan. The credit risk estimation engine2840uses historical credit default rates to predict future default rates and apply scaling factors to expected cash flows into the Individual Spending Account. The liability profile calculation engine2845translates parameters input by a user into a proposed liability profile (also known as an Individual Spending Plan), and manages reporting and status of currently executing plans. The attribution engine2850calculates portfolio performance/yield, and expected surplus and attributes changes to those metrics to changes in market data or analytics. Finally, the investment vehicle manager2855ingests and calculates prices and expected cashflows related to investment vehicles used in the solution.

FIG.29illustrates an operational flow2900of the calculation engine545components during operation of the asset management tool500of the asset management system100. The credit risk estimation engine2840receives inputs of historical default rates2905and fund holding information2910of the individual spending plan. The credit risk estimation engine2840uses this historical credit default rates2905to predict future default rates and apply scaling factors to expected cash flows of the fund holdings. Data from the credit risk estimation engine2840is then inputted into the investment allocation optimizer2820. The investment allocation optimizer2820provides a set of target asset allocations from the receive liability profile2915of the end user to achieve the objectives set forth in the portfolio. From there, data from the investment allocation optimizer2820is then inputted into the payment engine2835of the calculation engine545. The payment engine2830manages cash flow into the liability profile or accounts of the end user through contributions, dividends, or other means, and manages cash flow out to the end user of the liability profile or account to meet the objectives set by the end user. Once payment are sent out to the end user, or if new funds are added to the accounts of the end user, rebalancing of the operative profile is needed. Data from the payment engine2835is then sent to the rebalance engine2830to adjust the corresponding investment vehicles of the end user's profile or account. The rebalance engine2830uses a decision tree of the rebalance model2920described above to determine when to reallocate assets among instruments within the end user's account or profile to more closely match targets produced by the investment allocation optimizer2820representing the end user's account or profile. From there, data is then passed to the liability profile calculation engine2845which translates any outlying parameters inputs by the end user into the proposed liability profile. The calculation engine545may continuously reprocess the above data sets as overall changes occur within the system. For example, when payments are distributed to the end user, or if funds are added to the end user's account, the calculation engine545may undergo the above steps to make sure the liability profile of the end user is aligned with the end user's overall investment goals.

FIG.30illustrates the pattern3000of organization of components within the calculation engine545and communication mechanisms between them by demonstrating the interactions between two components3005and3010.FIG.30refers to “Component1”3005and “Component2”3010for illustration, but the term “Components” may refer to a plurality of independent systems within the calculation engine545, such as the investment allocation optimizer2820, analytic engine2825, rebalance engine2830, payment engine2835, credit risk estimation engine2840, liability profile calculation engine2845, attribution engine2850, investment vehicle manager2855, or other independent systems. Each component3005and3010is intended to perform a distinct function or grouping of functions within the calculation engine545. Each component3005and3010is distinctly defined and isolated within the calculation engine545such that it can be operated, duplicated (scaled), and modified independently, with minimal impact to the overall calculation engine545. Each component3005and3010can define an internal engine3020or3030that may contain functions and data access mechanisms that are independent from and not directly used by other components3005or3010in the calculation engine. Each component3005and3010can also define data storage mechanisms3015or3065that are distinct to the component3005or3010and are independent from and not directly used by other components3005and3010in the calculation engine545.

Each component3005and3010can define an interface3025or3040which defines and describes all functions and data models of the component3005or3010that are exposed to other components in the calculation engine545. Functions and data models defined in the interface3025or3040may be implemented by the component3005or3010and exposed for use by other components using functions and data in the internal engine3020or3035and data storage3015or3065. The utilization of interfaces3025and3040in this manner allows the internal engine3020or3035and data storage3015or3065utilized by a component3005or3010to change with minimal impact to the calculation engine545, so long as the interface3025or3040is still properly implemented by the component3005or3010.

The components3005and3010can communicate with one another both synchronously and asynchronously. For synchronous communication, the components3005and3010leverage high-performance remote procedure call (RPC) technology3030that uses a binary serialization protocol and allows data exchange and function invocation between components3005and3010according to the defined interfaces3025and3040specified above. For asynchronous communication, the components3005and3010utilize publication/subscription techniques. A component3005can publish3045an event intended to notify another component3010of a change in the data or status of the component3005to a centralized service bus3070, according to a topic3060. “Topic”3060in this sense refers to an area of interest or concern. Other components3005or3010that have a need to be informed of events pertaining to one or more topics3060can subscribe3050to the topics3060for which they have concern on the service bus3070, which will allow them to be notified of all events published that reference those topics3070.

The embodiments were chosen and described in order to best explain the principles of the invention and its practical application to thereby enable others skilled in the art to best utilize the invention in various embodiments and with various modifications as are suited to the particular use contemplated.

As various modifications could be made in the constructions and methods herein described and illustrated without departing from the scope of the invention, it is intended that all matter contained in the foregoing description or shown in the accompanying drawings shall be interpreted as illustrative rather than limiting. For example, different algorithms may be available for additional processing options by the calculation engine of the asset management tool or different user input options may be available on the graphical user interface for additional data to be inputted by the end user and used for computation by the calculation engine not expressly discussed within the present application. Thus, the breadth and scope of the present invention should not be limited by any of the above-described example embodiments, but should be defined only in accordance with the following claims appended hereto and their equivalents.