System and method for providing financial planning and advice

• US 8,407,125 B2
• Filed: 07/31/2002
• Issued: 03/26/2013
• Est. Priority Date: 07/31/2001
• Status: Active Grant

• Current Assignee: Ameriprise Financial, Inc.
• Original Assignee: Ameriprise Financial, Inc.
• Inventors: Jenson, Jodi, Lipinski, Patti P., Moran, William J., Ostrem, Linda, Vanney, Beth M., Ledson, Mark, Brose, Gabriella, Scarlato, Patricia
• Primary Examiner(s): Bartley, Kenneth
• Application Number:
  US10/210,827
• Publication Number:
  US 20030028466A1
• Time in Patent Office: 3,891 Days
• Field of Search: 705/36, 705/35, 705/36.R
• US Class Current: 705/36.R
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis
• CPC Class Codes: G06Q 40/08 Insurance

Abstract

The system facilitates portfolio advising and planning for a client. The system includes a portfolio integration module configured to receive and integrate data, wherein the data includes a client'"'"'s goals, assets, savings and risk tolerance, wherein the portfolio integration module is further configures to compute a customized strategy based on the received data. The portfolio reconciler module is configured to compare a customized strategy to at least one of the other strategies, based on the received and integrated data in order to further facilitate the financial portfolio planning of the client, wherein the financial portfolio planning analyzes both planned hypothetical events and unplanned hypothetical events. The stochastic modeling module is configures to further analyze proposed situation portfolios based on future uncertainties, wherein future uncertainties include the unplanned hypothetical events. The simulation module is configured to compute the probability of meeting the client'"'"'s goals.

Claims

1. A probability modeling system comprising:

a processor for facilitating financial portfolio advising and planning; and

a tangible memory configured to communicate with the processor, the memory having a plurality of software modules stored thereon, each module in the plurality of software modules having instructions executed by the processor, wherein the plurality of modules comprises;

a portfolio integration module configured to receive and integrate data, wherein the data includes goals, assets, savings and risk tolerance of a client, wherein the portfolio integration module is further configured to compute a customized strategy based on the received data;

a portfolio reconciler module configured to compare a customized strategy to at least one of other strategies, based on the received and integrated data in order to further facilitate the financial portfolio planning of the client, wherein the financial portfolio planning analyzes both planned hypothetical events and unplanned hypothetical events, wherein the unplanned hypothetical events comprise at least one of a disability, a retirement date, a death or a divorce;

a stochastic modeling module configured to further analyze proposed situation portfolios based on future uncertainties, wherein future uncertainties include the unplanned hypothetical events;

a simulation module configured to compute a probability of meeting the client'"'"'s goals;

the probability modeling system further configured to;

analyze results from at least one of a financial advice application, the portfolio reconciler module or the stochastic modeling module and, based at least partially on the results, determine a plurality of proposed allocations for the client based upon a risk tolerance, a goal timing schedule and a goals value, wherein the determining the plurality of proposed allocations comprises determining;

a proposed regular assets allocation;

a proposed regular contributions allocation for ongoing contributions to regular accounts;

a proposed retirement allocation for retirement plan ongoing contributions;

a proposed variable universal life (“

VUL”

) goal funding allocation comprising insurance cash values and premiums for VUL policies used to fund goals; and

a proposed VUL non-goal funding allocation for policies comprising insurance cash values and premiums for VUL policies not used to fund goals.

2. The probability modeling system of claim 1, wherein the portfolio reconciler module is further configured to monitor buy, sell, and hold recommendations for the proposed situation portfolio and dynamically update the proposed situation portfolio with the at least one of:

buy, sell, and hold recommendations.

3. The probability modeling system of claim 1, wherein the stochastic modeling module uses at least one of a stationary bootstrap sampling method and a synchronous stationary bootstrap sampling method.

4. The probability modeling system of claim 1, wherein the stochastic modeling module is further configured to measure a probability of the client reaching a goal of the client.

5. The probability modeling system of claim 1, wherein the stochastic modeling module is further configured to forecast effects based on at least one of:

current economic data of a country, historical economic data of a country, current world economic data;

or historical world economic data.

6. The probability modeling system of claim 1, wherein the stochastic modeling module is further configured to facilitate use of at least one of:

historical portfolio data, historical economic data, financial decisions, investment strategy, present cash flow, future cash flow, or goals of the client in order to facilitate forecasting effects of the proposed situation portfolio on the portfolio of the client.

7. The probability modeling system of claim 1, wherein the stochastic modeling module is further configured to:

facilitate forecasting effects of the proposed situation portfolio on a current situation portfolio of the client;

monitor at least one of the portfolio integration module, the portfolio reconciler module, and the stochastic modeling module;

simulate at least one of the portfolio integration module, the portfolio reconciler module, and the stochastic modeling module; and

test at least one of the portfolio integration module, the portfolio reconciler module, and the stochastic modeling module.

8. The probability modeling system of claim 1, wherein the stochastic modeling module is further configured to estimate a p value in a bootstrap sampling method using at least one of the following:

autocorrelation depending on lag alone;

or

estimating standard errors of bootstrapped samples for an original series and comparing the estimated standard errors for independent bootstraps.

9. The probability modeling system of claim 8, wherein the estimating the p value includes arriving at a 1/p value between 25 and 50.

10. The probability modeling system of claim 1, wherein the proposed situation portfolio is created by analysis of at least one of a normal life span, disability, long-term care, early death, life insurance information, disability insurance information, Social Security, social pension, asset structure, savings, goals, retirement information, expenses, tax structure of assets, savings, company or pension information.

11. The probability modeling system of claim 1, wherein the proposed situation portfolio is created by:

creating, based at least in part on a stochastic analysis, first customized timelines for at least one of;

normal life span, disability, long-term care, early death, life insurance information, disability insurance information, Social Security, social pension, asset structure, savings, goals, retirement information, expenses, tax structure of assets, savings, or company pension information;

creating, based at least in part on a stochastic analysis, second customized timelines for each member of a family; and

analyzing, based at least in part on a stochastic analysis, life expectancy on factors other than retirement information.

12. The probability modeling system of claim 1, wherein the portfolio integration module further includes:

inputs and associates a group goal and a group assumption with economic group data;

simulates a group passage of time, to analyze available financial resources for an economic group with respect to fulfilling the group goal based upon the group assumption;

processes a financial position for the economic group based upon an analysis of the available financial resources and in response to the at least one of;

planned hypothetical events, unplanned hypothetical events, and transactions, wherein the portfolio integration module obtains demographic data and financial data pertaining to the economic group and, wherein the portfolio integration module analyzes and solves the group goal in an efficient order, accounting for any dependency of future goals upon prior goals, based upon the group assumption, the demographic data, and the financial data.

13. The probability modeling system of claim 1, wherein the simulation module is further configured to:

identify historical test data to apply to a simulation, wherein the test data includes at least one of client input, market projections, economic projections, historical portfolio data, and cash flow from a plurality of scenarios;

receive the historical test data;

select a test scenario, wherein the test scenario includes a test goal;

simulate the portfolio integration module, the portfolio reconciler module, and the stochastic modeling module using the historical test data and test scenario;

assemble simulation results from the simulation;

compare the simulation results to actual results produced by the portfolio integration module, the portfolio reconciler module, and the stochastic modeling module; and

in response to the simulation results not matching the actual results, modify at least one of;

the portfolio integration module, the portfolio reconciler module, and the stochastic modeling module.

14. The probability modeling system of claim 1, wherein the portfolio integration module is further configured to at least one:

advise, simulate or select a specific investment instrument based on the customized strategy.

15. The probability modeling system of claim 1, wherein the planned hypothetical events include at least one of:

an expected lifespan of the client or an average lifespan of a family member associated with the client, wherein the expected lifespan is based on health of the client and family history, and wherein the average lifespan is based on health of the client and family history.

16. The probability modeling system of claim 1, wherein each of the goals are weighted based on at least one of:

client preference for importance of a respective one of the goals, importance of a timeframe for achieving the respective one of the goals, or a timeframe for achieving the respective one of the goals.

17. The probability modeling system of claim 1, wherein the determining the plurality of proposed allocations further comprises determining a short-term allocation regular assets amount.

18. The probability modeling system of claim 1, further configured to:

receive client input relating to goals of the client;

communicate with the portfolio integration module, the portfolio reconciler module, and the stochastic modeling module;

receive, from a plurality of disparate providers, a plurality of distinct financial advice client input.

19. A non-transitory tangible computer-readable medium having computer-executable instructions stored thereon comprising modules that, when executed by a computer for facilitating financial portfolio advising and planning, cause the computer to perform operations comprising:

receive and integrate data, by a portfolio integration module, wherein the data includes goals, assets, savings and risk tolerance of a client, wherein the portfolio integration module is further configured to compute a customized strategy based on the received data;

compare, by a portfolio reconciler module, a customized strategy to at least one of other strategies, based on the received and integrated data in order to further facilitate the financial portfolio planning of the client, wherein the financial portfolio planning analyzes both planned hypothetical events and unplanned hypothetical events, wherein the unplanned hypothetical events comprise at least one of a disability, a retirement date, a death or a divorce;

further analyze, by a stochastic modeling module, proposed situation portfolios based on future uncertainties, wherein future uncertainties include the unplanned hypothetical events;

compute, by a simulation module, a probability of meeting the client'"'"'s goals;

analyze results from at least one of a financial advice application, the portfolio reconciler module or the stochastic modeling module and, based at least partially on the results, determine a plurality of proposed allocations for the client based upon a risk tolerance, a goal timing schedule and a goals value, wherein the determining the plurality of proposed allocations comprises determining;

a proposed regular assets allocation;

a proposed regular contributions allocation for ongoing contributions to regular accounts;

a proposed retirement allocation for retirement plan ongoing contributions;

a proposed variable universal life (“

VUL”

) goal funding allocation comprising insurance cash values and premiums for VUL policies used to fund goals; and

a proposed VUL non-goal funding allocation for policies comprising insurance cash values and premiums for VUL policies not used to fund goals.