AU2018363884A1

AU2018363884A1 - System and method of automated preparation of a visual representation for goal achievability

Info

Publication number: AU2018363884A1
Application number: AU2018363884A
Authority: AU
Inventors: Adam ASHER; Matthew GURNEY; Alex HARKEN-YUMRU; Graeme Jones; Anoop VARGHESE
Original assignee: Spark Operating System Pty Ltd
Current assignee: Spark Operating System Pty Ltd
Priority date: 2017-11-10
Filing date: 2018-11-09
Publication date: 2020-05-28
Also published as: WO2019090394A1; JP2021502653A; WO2019090394A9; CN111328406A

Abstract

Disclosed herein is a computer-implemented method for automating the preparation of a visual representation for goal achievability and dynamically updating the visual representation. The method may comprise retrieving a user data set and a goal data set having at least one goal record, compiling a first input data set and communicating the first input data set to a modelling engine via a data network. The modelling engine may be configured to model a projection for the at least one goal record in dependence on the first input data set. The method may further comprise receiving at a data representation engine the projection for the at least one goal record, calculating a goal achievability for the at least one goal record by processing the projection for the at least one goal record and preparing the visual representation of goal achievability by converting the calculated goal achievability into the visual representation of goal achievability.

Description

System and method of automated preparation of a visual representation for goal achievability

Technical Field [0001] Disclosed herein is a computer-implemented method and system of automated preparation of a visual representation for goal achievability. The method and system may include the steps of compiling an input data set that includes a goal record and modelling a projection for the goal record in dependence on the input data set. A visual representation of goal achievability may be prepared from the modelled projection and outputted to a user to allow the user to assess the likelihood of achieving the goal record.

Background Art [0002] Traditional financial advice is generally directed towards trying to maximise total wealth of a user, based on the user’s risk rating or risk profile. Traditional processes may capture a risk rating for a user. Recommended solutions are then based upon this risk rating. For example, a risk averse risk profile rating for a user may result in recommended investments solutions matching this rating (e.g. 70% defensive assets and 30% growth assets in their recommended investment portfolio).

[0003] Traditional advice processes may also focus on specific advice phases (e.g. transition to retirement), solutions (e.g. income or annuities) or market segments (e.g. private wealth).

This can be problematic, as users may have several advice phases and solutions that they want to analyse concurrently.

[0004] Traditional advice processes may only rely on current financial information to calculate projections for future financial situation of a user. This may result in a projection that is unrealistic, in that the user’s financial information may change over time.

[0005] Goals based advice (as opposed to traditional advice) is a concept that has existed in the financial planning and investment management industries for several years. At its most basic, it provides a framework for understanding the financial objectives and needs of a user for the purposes of providing advice. In essence, goals have been viewed as an articulation of future economic needs, and the objective of advice and the role of financial products is to meet those economic needs given current and future resources available to them.

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PCT/AU2018/051208 [0006] The existing goals based advice process has been designed using the traditional advice process, with a simple overlay introduced to incorporate the user’s goals utilising simplistic calculation of probability of achieving such goals. This type of output typically presents an overly simplistic view of the future situations of a user, and may potentially be confusing for the user, and therefore adversely affect the user’s decision making process with respect to the funding of goals.

[0007] In this specification where a document, act or item of knowledge is referred to or discussed, this reference or discussion is not an admission that the document, act or item of knowledge or any combination thereof was at the priority date, publicly available, known to the public, part of common general knowledge; or known to be relevant to an attempt to solve any problem with which this specification is concerned.

Summary [0008] Disclosed herein is a computer-implemented method for automating the preparation of a visual representation for goal achievability and dynamically updating the visual representation. The method may comprise: (a) retrieving from a user system via a data network a user data set, the user data set comprising a plurality of user data records; (b) retrieving from the user system via the data network a goal data set, the goal data set comprising at least one goal record; (c) identifying a further data set by comparing the user data set with information stored on a database and, in dependence on the identification of the further data set, retrieving further data records via the data network; (d) compiling a first input data set, the first input data set comprising the plurality of user data records, the plurality of further data records if identified and the at least one goal record; (e) communicating the first input data set to a modelling engine via the data network, the modelling engine being configured to model a projection for the at least one goal record in dependence on the first input data set; (f) receiving at a data representation engine via the data network the projection for the at least one goal record, wherein the data representation engine is configured to prepare the visual representation of goal achievability by: calculating a goal achievability for the at least one goal record by processing the projection for the at least one goal record; and preparing the visual representation of goal achievability by converting the calculated goal achievability into the visual representation of goal achievability for the at least one goal record; (g) outputting the visual representation of goal achievability via the data network to the user system, wherein the visual representation of goal

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PCT/AU2018/051208 achievability allows the user to assess the likelihood of achieving the at least one goal record;

(h) retrieving from the user system via the data network an updated user data set and/or an updated goal data set, the updated user data set comprising a plurality of updated user data records and the updated goal data set comprising at least one updated goal record; (i) compiling a second input data set, the second input data set comprising the plurality of further data records if identified, the updated plurality of updated user data records if retrieved otherwise the plurality of user data records, and the updated at least one goal record if retrieved otherwise the at least one goal record; (j) communicating the compiled second input data set to the modelling engine via the data network, the modelling engine being configured to dynamically model an updated projection for the at least one goal record in dependence on the second input data set; (k) receiving at the data representation engine via the data network the updated projection for the at least one goal record or the at least one updated goal record if retrieved, wherein the data representation engine is configured to dynamically prepare an updated visual representation of goal achievability for the updated projection; and (1) outputting the dynamically updated visual representation of goal achievability via the network to the user system.

[0009] In some forms, the steps (h) to (1) may be repeated upon retrieval of further updated user data records and/or further updated goal records to dynamically update the goal achievability in dependence on the updated records. In some forms, the method may further comprise steps disclosed below.

[0010] Also disclosed herein is a computer-implemented method of automated preparation of a visual representation for goal achievability. The method may comprise the steps of: retrieving from a user system via a data network a user data set, the user data set comprising a plurality of user data records; retrieving from the user system via the data network a goal data set, the goal data set comprising at least one goal record; identifying a further data set by comparing the user data set with information stored on a database and, in dependence on the identification of the further data set, retrieving further data records via the data network; compiling an input data set, the input data set comprising the plurality of user data records, the plurality of further data records if identified and the at least one goal record; communicating the input data set to a modelling engine via the data network, the modelling engine being configured to model a projection for the at least one goal record in dependence on the input data set; receiving at a data representation engine via the data network the projection for the at least one goal record,

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PCT/AU2018/051208 wherein the data representation engine is configured to prepare the visual representation of goal achievability by: calculating a goal achievability for the at least one goal record by processing the projection for the at least one goal record; and preparing the visual representation of goal achievability by converting the calculated goal achievability into the visual representation of goal achievability for the at least one goal record; and outputting the visual representation of goal achievability to a user, wherein the visual representation of goal achievability allows the user to assess the likelihood of achieving the at least one goal record.

[0011] In some forms, the projection for the at least one goal record comprises a probability of achieving the at least one goal record and a potential shortfall calculated for the at least one goal record.

[0012] In some forms, the goal data set comprises a plurality of goal records.

[0013] In some forms, the data representation engine is configured to calculating a goal achievability for each goal record by processing the probability and potential shortfall for each goal record.

[0014] In some forms, the goal achievability for a goal record of the plurality of goal records is calculated by multiplying a first weighting with the probability of achieving the goal record and adding a multiplication of a second weighting with one minus the potential shortfall for the goal record.

[0015] In some forms, the first weighting and second weighting are each a figure within a range of 25 to 75% .

[0016] In some forms, each goal record is assigned a priority by the user.

[0017] In some forms, the method further comprises: retrieving from the user system via the data network an updated user data set, the updated user data set comprising a plurality of updated user data records; and re-compiling the input data set, the input data set comprising the plurality of updated user data records, the plurality of further data records if identified and the goal data set; communicating the re-compiled input data set to the modelling engine via the data network, the modelling engine being configured to model an updated projection for the at least one goal record in dependence on the input data set; receiving at the data representation engine

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PCT/AU2018/051208 via the data network the updated projection for the at least one goal record, wherein the data representation engine is configured to prepare an updated visual representation of goal achievability for the updated projection; and outputting the updated visual representation of goal achievability to the user.

[0018] In some forms, the method further comprises retrieving from the user system via the data network an updated goal data set, the updated goal data set comprising at least one updated goal record, wherein the input data set comprises the plurality of updated user data records, the plurality of further data records if identified and the at least one updated goal record.

[0019] In some forms, the method further comprises retrieving from the user system via the data network an updated goal data set, the updated user data set comprising a plurality of updated goal data records; re-compiling the input data set, the input data set comprising the user data records, the plurality of further data records if identified and the updated goal data set; communicating the re-compiled input data set to the modelling engine via the data network, the modelling engine being configured to model an updated projection for the plurality of updated goal records in dependence on the input data set; receiving at the data representation engine via the data network the updated projection for the plurality of updated goal records, wherein the data representation engine is configured to prepare an updated visual representation of goal achievability for the updated projection; and outputting the updated visual representation of goal achievability to the user.

[0020] In some forms, the modelling engine is configured to stochastically and deterministically model the projection for the at least one goal record.

[0021] In some forms, the outputting the visual representation of goal achievability comprises communicating the visual representation to a user interface.

[0022] In some forms, the method further comprises receiving a pre-determined strategy data set comprising a plurality of strategy records, reducing the plurality of strategy records in dependence on the user data set, and communicating the reduced strategy records to the modelling engine for modelling.

[0023] In some forms, the method further comprises preparing a scenario model. The scenario model may comprise a plurality of folded layers of scenario data.

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PCT/AU2018/051208 [0024] In some forms, the method further comprises preparing an interim data model. The interim data model may be prepared from at least the scenario model. The interim data model may comprise a plurality of folded layers of interim data. The interim data model may allow for the data to be efficiently accessed independent of the user.

[0025] Also disclosed herein is a system for automated preparation of a visual representation for goal achievability. The system may comprise at least one processor; and memory storing instructions that, when executed by the processor, cause the system to: retrieve from a user system via a data network a user data set, the user data set comprising a plurality of user data records; retrieve from the user system via the data network a goal data set, the goal data set comprising at least one goal record; identify a further data set by comparing the user data set with information stored on a database and, in dependence on the identification of the further data set, retrieve further data records via the data network; compile an input data set, the input data set comprising the plurality of user data records, the plurality of further data records if identified and the at least one goal record; communicate the input data set to a modelling engine via the data network, the modelling engine being configured to model a projection for the at least one goal record in dependence on the input data set; receive at a data representation engine via the data network the projection for the at least one goal record, wherein the data representation engine is configured to prepare the visual representation of goal achievability by: calculating a goal achievability for the at least one goal record by processing the projection for the at least one goal record; and preparing the visual representation of goal achievability by converting the calculated goal achievability into the visual representation of goal achievability for the at least one goal record; and output the visual representation of goal achievability to a user, wherein the visual representation of goal achievability allows the user to assess the likelihood of achieving the at least one goal record.

[0026] In some forms, the memory stores instructions that, when executed by the processor, cause the system to retrieve from the user system via a data network an updated user data set, the updated user data set comprising a plurality of updated user data records; re-compile the input data set, the input data set comprising the updated plurality of updated user data records, the plurality of further data records if identified and the goal data set; communicate the recompiled input data set to the modelling engine via the data network, the modelling engine being configured to model an updated projection for the at least one goal record in dependence

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PCT/AU2018/051208 on the input data set; receive at the data representation engine via the data network the updated projection for the at least one goal record, wherein the data representation engine is configured to prepare an updated visual representation of goal achievability for the updated projection; and output the updated visual representation of goal achievability to the user.

[0027] In some forms, the memory stores instructions that, when executed by the processor, cause the system to retrieve from the user system via the data network an updated goal data set, the updated goal data set comprising at least one updated goal record, wherein the input data set comprises the plurality of updated user data records, the plurality of further data records if identified and the at least one updated goal record.

[0028] In some forms, the memory stores instructions that, when executed by the processor, cause the system to: retrieve from the user system via the data network an updated goal data set, the updated user data set comprising a plurality of updated goal data records; re-compile the input data set, the input data set comprising the user data records, the plurality of further data records if identified and the updated goal data set; communicate the re-compiled input data set to the modelling engine via the data network, the modelling engine being configured to model an updated projection for the plurality of updated goal records in dependence on the input data set; receive at the data representation engine via the data network the updated projection for the plurality of updated goal records, wherein the data representation engine is configured to prepare an updated visual representation of goal achievability for the updated projection; and output the updated visual representation of goal achievability to the user.

[0029] In some forms, the memory stores instructions that, when executed by the processor, cause the system to receive a pre-determined strategy data set comprising a plurality of strategy records, reduce the plurality of strategy records in dependence on the user data set, and communicate the reduced strategy records to the modelling engine for modelling.

[0030] In some forms, the memory stores instructions that, when executed by the processor, may cause the system to: prepare a scenario model, the scenario model comprising a plurality of folded layers of scenario data.

[0031] In some forms, the memory stores instructions that, when executed by the processor, may cause the system to: prepare an interim data model from at least the scenario model, the interim data model comprising a plurality of folded layers of interim data, the interim data

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PCT/AU2018/051208 model being accessible by the modelling engine via the data network for modelling the projection for the at least one goal record.

Brief Description of Drawings [0032] Various embodiments/aspects of the invention will now be described with reference to the following drawings in which, [0033] Fig. 1 shows a schematic of the system in accordance with at least one embodiment of the disclosure;

[0034] Fig. 2 shows a schematic of the data acquisition and preparation engine;

[0035] Fig. 3 shows the visual representation of goal achievability in accordance with at least one embodiment of the disclosure;

[0036] Fig. 4 shows another visual representation of goal achievability in accordance with at least one embodiment of the disclosure;

[0037] Fig. 5 shows another visual representation of goal achievability in accordance with at least one embodiment of the disclosure;

[0038] Fig. 6 shows another visual representation of goal achievability in accordance with at least one embodiment of the disclosure;

[0039] Figs. 7a-b shows an example of the prepared visual representation of goal achievability for a retirement goal for two users in accordance with at least one embodiment of the disclosure;

[0040] Fig. 8 shows an example of rules that may be applicable to a selection of appropriate strategies to assist a user with debt management; and [0041] Fig. 9 shows an example output of the system disclosed in Fig. 1.

Detailed Description [0042] A system for automating the preparation of a visual representation of goal achievability disclosed. Referring firstly to Fig. 1, a data management system 1 implemented

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PCT/AU2018/051208 using computer processing 2 and memory resources 4 accessible by at least one user system 5 via a data network 7 is shown. The user system is in the form of a processing device 5 (e.g. a PC, tablet, smartphone, etc.) that enables the user (e.g. customer) to access the network 7.

[0043] The system 1 includes a data acquisition and preparation engine 3 configured to retrieve from a user system 5 via a data network 7 a user data set, in the form of user profile information. The user profile information may include information on the user's assets, liabilities, income, expenses, age, etc. The data acquisition and preparation engine 3 is configured to retrieve from the user system 5 via the data network 7 a goal data set. In one form, the goal data set may include a single goal record, in the form of user goal. In another form, the goal data set may include a plurality of goal records and may include priority information set by the user.

[0044] The disclosed system is able to develop a understanding of the user's life goals and to determine which of the goals are most important to the user. In the detailed embodiment, the user selects their life goals from the pre-existing list (e.g. “Be debt free”, “Retire right” “Start my own business”, etc.) or creates their own goals. In some forms, the pre-existing list may include a single goal. In another form, the pre-existing list may include a plurality of goals (e.g. more than one goal). In the detailed embodiment, the pre-existing list includes between 20 and 40 goals. In the detailed embodiment, once goals are chosen, users then define their goals stipulating priority, timeframes and financial targets. By the end of this session, the user is likely to have identified a plurality of life goals (e.g. 4 to 6 goals) and ranked their relative importance - high, medium and low. In one embodiment, the user may at this point in the process be provided with a goals exploration summary document which sets out their goals and their broad life-style implications. This allows the user to review the information entered into the system and make changes if required before proceeding with the next steps.

[0045] The data acquisition and preparation engine 3 is configured to identify a further data set by comparing the user data set with information stored on a database and, in dependence on the identification of the further data set, retrieving further data records from non-volatile memory 4 via the data network 7. The further data set comprises data required to model projections for each goal that does not form part of the user data set (e.g. the user may not have provided required data and, as such, the system needs to determine the additional data required to model projections for each goal). In the detailed embodiment, the further data set is assumed

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PCT/AU2018/051208 from heuristics (e.g. retirement age, superannuation guarantee, mortgage details, retirement living costs, lifestyle assets). The retrieval of the further data set ensures that the required data is available for modelling purposes. In some forms (e.g. where the user has provided all of the necessary data), no further data records are identified.

[0046] In some forms, the further data set may comprise non-economic assumptions that enables initial modelling to be performed without requiring detailed information to be retrieved from the user. This ensures that users are asked, by the system, relevant information at the right time, by assuming information for the initial modelling. The benefits of the non-economic assumptions are that users are not required to provide detailed information early in the advice experience. Further, the use of non-economic assumptions improves the validity of modelling and enable the user to further engage with the system. As such, the method may utilise a user's behavioural profile rather than their risk profile.

[0047] The data acquisition and preparation engine 3 outputs the user data set, the further data set and the goal data set to an application programming interface (API) 9. The API 9 is configured to compile an input data set and provides a doorway to a modelling engine 11. The input data set includes the plurality of user data records (i.e. the data retrieved directly from the user), the plurality of further data records, if identified (i.e. the data that was not provided by the user), and the goal data set. When the input data set has been compiled, it is communicated to the modelling engine 11 via the data network.

[0048] The modelling engine 11 is configured to model a projection for each goal record in dependence on the input data set. The projection includes a probability and a potential shortfall, as will be described below. The modelling engine draws on economic and non-economic information to then calculate the projections of goals. In some forms, the modelling engine uses stochastic modelling to generate the projections. A stochastic model is a type of financial model that models unknown future values for the purpose of estimating the likelihood of certain outcomes. In the economic scenario generator (ESG), certain mathematical relationships are assumed that are relevant to estimating different asset class returns and correlation modelling (which outlines the dependence between asset class returns).

[0049] The modelling engine is a complex calculation engine designed to model, both stochastically and deterministically, forward projections of a household’s assets, liabilities,

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PCT/AU2018/051208 income and expenses as cash flows within an economic environment with assumed attributes (e.g. tax and regulatory constraints). In the detailed embodiment, the modelling engine includes an ESG and a cashflow model.

[0050] The modelling approach disclosed herein is different from traditional deterministic modelling. In contrast to traditional deterministic modelling, a stochastic approach is implemented that differs in the use of randomness in the Monte Carlo simulation of a range of possible future values. This approach allows for the model to determine the probability of achieving client’s goals, and the risk, i.e. the potential shortfall, if the goals are not met.

[0051] The ESG simulates future scenarios for a range of asset classes and economic variables under a pre-specified quantitative model and is designed to simulate future paths of the economy and financial markets, and gives the modelling engine the ability to produce stochastic scenarios. The ESG in conjunction with the cashflow model enables a user to test the goal achievability before a formal advice recommendation document is created. The combination of the ESG and cashflow model also enables users to assess a projected shortfall of set goals. In one form, the ESG is updated with new economic and market information quarterly. The economic variables that may be used include the Consumer Price Inflation (CPI), Average Weekly Earnings (AWE), interest rates and other asset class returns, volatility and correlation assumptions. For example, in Australia, Australian Equities, Global Government Bonds, and Australian Direct Property may be useful. The ESG takes market data and economic assumptions as inputs and models at least 1,000 future scenarios of interest rate, inflation, and asset class returns. Within the ESG, the yield curve model may be used for modelling future cash returns and the entire yield curve, the inflation model may be used for modelling future CPI and AWE; and the equity model may be used for modelling future equity, fixed-interest and property excess returns (i.e., risk premium).

[0052] There are two primary uses of the economic variables. The first use is in the deterministic projection of a customer’s balance sheet. This enables the user to refer to just one set of returns for future estimations of the user’s salary, investments, and expenses, for example. This single set of returns utilises the user’s profile and goal information and economic values that aims to reflect the median, or middle, of the scenarios generated. The median result is the return for which there is an equal probability of the future return falling above or below this figure. The reason the user may refer to this deterministic model based estimate is to provide an

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PCT/AU2018/051208 understanding of estimated balances and cash flows over time. This may help the user to determine what action might be needed to meet certain goals and to assist in possible goal tradeoff decisions. The second use of economic variables may be to test the achievability of a user's set goals in conjunction with the cashflow model. The cashflow model is a calculator in the modelling engine made up of routines, rules and procedures that is utilised in conjunction with the ESG to forecast a user's financial position and the goal achievability for set goals.

[0053] In the detailed embodiment, the modelling engine is used to model the potential future financial circumstances of users given information provided about their current circumstances, demographic details, future goals and needs (i.e. in dependence on the input data set). The modelling engine accommodates the scenario testing of different combinations of advice strategies and provides the financial modelling used to inform achievability of goals and demonstrate relative impacts of financial and goal trade-off decisions. In the detailed embodiment, the modelling engine outputs a probability and potential shortfall for each of the users set goals in numerical form.

[0054] The probability of achieving a goal is calculated using the number of times (out of the, for example, 1,000 stochastic model scenarios run) that a goal is achieved, converted to a percentage e.g. if the goal is achieved in 700 cases the probability is 70%. The modelling engine calculates this by modelling the goals data, user data (e.g. personal data) and further data (e.g. heuristic information) (i.e. together the input data).

[0055] The potential shortfall amount is calculated using the same stochastic modelling technique for calculating the probability of achieving a goal. The potential shortfall amount when a customer’s goal is not achieved is also calculated. This is expressed as a percentage (%) of the goal amount (e.g. 40% of a goal amount of say $100,000). For example, from 1,000 possible scenarios, the average of the worst (i.e. left tail of the distribution curve) portion (e.g. 550%) of the 1,000 outcomes is calculated. This may be expressed as a percentage of the goal amount (e.g. for a goal of $100,000 if the average of the bottom portion (e.g. 5-50% of scenario results was $40,000, then this would represent a 40% potential shortfall).

[0056] The data acquisition and preparation engine 3 will now be described in further detail with reference to Fig. 2. A technical consideration that was considered when developing the system for automating the preparation of a visual representation of goal achievability was the

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PCT/AU2018/051208 speed at which the processing executes. In one embodiment, the preparation of the visual representation of goal achievability is intended to be performed in front of a user. As such, a response time of less than six seconds was targeted. The benefit of reducing the processing time is that users can engage in the process several times (e.g. to update the input data to dynamically explore the displayed goal achievability). The modelling engine 11 typically requires a significant proportion of the processing time (e.g. more than half of the targeted six seconds) due to the complexity of the modelling (e.g. in at least one embodiment the system models in excess of five thousand different scenarios). Therefore, there is minimal time remaining (e.g. less than half of the targeted six seconds) to conduct the further processing required to collate the data to be modelled and prepare the modelled data to then be visually represented to a user.

[0057] In order to significantly reduce the processing time, the data acquisition and preparation engine 3 includes several modules to minimise the processing and data transfer requirements of the system. In the detailed embodiment, the data acquisition and preparation engine 3 includes a data generator module 21, a data projection module 23 and a cache interface

25. In some forms, the data acquisition and preparation engine 3 includes further modules that are not described in detail.

[0058] The data acquisition and preparation engine 3 gathers several different groups of related data in order to understand the position of each user. This data is then overlaid with assumptions, life events and advice strategies in order to construct a fully formed advice scenario. This data may then be used for a number of different purposes, such as rendering user engagement tools on the user interface, for generating documents and for feeding the modelling process. In each case different aspects and combinations of the advice scenario data may be used. In addition to this, the numerical values may be combined, aggregated and rolled up at various different levels. In order to avoid creating a highly complex set of point-to-point independent data mappings, an interim data model was developed to provide a technical solution that allows the system to combine the data into a multi-dimensional 'cube'. The interim data model allows for the data to be efficiently accessed independent of the user (e.g. the required output to the user interface, the required output of a document, the data input required by the modelling engine 11, etc.). The development of the interim data model is described herein with reference to the provision of financial advice. As will be understood by the skilled addressee, the development of the disclosed interim data model may be applicable for reducing the processing

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PCT/AU2018/051208 time for other applications (e.g. not related to the modelling of financial data). The development of the interim data model will now be described in further detail.

[0059] As previously described, the data acquisition and preparation engine 3 initially retrieves data from the user 5 and potentially also from a database 2 (e.g. assumptions). The data acquisition and preparation engine 3 then retrieves further data (e.g. data to produce a scenario model) from the database 2 in preparation for outputting data to the modelling engine 11. The scenario model includes various layers of scenario data that are processed by the data acquisition and preparation engine 3 in order to enable calls to the modelling engine 11. The scenario model is based on a snapshot of the profile information, scenario assumptions and user created goals, events and/or strategies. In some forms, the scenario model may be similar to a star-schema model. The main blocks of the scenario model response may include: sources, parties, strategies, objectives, events and facts (e.g. financial and non-financial). Sources may include any data that relates to financial information and may form a dimension of the scenario model. Parties may include parties that are in consultation (e.g. a married couple) and may form a further dimension of the scenario model. Strategies may include, for example, a list of adviser defined actions that modify the financial situation of the user, and may form a further dimension of the scenario model. Objectives may include a list of objectives in the scenario to be modelled. Events may include a list of events in the scenario model. Financial facts may include financial information related to the aforementioned dimensions of the scenario model (e.g. annual salary sacrifice amount for a party). Every financial fact may include chart-of-accounts dimension in the scenario model. Every financial fact may also include a reference to the system it originated from (e.g. assumption, profile or strategy). Non-financial facts may include non-dollar facts. Every non-financial fact may include a reference to the system it originated from (e.g. assumption, profile or strategy).

[0060] The data generator module 23 enhances the scenario model and produces the interim data model. The production of the interim data model provides a reduction in processing time required to generate the disclosed visual representation of goal achievability. The produced scenario model may be used as the raw input in the production of the interim data model. In order to be able to efficiently provide the required data to downstream processing modules (e.g. the modelling engine 11), the stages in the data projection module 23 fold together the profile, assumption, goal, event and action data. The term 'fold' is used to describe the combination of

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PCT/AU2018/051208 data by classifying the data according to a set of rules (e.g. data obtained from a user profile may be prioritised relative to data obtained from an assumption). This step adds a number of newly derived layers of interim data that augment the original raw scenario model data and can then be mapped to a user interface, as input data to the modelling engine 11 or as input data to a document generation process.

[0061] Importantly, each downstream processing module (e.g. the modelling engine 11) may have different requirements with respect to particular input data points. The production of the interim data model allows for the data points to be accessed efficiently irrespective of the data point requirements of the downstream processing module. For example, depending on the system requirements, the modelling engine 11 used to model a projection for each goal record may have different input data point requirements. Thus, the interim data model provides a flexible data model that allows for each data point to be represented at different levels of aggregation and be compiled by a combination of different raw scenario model components. The interim data model includes layers of information that can be efficiently accessed by downstream processing modules irrespective of the downstream modules input data requirements. The data generator module 21 may generate synthetic artefacts, generate financial fact rollups across different dimensions, generate non-financial facts as a best view and/or rollup and/or evaluate values stored in the scenario model as references. Advantageously, the generation of an interim data model increases processing efficiency, and as a result reduces the overall processing time. For example, rules that may be applied to several scenarios are performed up front such that downstream processing may be limited to specific consumption needs (e.g. the generation of a modelling request). This is an important performance consideration as the interim data model must be recalculated each time a change is made to a layer in the scenario model (usually the profile or advice strategy layers), hence without this interim structure the impact of these regular and user facing changes would cause a recurring performance issue as common logic is executed and data structure rebuilt multiple times.

[0062] Generating derived artefacts, where applicable, may include generating advice fee source and financial facts based on assumptions in the scenario model. Generating financial fact rollups across different dimensions (e.g. sources, parties, charter of accounts nodes) may include generating a rollup(s) of all investment income for a party and an additional rollup(s) for household investment income. Generating a non-financial fact best view (or rollup) may include

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PCT/AU2018/051208 applying priority rules based on system of origin to determine a value that will propagate into the interim data model. For example, if the scenario model includes both profile provided and assumption provided interest rate, profile value may be used in the generated best view. Evaluating values stored in scenario model as references (e.g. derivatives) may include pointers to the financial model values. The pointers may be found in actions and events, but may be used throughout the process.

[0063] The data projection module 23 generates goal achievability and deterministic projections results for a particular interim data model. The data projection module 23 may include three blocks, namely a modelling engine input generator, a modelling engine output processor and a modelling engine service interface (not shown in Fig. 2). The modelling engine input generator may generate input data for the modelling engine 11 by accessing the interim data model. In some forms, the accuracy of the modelling engine 11 depends on the data being provided (e.g. the input data needs to be correctly prepared). This may include the aggregation of numerical fields into a particular level in, for example, chart-of-accounts, the creation of synthetic products to simulate certain advice strategies and various other calculations to ensure that the modelling engine is able to process the data accurately. For example, the modelling engine input generator may perform the following operations: generating synthetics (e.g. sources, financial and non-financial facts) and using business rules. The synthetics generated and added to the interim data model before the modelling engine 11 input mapping, are referred to as synthetics. For example, a property goal may be mapped into the modelling engine 11 in a property-goal input block. The modelling engine 11 may then generate asset and mortgage within the output produced by the modelling engine 11. Synthetic inputs and passing identifiers in the input elements may be generated so that deterministic projections are related to the correct financial facts. The modelling engine input generator may use business rules outlined in a mapping document to generate a modelling engine input data (e.g. in XML form) from the interim data model. A base request may be generated and then turned into stochastic and deterministic requests.

[0064] The modelling engine output processor may generate a projection model from the interim data model, along with the modelling engine 11 stochastic and deterministic outputs. The data may then be returned from the modelling engine 11, reconciled back to the original input, and added to the interim data model to form new projection layers and components used

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PCT/AU2018/051208 to determine goal achievability. In some forms, this process ensures that the To data (i.e. the initial data points) used as the basis for the input data to the modelling engine 11 is aligned to the Ti-Tn (i.e. the later data points) projection datasets that are received as an output from the modelling engine 11. For example, the modelling engine output processor may generate synthetics, map deterministic outputs into the interim data model and perform a rollup(s) of the projected data.

[0065] Generated synthetics (e.g. sources, financial and non-financial facts) may be added before modelling engine 11 output mapping. Synthetics may be generated when the modelling engine creates outputs on chart-of-account nodes that are not included in the interim data model. For example, data relating to contributions for individual super accounts may be outputting to the modelling engine 11 and the modelling engine may then return contributions rolled up in the single amount across all super accounts. A financial fact with appropriate chart-of-accounts then may be created to receive projections. The mapping of deterministic outputs into the interim data model may be based on the definition of the modelling engine 11 output keys. Projections may be added as arrays of values to financial facts. Time may not always be a dimension in the interim data model, despite the fact that there may be a time period field (i.e. T may remain at To). Performing a rollup of projected data may also be required. For example, in some cases projections may need to be apportioned to more granular chart-of-account items that are then made available within a modelling engine 11 output. Appending the interim data model with a modelled goal achievability may also be required as the goal achievability calculated from the percentile of probability and shortfall/risk percentage (both values provided by stochastic projections as per goal/objective).

[0066] The modelling engine 11 may be the largest consumer of the interim data model. As such, the data projection module may include a modelling engine service interface to marshal service calls to and from the modelling engine. The interface may create stochastic and deterministic modelling engine 11 requests from a base input (e.g. that provided by the data generator module 21). Requests may be concurrent. Projection requests may be followed by projection retrieval calls to modelling engine 11.

[0067] The cache interface 25 may handle user cache interactions. Considering that the response time of the data acquisition and preparation engine 3 processing was, in some forms, an important technical consideration, reducing unnecessary calls to the modelling engine 11

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PCT/AU2018/051208 provides a contributor to ensuring processing load is minimised where possible. The caching mechanism ensures that if an identical call had already been made to the modelling engine 11, the system could then reuse the previous response rather than running a full modelling cycle. This both reduces load on the modelling engine and cuts out a relatively time consuming part of the processing cycle. In some forms, the scenario model artefacts may be stored under a hash key generated from the scenario model. In some forms, the cache interface may include economic assumptions used for simulations in the modelling engine 11.

[0068] In some forms, the system may include an audit module (not shown) that stores to memory the input data, assumed data and the output of the modelling engine 11. This ensure that, if required, the modelled data can be interrogated at a later date.

[0069] The modelled projections are communicated from the modelling engine 11 to a data representation engine 13 via the data network 7. The data representation engine 13 is configured to prepare a visual representation of goal achievability. The visual representation of goal achievability is a visual aid to communicate the projected achievability of each of a user’s goals. The purpose of this indicator is to help guide the user with respect to the achievability of the set goals and to facilitate trade-off decisions. The indicator is also designed to dynamically reflect the impact of changes to user scenarios (e.g. if the input data is manipulated), such as saving more or incurring an unexpected cost, to better inform trade-off decisions. The visual representation of goal achievability improves understanding and confidence of the modelled projections. In the detailed embodiment, the visual representation is in the form of a five bar signal 100, as is shown in Fig. 3.

[0070] The determination of the visual representation is performed by processing of the probability of achieving a set goal and the potential shortfall when the set goal is not achieved. Combining the numerical measures of goal probability and shortfall provides a useful representation of goal achievability. Fig. 4 shows how the visual representation of goal achievability is mapped to probability 300 (shown on the x-axis) and potential shortfall 200 (shown on the y-axis). Probability is only one indicator of goal success and provides a likelihood of goal achievability. It is implicitly also a risk indicator. For example, if the modelled projections provide an 80% probability of achieving a set goal, there is a 20% chance of failing to meet that set goal. While this measure is important to a goals based advice framework, it does not provide any information about the potential size of the goal shortfall if

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PCT/AU2018/051208 the user fails to meet the goal target. The Applicant has determined that the provision of potential shortfall information is also important and enables the user to make a meaningful goal trade-off decisions in dependence on visual representation.

[0071] For example, a user is 10 years from planned retirement and is targeting an accumulation goal of $1 million. The modelled probability of reaching that target is determined to be 85% and their potential shortfall is modelled to be 7%. This means that in the 15% of projected returns where the user falls short of their target, they could fall short by $70,000. If this shortfall risk is deemed too great, the user can take steps to mitigate the risk.

[0072] In the detailed embodiment, a five bar symbol as the visual representation of goal achievability provides users with a familiar visual indicator, in that it conveys an intuitive meaning of strength. In some forms, the visual representation may take another form (e.g. a score out of 5, a visual category of red/amber/green and a text category of High/Medium/Low).

[0073] When the visual indicator is in the form of a five bar symbol, each bar of the five bar symbol represents a combination of probability and potential shortfall. This means that it is possible for a user's modelled goal achievability to improve or deteriorate with no change in the number of bars shown (i.e. when the input data is manipulated such that goal achievability is then re-modelled). As is shown in Fig. 5, to provide a further indicator of user progress in this regard, the visual representation includes an up 401 (e.g. green) or down 403 (e.g. red) arrow with each goal’s achievability signal 400. This arrow may be shown to illustrate whether a particular modelling update has improved or worsened the modelled projection for each of the set user goals.

[0074] In at least one embodiment, underlying data is mapped to the indicator to form a grid. The grid 500 is shown in Fig. 6. The grid shows ranges of probability 501 (on the x-axis) being drawn up with the number of indicator bars 505 assigned according to probability range. Bars 505 are then penalised according to the size of the potential shortfall 503 (on the y-axis).

[0075] There are many different measurements of shortfall. One useful measurement is conditional tail expectation of shortfall at the 5-50% level. This is a measure that averages the worst 5-50% of stochastically modelled shortfall outcomes, e.g. the shortfalls in the worst 200 to 500 simulations of the 1,000 are averaged for each user goal. Where there are positive outcomes (i.e. no shortfall) in the range, these are given a value of 0%. Another useful measurement is

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PCT/AU2018/051208 average shortfall across all cases with a shortfall. This is a measure that averages all the shortfalls when the target goal amount is not achieved. The conditional tail expectation of shortfall at the 5-50% level measure is useful in the user's goals trade off decision making. The conditional tail expectation of shortfall at the 5-50% level is standardised and therefore a risk measure that is more comparable across goals and scenarios. The conditional tail expectation of shortfall at the 5-50% level measure also gives a good indication of adverse future outcomes or the risks in goal achievability.

[0076] For example, two goals with the same average shortfall may have substantially different outcomes in the “tail” scenarios. Moreover, actions intended to specifically reduce conditional tail expectation of shortfall at the 5-50% level related shortfalls may not reduce the average shortfall measures. This is because the average shortfall is like a moving target whereas the conditional tail expectation of shortfall at the 5-50% level shortfall is the worst 5-50% of outcomes. In the detailed embodiment, the goal achievability (GA) is calculated as:

GA = P x Probability + (1-P) x (1-Percentage Shortfall)

Where P = a figure within a range between 25% and 75% [0077] This weighting approach is a method of simplifying the multitude of possible band placements and weightings that would apply to each user and their goals. In other words, the actual or ‘true’ weightings and band placements of each user's goals are subjective and are based on complex consumer utility theory which is dependent on the specifics of each goal, the user's financial situation and risk profile. However, identifying a different combination of weightings and risk profiles for each user's goals is typically not feasible.

[0078] In summary, adjustment of the mapping of probability and potential shortfall to the five bar signal:

. removes inconsistent weightings of probability and shortfall across the grid and associated issues. Fixed weighting provides linear banding resulting in minimal builtin subjective views of how probability and shortfall should be weighted across the grid;

. applies consistent ranges of probability and shortfall to each bar; and

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PCT/AU2018/051208 . allows for a formula to be applied to show whether the achievability position has improved or deteriorated. This formula may also be used to calculate the up and down arrow indicating an improvement or deterioration in the calculation of goal achievability.

[0079] Figs. 7a-b show an example of the prepared visual representation of goal achievability for a retirement goal for two users, Craig & Christine, who are aged 52 and 53. Both are employed and plan to retire at age 66 and 65. Their primary goal is to retire right with joint retirement income of $65,000 per annum for 30 years. They have two other goals - take a $5,000 holiday next year and pay off their mortgage in 8 years. The table shown in Fig. 7a illustrates the impact of changing the goal amount and increasing the funding level by reducing “lifestyle” expenses while they are savings towards retirement.

[0080] Considerable value can be added for the user if the process is regularly reviewed (i.e. revisited to model changes), as circumstances change over time. The user's goals and/or priorities may change over time. Whether an update be fine tuning the input data or a wholesale change to the input data, updating this data and again running the modelling presents an opportunity for users to investigate circumstance changes and take appropriate action if required.

[0081] In one embodiment, the system also includes a selection module 19. This embodiment will now be described with reference to Fig. 9. The selection module 19 is configured to deselect strategies to be modelled by the modelling engine based on the user input data set. The selection module 19 is configured to determine which advice strategies apply to each user. The selection module 19 applies a set of rules using the user input data set to filter out strategies that are not relevant and select a list of those that remain ‘in scope’ for the user. The selection module 19 is configured to de-select strategies in dependence on the user data set (i.e. profile information), not on the user's objectives (i.e. goals). This is because potentially applicable strategies that may be appropriate to a user should not be constrained by their goal selection. For example, even though a user does not explicitly select a goal “to be debt free”, this should not result in debt management strategies not being explored.

[0082] In the detailed embodiment, eligibility rules are binary rules, therefore can only either be true or false. When an eligibility rule fails, the strategy will be de-selected by the selection

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PCT/AU2018/051208 module 19. For example, spouse contributions is not an applicable strategy if the user doesn't have a spouse.

[0083] In at least one embodiment, the selection module 19 includes business rules that generate messages for users to explain the situations where advice strategies are suitable for a particular scenario (e.g. a scenario specific to an user). The suitability rules require the user to make a judgement on whether the advice strategy is suitable based upon based upon the user data set. This builds on the framework the eligibility rules established to provide further consistent guidance to users to model (and potentially recommend) the optimum advice strategies (e.g. to a specific user).

[0084] When the selection module 19 is called on by the system to provide the list of strategies for modelling, the user’s profile input data is used. The user's profile input data includes any updates to the input data during advice modelling for the strategy selection model to refresh the latest list of the eligible advice strategies. In the detailed embodiment, actions, regardless of whether they are setup as self-configured or pre-configured, are made up of the following information:

. Owner - the action will be associated with a particular person or jointly - this is the owner of the action only and not necessarily the owner of the underlying product;

. Action name - the action name will represent the specific strategy that is to be modelled and potentially recommended for the user;

. Action ‘attributes’ - the details or specific configuration of that action (e.g. amount, etc.); and . Product - the applicable strategy, which may be an actual product provided by the user profile input data or a generic product type representing a certain level balance (e.g. super fund, income, etc.).

[0085] Eogic is required to ensure the selection module 19 and modelling engine 11 are called in the correct order and at the correct time in the process. For example, when an action is ticked/unticked in a change log of the system, the selection module 19 may be called to ensure the list of strategies is still correct before running the modelling using the modelling engine 11.

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PCT/AU2018/051208

For example, if a self-configured action is added which includes a new home or home loan, running the selection module 19 will include any strategies relevant for a user with a home or home loan.

[0086] In order to model using modelling engine 11 and accurately assess the rules of the selection module 19, the specific strategy at the lowest level is required. For example, an existing list of strategies stored in memory 4 may be restructured to have three levels of granularity. In one embodiment, the list of strategies may include between two and fifty strategies. The scoped strategies may then be sent to the modelling engine 11 for modelling at the 3rd level. An example of a strategy structure is provided in the table below.

Level Purpose Example

1. High Level: Area of advice	General grouping of strategies. May be made up of one or more strategy groups and low level strategies.	~Superannuation, ~Debt ~Protection
2. Mid/user level: User-facing strategy category/grouping	For simplicity, the system may be configured to display a slightly reduced level of strategy to users. It may be the same name as the strategy or a general grouping depending on what’s appropriate. This may be made up of one or more low level strategies. Scoping may occur at this level.	~Super contributions, ~Debt repayments ~lncome protection
3. Low level: Strategy	Actual strategy to be modelled and may have rules assessed at this level. Users may need to configure strategies required for modelling at this level. Where the strategy is not supportable by the modelling engine, the strategy can be modelled outside the system. This level may not be visible in scoping.	~Make a lump sum non-concessional super contribution ~Make a lump sum repayment on deductable debt ~Start a new income protection policy (inside super)

[0087] In at least one embodiment, there may be a plurality of rules (e.g. between five and fifty rules) within the selection module 19. These rules can be applied individually or in conjunction with each other to determine eligibility for a particular strategy. Some strategies will not require any rules as they will always be available. For example, the strategy “Make a lump sum debt drawdown” only requires the “Number of debts” rule to be assessed to determine eligibility. The “Consider a home equity loan” strategy, however, may need the user to pass both the “Own home” rule and the “Employment status” rule in order to be eligible. Whereas

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PCT/AU2018/051208 the strategy “Establish a new loan” does not require any rules to determine eligibility - as users may always have the ability to establish a new loan.

[0088] The below table describes a sample of examples rules and the criteria assessed.

Rule Name	Description	How used
Number of Debts (type of debt does not matter)	This rule will determine the number of debts a user has. For a joint consultation, debts owned by the user directly as well as jointly will be counted. For example, if the user solely has an investment property loan and also jointly holds a mortgage on their home with their partner, they will be considered to have two debts.	The number of debts rule is used to determine which debt strategies are appropriate such as debt restructuring and consolidation. The user will need to have at least one debt to be eligible generally for debt strategies and have two or more debts to be eligible for debt consolidation strategies.
Own Home	This rule will determine if the user owns their own home. For a joint consultation, if the user owns their home jointly with their partner they will be deemed to own their home.	The home ownership rule determines eligibility for strategies which requires the user own their own home, such as the ability to take out a home equity loan and downsize their home.
Home Loan (not investment property)	This rule will determine if the user specifically has a mortgage against their home. For a joint consultation, if the user jointly holds the mortgage with their partner, they will be deemed to have a home loan.	The home loan rule determines eligibility for strategies which requires the user has a mortgage, such as debt recycling.
ABP (commutable account based pension)	The ABP rule will calculate the total commutable account based pension balance that the user has. NCAP (noncommutable allocated pension) and accumulation super account balances are not included.	The user's commutable account based pension balance is used to determine the eligibility of pension strategies, such as when a reversionary beneficiary should be nominated and general retirement related strategies. For example, the retention of an existing account based pension would check that the user's balance is greater than zero, whereas setting up of a new account based pension will require a user have a total balance less than, for example, $1.6M based on current legislation limits.
Income Protection Insurance	The insurance rules will check whether the user has the requisite insurance type. The rule will check for insurance where the user is the policy owner (e.g. they are paying the premium).	The insurance rule will determine if the user is eligible for strategies that include retaining, changing or cancelling of an existing insurance policy.
Life Insurance
TPD (Total & Permanent Disability) Insurance
Trauma Insurance

[0089] Fig. 8 shows an example of rules that may be applicable to a selection of appropriate strategies to assist a user with debt management. For example, if the user data indicates that the

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PCT/AU2018/051208 user has more than one debt 701, the selection module may select a 're-structure my debt' strategy 703 for modelling. Once the selection module has compiled a list of applicable advice strategies, the strategies can be communicated to the modelling engine to assess the impact of these strategies on the user's financial position. In at least one embodiment, the strategies may be communicated to the modelling engine via the user interface. In at least one embodiment, the compiled strategies may be communicated to the user interface for further filtering (e.g. a user may be able to review the list of strategies selected by the strategy module and manually select the strategies of interest). In this embodiment, the manually selected strategy (or strategies) is then communicated to the modelling engine to assess the impact of the selected strategies on the user's financial position.

[0090] For example, the modelling engine may be configured to model more than 100 investment strategies. However, these strategies may not be applicable for all users. Therefore, in dependence on the user information (e.g. age), strategies may be de-selected from a list of available strategies (e.g. strategies relating to aged care). The remaining strategies are then communicated via the data network 7 via the API 9 to the modelling engine 11. Again, the API 9 provides a doorway to the modelling engine 11 where data (the user's cashflows, assets, liabilities, and selected strategies) is assembled. The selection module 19 may improve productivity (e.g. reduced time relative to manually deselecting non-applicable strategies), user advocacy (e.g. less confusion around why strategies that do not apply are not displayed) and adviser advocacy.

[0091] An example of the disclosed method for automated preparation of a visual representation for goal achievability will now be described. Initially some basic level information of the user's financial circumstances is captured via an online questionnaire through a user interface. The user's financial information may be represented by way of a household balance sheet (with assets and liabilities) and cashflows. The customer may also undertake behavioural profiling. The behavioural profile may provide insights on the user's behaviours visa-vis their goal, and their innate tolerance of risk and loss.

[0092] The system validates that a minimum level of information is required to be captured to assess the user's goals readiness i.e. that there is enough profile information provided to continue. If the basic information (user data) with any assumed heuristic information (i.e. the further data) is sufficient, then the baseline goal achievability is generated by the system by

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PCT/AU2018/051208 communicating the customer information firstly to applicable advice strategies based on the goals selected and the data captured from the user. This information is then fed to the modelling engine to generate the projections for each goal. The goal’s positions are calculated using sophisticated stochastic modelling calculations and algorithms interfacing with the modelling engine 11. Referring now to Fig. 10, an example output of the system is shown, in the form of a report 900 presented on a user interface. The report includes the visual representation of goal achievability generated by the data representation engine 13.

[0093] The output screen anchors to a timeline 903 (shown on the x-axis), which may become the persistent visual throughout a user's experience (e.g. when the user returns to the system at a later date they may be able to access the originally presented visual representation, which may also be configured to update dynamically). The timeline 903 provides users with the ability to see all goals and events based on an axis that represents time.

[0094] Each goal chosen is displayed across the timeline 903. In this example, goals can be edited or discarded or ‘parked’ by dragging them below the timeline, and new goals added to by clicking on an ‘add goal’ button. Details for each goal can be seen by clicking on the goal or event - this includes the description, the value of the goal/asset, timeframe (date of achieving it) and the priority (high, medium, low). Like goals, events may be added and deleted as the user decides and by dragging them below the line may ‘park’ the event.

[0095] Goals are aspirational, something that users hope to achieve in the future and want to plan towards achieving. Events are something which may or may not happen in the future and will impact goal achievability. Events can be both negative and positive. In at least one embodiment, ‘events’ may be added to the timelines - e.g. What if the user’s partner dies in 2020, what would this do to goal achievability? In one embodiment, there are six events: temporarily unable to work, change of salary, death, permanently unable to work, change of household expenditure, and receiving a lump sum. Events allow the user to investigate events that may occur in future to understand the impact on the goals/plans to determine if any changes are necessary. For example, if a partner dies without life insurance, goal achievabilities are materially impacted therefore life insurance cover for the partner should be investigated.

[0096] In at least one embodiment, events are not included within the output documentation as they are hypothetical and may be used for illustrative purposes. Events may be displayed

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PCT/AU2018/051208 differently to goals on the user interface - e.g. a dark blue box to stand out. Events may affect the goal achievability, and interface dynamically with the modelling engine. In at least one embodiment, re-modelling may be triggered upon receipt of updated (e.g. user, goal, etc.) data.

[0097] User interface outputs may include a visual representation of goal achievability, a sub-set or full set of facts about a user's financial situation assumptions and defaults. These inputs are fed into the data acquisition and preparation engine 3 where they are combined and overlayed with the economic assumptions, legislative rules and business logic. Outputs are then sent to the user interface, which includes the visual representation of goal achievability. Within the user interface the facts and assumptions can be edited to trigger a re-run of the data acquisition and preparation engine 3 and a re-run of the data representation module.

[0098] The system may be used to run particular scenarios. Scenario testing involves applying a hypothetical situation to the user's circumstances to see the effect on their financial situation. Extreme scenarios, known as stress tests, can also be assessed to judge the impact of a potentially damaging event (e.g. sudden death, an unexpected cost, a permanent disability, etc.).

[0099] The system also allows for users to investigate the priority of goals. This priority can be challenged in the scenario testing stage e.g. the user may decide that a medium priority goal is unaffordable. The next step is therefore to update the goals data e.g. the user might choose to fund a high priority goal. This is a useful function because it forms a component of the investment strategy selection and other advice processes. For all the goals, such as buying a car or paying university fees, users are prompted to assign their goal priority as either high, medium or low. The ranking is made relative to the user's other goals which ensures the user owns this part of the process. For instance, a user might have a high priority for “paying my daughter’s university fees in five years”, but a low priority for “buying a car in three years”. The priority of achieving the car purchase in three years is low relative to the priority of paying for university in five years.

[00100] The disclosed system provides users with a strong opportunity to live their desired life. This process can provide superior financial outcomes for which the measure of success is goal achievement as defined by the user, rather than wealth maximization through leveraging regulatory, tax, and product regimes like superannuation, as defined by an adviser. The disclosed methodology is centred around goal achievability and does not focus on the level of

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PCT/AU2018/051208 investment return received, or the outperformance of a market index or a peer group in the user's goals based advice process.

[00101] The disclosed system and methodology provides a fundamentally different approach of how financial advice is provided, one that is centred around users achieving specific goals, where the goals are the focus - from the first interaction with the system, to the recommended advice solutions through to the way performance is measured, monitored, and communicated.

[00102] The terms 'module' and 'engine' as used in the summary, description and claims refer to one or more sequences of coded software (e.g. software components) comprising instructions to be executed by a processor. In some forms, the referenced module or engine includes all of the instructions necessary to accomplish the referenced function. In some forms, the modules may overlap and together accomplish the referenced function. In some forms, the module or engine may include further sub-categories of modules or engines configured to accomplish certain functions.

[00103] The word ‘comprising’ and forms of the word ‘comprising’ as used in this description and in the claims does not limit the invention claimed to exclude any variants or additions.

[00104] Modifications and improvements to the invention will be readily apparent to those skilled in the art. Such modifications and improvements are intended to be within the scope of this invention.

Claims

Claims

1 A computer-implemented method for automating the preparation of a visual representation for goal achievability and dynamically updating the visual representation, the method comprising:

(a) retrieving from a user system via a data network a user data set, the user data set comprising a plurality of user data records;

(b) retrieving from the user system via the data network a goal data set, the goal data set comprising at least one goal record;

(c) identifying a further data set by comparing the user data set with information stored on a database and, in dependence on the identification of the further data set, retrieving further data records via the data network;

(d) compiling a first input data set, the first input data set comprising the plurality of user data records, the plurality of further data records if identified and the at least one goal record;

(e) communicating the first input data set to a modelling engine via the data network, the modelling engine being configured to model a projection for the at least one goal record in dependence on the first input data set;

(f) receiving at a data representation engine via the data network the projection for the at least one goal record, wherein the data representation engine is configured to prepare the visual representation of goal achievability by:

calculating a goal achievability for the at least one goal record by processing the projection for the at least one goal record; and preparing the visual representation of goal achievability by converting the calculated goal achievability into the visual representation of goal achievability for the at least one goal record;

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PCT/AU2018/051208 (g) outputting the visual representation of goal achievability via the data network to the user system, wherein the visual representation of goal achievability allows the user to assess the likelihood of achieving the at least one goal record;

(h) retrieving from the user system via the data network an updated user data set and/or an updated goal data set, the updated user data set comprising a plurality of updated user data records and the updated goal data set comprising at least one updated goal record;

(i) compiling a second input data set, the second input data set comprising the plurality of further data records if identified, the updated plurality of updated user data records if retrieved otherwise the plurality of user data records, and the updated at least one goal record if retrieved otherwise the at least one goal record;

(j) communicating the compiled second input data set to the modelling engine via the data network, the modelling engine being configured to dynamically model an updated projection for the at least one goal record in dependence on the second input data set;

(k) receiving at the data representation engine via the data network the updated projection for the at least one goal record or the at least one updated goal record if retrieved, wherein the data representation engine is configured to dynamically prepare an updated visual representation of goal achievability for the updated projection; and (l) outputting the dynamically updated visual representation of goal achievability via the network to the user system.
2 A computer-implemented method according to claim 1, wherein the steps (h) to (1) are repeated upon retrieval of further updated user data records and/or further updated goal records to dynamically update the goal achievability in dependence on the updated records.
3 A computer-implemented method of automated preparation of a visual representation for goal achievability, the method comprising the steps of:

retrieving from a user system via a data network a user data set, the user data set comprising a plurality of user data records;

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PCT/AU2018/051208 retrieving from the user system via the data network a goal data set, the goal data set comprising at least one goal record;

identifying a further data set by comparing the user data set with information stored on a database and, in dependence on the identification of the further data set, retrieving further data records via the data network;

compiling an input data set, the input data set comprising the plurality of user data records, the plurality of further data records if identified and the at least one goal record;

communicating the input data set to a modelling engine via the data network, the modelling engine being configured to model a projection for the at least one goal record in dependence on the input data set;

receiving at a data representation engine via the data network the projection for the at least one goal record, wherein the data representation engine is configured to prepare the visual representation of goal achievability by:

calculating a goal achievability for the at least one goal record by processing the projection for the at least one goal record; and preparing the visual representation of goal achievability by converting the calculated goal achievability into the visual representation of goal achievability for the at least one goal record; and outputting the visual representation of goal achievability to a user, wherein the visual representation of goal achievability allows the user to assess the likelihood of achieving the at least one goal record.
4 A computer-implemented method according to claim 3, wherein the projection for the at least one goal record comprises a probability of achieving the at least one goal record and a potential shortfall calculated for the at least one goal record.
5 A computer-implemented method according to claim 4, wherein the goal data set comprises a plurality of goal records.

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6 A computer-implemented method according to claim 5, wherein the data representation engine is configured to calculating a goal achievability for each goal record by processing the probability and potential shortfall for each goal record.
7 A computer-implemented method according to claim 6, wherein the goal achievability for a goal record of the plurality of goal records is calculated by multiplying a first weighting with the probability of achieving the goal record and adding a multiplication of a second weighting with one minus the potential shortfall for the goal record.
8 A computer-implemented method according to claim 7, wherein the first weighting and second weighting are each a figure in a range between 25-75%.
9 A computer-implemented method according to any one of claims 5 to 8, wherein each goal record is assigned a priority by the user.
10 A computer-implemented method according to any one of the preceding claims, further comprising:

retrieving from the user system via the data network an updated user data set, the updated user data set comprising a plurality of updated user data records;

re-compiling the input data set, the input data set comprising the updated plurality of updated user data records, the plurality of further data records if identified and the goal data set;

communicating the re-compiled input data set to the modelling engine via the data network, the modelling engine being configured to model an updated projection for the at least one goal record in dependence on the input data set;

receiving at the data representation engine via the data network the updated projection for the at least one goal record, wherein the data representation engine is configured to prepare an updated visual representation of goal achievability for the updated projection; and outputting the updated visual representation of goal achievability to the user.

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11 A computer-implemented method according to claim 10, further comprising retrieving from the user system via the data network an updated goal data set, the updated goal data set comprising at least one updated goal record, wherein the input data set comprises the plurality of updated user data records, the plurality of further data records if identified and the at least one updated goal record.
12 A computer-implemented method according to any one of claims 3 to 10, further comprising:

retrieving from the user system via the data network an updated goal data set, the updated user data set comprising a plurality of updated goal data records;

re-compiling the input data set, the input data set comprising the user data records, the plurality of further data records if identified and the updated goal data set;

communicating the re-compiled input data set to the modelling engine via the data network, the modelling engine being configured to model an updated projection for the plurality of updated goal records in dependence on the input data set;

receiving at the data representation engine via the data network the updated projection for the plurality of updated goal records, wherein the data representation engine is configured to prepare an updated visual representation of goal achievability for the updated projection; and outputting the updated visual representation of goal achievability to the user.
13 A computer-implemented method according to any one of claims 3 to 12, wherein the modelling engine is configured to stochastically and deterministically model the projection for the at least one goal record.
14 A computer-implemented method according to any one of claims 3 to 13, wherein outputting the visual representation of goal achievability comprises communicating the visual representation to a user interface.
15 A computer-implemented method according to any one of claims 3 to 14, further comprising receiving a pre-determined strategy data set comprising a plurality of strategy

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PCT/AU2018/051208 records, reducing the plurality of strategy records in dependence on the user data set, and communicating the reduced strategy records to the modelling engine for modelling.
16 A computer-implemented method according to any one of the preceding claims, further comprising preparing a scenario model, the scenario model comprising a plurality of folded layers of scenario data.
17 A computer-implemented method according to claim 16, further comprising preparing an interim data model from at least the scenario model, the interim data model comprising a plurality of folded layers of interim data.
18 A system for automated preparation of a visual representation for goal achievability, the system comprising:

a processor; and memory storing instructions that, when executed by the processor, cause the system to:

retrieve from a user system via a data network a user data set, the user data set comprising a plurality of user data records;

retrieve from the user system via the data network a goal data set, the goal data set comprising at least one goal record;

identify a further data set by comparing the user data set with information stored on a database and, in dependence on the identification of the further data set, retrieve further data records via the data network;

compile an input data set, the input data set comprising the plurality of user data records, the plurality of further data records if identified and the at least one goal record;

communicate the input data set to a modelling engine via the data network, the modelling engine being configured to model a projection for the at least one goal record in dependence on the input data set;

receive at a data representation engine via the data network the projection for the at least one goal record, wherein the data representation engine is configured to prepare the visual representation of goal achievability by:

WO 2019/090394

PCT/AU2018/051208 calculating a goal achievability for the at least one goal record by processing the projection for the at least one goal record; and preparing the visual representation of goal achievability by converting the calculated goal achievability into the visual representation of goal achievability for the at least one goal record; and output the visual representation of goal achievability to a user, wherein the visual representation of goal achievability allows the user to assess the likelihood of achieving the at least one goal record.
19 A system according to claim 18, wherein the projection for the at least one goal record comprises a probability of achieving the at least one goal record and a potential shortfall calculated for the at least one goal record.
20 A system according to claim 19, wherein the goal data set comprises a plurality of goal records.
21 A system according to claim 120, wherein the data representation engine is configured to calculating a goal achievability for each goal record by processing the probability and potential shortfall for each goal record.
22 A system according to claim 21, wherein the goal achievability for a goal record of the plurality of goal records is calculated by multiplying a first weighting with the probability of achieving the goal record and adding a multiplication of a second weighting with one minus the potential shortfall for the goal record.
23 A system according to claim 21, wherein the first weighting and second weighting are each a figure in a range between 25-75%.
24 A system according to any one of claims 20 to 23, wherein each goal record is assigned a priority by the user.
25 An system according to any one of claims 18 to 24, wherein the memory stores instructions that, when executed by the processor, cause the system to

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PCT/AU2018/051208 retrieve from the user system via a data network an updated user data set, the updated user data set comprising a plurality of updated user data records;

re-compile the input data set, the input data set comprising the updated plurality of updated user data records, the plurality of further data records if identified and the goal data set;

communicate the re-compiled input data set to the modelling engine via the data network, the modelling engine being configured to model an updated projection for the at least one goal record in dependence on the input data set;

receive at the data representation engine via the data network the updated projection for the at least one goal record, wherein the data representation engine is configured to prepare an updated visual representation of goal achievability for the updated projection; and output the updated visual representation of goal achievability to the user.
26 A system according to claim 25, wherein the memory stores instructions that, when executed by the processor, cause the system to retrieve from the user system via the data network an updated goal data set, the updated goal data set comprising at least one updated goal record, wherein the input data set comprises the plurality of updated user data records, the plurality of further data records if identified and the at least one updated goal record.
27 A system according to any one of claims 18 to 26, wherein the memory stores instructions that, when executed by the processor, cause the system to:

retrieve from the user system via the data network an updated goal data set, the updated user data set comprising a plurality of updated goal data records;

re-compile the input data set, the input data set comprising the user data records, the plurality of further data records if identified and the updated goal data set;

communicate the re-compiled input data set to the modelling engine via the data network, the modelling engine being configured to model an updated projection for the plurality of updated goal records in dependence on the input data set;

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PCT/AU2018/051208 receive at the data representation engine via the data network the updated projection for the plurality of updated goal records, wherein the data representation engine is configured to prepare an updated visual representation of goal achievability for the updated projection; and output the updated visual representation of goal achievability to the user.
28 A system according to any one of claims 18 to 27, wherein the modelling engine is configured to stochastically and deterministically model the projection for the at least one goal record.
29 A system according to any one of claims 18 to 28, wherein outputting the visual representation of goal achievability comprises communicating the visual representation to a user.
30 A system according to any one of claims 18 to 29, wherein the memory stores instructions that, when executed by the processor, cause the system to receive a pre-determined strategy data set comprising a plurality of strategy records, reduce the plurality of strategy records in dependence on the user data set, and communicate the reduced strategy records to the modelling engine for modelling.
31 A system according to any one of claims 18 to 30, wherein the memory stores instructions that, when executed by the processor, cause the system to: prepare a scenario model, the scenario model comprising a plurality of folded layers of scenario data.
32 A system according to claim 31, wherein the memory stores instructions that, when executed by the processor, cause the system to: prepare an interim data model from at least the scenario model, the interim data model comprising a plurality of folded layers of interim data, the interim data model being accessible by the modelling engine via the data network for modelling the projection for the at least one goal record.