Family financial health assessment method, device, equipment and medium

Abstract

The invention provides a family finance health assessment method and device, equipment and a medium, and relates to the technical field of data analysis, and the method comprises the steps: obtaining the personal finance data of different members in a target family in a target time period, and the macroeconomic parameters of an area where the target family is located in the target time period; according to the macroeconomic parameters and the personal financial data of different members, calculating an income elasticity coefficient and an income acceleration prediction value; inputting the configured family finance target of the target family, the personal finance data of different members, the income elasticity coefficient and the income acceleration prediction value into a pre-trained family finance dynamic simulation model to obtain a family finance dynamic simulation result; and determining a family financial health assessment result of the target family according to the personal financial data of the different members, the macroeconomic parameters and the family financial dynamic simulation result. According to the invention, an accurate and reliable family financial health assessment result can be obtained.

Description

Technical Field

[0001] This application relates to the field of data analysis technology, and more specifically, to a method, apparatus, device, and medium for assessing the health of family finances. Background Technology

[0002] In the traditional field of household financial health assessment, most assessment methods focus only on static financial data such as the household's current balance sheet and cash flow statement, and judge the financial health status by calculating basic indicators such as liquidity ratio and debt-to-income ratio. They lack consideration of changes in the external macroeconomic environment, which makes it impossible to effectively quantify the impact of macroeconomic factors on household income and to predict the financial risks that households may face in future economic cycles, resulting in inaccurate and unreliable assessment results. Summary of the Invention

[0003] The purpose of this application is to provide a method, apparatus, device and medium for assessing the health of family finances, so as to solve the above-mentioned problems existing in the prior art and obtain accurate and reliable family financial health assessment results.

[0004] Firstly, a method for assessing the health of family finances is provided, which may include: Obtain personal financial data of different members of the target family during the target time period, as well as macroeconomic parameters of the region where the target family is located during the target time period; Based on the macroeconomic parameters and the personal financial data of different members, calculate the income elasticity coefficient and the predicted income growth rate. The family financial goals of the target family, the personal financial data of different members, the income elasticity coefficient and the predicted income growth rate are input into the pre-trained family financial dynamic simulation model to obtain the family financial dynamic simulation results. Based on the individual financial data of different members, the macroeconomic parameters, and the results of dynamic simulation of family finances, the family financial health assessment results of the target family are determined.

[0005] In an optional implementation, the personal financial data includes: income, expenses, assets, liabilities, industry, occupation type, years of service, education level, subjective income growth rate, and income growth rate confidence coefficient; The macroeconomic parameters include a first economic parameter and a second economic parameter; the first economic parameter includes: industry volatility index and industry unemployment rate for different industries, consumer price index, house price-to-income ratio and occupational stability coefficient for different educational levels; The second economic parameter includes: GDP growth rate, projected GDP growth rate, per capita income and GDP growth rate coefficient, inflation rate, interest rate, average market investment return rate, and projected inflation rate.

[0006] In an optional implementation, the income elasticity coefficient is calculated based on the macroeconomic parameters and the personal financial data of different members, including: The total family income is the sum of the incomes of different members of the target family within the target time period. Regression analysis was performed on total household income and GDP growth rate to obtain the macroeconomic elasticity coefficient; From the first economic parameter, extract the industry volatility index and industry unemployment rate of the industries to which different members belong, as well as the occupational stability coefficient corresponding to the education level of different members; Calculate the industry volatility elasticity coefficient based on the extracted industry volatility index, unemployment rate, and total household income; Based on the extracted occupational stability coefficient, consumer price index, house price-to-income ratio, and total household income for different members, calculate the regional-individual characteristic elasticity coefficient. The income elasticity coefficient is calculated based on the macroeconomic elasticity coefficient, industry volatility elasticity coefficient, and regional-individual characteristic elasticity coefficient.

[0007] In an optional implementation, the projected income growth rate is calculated based on the macroeconomic parameters and the individual financial data of different members, including: The predicted income growth rate is calculated based on the per capita income and GDP growth rate coefficient, the predicted GDP growth rate, the income elasticity coefficient, the subjective income growth rate, and the income growth rate confidence coefficient.

[0008] In an optional implementation, the household financial dynamic simulation model includes: The input layer is used to input the target family's financial goals, individual financial data of different members, income elasticity coefficient, and income growth forecast. The scene rule configuration layer is used to configure the parameter calculation rules for different simulation scenarios; The multi-scenario simulation layer is used to simulate the evolution of family finances within a preset future time period based on the parameter calculation rules configured for different simulation scenarios, and to obtain family financial reports at different points in time within the preset future time period. The output layer is used to obtain dynamic simulation results of family finances based on family financial reports at different points in time within a preset future time period.

[0009] In an optional implementation, the simulation scenario includes a baseline scenario, a stress scenario, and a target scenario.

[0010] In an optional implementation, the family financial health assessment result of the target family is determined based on the individual financial data of different members, the macroeconomic parameters, and the results of the family financial dynamic simulation, including: The first health assessment result is obtained by analyzing the personal financial data of different members and the macroeconomic parameters. The results of the household financial dynamic simulation and the macroeconomic parameters are analyzed to obtain the second health assessment results; Based on the results of the first health assessment and the second health assessment, the results of the family financial health assessment are determined.

[0011] Secondly, a household financial health assessment device is provided, which may include: The acquisition unit is used to acquire personal financial data of different members of the target family during the target time period, as well as macroeconomic parameters of the region where the target family is located during the target time period. The calculation unit is used to calculate the income elasticity coefficient and the predicted income growth rate based on the macroeconomic parameters and the personal financial data of different members. The simulation unit is used to input the configured target family's family financial goals, individual financial data of different members, income elasticity coefficient and income growth forecast into the pre-trained family financial dynamic simulation model to obtain the family financial dynamic simulation results; The determining unit is used to determine the family financial health assessment result of the target family based on the personal financial data of different members, the macroeconomic parameters, and the results of dynamic simulation of family finance.

[0012] Thirdly, an electronic device is provided, which includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When a processor executes a program stored in memory, it implements any of the steps described in the first aspect above.

[0013] Fourthly, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when executed by a processor, the computer program implements the steps of any of the methods described in the first aspect above.

[0014] This application establishes a quantitative correlation model between macroeconomic parameters and household income by introducing income elasticity coefficients and income growth forecasts, breaking through the limitations of traditional static assessments. With the help of a dynamic household financial simulation model, it can conduct multi-year dynamic simulations based on different macroeconomic scenarios and household financial goals. It can not only accurately predict future trends in household cash flow and the evolution path of assets and liabilities, but also effectively identify potential problems such as liquidity crises and debt repayment risks under stress scenarios, significantly improving the foresight and risk warning capabilities of the assessment results.

[0015] This application integrates three core dimensions: personal financial data, macroeconomic parameters, and dynamic simulation results. It achieves the dual goals of current situation assessment and future trend prediction. Based on static financial data, it objectively evaluates a family's current emergency response capacity, debt repayment capacity, and savings capacity. Furthermore, it combines dynamic simulation results to analyze the feasibility of family financial goals, providing families with a comprehensive solution covering weakness identification, risk warning, and strategy optimization. The assessment results of this application are more personalized and instructive, directly supporting family decisions regarding asset allocation, debt optimization, and insurance planning, thus comprehensively improving the family's financial health management level. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 An architecture diagram of a family financial health assessment system provided in this application embodiment; Figure 2 A flowchart illustrating a method for assessing the health of family finances provided in an embodiment of this application; Figure 3 A schematic diagram of a family financial health assessment device provided in this application embodiment; Figure 4 This application provides a schematic diagram of the structure of an electronic device. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Unless otherwise defined, the technical or scientific terms used in this application should have the ordinary meaning understood by those skilled in the art. The words "first," "second," and similar terms used in this application do not indicate any order, quantity, or importance, but are only used to distinguish different components. The words "comprising" or "including," etc., mean that the element or object preceding the word covers the element or object listed after the word and its equivalents, but do not exclude other elements or objects. The words "connected," "coupled," or "connected," etc., are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up," "down," "left," "right," etc., are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0019] The family financial health assessment method provided in this application embodiment can be applied to... Figure 1 In the system architecture shown, such as Figure 1 As shown, the system may include a server and a terminal. The server can be a physical server, a server cluster consisting of multiple physical servers, or a distributed system. It can also be a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms. The terminal may be a user equipment (UE) such as a mobile phone, smartphone, laptop, digital radio receiver, personal digital assistant (PDA), tablet computer (PAD), handheld device, in-vehicle device, wearable device, computing device, or other processing device connected to a wireless modem, mobile station (MS), mobile terminal, etc. The terminal and server can be directly or indirectly connected via wired or wireless communication methods, which is not limited herein.

[0020] The preferred embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit this application. Furthermore, the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0021] Figure 2 This is a flowchart illustrating a method for assessing the health of family finances, as provided in an embodiment of this application. Figure 2 As shown, the method may include: Step S210: Obtain the personal financial data of different members of the target family during the target time period, as well as the macroeconomic parameters of the region where the target family is located during the target time period.

[0022] The target timeframe can be 5-10 years; personal financial data includes: income, expenditure, assets, liabilities, industry, occupation type, years of work experience, education level, subjective income growth rate, and income growth confidence coefficient; macroeconomic parameters include primary and secondary economic parameters; primary economic parameters include: industry volatility index and industry unemployment rate for different industries, consumer price index, house price-to-income ratio, and occupational stability coefficient for different education levels; secondary economic parameters include: GDP growth rate, projected GDP growth rate, per capita income and GDP growth rate coefficient, inflation rate, interest rate, average market investment return rate, and projected inflation rate.

[0023] Step S220: Calculate the income elasticity coefficient and the predicted income growth rate based on macroeconomic parameters and the personal financial data of different members.

[0024] In practice, the sum of the incomes of different members of the target family within the target time period is taken as the total family income. Regression analysis was performed on total household income and GDP growth rates to obtain the macroeconomic elasticity coefficient. Specifically, based on the configured anomaly rules, abnormal total income and GDP growth rate data for abnormal periods were selected from the total household income and GDP growth rate data at different time points within the target time period. These different time points within the target time period could be each year or each half-year; abnormal periods were periods of major economic crises or other special periods. The abnormal total income and abnormal GDP growth rates were smoothed or weighted to obtain adjusted abnormal total income and abnormal GDP growth rates. Linear regression analysis was then performed using the least squares method on the adjusted abnormal total income and abnormal GDP growth rates, as well as on the total household income and GDP growth rates at different time points within the target time period excluding abnormal periods. A regression model was constructed with GDP growth rate as the independent variable and total household income growth rate as the dependent variable to obtain the macroeconomic elasticity coefficient. The regression model can be expressed as: Total Household Income Growth Rate = α + β × GDP Growth Rate + ε, where β represents the macroeconomic elasticity coefficient, indicating the percentage change in total household income for every 1 percentage point increase in GDP; α represents the regression intercept; and ε is the regression residual. From the primary economic parameters, we extract the industry volatility index and industry unemployment rate of different members' industries, as well as the occupational stability coefficient corresponding to different members' educational levels. Based on the extracted industry volatility index, unemployment rate, and total household income, the industry volatility elasticity coefficient is calculated. Specifically, based on the extracted total household income, the annual growth rate of total household income at different time points within the target period is calculated; based on the extracted industry volatility index, the annual rate of change of the industry volatility index at different time points within the target period is calculated; based on the extracted unemployment rate, the annual rate of change of the unemployment rate at different time points within the target period is calculated; based on the industry and occupation type of different members within the target household, the first and second weights at different time points are determined; the annual rates of change of the industry volatility index and the annual rates of change of the unemployment rate at different time points within the target period are weighted and summed using the first and second weights respectively to obtain the comprehensive industry risk index at different time points within the target period; regression analysis is performed on the annual growth rate of total household income and the corresponding comprehensive industry risk index at different time points within the target period to obtain the industry volatility elasticity coefficient. Based on the extracted occupational stability coefficient, consumer price index (CPI), house price-to-income ratio, and total household income for different members, the regional-individual characteristic elasticity coefficient is calculated. Specifically, based on the extracted CPI at different points in time within the target time period, the annual rate of change of the CPI at the current point in time within the target time period is calculated; based on the extracted house price-to-income ratio at different points in time within the target time period, the annual rate of change of the house price-to-income ratio at the current point in time is calculated; based on the extracted occupational stability coefficient at different points in time within the target time period, the annual rate of change of the occupational stability coefficient at the current point in time is calculated; based on a pre-trained multiple regression model, the regression coefficients of the annual rate of change of the CPI, the annual rate of change of the house price-to-income ratio, and the annual rate of change of the occupational stability coefficient on the growth rate of total household income are determined; the determined regression coefficients are then used to perform a weighted summation of the annual rate of change of the CPI at each point in time, the annual rate of change of the house price-to-income ratio at the current point in time, and the annual rate of change of the occupational stability coefficient at the current point in time to obtain the regional-individual characteristic elasticity coefficient. The income elasticity coefficient is calculated based on the macroeconomic elasticity coefficient, industry volatility elasticity coefficient, and regional-individual characteristic elasticity coefficient. Specifically, based on the configured expert evaluation model, the weights corresponding to the macroeconomic elasticity coefficient, industry volatility elasticity coefficient, and regional-individual characteristic elasticity coefficient are determined respectively. The weighted composite elasticity value is calculated based on the macroeconomic elasticity coefficient, industry volatility elasticity coefficient, regional-individual characteristic elasticity coefficient, and their corresponding weights. The weighted composite elasticity value is input into a preset normalization function to map it to a standard elasticity coefficient range, thus obtaining the income elasticity coefficient. The projected income growth rate is calculated based on the per capita income and GDP growth rate coefficient, the projected GDP growth rate, the income elasticity coefficient, the subjective income growth rate, and the income growth rate confidence coefficient. Specifically, the product of the extracted per capita income and GDP growth rate coefficient and the projected GDP growth rate is used as the macro benchmark income growth rate. Based on the income elasticity coefficient and the macro benchmark income growth rate, the elastically adjusted macro income growth rate is calculated using a pre-trained adjustment coefficient. The projected income growth rate is then calculated based on the elastically adjusted macro income growth rate, the subjective income growth rate, and the income growth rate confidence coefficient. ;in, This represents the projected revenue growth rate; Indicator of confidence in income growth rate; Indicates the growth rate of subjective income; This represents the macroeconomic income growth rate after a flexible adjustment.

[0025] Step S230: Input the configured target family's family financial goals, individual financial data of different members, income elasticity coefficient and income growth forecast into the pre-trained family financial dynamic simulation model to obtain the family financial dynamic simulation results.

[0026] The dynamic simulation model of household finances includes: The input layer is used to input the target family's financial goals, individual financial data of different members, income elasticity coefficient, and income growth forecast. The scene rule configuration layer is used to configure the parameter calculation rules for different simulation scenarios; The multi-scenario simulation layer is used to simulate the evolution of family finances within a preset future time period based on the parameter calculation rules configured for different simulation scenarios, and to obtain family financial reports at different points in time within the preset future time period. The simulation scenarios include a baseline scenario, a stress scenario, and a target scenario. The baseline scenario can be income growing at the GDP rate and expenditure growing at 3%. The stress scenario can be a decrease in the income of the main income earner of Ei×30% lasting for 1 year or a large medical expense. The target scenario can be the additional financial expenditure required to meet the family's financial goals. The output layer is used to obtain dynamic simulation results of household finances based on household financial reports at different points in time within a preset future period. The dynamic simulation results of household finances may include: baseline, stress, and target scenarios, projected cash flow statements for each future year, projected balance sheets, and key indicator sequences (liquidity ratio, debt-to-income ratio, net assets, etc.).

[0027] Step S240: Based on the individual financial data of different members, macroeconomic parameters, and the results of dynamic simulation of family finances, determine the family financial health assessment results of the target family.

[0028] In practice, the personal financial data and macroeconomic parameters of different members are analyzed to obtain the first health assessment result. Specifically, based on the personal financial data of different members, a family balance sheet and a family cash flow statement for the target family within a preset time period are generated. The preset time period can be the past year, the past month, or the past six months. The family balance sheet can include: total assets, total liabilities, and net assets. The family cash flow statement can include: total income, total expenditure, net cash flow, and free savings. Based on the family balance sheet and the family cash flow statement, the liquidity ratio is calculated. The liquidity ratio = liquid assets / average monthly expenditure. An emergency safety threshold is determined based on macroeconomic parameters. Based on the comparison between the liquidity ratio and the emergency safety threshold, the emergency response capability score of the target family is determined. The emergency safety threshold can include a first threshold and a second threshold. If the liquidity ratio is not less than the second threshold, the emergency response capability score can be 90 points. If the liquidity ratio is not greater than the first threshold, the score can be 90 points. If the value is 40, the emergency response capability score can be 40 points. The debt repayment capability of the target family is assessed based on the family balance sheet and family cash flow statement. Specifically, the debt-to-income ratio is calculated as: annual debt repayment (principal + interest) / annual after-tax total income. The benchmark interest rate trend and benchmark safety threshold in macroeconomic parameters are used as indicators. The debt repayment capability score of the target family is determined based on the comparison between the liquidity ratio and the benchmark safety threshold. The savings capability score of the target family is determined based on the family balance sheet and family cash flow statement. The family life cycle stage is determined based on the age of different members. The first, second, and third coefficients are determined based on the family life cycle stage. The emergency response capability score, debt repayment capability score, and savings capability score are weighted and summed based on the first, second, and third coefficients to obtain the first health assessment result. For young families, the savings capability weight is 0.4, and the others are 0.3 each; for middle-aged families, the debt repayment capability weight is 0.5, and the others are 0.25 each; for retired families, the emergency response capability weight is 0.5, and the others are 0.25 each. The results of dynamic simulations of household finances and macroeconomic parameters are analyzed to obtain the second health assessment result. Specifically, for each preset financial goal, the available funds (cash + investment assets) in the target year are checked in the target scenario simulation to see if they are not less than the required amount. Based on the required amount and available funds, the target achievement gap rate is calculated. The target achievement score is determined based on the target achievement gap rate. In the stress scenario simulation, risk years are identified: liquidity crisis: liquidity ratio below 2 for three consecutive months; solvency crisis: debt-to-income ratio exceeding 50% for two consecutive years; negative net asset growth: net assets decrease by more than 10% compared to the previous year. The number of years for crisis recovery (the number of years required for indicators to return to a safe value from the occurrence of a crisis) is calculated to obtain a risk resistance score: no crisis = 100 points; crisis occurs but recovery years ≤ 2 = 60 points; recovery years > 2 = 30 points. The weights are adjusted according to the GDP forecast growth rate: if the GDP growth rate is expected to decline (below the potential growth rate), the risk resistance weight is 0.7 and the target achievement weight is 0.3; otherwise, each is 0.5. The second health assessment result is determined based on the target achievement score and the risk resistance score. The second health assessment result (dynamic health score) = Target achievement score × weight + Risk resistance score × (1 - weight).

[0029] Based on the results of the first and second health assessments, the family financial health assessment result is determined. Specifically, the weights of the first and second health assessment results are determined according to the economic climate index in the macroeconomic parameters: if the economic climate index is >50 (expansion period), the weight of the first health assessment result is 0.4, and the weight of the second health assessment result is 0.6; if it is ≤50 (contraction period), the weight of the first health assessment result is 0.3, and the weight of the second health assessment result is 0.7. The first and second health assessment results are weighted and summed according to their respective weights to obtain a comprehensive health score. The indicator with the lowest score among emergency response, debt repayment, and savings capacity is selected to obtain the static weakness. If the target achievement score or risk resistance score is <70, the corresponding problem is marked to obtain the dynamic weakness. Optimization suggestions are generated based on the static and dynamic weaknesses. Based on the comprehensive health score, the weakness list, and the optimization suggestions, the family financial health assessment result is generated.

[0030] Corresponding to the above method, embodiments of this application also provide a household financial health assessment device, such as... Figure 3 As shown, the device includes: The acquisition unit 310 is used to acquire the personal financial data of different members of the target family during the target time period, as well as the macroeconomic parameters of the region where the target family is located during the target time period. The calculation unit 320 is used to calculate the income elasticity coefficient and the predicted income growth rate based on macroeconomic parameters and the personal financial data of different members. The simulation unit 330 is used to input the configured target family's family financial goals, the personal financial data of different members, the income elasticity coefficient and the predicted income growth rate into the pre-trained family financial dynamic simulation model to obtain the family financial dynamic simulation results. Unit 340 is used to determine the family financial health assessment results of the target family based on the personal financial data of different members, macroeconomic parameters, and the results of dynamic simulation of family finance.

[0031] The functions of each functional unit of the family financial health assessment device provided in the above embodiments of this application can be realized through the above methods and steps. Therefore, the specific working process and beneficial effects of each unit in the family financial health assessment device provided in the embodiments of this application will not be repeated here.

[0032] This application also provides an electronic device, such as... Figure 4 As shown, it includes a processor 410, a communication interface 420, a memory 430, and a communication bus 440, wherein the processor 410, the communication interface 420, and the memory 430 communicate with each other through the communication bus 440.

[0033] Memory 430 is used to store computer programs; When the processor 410 executes the program stored in the memory 430, it performs the following steps: Obtain personal financial data of different members of the target family during the target time period, as well as macroeconomic parameters of the region where the target family is located during the target time period; Based on macroeconomic parameters and the personal financial data of different members, calculate the income elasticity coefficient and the predicted income growth rate. The family financial goals of the target family, the personal financial data of different members, the income elasticity coefficient and the predicted income growth rate are input into the pre-trained family financial dynamic simulation model to obtain the family financial dynamic simulation results. Based on the individual financial data of different members, macroeconomic parameters, and the results of dynamic simulation of family finances, the family financial health assessment results of the target family are determined.

[0034] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0035] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0036] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0037] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0038] The implementation methods and beneficial effects of the various components of the electronic device in the above embodiments for solving the problem can be found in [reference needed]. Figure 2 The steps in the illustrated embodiments are used to implement the electronic device. Therefore, the specific working process and beneficial effects of the electronic device provided in this application will not be repeated here.

[0039] In another embodiment provided in this application, a computer-readable storage medium is also provided, which stores instructions that, when executed on a computer, cause the computer to perform any of the family financial health assessment methods described in the above embodiments.

[0040] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to perform any of the family financial health assessment methods described above.

[0041] Those skilled in the art will understand that the embodiments in this application can be provided as methods, systems, or computer program products. Therefore, the embodiments in this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the embodiments in this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0042] This application describes embodiments of methods, apparatus (systems), and computer program products according to embodiments of this application with reference to flowchart illustrations and / or block diagrams. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0043] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0044] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0045] Although preferred embodiments have been described in this application, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of this application.

[0046] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of the embodiments of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of this application and its equivalents, then these modifications and variations are also intended to be included in the embodiments of this application.

Claims

1. A method for assessing the health of family finances, characterized in that, The method includes: Obtain personal financial data of different members of the target family during the target time period, as well as macroeconomic parameters of the region where the target family is located during the target time period; Based on the macroeconomic parameters and the personal financial data of different members, calculate the income elasticity coefficient and the predicted income growth rate. The family financial goals of the target family, the personal financial data of different members, the income elasticity coefficient and the predicted income growth rate are input into the pre-trained family financial dynamic simulation model to obtain the family financial dynamic simulation results. Based on the individual financial data of different members, the macroeconomic parameters, and the results of the family financial dynamic simulation, the family financial health assessment results of the target family are determined.

2. The method as described in claim 1, characterized in that, The personal financial data includes: income, expenses, assets, liabilities, industry, occupation type, years of work experience, education level, subjective income growth rate, and income growth rate confidence coefficient; The macroeconomic parameters include a first economic parameter and a second economic parameter; the first economic parameter includes: industry volatility index and industry unemployment rate for different industries, consumer price index, house price-to-income ratio and occupational stability coefficient for different educational levels; The second economic parameter includes: GDP growth rate, projected GDP growth rate, per capita income and GDP growth rate coefficient, inflation rate, interest rate, average market investment return rate, and projected inflation rate.

3. The method as described in claim 2, characterized in that, Based on the aforementioned macroeconomic parameters and the personal financial data of different members, the income elasticity coefficient is calculated, including: The total family income is the sum of the incomes of different members of the target family within the target time period. Regression analysis was performed on total household income and GDP growth rate to obtain the macroeconomic elasticity coefficient; From the first economic parameter, extract the industry volatility index and industry unemployment rate of the industries to which different members belong, as well as the occupational stability coefficient corresponding to the education level of different members; Calculate the industry volatility elasticity coefficient based on the extracted industry volatility index, unemployment rate, and total household income; Based on the extracted occupational stability coefficient, consumer price index, house price-to-income ratio, and total household income for different members, calculate the regional-individual characteristic elasticity coefficient. The income elasticity coefficient is calculated based on the macroeconomic elasticity coefficient, industry volatility elasticity coefficient, and regional-individual characteristic elasticity coefficient.

4. The method as described in claim 3, characterized in that, Based on the aforementioned macroeconomic parameters and the personal financial data of different members, the projected income growth rate is calculated, including: The predicted income growth rate is calculated based on the per capita income and GDP growth rate coefficient, the predicted GDP growth rate, the income elasticity coefficient, the subjective income growth rate, and the income growth rate confidence coefficient.

5. The method as described in claim 1, characterized in that, The dynamic simulation model of household finances includes: The input layer is used to input the target family's financial goals, individual financial data of different members, income elasticity coefficient, and income growth forecast. The scene rule configuration layer is used to configure the parameter calculation rules for different simulation scenarios; The multi-scenario simulation layer is used to simulate the evolution of family finances within a preset future time period based on the parameter calculation rules configured for different simulation scenarios, and to obtain family financial reports at different points in time within the preset future time period. The output layer is used to obtain dynamic simulation results of family finances based on family financial reports at different points in time within a preset future time period.

6. The method as described in claim 5, characterized in that, The simulation scenarios include a baseline scenario, a stress scenario, and a target scenario.

7. The method as described in claim 1, characterized in that, Based on the individual financial data of different members, the macroeconomic parameters, and the results of the dynamic simulation of family finances, the family financial health assessment results of the target family are determined, including: The first health assessment result is obtained by analyzing the personal financial data of different members and the macroeconomic parameters. The results of the household financial dynamic simulation and the macroeconomic parameters are analyzed to obtain the second health assessment results; Based on the results of the first health assessment and the second health assessment, the results of the family financial health assessment are determined.

8. A household financial health assessment device, characterized in that, The device includes: The acquisition unit is used to acquire personal financial data of different members of the target family during the target time period, as well as macroeconomic parameters of the region where the target family is located during the target time period. The calculation unit is used to calculate the income elasticity coefficient and the predicted income growth rate based on the macroeconomic parameters and the personal financial data of different members. The simulation unit is used to input the configured target family's family financial goals, individual financial data of different members, income elasticity coefficient and income growth forecast into the pre-trained family financial dynamic simulation model to obtain the family financial dynamic simulation results; The determining unit is used to determine the family financial health assessment result of the target family based on the personal financial data of different members, the macroeconomic parameters, and the results of the family financial dynamic simulation.

9. An electronic device, characterized in that, The electronic device includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the method of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method described in any one of claims 1-7.

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