Patent Publication Number: US-2019171982-A1

Title: Financial Modeling and Prediction System

Description:
FIELD 
     This invention relates to the field of finances and more particularly to a system for modeling and predicting future financial status. 
     BACKGROUND 
     Many individuals are concerned about their future. Besides health and other family concerns most are concerned about finances. “Will I have enough money to pay for a house, a car, raising a family, my children&#39;s education, healthcare, eldercare, and the biggest question, retirement?” 
     Today, there are many tools for individuals or for financial advisors that attempt to provide insights into the individual&#39;s financial future. Some use a “goals-based” system, while some use a cash-flow based system in their approach. In these tools, the individual&#39;s age, income, and assets are used in calculations that make broad assumptions to arrive at yearly financial status or, at financial status at a particular point in time such as at retirement. Some use actuary tables to make assumptions as to how long an individual is expected to live. No present planning and/or prediction system utilizes future cash flow requirements based upon life events and the expected age of the individual at which these live events are likely to occur, thereby generating financial impact when they occur. Likewise, none of the tools incorporate the uniqueness of each individual into the planning, such as health, backgrounds, education, ethnicity, etc. 
     These financial planning tools provide very vague and often inaccurate financial status, as everybody who uses these tools is different. Consider two individuals, both age 30, one male and Hispanic and one female who is Caucasian. The male is an engineer and the female is a construction worker. Both earn substantially the same salary and bonus of $35,000.00 per year. Both have minimal assets but want to own a home by the time they are 40. Using the existing financial analysis tools, the financial outlook for both individuals will be virtually the same because existing financial analysis tools do not take into consideration likely events that will occur in the future, for example, over the next ten years. There are many considerations that existing financial analysis tools do not consider. For example, does either individual have a higher risk of a certain illness that will impact earning potential? Does either individual have an earning cap or a lower potential for pay increases (e.g. glass ceiling)? Will either individual experience a birth of one or more children, a divorce, an inheritance, an elderly parent living with them? Where do each live? What is the future expected demand for each individual&#39;s work/career in the location that the individual lives, and hence expected future earnings? 
     There are many more parameters that will, in general, have effect on an individual&#39;s future financial status. Most or all of these parameters are not considered as existing financial analysis tools typically capture individual data such as age, marital status, asset information, loan amounts and payments, list of known future expenditures (e.g. plans to attend college, schooling for dependents, weddings, vacations), and some data regarding yearly expenses. From this minimal data, calculations are made as to life expectancy and future assets based upon earnings, expected investment returns, interest rates, taxes, and current assets. There is no account taken into any of the parameter&#39;s listed above, though it is known that people in one occupation have greater earnings potential than in another, people who live in cities have greater expenses than those outside of cities, (unfortunately) males have greater earnings potential than females in today&#39;s US society, certain individuals are likely to have more children than others, certain individuals are more likely to experience certain illnesses and associated expenses, individuals are often likely to marry, divorce, remarry, etc., each having associated financial impacts. 
     As an example of the limitations of existing financial planning systems, using the user&#39;s name, birthdate, age, sex of the user, current assets and asset types, housing data (e.g. home value, mortgage), these tools utilize, for example, actuary tables to predict the end of life of the user, possibly to determine cash flow during retirement. The predictions have little accuracy, as without also understanding certain health-related issues of the user, the end of life will be very inaccurate. For example, many things effect life expectancy including the user&#39;s genetic background (e.g. a parent with high blood pressure), the user&#39;s lifestyle (e.g. exercise levels, overweight, underweight, diet), user&#39;s medical status (e.g. diabetes, high blood pressure), etc. Knowing more about the health of the user enables greater accuracy in predicting the end of life (as well as other life events). The prior art does not utilize such information to make life event predictions such as end of life. 
     Predictions of financial futures require much more information to be reasonably accurate and to provide a more realistic summary of what will be given the current situation and course of the individual. 
     None of the existing systems incorporate future cash flow requirements of upcoming life events, the expected age for when they occur, the financial impact, or the uniqueness specifically associated to each of us as individuals. 
     What is needed is a system that will utilize personal data and demographic data to generate future financial models that more accurately depict what will be for an individual. 
     SUMMARY 
     A financial analysis system retrieves data from a myriad of public and/or private data sources to develop a financial model that takes into account many life situations that an individual may experience. Together with detailed information regarding the individual such as age, race/ethnic background, marital status, occupation, family information, health data, career data, place of living/work, expected future expenditures, goals, lifestyles, etc. From these, the financial analysis system predicts future financial situations such as savings/assets, cash flow, etc., based upon the individual&#39;s data in view of the myriad of data sources that are available, providing a more accurate view of what the future financial situation shall be for that individual. 
     In one embodiment, a system for financial prediction is disclosed including a computer and a plurality of data sources that are accessible by the computer (e.g. government or private data sources). For each data source, software running on the computer accesses the each data source, extracts data from the each data source, and inputs the data into a knowledge base having artificial intelligence. The software the gathers user data including a name of a user, an age of the user, a gender of the user, an ethnicity of the user, and a current financial profile related to the user of the system for financial prediction. Next, using the user data and the knowledge base, the software predicts future life events, the life events associated with financial impact to a financial profile of the user and using the future life events and the financial impact, the software generates a report showing the life events and financial data over time. 
     In another embodiment, a method of making financial predictions is disclosed including identifying a plurality of data sources. For each data source of the plurality of data sources, each data source is accessed, data is extracted from the each data source, and the data is imported into a knowledge base. The knowledge base has artificial intelligence. Next, user data is gathered from a user. The user data includes a name of a user, an age of the user, a gender of the user, an ethnicity of the user, and a current financial profile related to the user. Next, the user data and the knowledge base is used to predict future life events, the life events associated with financial impact to a financial profile of the user. Using the future life events and the financial impact, a report showing the life events and financial data over time is generated. 
     In another embodiment, a system for financial prediction is disclosed including a computer and a plurality of data sources that are accessible by the computer. The plurality of data sources includes, at least, a bureau of labor and statistics data source, a center for disease control data source, and a world health organization data source. First, for each data source of the plurality of data sources, software running on the computer accesses the each data source, extracts data from the each data source, and inputs the data into a knowledge base having artificial intelligence. Second, the software gathers user data including a name of a user, an age of the user, a gender of the user, an ethnicity of the user, and a current financial profile related to the user of the system for financial prediction. Third, using the user data and the knowledge base, the software predicts future life events, the life events associated with financial impact to a financial profile of the user. Fourth, using the future life events and the financial impact, the software generates a report showing the life events and financial data over time. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The invention can be best understood by those having ordinary skill in the art by reference to the following detailed description when considered in conjunction with the accompanying drawings in which: 
         FIG. 1  illustrates a schematic view of a system for financial prediction. 
         FIG. 2  illustrates a schematic view of a computer as used by the system for financial prediction. 
         FIG. 3  illustrates a sample output of the system for financial prediction. 
         FIG. 4  illustrates a second sample output of the system for financial prediction. 
         FIGS. 5-14  illustrate exemplary data input user interfaces of the system for financial prediction. 
         FIG. 15  illustrates an operational model of the system for financial prediction. 
         FIG. 16  illustrates a list of sample life events and live stages in the system for financial prediction. 
         FIG. 17  illustrates a list of various information used for predictions in the system for financial prediction. 
         FIG. 18  illustrates a list of sample life stages as used for predictions in the system for financial prediction. 
         FIGS. 19-21  illustrate sample program flow charts for the system for financial prediction. 
         FIGS. 22A-22F  illustrate sample data sources used by the system for financial prediction. 
     
    
    
     DETAILED DESCRIPTION 
     Reference will now be made in detail to the presently preferred embodiments of the invention, examples of which are illustrated in the accompanying drawings. Throughout the following detailed description, the same reference numerals refer to the same elements in all figures. 
     Throughout this description, the term, “Life Event,” refers to any event that has the potential to inflict financial changes. There are many life events, both having positive or negative financial impact. Examples of life events include, but are not limited to, bankruptcy, birth/adoption, buy/sell car, buy/sell home, change jobs, child care, college, death, disabled, disaster, divorce/separate, first job, foreclosure, foreign national, gamble/gift/prize, home equity loan, hosting people (e.g., parents), inheritance, injury, lawsuit, live together/marriage, major illness, military, moved residence, non-bus debt (bad), IRA/401k pre-distribution, 2nd car/home, receiving an IRS notice, receiving additional income, retirement, start a new business, stock options, etc. 
     Referring to  FIG. 1  illustrates a data connection diagram of the system for financial prediction. In this example, one or more user devices  10  communicate through the wide area network  506  (e.g. the Internet) to a server computer  500 . That which is shown in  FIG. 1  is but an exemplary connection layout and is in no way limiting as other networking configurations are anticipated as known in the art. 
     The server computer  500  has access to data storage  502 . The server computer  500  transacts with the user devices  10  through the network  506  to present menus to/on the user devices  10 , obtain inputs from the user devices  10 , and provide data to the user devices  10 . In some embodiments, login credentials (e.g., passwords, pins, secret codes) are stored local to the user devices  10 ; while in other embodiments, login credentials are stored in a data storage  502  (preferably in a secured area) requiring a connection to login. 
     Referring to  FIG. 2 , a schematic view of a typical computer system (e.g., server  500  or user devices  10 ) is shown. The example computer system  500  represents a typical computer system used for back-end processing, calculating financial models, generating reports, displaying data, etc. This exemplary computer system is shown in its simplest form. Different architectures are known that accomplish similar results in a similar fashion and the present invention is not limited in any way to any particular computer system architecture or implementation. In this exemplary computer system, a processor  570  executes or runs programs in a random-access memory  575 . The programs are generally stored within a persistent memory  574  and loaded into the random-access memory  575  when needed. The processor  570  is any processor, typically a processor designed for computer systems with any number of core processing elements, etc. The random-access memory  575  is connected to the processor by, for example, a memory bus  572 . The random-access memory  575  is any memory suitable for connection and operation with the selected processor  570 , such as SRAM, DRAM, SDRAM, RDRAM, DDR, DDR-2, etc. The persistent memory  574  is any type, configuration, capacity of memory suitable for persistently storing data, for example, magnetic storage, flash memory, read only memory, battery-backed memory, magnetic memory, etc. The persistent memory  574  is typically interfaced to the processor  570  through a system bus  582 , or any other interface as known in the industry. 
     Also shown connected to the processor  570  through the system bus  582  is a network interface  580  (e.g., for connecting to a data network  506 ), a graphics adapter  584  and a keyboard interface  592  (e.g., Universal Serial Bus—USB). The graphics adapter  584  receives commands from the processor  570  and controls what is depicted on a display image on the display  586 . The keyboard interface  592  provides navigation, data entry, and selection features. 
     In general, some portion of the persistent memory  574  is used to store programs, executable code, data, contacts, and other data, etc. 
     The peripherals are examples and other devices are known in the industry such as speakers, microphones, USB interfaces, Bluetooth transceivers, Wi-Fi transceivers, image sensors, temperature sensors, etc., the details of which are not shown for brevity and clarity reasons. 
     In  FIG. 1 , multiple data sources  20  as shown connected to the server  500  through the network  506 . The type of connection is not important and can be through the network  506 , through any form of data communication, including data transfer by bulk means such as magnetic tape, disk, drive, etc. The data sources  20  are used to feed a knowledge base  210  (see  FIG. 15 ) stored in the data storage  502  that is connected to the server  500 . The knowledge base  210 , as will be shown, is used to predict life events once the user&#39;s information is entered, as will be shown. Any number of data sources  20  are anticipated from public agencies or from private companies. Any or all data sources are anticipated including any of the following, but not limited to the following: Data.gov, Worldbank, Fed Stats, BLS, World Health Organization, WHO II, Department of Justice, Federal Bureau of Investigation, Department of Health Services, various other US Departments (i.e. Agriculture, Commerce, Education, Energy, Health &amp; Human Services, Interior, Justice, State, Transportation), various National Centers and Institutes such as BTES, National Center for Education Statistics (NCES), National Center for Health Statistics (NCHS), Federal Emergency Management Administration (FEMA), National Cancer Institute (NCI), NCMSD, National Institute on Drug Abuse (NIDA). In addition, some data sources include private sources such as Realtor.org, Zillow.com, etc. A partial list of data sources  20  is included in  FIGS. 22A-22F . Again, any or all of the data sources  20  that are suggested, as well as other data sources is anticipated to develop the knowledge base  210  of the system for financial prediction. 
     Throughout this document, the term “user” refers to the person or persons for which the financial prediction is to be made. 
     Referring to  FIG. 3 , a sample output of the system for financial prediction is shown. As is shown in  FIG. 3 , there are several life events  601 , though not necessarily the complete list of life events (for example, a robbery or a lawsuit). Prior financial modeling systems take into account the age of the user, the life expectancy of the user, but little else. For example, not one prior art financial modeling system attempts to predict that the user will divorce, when the user will likely divorce, and what the financial impacts of the divorce will be. No prior modeling system utilizes a location of the user to better predict costs such as housing and college, medical costs, etc. 
     The system for financial prediction utilizes data provided by the user (see  FIGS. 5-13  for examples of such) to make such predictions based upon knowledge extracted from the multitude of data sources  20  such as any or all of: Data.gov, Worldbank, Fed Stats, BLS, World Health Organization, WHO II, Department of Justice, Federal Bureau of Investigation, Department of Health Services, various other US Departments (i.e. Agriculture, Commerce, Education, Energy, Health &amp; Human Services, Interior, Justice, State, Transportation), various National Centers and Institutes such as BTES, National Center for Education Statistics (NCES), National Center for Health Statistics (NCHS), Federal Emergency Management Administration (FEMA), National Cancer Institute (NCI), NCMSD, National Institute on Drug Abuse (NIDA), Realtor.org, Zillow.com, etc. By combining data from the data sources  20  into a knowledge base  210 , questions can be asked of the knowledge base  210  regarding the user. Given the age, educational background, sex, race, and location of the user, the knowledge base  210  will provide predictions of when the user will marry, have children, buy a home, enter/exit college, divorce, etc. Further, additionally having detailed medical history and family medical history, the knowledge base  210  will provide predictions of medical issues (e.g. heart attack, cancer, renal problems, etc.) and, having user data indicating location, the cost of such issues are predicted. Further, although actuarial tables are often used to predict life expectancy, such tables take into account only sex and smoking (yes or no). Having the knowledge base  210  that incorporates data from, for example, the World Health Organization, WHO II, the National Center for Health Statistics (NCHS), and the National Cancer Institute, given the user health data and family history, a much more realistic life expectancy of the user is predicted. 
     As an example, the chart  600  shown in  FIG. 3  shows an abbreviated list of  19  life events  601  listed at the left (Bankruptcy . . . injury) but there are many other life events that are considered such as a lawsuit, living together/marriage, major illness, military, moved, residence, non-business debt (Bad), IRA/401k pre-retirement distribution, 2 nd  car, 2 nd  home, receipt of an IRS notice, receipt of additional income, retirement, start of a new business, stock options, etc. In the chart  600 , the X-axis is age (typically shown in years starting at the current age of the user). 
     In the chart  600 , many life events that have been predicted by using the knowledge base  210  are shown as rectangles under the age of the user when they are predicted to occur. For example, there are predictions for two children  602 / 604  (birth or adoption) on the line labeled “birth/adoption.” There are predictions for purchases of two cars  606 / 608  (birth or adoption) on the line labeled “birth/adoption.” It should be noted that the prediction machine made predictions of purchases of two cars  606 / 608  shortly after the predictions of each of the children  602 / 604  as new vehicles are often needed to accommodate larger families. Another notable prediction is a home purchase and/or sale  610 . It is hard to believe that financial models of the past can predict any level of financial achievement (e.g. cash available at retirement) without taking into account these major financially impacting events. 
     Referring now to  FIG. 4 , a second sample output of the system for financial prediction is shown. In  FIG. 4 , an income utilization chart  620  is presented for the user. The Y-axis is in money increments (e.g. dollars for the USA from $10K up to $100K). Each vertical bar represents one year and each vertical bar contains color-coded sub-bars indicating yearly costs associated with the life events. 
     In this exemplary income utilization chart  620 , the first years (e.g. age 15 to 25) show only disposable income  624 , each year increasing as the user receives pay increases and gains investments from savings, etc. Then, a first life event is predicted at age 26—a home purchase. This is represented by a tall vertical bar  622  that includes a dark sub-bar representing house payments  626  during the year the user is 26 years old (e.g. the down payment for a house and monthly payment in that year totaling $90,000) and a lighter sub-bar at the top for medical costs for depression. Note, in that year there is no disposable income. 
     In subsequent years (age 27-28) vertical bars include the house payments  626  and disposable income  624 . Then, in year 29, another life event occurs—a first child is born. The costs associated with the first child  632  reduces the disposable income  624  (top of each bar). In year 31, another life event occurs—a second child is born. The costs associated with the second child  634  further reduces the disposable income  624  (top of each bar). In year 38, another life event occurs—a third child is born. The costs associated with the third child  636  further reduces the disposable income  624  (top of each bar). Note that the predicted financial impact of the first child ends at age 54, the second child at age 56, and the third child at age 63. Then, at age 65, another life event occurs, a cancer diagnosis. The cost of cancer treatment is shown for ages 65-80 at which the user&#39;s life is predicted to end. 
     All of this prediction is based upon the knowledge extracted from data sources  20  and the detail data provided by the user (see below). Take for example the income utilization chart  620 . Assume the chart  600  is for an Asian female of age 21 living in Dallas, Tex. and working in the Biology Medical field and a certain set of data regarding health, etc. Now assume that the same person, with the same age, field, and data is living in rural Minnesota. In such, the income predictions will be lower based upon the average income for one in the Biology Medical field in Minnesota vs. Dallas Tex. The costs for raising each child are reduces as many family-related costs are lower in rural Minnesota vs. Dallas Tex. The costs for cancer treatment increase as travel and lodging are necessary to seek specialized care. This is but a sample of many differences in life events, disposable income, life expectancy, based upon changing only one input datum—location. Further differences may also include number of children, other costs, commuting costs, etc., based upon only a change of location. Such predictions are not only useful in predicting financial status, but are also useful in comparing where you will live/move and your occupation. Being that you cannot change your genetic makeup, keeping such static and changing location or occupation, the user will see different income utilization charts  620  reflective of each change, leading to making informed decisions regarding location and/or occupation. 
     Referring to  FIGS. 5-14 , exemplary data input user interfaces of the system for financial prediction are shown. The detail data is typically provided by the user, perhaps during one or meetings with a financial advisor or by direct entry into an application user interface. The detail data provided by the user in  FIGS. 5-15  is meant to be an example as in some embodiments, more or less detail data is provided by the user. For example, to better predict certain medical life events, including life expectancy, genealogical data is another data input that, in some embodiments, is provided by the user, for example, as provided by a DNA screening. 
     In  FIG. 5 , a first user interface  660  is used to enter the user&#39;s present age  662  and target time period  664  that the user wants to attain a certain goal (e.g. retirement, start a business, buy a home, have children). 
     In  FIG. 6 , a second user interface  680  is used to enter the user&#39;s initial funding  682  and monthly additions/deposits  684 . 
     In  FIG. 7 , a third user interface  700  is used to enter the user&#39;s annual income  702  and liquid net worth  704 . 
     In  FIG. 8 , a fourth user interface  720  is used to enter the user&#39;s investment sectors. In this example, for brevity and clarity reasons, the user has opted to follow the advisor&#39;s picks  722 . 
     In  FIG. 9 , a fifth user interface  740  shows an allocation pie chart  742  of asset allocations per the advisor&#39;s picks  722 . 
     In  FIG. 10 , a sixth user interface  760  shows an asset growth chart  762  showing a range of returns on the initial funding  682  and monthly deposits  684 . 
     In  FIG. 11 , a seventh user interface  780  is used to enter the user&#39;s demographic information. In this example, the user enters demographic data  782  for race, age, gender, education level, degree type, residence, and whether they live in a rural or urban area. 
     In  FIG. 12 , an eighth user interface  800  is used to enter the user&#39;s family information. In this example, the user enters family data  802  for marital status (single, married, divorced, living together, etc.), age when married, number of children and age when each child was born. 
     In  FIG. 13 , a ninth user interface  820  is used to enter the user&#39;s housing information. In this example, the user enters housing data  822  for housing type (e.g. own, lease, rent), age when the user purchase the home, purchase price of the home, mortgage terms, down payment, and interest rate. 
     In  FIG. 14 , a tenth user interface  840  is used to enter the user&#39;s healthcare information. In this example, the user enters healthcare data  842  for arthritis, asthma, depression, diabetes, cancer, and stroke. This is just an example of health-related data that the user provides, as many more health-related data are anticipated, all or some of which are included in various embodiments of the present invention. For example, certain other diseases (e.g. HIV, Hepatitis), family/genetic issues (e.g. a parent with high blood pressure or high cholesterol), current diet (e.g. fast food and soda, vegetarian, Mediterranean, moderately healthy), exercise (e.g. daily exercise, twice a week, couch potato), etc. 
     Referring to  FIG. 15 , an operational model of the system for financial prediction is shown. In operation, the system for financial prediction receives data  200  from the user (investor), often by way of a financial advisor. The inputs/data  200  are saved as the customer needs. 
     Independent of such, a model is continually refined using a semantic engine, retrieving and analyzing the data sources  20 , developing the knowledge base  210  that is used to predict life events. 
     Industry data  220 , market data  214  (e.g. stock market, bond market), social data  216  (e.g. political events, social trends), and customer inputs  212  also feed the knowledge base  210 . 
     Feeding the customer needs  202  (e.g. data provided by the user as in  FIGS. 5-13 ) into the artificial intelligence engine (knowledge base  210 ), using a set of process rules, output data/reports  240  are generated, for example, reports as in  FIGS. 3 and 4 . These reports include predicted life events (e.g. life events  250 —see  FIG. 16 ), life stages (e.g. life stages  260 —see  FIG. 16 ), risk parameters, etc. These reports become the voice of a financial advisor in consulting the user. 
     Referring to  FIG. 16 , exemplary sets of life events  250  and life stages  260  are shown. Although  32  life events  250  are shown, any number of life events are anticipated and, in some embodiments, the life events  250  are named differently. For example, one life event  250  is “Change jobs.” It is anticipated that this life event  250 , in some embodiments, will have different names such as “resigned,” “laid-off,” “fired,” and “new job.” Likewise, in some embodiments, “Injury” is divided into short-term disability and long-term disability. There are no limitations as to the number, names, and types of life events  250 . 
     The same is true with the life stages  260 . 
     Referring to  FIG. 17 , a list of various information used for predictions in the system for financial prediction is shown. For example, to provide sensible financial data and predictions, it is important to know the user&#39;s net worth, income, tax bracket, state in which earnings are made, goals, risk adversity, spending trends (e.g. vehicles, groceries, dining, utilities, mortgage/rent), etc. Further, to provide guidance to the user, the system needs to know what types of investments are available and their expected returns. For example, fixed income, bonds, stocks, mutual funds, etc. Other data is used to help predict life events, for example education levels, ethnicity, marital status, religion, weight/height, health, etc. For example, a person with a Master&#39;s Degree may be predicted to live longer than a person of similar health, etc., without any degree. 
     Referring to  FIG. 18 , a list of sample life stages  260  as used for predictions in the system for financial prediction is shown. Different life stages  260  dictate or predict different life events  250 . For example, one who is in the life stage  260  of infant or child will likely not have a life event  250  of, for example, having a child for a much longer period of time than one who is in the life stage  260  of young couple. Likewise, one who is in the life stage  260  of Vulnerable Elder will likely not have a life event  250  of having a child. Therefore, the life stages  260  are used with the knowledge base  210  to better predict future life events  250 . 
     Referring to  FIGS. 19-21 , sample program flow charts for the system for financial prediction are shown. The high-level flow charts of  FIGS. 19-21  are for illustration purposes and are described in brief form to convey the overall operation of the system for financial prediction. One skilled in the art of programing and artificial intelligence, without undue experimentation and using this description, would have little difficulty developing the described system for financial prediction. 
     In  FIG. 19 , the knowledge base  210  is generated. To start, an identifier is set  300  to a first data source  20 . That data source  20  is read  302  and the significant data from that data source  20  is stored  304 . Next, a test  306  is performed to determine if the identifier is set to the last data source  20 . If the identifier is not set to the last data source  20 , then the above steps are repeated. 
     If the identifier is set to the last data source  20 , then the stored data sources are imported  314  into the knowledge base  210  as known in the field of artificial intelligence. For example, nodes and weights are created in an artificial intelligence program or hardware for each datum in the stored data sources. 
     In  FIG. 20 , the knowledge base  210  is updated when a data source  20  is updated or changed. To start, an identifier is set  320  to a first data source  20 . Next, a test  322  is performed to determine if that data source  20  has changed. If the test  322  determines that the data source  20  has changed, that data source  20  is read  324  and the significant data from that data source  20  is stored  326 . Next, a test  330  is performed to determine if the identifier is set to the last data source  20 . If the identifier is not set to the last data source  20 , then the above steps are repeated. 
     If the identifier is set to the last data source  20 , then the stored data sources are imported  334  into the knowledge base  210  as known in the field of artificial intelligence. For example, nodes and weights are created in an artificial intelligence program or hardware for each datum in the stored data sources. 
     In  FIG. 21 , after the knowledge base  210  is generated and/or updated, the knowledge base  210  is available to generate reports using the user data. To start, the user data is retrieved  350  (e.g. as shown in  FIGS. 5-13 ). Next, using the user data and the knowledge base  210 , a set of life events  250  are generated  352 . An identifier is set  354  to the first life event  250  and a cost associated with the current life event is generated  356 . For example, if the current life event is having a baby, then, using the knowledge base  210 , a yearly cost for supporting the baby is generated for the number of years that the baby will be under the care of the user (e.g. until age 18 or age 21). The cost associated with the current life event is saved  358  and the identifier is set  360  to the next life event. A test  362  is performed to determine if current life event  250  is the last live event  250  that was generated (step  352 ). If the test  362  determines that the current life event  250  is not the last live event  250 , the above steps  356 - 362  are repeated. 
     If the test  362  determines that the current life event  250  is the last live event  250 , a financial model is generated  370  for the user using the life events, the costs associated with the life events, financial prediction data from the stored data sources (e.g. expected asset growth dependent upon asset type, expected income growth, inflation, taxes, expected tax changes, etc.). The financial model is used to generate reports  372  that are then delivered  374  to the user and/or financial advisor. 
     Again, the above program flow is a simplified program overview of one possible implementation and is shown for understanding and is not limiting of the present application in any way. 
     Equivalent elements can be substituted for the ones set forth above such that they perform in substantially the same manner in substantially the same way for achieving substantially the same result. 
     It is believed that the system and method as described and many of its attendant advantages will be understood by the foregoing description. It is also believed that it will be apparent that various changes may be made in the form, construction and arrangement of the components thereof without departing from the scope and spirit of the invention or without sacrificing all of its material advantages. The form herein before described being merely exemplary and explanatory embodiment thereof. It is the intention of the following claims to encompass and include such changes.