Financial scheme determination method and system

Abstract

The invention relates to the technical field of finance, and particularly discloses a financial scheme determination method and system, and the method comprises the following steps: 1, constructing and storing a financial template set, template parameters, a risk assessment model set and a value assessment index library, in particular, standardized processes and core elements of business such as credit, investment, financing and the like are sorted according to financial business types, during application of the technical scheme, ordered management and accurate calculation of data are realized by setting modules such as a data storage module and a calculation engine module, and multi-dimensional risk assessment and value measurement are completed by matching modules such as a risk engine and a value evaluation module. The interaction efficiency and the technical precision are improved by combining a terminal module, a model optimization module and the like, so that the risk can be efficiently controlled, the value can be scientifically evaluated and the user experience can be optimized in use, and the problems of data confusion, one-sided risk evaluation, single value dimension, tedious interaction and no model optimization in the prior art are solved.

Description

Technical Field

[0001] This invention relates to the field of financial technology, and in particular to a method and system for determining financial solutions. Background Technology

[0002] Against the backdrop of increasing demands for digital transformation and refined operations in the financial sector, financial solutions, as the core carrier connecting financial institutions and customer needs, are directly affected by the scientific and comprehensive nature of their determination methods. This impacts the risk controllability, return stability, and strategic alignment of financial operations. However, current mainstream methods for determining financial solutions still have significant shortcomings, particularly in the application of background technologies related to risk assessment, value assessment, and risk monitoring. These deficiencies make it difficult to meet the comprehensive needs of financial operations in complex market environments. Existing methods for determining financial solutions generally lack systematic and multi-dimensional risk assessment technology support. Traditional methods often focus on preliminary judgments of single risk types, relying on human experience or... Risk assessments are often based on simple parameter comparisons, failing to establish a comprehensive risk assessment model system covering credit risk, market risk, liquidity risk, and operational risk. For example, in credit risk assessment, only basic data such as customer credit ratings are relied upon, without combining statistical analysis models and machine learning algorithms to accurately quantify core indicators such as probability of default and loss rate of default. In market risk assessment, there is a lack of dynamic measurement techniques for fluctuations in market factors such as interest rates, exchange rates, and stock prices, making it difficult to calculate key indicators such as value at risk and scenario loss through historical simulations and Monte Carlo simulations. This results in one-sided and lagging risk assessment results that fail to fully reflect the risk exposure level of financial solutions in complex market environments. Existing methods for determining financial solutions suffer from problems such as a single value assessment dimension and fragmented assessment logic. They lack a multi-dimensional and collaborative value assessment framework. Traditional methods often use revenue indicators as the core assessment basis, neglecting a comprehensive consideration of costs, capital efficiency, and strategic value. In cost assessment, only the cost of funds is simply calculated without incorporating risk costs and operating costs into a unified assessment system, making it impossible to accurately calculate indicators reflecting cost-benefit matching, such as cost-return ratio and risk-adjusted return on capital. In capital efficiency assessment, there is a lack of quantitative analysis techniques for the impact of risk-weighted asset size and capital adequacy ratio, making it difficult to assess capital utilization efficiency through indicators such as capital turnover and economic value added. In strategic value assessment, no correlation analysis techniques have been established between the solution and the institution's long-term strategic goals, making it impossible to measure the solution's contribution to customer structure optimization, business structure upgrading, and enhanced market competitiveness. Consequently, the value assessment results cannot support long-term strategic decision-making for financial solutions. Current financial scheme determination methods generally lack a full-lifecycle, dynamic risk monitoring technology system. There are significant gaps in risk monitoring. Traditional methods often focus risk management on static assessments before scheme generation, failing to build real-time monitoring technology during scheme execution: They lack dynamic data collection technology for real-time market data changes, customer performance, and fund flow information, making it impossible to promptly detect risk anomalies during scheme execution; they lack risk warning trigger mechanisms, making it impossible to compare real-time risk data with risk calculations and simulation results from the scheme generation stage, and difficult to automatically trigger warnings and push adjustment suggestions when actual data exceeds preset warning ranges; furthermore, they lack linkage optimization technology between risk monitoring data and risk assessment models and value assessment algorithms, failing to use historical monitoring data to feed back into model parameter adjustments, resulting in a failure to form a "assessment-monitoring-optimization" risk monitoring system. The closed-loop approach is insufficient to address the dynamic risk challenges during the execution of financial solutions. It is evident that the existing methods for determining financial solutions lack background technologies related to risk assessment, value assessment, and risk monitoring, resulting in insufficient risk controllability, sustainable returns, and strategic alignment of financial solutions. There is an urgent need to construct a systematic risk assessment model, a multi-dimensional value assessment framework, and a full life-cycle risk monitoring system to improve the scientific rigor and comprehensiveness of methods for determining financial solutions. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for determining financial solutions, so as to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides a method and system for determining financial solutions, comprising the following steps: Step 1: Construct and store a set of financial templates, template parameters, a set of risk assessment models, and a library of value assessment indicators. Specifically, classify and sort out the standardized processes and core elements of credit, investment, wealth management, financing, and other businesses according to the type of financial business. Based on this, design at least one financial template, and embed corresponding query tags in each financial template to identify business attributes. Collect various parameters involved in business operations, including basic data parameters and calculation rule parameters, to form a template parameter library and associate and match it with financial templates; Based on the types of financial risks, credit risk assessment models, market risk assessment models, liquidity risk assessment models, and operational risk assessment models are constructed respectively, and integrated to form a risk assessment model set. The models have preset parameter input interfaces and calculation logic. Key indicators are selected from four dimensions: revenue, cost, efficiency, and strategy. The results include: annualized rate of return and internal rate of return; cost indicators such as cost of capital and risk reserve ratio; efficiency indicators such as capital turnover and return on capital; and strategic value indicators such as market share growth rate and customer lifetime value. A value assessment indicator library is established and the calculation method and application scenarios of each indicator are defined. Step 2: Obtain product attributes. Product attributes include template attributes, parameter attributes, risk characteristic attributes, and value characteristic attributes. Based on the product attributes, determine the target template, target parameters, applicable risk assessment model, and value assessment indicators. The target template is a financial template that matches the query tags and template attributes. The target parameters are template parameters that match the parameter attributes. The applicable risk assessment model is a risk assessment model that matches the risk characteristic attributes. The value assessment indicators are the indicator library content that matches the value characteristic attributes. Step 3: Inject the target parameters into the target template to generate a set of calculation formulas. The set of calculation formulas covers various calculation logics and numerical derivation methods related to financial business, providing a basic calculation framework for subsequent risk calculation, value assessment, etc. Step 4: Conduct financial risk calculations, including credit risk calculation, market risk calculation, liquidity risk calculation, and operational risk calculation; Credit risk calculation is based on parameters such as customer credit rating, debt repayment ability indicators, historical performance record, and industry credit level. It uses a credit risk assessment model to calculate indicators such as default probability, default loss rate, and credit risk exposure, and achieves risk quantification through statistical analysis models and machine learning algorithms. Market risk calculation combines market factor fluctuation data such as interest rates, exchange rates, stock prices, and commodity prices, and calculates risk value, sensitivity indicators, scenario losses, etc. through market risk assessment models, using methods such as historical simulation and Monte Carlo simulation for measurement; Liquidity risk is calculated based on data such as asset liquidity, the matching degree of capital supply and demand, and the stability of financing channels. It uses a liquidity risk assessment model to calculate indicators such as liquidity gap, liquidity coverage ratio, and net stable funding ratio. Operational risk calculation is based on data such as internal process defects, human operational errors, system failures, and external events. The operational risk capital requirements are calculated using the basic indicator method, the standard method, and the advanced measurement method through the operational risk assessment model. Step 5: Conduct financial risk simulations, including multi-scenario simulations, stress tests, sensitivity analysis, and dynamic simulations; Multi-scenario simulation sets up a baseline scenario, an optimistic scenario, a pessimistic scenario, and an extreme scenario. The extreme scenario includes economic cycle fluctuations, policy and regulatory adjustments, and global emergencies. Corresponding parameters are configured for each scenario to simulate the risk status and return performance of financial solutions under different scenarios. Stress tests are designed with different levels of stress scenarios, including large-scale market factor fluctuations, large-scale defaults, and liquidity crises, to test the financial solutions’ ability to withstand extreme stress and assess the maximum possible loss and risk boundary. Sensitivity analysis targets key parameters that affect financial solutions, including interest rates, customer credit ratings, and market volatility. The parameter values ​​are adjusted to a certain extent to analyze the impact of parameter changes on the risk and return of the financial solutions, and to determine the sensitive parameters and their impact coefficients. Dynamic simulation, based on time series data, simulates the risk evolution trend of financial solutions at different future time periods and predicts the change path of risk indicators. Step Six: Develop financial risk response strategies, including risk avoidance, risk reduction, risk transfer, and risk acceptance; Risk avoidance involves taking measures such as refusing to conduct business or terminating cooperation with financial businesses or products whose risk levels exceed the acceptable range. Risk reduction can be achieved by adjusting the structure, parameters, or terms of financial solutions, optimizing customer eligibility criteria, diversifying investment portfolios, and setting risk limits. Risk transfer utilizes insurance, guarantees, and hedging tools to transfer part or all of the risk to a third party, thereby reducing one's own risk exposure. Hedging tools include forward contracts, options, and swaps. Risk tolerance involves developing a risk tolerance plan, setting aside risk reserves, and establishing risk response plans for risks that are within an acceptable range, in order to deal with potential risk losses. Step 7: Conduct a profitability review. Using the profitability model in the calculation formula set, calculate indicators such as expected annualized rate of return, internal rate of return, and net present value. Compare the industry benchmark rate of return and the historical return level of similar projects to analyze the stability of returns. Return stability includes return volatility and maximum drawdown rate. Assess the profitability potential and sustainability of the project. Step 8: Conduct a cost-benefit and capital efficiency review, calculate the cost of capital, risk cost, and operating cost. The cost of capital includes the financing interest rate and the reserve requirement ratio; the risk cost includes the amount of risk reserve provision and the expected loss compensation cost; and the operating cost includes human resource input and system maintenance expenses. Calculate the cost-benefit ratio and the risk-adjusted return on capital. The cost-benefit ratio is the ratio of revenue to total cost. Assess the matching degree between costs and benefits and identify cost optimization opportunities. At the same time, analyze the capital occupation of the plan. Capital occupation includes the impact of risk-weighted asset size and capital adequacy ratio. Calculate the capital turnover rate and economic value added. The capital turnover rate is the ratio of operating revenue to average capital occupation. Compare with capital regulatory requirements and internal capital allocation efficiency standards to assess capital utilization efficiency and value-added capacity. Step 9: Conduct a strategic value and risk-return balance assessment. In conjunction with the market environment and the institution's strategic objectives, analyze the contribution of the plan to customer structure optimization, business structure upgrade, and market competitiveness enhancement. Customer structure optimization includes an increase in the proportion of high-net-worth customers, business structure upgrade includes an increase in the proportion of intermediary business revenue, and market competitiveness enhancement includes product differentiation advantages. Assess the fit between the plan and the long-term strategy and the potential synergistic value. Establish a risk-return matrix and couple risk indicators with return indicators for analysis. Risk indicators include value at risk and default rate, while return indicators include rate of return and net present value. Calculate the Sharpe ratio and information ratio. The Sharpe ratio is the ratio of excess return to risk volatility, and the information ratio is the ratio of excess return to tracking error. Evaluate the cost-effectiveness of returns at a specific risk level and clarify the rationality of the risk compensation of the plan. Step 10: Generate a financial solution based on the set of calculation formulas, financial risk calculation results, financial risk simulation results, financial risk response strategies, and financial value assessment. The financial solution includes a financial product or business plan, corresponding calculation formulas, risk assessment report, risk simulation analysis report, risk response plan, and value assessment report. All parts are interconnected and together constitute a complete solution system. Step 11: Conduct compliance verification of the generated financial plan. Based on financial regulatory laws and internal management systems, comprehensively check whether the terms of the plan comply with regulatory and internal regulations, whether the risk indicators are within the scope of compliance, and whether there are any illegal assumptions or calculations in the value assessment logic. Generate a compliance verification report and identify compliant and non-compliant items. Step 12: Adjust and optimize the financial plan based on the compliance verification report, formulate specific adjustment measures for non-compliant items, modify the plan terms, adjust the calculation method of risk indicators or revise the value assessment logic to ensure that the plan fully complies with compliance requirements. Step 13: Conduct a second verification of the adjusted and optimized financial plan. Check again whether the plan fully complies with the compliance verification rules. If there are still non-compliant items, return to Step 12 to continue the adjustment until the plan is fully compliant. Step Fourteen: Output the final financial solution, and store various data and reports generated during the solution generation process, including calculation formula set, risk calculation results, simulation data, response strategies, value assessment content, compliance verification records, etc., to provide data support for subsequent solution traceability, analysis and optimization.

[0005] Furthermore, it includes an application platform server, a data storage module connected to the application platform server, the data storage module being either internal to the application platform server or independently configured, and the application platform server being connected to a terminal module via the Internet; the terminal module is used to send data request conditions to the backend application platform server and receive financial solutions returned by the application platform server; the data request conditions include financial business requirements, risk preferences, customer information, and value assessment requirements; the application platform server has a built-in computing engine module, risk engine module, value assessment module, solution generation module, and compliance verification module; wherein, the application platform server is used to: store financial templates. The system includes a set of template parameters, a risk assessment model set, a value assessment indicator library, a value evaluation algorithm, and a compliance verification rule library. The risk assessment model set includes credit risk assessment models, market risk assessment models, liquidity risk assessment models, operational risk assessment models, etc., covering various risk measurement algorithms and model parameters. The financial template set includes at least one financial template covering various financial businesses such as credit, investment, wealth management, and financing; each financial template includes at least one query tag. The value assessment indicator library includes return on investment indicators, cost indicators, efficiency indicators, and strategic value indicators. Return on investment indicators include annualized return and internal rate of return; cost indicators include cost of capital ratio and risk reserve ratio; efficiency... The metrics include capital turnover rate and return on capital; strategic value metrics include market share growth rate and customer lifetime value; the compliance verification rule base includes financial regulatory provisions and internal management system provisions; product attributes are obtained through the solution generation module, and based on these product attributes, target templates, target parameters, applicable risk assessment models, and value assessment metrics are determined. The product attributes include template attributes, parameter attributes, risk characteristic attributes, and value characteristic attributes. The target template is a financial template whose query tags match the template attributes; the target parameters are template parameters that match the parameter attributes; and the applicable risk assessment model is a risk assessment model that matches the risk characteristic attributes. The valuation model uses a library of indicators that match the value characteristics. The calculation engine module injects target parameters into the target template to generate a set of calculation formulas. The risk engine module calls an applicable risk assessment model and combines it with the set of calculation formulas to calculate financial risk, outputting quantitative results for various risk indicators. Based on the financial risk calculation results, the risk engine module performs financial risk simulations, including multi-scenario simulations, stress tests, sensitivity analysis, and dynamic simulations, generating a simulation analysis report. Finally, based on the financial risk calculation and simulation results, the risk engine module formulates risk response strategies, including risk avoidance conditions, risk reduction measures, risk transfer plans, and risk-bearing plans.The value assessment module utilizes a value evaluation indicator library and algorithm to conduct financial value assessments based on a set of calculation formulas, risk calculation results, and risk response strategies. It outputs profitability, cost-effectiveness, capital efficiency, strategic value, and risk-return balance assessments. The solution generation module integrates the calculation formulas, risk calculation results, risk simulation analysis reports, risk response plans, and value assessment results to generate preliminary financial solutions. These preliminary financial solutions include financial product or business plans, corresponding calculation formulas, risk assessment reports, simulation analysis reports, response plans, and value assessment reports. Finally, a compliance verification module... The module invokes the compliance verification rule library to perform compliance verification on the preliminary financial plan and generates a compliance verification report. The plan generation module then adjusts and optimizes the preliminary financial plan based on the compliance verification report, outputting the final financial plan. The data storage module stores relevant data, including financial template sets, template parameters, risk assessment model sets and algorithm parameters, value assessment indicator library, compliance verification rule library, product attribute database, market data, customer information database, risk calculation results, simulation process data, value evaluation data, compliance verification data, and historical financial plan archives. Market data includes interest rates, exchange rates, and stock prices.

[0006] Furthermore, the computing engine module is also used to: acquire an input information set, the input information set including at least one input information, wherein the input information is a calculation formula corresponding to a financial product or business with embedded relevant information, the calculation formula of the financial product or business is configured with a wrapper and a parameter expression; traverse the input information set, parse the input information respectively, and generate parsed data; and calculate the parsing result based on the parsed data.

[0007] Furthermore, the calculation engine module is also used for: traversing the input information in the input information set; determining whether the input information has parameters; if the input information has parameters, continuing to track the input information in the input information set; if the input information does not have parameters, calculating the input information to obtain the calculated input information, storing the calculated input information to obtain temporary data; and parsing the temporary data to generate parsed data.

[0008] Furthermore, the computing engine module is also used to: traverse the input information in the input information set; determine whether the wrapper of the input information is a preset wrapper; when the wrapper of the input information is a preset wrapper, parse out the Boolean value of the input information and the result branch of the input information; and generate the parsed result according to the ternary operation rules, the Boolean value, and the result branch.

[0009] Furthermore, the calculation engine module is also used to: round the parsed data according to preset rounding rules to obtain rounded data; calculate the parsing result based on the rounded data; the risk engine module is also used to: use the rounded data as input parameters in financial risk calculation, and combine it with a risk assessment model for indicator quantification; combine the rounded data with scenario parameters in financial risk simulation to improve the reliability of simulation results; and set response thresholds based on the rounded risk data when formulating risk response strategies; the value assessment module is also used to: use the rounded data as the basic data for calculating value assessment indicators to ensure the calculation accuracy of indicators such as returns and costs, and improve the credibility of value assessment results.

[0010] Furthermore, the risk engine module also includes a risk monitoring submodule, which is used to collect dynamic data in real time during the execution of financial solutions. The dynamic data includes changes in market data, customer performance, and capital flow information. The data is compared with the risk calculation and simulation results. When the actual data exceeds the preset warning range, a risk warning is automatically triggered, and risk response adjustment suggestions are pushed to the terminal module.

[0011] Furthermore, the application platform server also includes a model optimization module, which is used to periodically analyze the deviation between historical risk data and model prediction data, the deviation between the actual value of historical value indicators and the predicted value of the evaluation, evaluate the effectiveness of the risk assessment model and the value evaluation algorithm, and optimize and adjust the model parameters and algorithm coefficients when the deviation exceeds a set threshold, and update the risk assessment model and the value evaluation algorithm to improve the accuracy of risk assessment and value evaluation.

[0012] Furthermore, the compliance verification module is also used to: regularly update the compliance verification rule base, synchronize the latest financial regulatory laws and internal management systems; mark the violations and provide compliance adjustment suggestions for preliminary financial solutions that fail compliance verification; and store compliance verification history records to provide data support for compliance audits.

[0013] Furthermore, the terminal module also includes an interaction submodule, which is used to receive user instructions to adjust the financial plan, transmit the adjustment instructions to the application platform server, receive the final financial plan and related reports returned by the application platform server, and support users to query, download and print the plan content.

[0014] Compared with the prior art, the beneficial effects of the present invention are: Firstly, in this invention, during the application of this technical solution, a data storage module is set up to centrally manage various basic and rule data. This is combined with a built-in module on the application platform server to complete data initialization and loading. Then, the solution generation module extracts product attributes based on user-inputted business needs, risk preferences, and other information, accurately matching suitable target templates, parameters, and risk assessment models. Simultaneously, the calculation engine module generates a set of calculation formulas containing complete calculation logic according to a standardized process, and rounds the parsed data to ensure accuracy. This enables the orderly access and efficient management of various types of data during use, providing a precise data foundation for subsequent risk assessment. This achieves the effect of improving data utilization efficiency and ensuring the accuracy of calculation results, solving the problems of scattered data leading to chaotic access and large calculation errors affecting assessment results in existing technologies. Secondly, in this invention, during the application of this technical solution, a risk engine module is set up to conduct multi-dimensional risk calculation, multi-scenario simulation, and dynamic monitoring during the solution execution process. Combined with a value evaluation module, the solution value is systematically evaluated from multiple dimensions such as benefits, costs, efficiency, and strategy. At the same time, the solution generation module, together with the compliance verification module, verifies and optimizes the solution against regulatory laws and internal systems. This allows for accurate quantitative assessment of various types of risks involved in financial business, comprehensive measurement of the solution's overall value, and ensures the solution's compliance during use. This achieves the effects of strengthening the comprehensiveness of risk assessment, improving the scientific nature of solution decision-making, and avoiding compliance risks. It solves the problems of existing technologies where risk assessment only focuses on a single type and the results are lagging, value assessment has a single dimension and fragmented logic, and the lack of compliance verification in the solution easily leads to business risks. Thirdly, in this invention, during the application of this technical solution, the terminal module provides users with efficient interactive functions such as solution query, download, and adjustment command input. Combined with the model optimization module, which regularly analyzes historical data deviations to optimize the parameters of the risk assessment model and value evaluation algorithm, the efficiency of user interaction with the system can be improved during use. At the same time, the accuracy of the technical solution's evaluation and calculation is continuously optimized, thereby achieving the effect of optimizing the user experience and ensuring the long-term stability of the technical solution. This solves the problems of insufficient user interaction functions and cumbersome operation in the prior art, and the lack of iterative optimization mechanism for the evaluation model and algorithm, which leads to a decrease in accuracy. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the system module connection structure for determining the financial solution of the present invention. Detailed Implementation

[0016] Example Please see Figure 1 In this embodiment of the invention, a method and system for determining a financial scheme includes the following steps: Step 1: Construct and store a set of financial templates, template parameters, a set of risk assessment models, and a library of value assessment indicators. Specifically, classify and sort out the standardized processes and core elements of credit, investment, wealth management, financing, and other businesses according to the type of financial business. Based on this, design at least one financial template, and embed corresponding query tags in each financial template to identify business attributes. Collect various parameters involved in business operations, including basic data parameters and calculation rule parameters, to form a template parameter library and associate and match it with financial templates; Based on the types of financial risks, credit risk assessment models, market risk assessment models, liquidity risk assessment models, and operational risk assessment models are constructed respectively, and integrated to form a risk assessment model set. The models have preset parameter input interfaces and calculation logic. Key indicators are selected from four dimensions: revenue, cost, efficiency, and strategy. The results include: annualized rate of return and internal rate of return; cost indicators such as cost of capital and risk reserve ratio; efficiency indicators such as capital turnover and return on capital; and strategic value indicators such as market share growth rate and customer lifetime value. A value assessment indicator library is established and the calculation method and application scenarios of each indicator are defined. Step 2: Obtain product attributes. Product attributes include template attributes, parameter attributes, risk characteristic attributes, and value characteristic attributes. Based on the product attributes, determine the target template, target parameters, applicable risk assessment model, and value assessment indicators. The target template is a financial template that matches the query tags and template attributes. The target parameters are template parameters that match the parameter attributes. The applicable risk assessment model is a risk assessment model that matches the risk characteristic attributes. The value assessment indicators are the indicator library content that matches the value characteristic attributes. Step 3: Inject the target parameters into the target template to generate a set of calculation formulas. The set of calculation formulas covers various calculation logics and numerical derivation methods related to financial business, providing a basic calculation framework for subsequent risk calculation, value assessment, etc. Step 4: Conduct financial risk calculations, including credit risk calculation, market risk calculation, liquidity risk calculation, and operational risk calculation; Credit risk calculation is based on parameters such as customer credit rating, debt repayment ability indicators, historical performance record, and industry credit level. It uses a credit risk assessment model to calculate indicators such as default probability, default loss rate, and credit risk exposure, and achieves risk quantification through statistical analysis models and machine learning algorithms. Market risk calculation combines market factor fluctuation data such as interest rates, exchange rates, stock prices, and commodity prices, and calculates risk value, sensitivity indicators, scenario losses, etc. through market risk assessment models, using methods such as historical simulation and Monte Carlo simulation for measurement; Liquidity risk is calculated based on data such as asset liquidity, the matching degree of capital supply and demand, and the stability of financing channels. It uses a liquidity risk assessment model to calculate indicators such as liquidity gap, liquidity coverage ratio, and net stable funding ratio. Operational risk calculation is based on data such as internal process defects, human operational errors, system failures, and external events. The operational risk capital requirements are calculated using the basic indicator method, the standard method, and the advanced measurement method through the operational risk assessment model. Step 5: Conduct financial risk simulations, including multi-scenario simulations, stress tests, sensitivity analysis, and dynamic simulations; Multi-scenario simulation sets up a baseline scenario, an optimistic scenario, a pessimistic scenario, and an extreme scenario. The extreme scenario includes economic cycle fluctuations, policy and regulatory adjustments, and global emergencies. Corresponding parameters are configured for each scenario to simulate the risk status and return performance of financial solutions under different scenarios. Stress tests are designed with different levels of stress scenarios, including large-scale market factor fluctuations, large-scale defaults, and liquidity crises, to test the financial solutions’ ability to withstand extreme stress and assess the maximum possible loss and risk boundary. Sensitivity analysis targets key parameters that affect financial solutions, including interest rates, customer credit ratings, and market volatility. The parameter values ​​are adjusted to a certain extent to analyze the impact of parameter changes on the risk and return of the financial solutions, and to determine the sensitive parameters and their impact coefficients. Dynamic simulation, based on time series data, simulates the risk evolution trend of financial solutions at different future time periods and predicts the change path of risk indicators. Step Six: Develop financial risk response strategies, including risk avoidance, risk reduction, risk transfer, and risk acceptance; Risk avoidance involves taking measures such as refusing to conduct business or terminating cooperation with financial businesses or products whose risk levels exceed the acceptable range. Risk reduction can be achieved by adjusting the structure, parameters, or terms of financial solutions, optimizing customer eligibility criteria, diversifying investment portfolios, and setting risk limits. Risk transfer utilizes insurance, guarantees, and hedging tools to transfer part or all of the risk to a third party, thereby reducing one's own risk exposure. Hedging tools include forward contracts, options, and swaps. Risk tolerance involves developing a risk tolerance plan, setting aside risk reserves, and establishing risk response plans for risks that are within an acceptable range, in order to deal with potential risk losses. Step 7: Conduct a profitability review. Using the profitability model in the calculation formula set, calculate indicators such as expected annualized rate of return, internal rate of return, and net present value. Compare the industry benchmark rate of return and the historical return level of similar projects to analyze the stability of returns. Return stability includes return volatility and maximum drawdown rate. Assess the profitability potential and sustainability of the project. Step 8: Conduct a cost-benefit and capital efficiency review, calculate the cost of capital, risk cost, and operating cost. The cost of capital includes the financing interest rate and the reserve requirement ratio; the risk cost includes the amount of risk reserve provision and the expected loss compensation cost; and the operating cost includes human resource input and system maintenance expenses. Calculate the cost-benefit ratio and the risk-adjusted return on capital. The cost-benefit ratio is the ratio of revenue to total cost. Assess the matching degree between costs and benefits and identify cost optimization opportunities. At the same time, analyze the capital occupation of the plan. Capital occupation includes the impact of risk-weighted asset size and capital adequacy ratio. Calculate the capital turnover rate and economic value added. The capital turnover rate is the ratio of operating revenue to average capital occupation. Compare with capital regulatory requirements and internal capital allocation efficiency standards to assess capital utilization efficiency and value-added capacity. Step 9: Conduct a strategic value and risk-return balance assessment. In conjunction with the market environment and the institution's strategic objectives, analyze the contribution of the plan to customer structure optimization, business structure upgrade, and market competitiveness enhancement. Customer structure optimization includes an increase in the proportion of high-net-worth customers, business structure upgrade includes an increase in the proportion of intermediary business revenue, and market competitiveness enhancement includes product differentiation advantages. Assess the fit between the plan and the long-term strategy and the potential synergistic value. Establish a risk-return matrix and couple risk indicators with return indicators for analysis. Risk indicators include value at risk and default rate, while return indicators include rate of return and net present value. Calculate the Sharpe ratio and information ratio. The Sharpe ratio is the ratio of excess return to risk volatility, and the information ratio is the ratio of excess return to tracking error. Evaluate the cost-effectiveness of returns at a specific risk level and clarify the rationality of the risk compensation of the plan. Step 10: Generate a financial solution based on the set of calculation formulas, financial risk calculation results, financial risk simulation results, financial risk response strategies, and financial value assessment. The financial solution includes a financial product or business plan, corresponding calculation formulas, risk assessment report, risk simulation analysis report, risk response plan, and value assessment report. All parts are interconnected and together constitute a complete solution system. Step 11: Conduct compliance verification of the generated financial plan. Based on financial regulatory laws and internal management systems, comprehensively check whether the terms of the plan comply with regulatory and internal regulations, whether the risk indicators are within the scope of compliance, and whether there are any illegal assumptions or calculations in the value assessment logic. Generate a compliance verification report and identify compliant and non-compliant items. Step 12: Adjust and optimize the financial plan based on the compliance verification report, formulate specific adjustment measures for non-compliant items, modify the plan terms, adjust the calculation method of risk indicators or revise the value assessment logic to ensure that the plan fully complies with compliance requirements. Step 13: Conduct a second verification of the adjusted and optimized financial plan. Check again whether the plan fully complies with the compliance verification rules. If there are still non-compliant items, return to Step 12 to continue the adjustment until the plan is fully compliant. Step Fourteen: Output the final financial solution, and store various data and reports generated during the solution generation process, including calculation formula set, risk calculation results, simulation data, response strategies, value assessment content, compliance verification records, etc., to provide data support for subsequent solution traceability, analysis and optimization.

[0017] Please see Figure 1The system includes an application platform server, a data storage module connected to the application platform server (the data storage module is either located inside the application platform server or set up independently), and the application platform server connected to a terminal module via the Internet. The terminal module is used to send data request conditions to the backend application platform server and receive financial solutions returned by the application platform server. The data request conditions include financial business requirements, risk preferences, customer information, and value assessment requirements. The application platform server has a built-in computing engine module, risk engine module, value assessment module, solution generation module, and compliance verification module. The application platform server is used to store financial template sets and modules... The system includes board parameters, a risk assessment model set, a value assessment indicator library, a value evaluation algorithm, and a compliance verification rule library. The risk assessment model set includes credit risk assessment models, market risk assessment models, liquidity risk assessment models, operational risk assessment models, etc., covering various risk measurement algorithms and model parameters. The financial template set includes at least one financial template covering various financial businesses such as credit, investment, wealth management, and financing; each financial template includes at least one query tag. The value assessment indicator library includes return on investment indicators, cost indicators, efficiency indicators, and strategic value indicators. Return on investment indicators include annualized return and internal rate of return; cost indicators include cost of capital ratio and risk reserve ratio; efficiency indicators... The system includes capital turnover rate and return on capital; strategic value indicators include market share growth rate and customer lifetime value; the compliance verification rule base includes financial regulatory provisions and internal management system provisions; product attributes are obtained through the solution generation module, and based on these product attributes, target templates, target parameters, applicable risk assessment models, and value assessment indicators are determined. The product attributes include template attributes, parameter attributes, risk characteristic attributes, and value characteristic attributes. The target template is a financial template whose query tags match the template attributes; the target parameters are template parameters that match the parameter attributes; and the applicable risk assessment model is a risk assessment model that matches the risk characteristic attributes. The model comprises a value assessment index library that matches the value characteristic attributes; the target parameters are injected into the target template through the calculation engine module to generate a set of calculation formulas; the applicable risk assessment model is called through the risk engine module, and financial risk is calculated in combination with the set of calculation formulas, outputting quantitative results of various risk indicators; financial risk simulation is performed based on the financial risk calculation results through the risk engine module, including multi-scenario simulation, stress testing, sensitivity analysis, dynamic simulation, etc., generating a simulation analysis report; and risk response strategies are formulated based on the financial risk calculation and simulation results through the risk engine module, including risk avoidance conditions, risk reduction measures, risk transfer plans, risk tolerance plans, etc.The value assessment module utilizes a value evaluation indicator library and algorithm to conduct financial value assessments based on a set of calculation formulas, risk calculation results, and risk response strategies. It outputs profitability, cost-effectiveness, capital efficiency, strategic value, and risk-return balance assessments. The solution generation module integrates the calculation formulas, risk calculation results, risk simulation analysis reports, risk response plans, and value assessment results to generate preliminary financial solutions. These preliminary financial solutions include financial product or business plans, corresponding calculation formulas, risk assessment reports, simulation analysis reports, response plans, and value assessment reports. Finally, a compliance verification module... The module invokes the compliance verification rule library to perform compliance verification on the preliminary financial plan and generates a compliance verification report. The plan generation module then adjusts and optimizes the preliminary financial plan based on the compliance verification report, outputting the final financial plan. The data storage module stores relevant data, including financial template sets, template parameters, risk assessment model sets and algorithm parameters, value assessment indicator library, compliance verification rule library, product attribute database, market data, customer information database, risk calculation results, simulation process data, value evaluation data, compliance verification data, and historical financial plan archives. Market data includes interest rates, exchange rates, and stock prices.

[0018] Please see Figure 1The computing engine module is further configured to: acquire an input information set, the input information set including at least one input information, wherein the input information is a calculation formula corresponding to a financial product or business with embedded relevant information, the calculation formula of the financial product or business is configured with a wrapper and a parameter expression; traverse the input information set, parse the input information respectively, and generate parsed data; calculate the parsing result based on the parsed data; the computing engine module is further configured to: traverse the input information in the input information set; determine whether the input information has parameters; if the input information has parameters, continue to track the input information in the input information set; if the input information does not have parameters, calculate the input information to obtain calculated input information, store the calculated input information, and obtain temporary data;The device parses the temporarily stored data to generate parsed data. During application, it sets up an application platform server data storage module and a terminal module. This allows the terminal module to send data request conditions containing financial business needs, risk preferences, customer information, and value assessment requirements to the application platform server. The application platform server stores financial template sets, template parameters, risk assessment model sets, value assessment indicator libraries, value evaluation algorithms, and compliance verification rule libraries. The risk assessment model set includes credit risk assessment models, market risk assessment models, liquidity risk assessment models, operational risk assessment models, and various risk measurement algorithms and model parameters. The financial template set includes financial templates related to credit, investment, wealth management, and financing businesses with query tags. The value assessment indicator library includes yield indicators, cost indicators, efficiency indicators, and strategic value indicators, each with specific content. The compliance verification rule library includes financial regulatory provisions and internal management system provisions. The application platform server can also obtain product attributes through the solution generation module and determine the applicable risk assessment model and value assessment indicators for the target template and target parameters based on the product attributes. The calculation engine module injects the target parameters into the target template to generate a calculation formula set. The risk engine module calls the applicable model and combines it with the calculation formula set to perform financial risk calculation and output quantitative results. The system utilizes calculation results to perform multi-scenario simulations, stress tests, sensitivity analyses, and dynamic simulations to generate simulation analysis reports and formulate risk response strategies. The value assessment module calls upon an indicator library and algorithms to conduct financial value assessments, outputting multi-dimensional assessment results. The solution generation module integrates various results to generate preliminary financial solutions. The compliance verification module then calls upon a rule library to generate a compliance verification report, adjusting and optimizing the preliminary solution to output the final financial solution. The data storage module stores various relevant data, including financial template sets, template parameters, risk assessment model sets and algorithm parameters, value assessment indicator libraries, compliance verification rule libraries, product attribute databases, market data, customer information databases, risk calculation results, simulation process data, value assessment data, compliance verification data, and historical financial solution archives. Market data includes interest rates, exchange rates, and stock prices. This setup makes the financial solution generation process more systematic and comprehensive, accurately matching business needs for risk assessment and value assessment. It ensures that the generated financial solutions meet compliance requirements and cover various financial business scenarios, effectively improving the accuracy and applicability of financial solution generation. This meets the scientific and compliant needs of actual financial business. Simultaneously, the collaborative operation of each module makes the entire process coherent and efficient, reducing redundant steps in the solution generation process and ensuring the smoothness and reliability of the financial solution from input to final output.

[0019] Please see Figure 1The computation engine module is further configured to: traverse the input information in the input information set; determine whether the wrapper of the input information is a preset wrapper; when the wrapper of the input information is a preset wrapper, parse out the Boolean value of the input information and the result branch of the input information; generate the parsed result according to the ternary operation rule, the Boolean value, and the result branch; the computation engine module is further configured to: round the parsed data according to the preset rounding rule to obtain the rounded data; calculate the parsed result based on the rounded data; the risk engine module is further configured to: use the rounded data as input when calculating financial risk. The parameters are quantified using a risk assessment model. During financial risk simulation, the adjusted data is combined with scenario parameters to improve the reliability of the simulation results. When formulating risk response strategies, response thresholds are set based on the adjusted risk data. The value assessment module also uses the adjusted data as the basis for calculating value assessment indicators, ensuring the accuracy of calculations for indicators such as returns and costs, and improving the credibility of the value assessment results. The risk engine module also includes a risk monitoring submodule, used to collect dynamic data in real time during the execution of financial solutions. This dynamic data includes market data changes, customer performance, and capital flow information; and is integrated with risk calculation and simulation results. When the actual data exceeds the preset warning range, a risk warning is automatically triggered, and risk response adjustment suggestions are pushed to the terminal module. During the application of this device, by setting up a calculation engine module, a risk engine module, and a value assessment module, the calculation engine module can first traverse the input information set to determine whether the wrapper of the input information is a preset wrapper. When the wrapper is a preset wrapper, the Boolean value and result branch of the input information are parsed out. Then, according to the ternary operation rules, the Boolean value, and the result branch, the parsed result is generated. Subsequently, the parsed data can be rounded according to the preset rounding rules to obtain the rounded data, and the final result is calculated based on the rounded data. Analysis results: When calculating financial risks, the risk engine module uses the rounded data as input parameters and combines it with the risk assessment model to quantify indicators. When simulating financial risks, it combines the rounded data with scenario parameters. When formulating risk response strategies, it sets response thresholds based on the rounded risk data. At the same time, the risk monitoring sub-module included in the risk engine module can collect dynamic data in real time during the execution of financial solutions. This dynamic data includes changes in market data, customer performance, and capital flow information. After collection, it is compared with the risk calculation and simulation results. When the actual data exceeds the preset warning range, a risk warning is automatically triggered, and risk response adjustment suggestions are pushed to the terminal module.The value assessment module uses the rounded data as the basis for calculating value evaluation indicators, ensuring the accuracy of calculations for indicators such as revenue and cost. This setup guarantees the standardization and accuracy of data parsing and calculation in the calculation engine module, providing reliable data support for subsequent risk assessment and value assessment. The risk engine module leverages the rounded data to improve the accuracy of risk quantification, simulation, and strategy formulation. The risk monitoring submodule implements dynamic risk management during the implementation of the plan, providing timely warnings and adjustment directions. The value assessment module, based on accurate data, ensures the accuracy of value evaluation indicator calculations, thereby enhancing the credibility of the value assessment results. Overall, this makes the device more stable and reliable in data processing, risk management, and value assessment, meeting the requirements for data accuracy and risk management in the financial plan generation process.

[0020] Please see Figure 1The application platform server also includes a model optimization module, used to periodically analyze the deviation between historical risk data and model prediction data, and the deviation between the actual value of historical value indicators and the predicted value of the assessment, to evaluate the effectiveness of the risk assessment model and value assessment algorithm. When the deviation exceeds a set threshold, the model parameters and algorithm coefficients are optimized and adjusted, and the risk assessment model and value assessment algorithm are updated to improve the accuracy of risk assessment and value assessment. The compliance verification module is also used to: periodically update the compliance verification rule base, synchronize the latest financial regulatory laws and internal management systems; mark the violations and provide compliance adjustment suggestions for preliminary financial plans that fail compliance verification; and store compliance verification data. Historical records provide data support for compliance audits. The terminal module also includes an interaction submodule for receiving user adjustment instructions for financial solutions and transmitting these instructions to the application platform server; receiving the final financial solution and related reports returned by the application platform server; and supporting users to query, download, and print the solution content. During application, this device, through the setting of interaction submodules for the model optimization module, compliance verification module, and terminal module, enables each module to operate collaboratively according to its predetermined functions. The model optimization module periodically analyzes the deviation between historical risk data and model prediction data, and the deviation between the actual value of historical value indicators and the predicted value of the evaluation. Based on these deviations... The system assesses the effectiveness of the risk assessment model and value evaluation algorithm. When the deviation exceeds a set threshold, it optimizes and adjusts the model parameters and algorithm coefficients, thereby updating the risk assessment model and value evaluation algorithm. The compliance verification module regularly updates the compliance verification rule base to synchronize with the latest financial regulatory laws and internal management systems. Furthermore, when performing compliance verification on preliminary financial solutions, it marks violations and provides compliance adjustment suggestions if the solution fails. It also stores compliance verification history to provide data support for compliance audits. The terminal module's interaction submodule receives user instructions to adjust financial solutions and transmits these instructions to the application platform server. It can also receive the final financial solutions and related reports returned by the application platform server, and supports users to query, download and print the solution content. Through this setting, the model optimization module can continuously improve the accuracy of risk assessment and value evaluation, and ensure the reliability of subsequent risk assessment and value judgment. The compliance verification module can ensure that the financial solution always complies with the latest regulatory requirements and internal systems, reduce the risk of violations and provide a basis for auditing. The interaction sub-module improves the convenience of operation between users and the system, and meets users' needs for adjusting, querying and saving solutions. Overall, it makes the device more in line with the needs of actual business scenarios during use, and improves the stability and practicality of operation.

[0021] The working principle of this invention is as follows: In the initial stage of use, this technical solution requires the completion of preliminary data preparation and storage, as well as user requirement input. First, the data storage module of this technical solution needs to store all the basic data and rule data required for subsequent business operations. The application platform server, through internal logic, categorizes and sorts the standardized processes and core elements of businesses such as credit, investment, wealth management, and financing according to financial business types. It designs at least one financial template and embeds corresponding query tags to integrate it into a financial template set. It collects various business parameter items to form a template parameter library and associates and matches them with the financial templates. Based on four risk types, it constructs corresponding risk assessment models and integrates them into a risk assessment model set. It selects key indicators from four dimensions to establish a value assessment indicator library and collects legal clauses to form a compliance verification rule library. All data is stored in the data storage module. At the same time, the application platform server's built-in calculation engine module, risk engine module, value evaluation module, solution generation module, and model optimization module call various data in the data storage module through internal instructions to complete the initial loading. To ensure that each module can access the required data, the user input phase begins. The terminal module includes an interactive sub-module for users to input data request conditions including financial business needs, risk preferences, customer information, and value assessment requirements. The terminal module sends these conditions to the application platform server. After receiving the data, the solution generation module extracts product attributes, including template attributes, parameter attributes, risk characteristic attributes, and value characteristic attributes, from the data request conditions. Then, it calls the financial template set, template parameter library, risk assessment model set, and value assessment indicator library in the data storage module. Based on attribute matching, it determines the target template, target parameters, applicable risk assessment model, and value assessment indicator. In this phase, the data storage module centrally stores various types of data, avoiding the chaotic calling problem caused by data dispersion in existing methods. The solution generation module accurately matches the required templates and models based on attributes, solving the defects of arbitrary template selection and poor model adaptability in existing methods, and providing a precise data and model foundation for subsequent processes. After completing the preliminary preparations, this technical solution enters the calculation formula generation and risk processing stage. The calculation engine module receives the target template and target parameters from the solution generation module, injects the target parameters into the target template, and generates a set of calculation formulas covering various calculation logics of financial business according to the built-in calculation logic. At the same time, the calculation engine module obtains the input information set of financial business calculation formulas containing embedded relevant information and configured wrapper symbols and parameter expressions. It traverses this set to determine whether the input information contains parameters. If parameters exist, it continues to track them. If not, it calculates and stores them as temporary data. It parses and generates parsed data, then determines whether the wrapper symbol is a preset wrapper symbol. If so, it parses the Boolean value and result branch and generates the parsed result according to the ternary operation rule. Finally, it modifies the preset data. The rounding rules round the parsed data to obtain the rounded data and calculate the final parsing result, which is then synchronized to the risk engine module and the value assessment module. The risk engine module then receives the calculation formula set and rounded data from the calculation engine module, and calls the applicable risk assessment model determined in the data storage module to perform financial risk calculations. Credit risk calculation uses customer-related parameters and a credit risk assessment model to calculate key indicators, which are then quantified using statistical analysis models and machine learning algorithms. Market risk calculation combines market factor volatility data and a market risk assessment model to calculate indicators, employing historical simulation, Monte Carlo simulation, and other methods for measurement. Liquidity risk calculation uses asset and capital-related data and a liquidity risk assessment model. The indicators and operational risk calculations utilize internal and external data, employing an operational risk assessment model and three methods to calculate capital requirements. After risk calculation, the risk engine module performs financial risk simulation based on the results. Multi-scenario simulation sets four scenarios and configures parameters to simulate risk and return performance. Stress testing designs three stress scenarios to test resilience under extreme stress. Sensitivity analysis adjusts key parameters to analyze the impact on risk and return. Dynamic simulation predicts risk evolution trends based on time series data. Subsequently, risk response strategies are formulated based on the risk calculation and simulation results. Risk avoidance involves refusing to engage in or terminating cooperation; risk reduction involves adjusting the scheme structure; and risk transfer utilizes insurance guarantees and hedging tools to transfer risk. The system is subject to planning and reserve funds; simultaneously, the risk engine module includes a risk monitoring submodule that collects dynamic data in real time during the execution of the plan, compares it with the risk calculation and simulation results, and triggers an alert and pushes suggestions to the terminal module when the data exceeds the warning range. This technical solution generates an accurate set of calculation formulas and rounded data through the calculation engine module, providing an accurate data foundation for risk calculation and solving the problem of large calculation data errors in existing methods. The multi-dimensional risk assessment and simulation system built by the risk engine module makes up for the shortcomings of existing methods that only focus on a single risk type and lack dynamic simulation. The dynamic monitoring implemented by the risk monitoring submodule solves the problem of risk control gaps in existing methods, ensuring that the risks of the financial plan are controllable throughout its entire life cycle. After risk mitigation, this technical solution enters the value assessment and solution generation phase. The value assessment module receives the calculation formula set and rounded data from the calculation engine module, as well as the risk calculation results and risk response strategies from the risk engine module. It then calls upon the value assessment indicator library in the data storage module to conduct a multi-dimensional value assessment. The profitability assessment calculates relevant profitability indicators and compares and analyzes profitability stability. The cost-benefit and capital efficiency assessment calculates three types of costs and related ratios to evaluate cost-benefit matching and capital utilization efficiency. The strategic value and risk-return balance assessment analyzes the solution's contribution to customer structure, business structure, and competitiveness, and establishes a matrix to calculate ratios and evaluate the cost-effectiveness of returns. The solution generation module receives the calculation formula set from the calculation engine module, the risk calculation results and simulation results and response strategies from the risk engine module, and the value assessment results from the value assessment module. It integrates these contents to generate a solution containing financial statements. This technical solution generates a preliminary financial plan, including product or business solutions, calculation formulas, risk assessment reports, risk simulation analysis reports, risk response plans, and value assessment reports. The plan generation module then calls the compliance verification module, which in turn uses the compliance verification rule base in the data storage module to verify the preliminary financial plan's compliance. This generates a verification report containing compliant and non-compliant items, marks violations, and provides adjustment suggestions. The plan generation module then adjusts and optimizes the preliminary financial plan based on the report until it is fully compliant. This technical solution, through a multi-dimensional value assessment system built by the value assessment module, solves the problems of single-dimensional value assessment and fragmented logic in existing methods. The comprehensive value assessment provides sufficient basis for plan decisions. The collaborative operation of the plan generation module and the compliance verification module ensures that the final plan complies with regulatory and internal requirements, avoiding business risks caused by the lack of compliance considerations in existing methods. The solution generation module transmits the final compliant financial solution to the application platform server. The application platform server then sends the solution to the terminal module. The terminal module receives the solution and related reports through the interaction submodule. Users can query, download, and print the solution through the interaction submodule. If adjustments are needed, users input commands through the interaction submodule, which are then transmitted to the application platform server. The application platform server then drives the relevant modules to make adjustments. Simultaneously, the model optimization module periodically retrieves historical risk data, model prediction data, actual values ​​of historical value indicators, and predicted values ​​from the data storage module. It analyzes various data deviations to assess the risk assessment model and value assessment algorithm. The effectiveness of the model is assessed. When the deviation exceeds a threshold, the model parameters and algorithm coefficients are optimized and adjusted. The updated model and algorithm are then re-stored in the data storage module. This technical solution achieves efficient user interaction through the terminal module, solving the problem of poor user-system interaction in existing methods and improving the efficiency of solution delivery and adjustment. The model optimization module implements iterative model optimization, which solves the problem of decreased evaluation accuracy caused by the lack of a model update mechanism in existing methods. This ensures that the evaluation and calculation of subsequent financial solutions always maintain high accuracy. The collaborative operation of each module improves the efficiency and quality of financial solution generation and meets the comprehensive needs of financial business in complex market environments.

Claims

1. A method for determining a financial scheme, characterized in that, Includes the following steps: Step 1: Construct and store a set of financial templates, template parameters, a set of risk assessment models, and a library of value assessment indicators. Specifically, classify and sort out the standardized processes and core elements of credit, investment, wealth management, financing, and other businesses according to the type of financial business. Based on this, design at least one financial template, and embed corresponding query tags in each financial template to identify business attributes. Collect various parameters involved in business operations, including basic data parameters and calculation rule parameters, to form a template parameter library and associate and match it with financial templates; Based on the types of financial risks, credit risk assessment models, market risk assessment models, liquidity risk assessment models, and operational risk assessment models are constructed respectively, and integrated to form a risk assessment model set. The models have preset parameter input interfaces and calculation logic. Key indicators are selected from four dimensions: revenue, cost, efficiency, and strategy. The results include: annualized rate of return and internal rate of return; cost indicators such as cost of capital and risk reserve ratio; efficiency indicators such as capital turnover and return on capital; and strategic value indicators such as market share growth rate and customer lifetime value. A value assessment indicator library is established and the calculation method and application scenarios of each indicator are defined. Step 2: Obtain product attributes. Product attributes include template attributes, parameter attributes, risk characteristic attributes, and value characteristic attributes. Based on the product attributes, determine the target template, target parameters, applicable risk assessment model, and value assessment indicators. The target template is a financial template that matches the query tags and template attributes. The target parameters are template parameters that match the parameter attributes. The applicable risk assessment model is a risk assessment model that matches the risk characteristic attributes. The value assessment indicators are the indicator library content that matches the value characteristic attributes. Step 3: Inject the target parameters into the target template to generate a set of calculation formulas. The set of calculation formulas covers various calculation logics and numerical derivation methods related to financial business, providing a basic calculation framework for subsequent risk calculation, value assessment, etc. Step 4: Conduct financial risk calculations, including credit risk calculation, market risk calculation, liquidity risk calculation, and operational risk calculation; Credit risk calculation is based on parameters such as customer credit rating, debt repayment ability indicators, historical performance record, and industry credit level. It uses a credit risk assessment model to calculate indicators such as default probability, default loss rate, and credit risk exposure, and achieves risk quantification through statistical analysis models and machine learning algorithms. Market risk calculation combines market factor fluctuation data such as interest rates, exchange rates, stock prices, and commodity prices, and calculates risk value, sensitivity indicators, scenario losses, etc. through market risk assessment models, using methods such as historical simulation and Monte Carlo simulation for measurement; Liquidity risk is calculated based on data such as asset liquidity, the matching degree of capital supply and demand, and the stability of financing channels. It uses a liquidity risk assessment model to calculate indicators such as liquidity gap, liquidity coverage ratio, and net stable funding ratio. Operational risk calculation is based on data such as internal process defects, human operational errors, system failures, and external events. The operational risk capital requirements are calculated using the basic indicator method, the standard method, and the advanced measurement method through the operational risk assessment model. Step 5: Conduct financial risk simulations, including multi-scenario simulations, stress tests, sensitivity analysis, and dynamic simulations; Multi-scenario simulation sets up a baseline scenario, an optimistic scenario, a pessimistic scenario, and an extreme scenario. The extreme scenario includes economic cycle fluctuations, policy and regulatory adjustments, and global emergencies. Corresponding parameters are configured for each scenario to simulate the risk status and return performance of financial solutions under different scenarios. Stress tests are designed with different levels of stress scenarios, including large-scale market factor fluctuations, large-scale defaults, and liquidity crises, to test the financial solutions’ ability to withstand extreme stress and assess the maximum possible loss and risk boundary. Sensitivity analysis targets key parameters that affect financial solutions, including interest rates, customer credit ratings, and market volatility. The parameter values ​​are adjusted to a certain extent to analyze the impact of parameter changes on the risk and return of the financial solutions, and to determine the sensitive parameters and their impact coefficients. Dynamic simulation, based on time series data, simulates the risk evolution trend of financial solutions at different future time periods and predicts the change path of risk indicators. Step Six: Develop financial risk response strategies, including risk avoidance, risk reduction, risk transfer, and risk acceptance; Risk avoidance involves taking measures such as refusing to conduct business or terminating cooperation with financial businesses or products whose risk levels exceed the acceptable range. Risk reduction can be achieved by adjusting the structure, parameters, or terms of financial solutions, optimizing customer eligibility criteria, diversifying investment portfolios, and setting risk limits. Risk transfer utilizes insurance, guarantees, and hedging tools to transfer part or all of the risk to a third party, thereby reducing one's own risk exposure. Hedging tools include forward contracts, options, and swaps. Risk tolerance involves developing a risk tolerance plan, setting aside risk reserves, and establishing risk response plans for risks that are within an acceptable range, in order to deal with potential risk losses. Step 7: Conduct a profitability review. Using the profitability model in the calculation formula set, calculate indicators such as expected annualized rate of return, internal rate of return, and net present value. Compare the industry benchmark rate of return and the historical return level of similar projects to analyze the stability of returns. Return stability includes return volatility and maximum drawdown rate. Assess the profitability potential and sustainability of the project. Step 8: Conduct a cost-benefit and capital efficiency review, calculate the cost of capital, risk cost, and operating cost. The cost of capital includes the financing interest rate and the reserve requirement ratio; the risk cost includes the amount of risk reserve provision and the expected loss compensation cost; and the operating cost includes human resource input and system maintenance expenses. Calculate the cost-benefit ratio and the risk-adjusted return on capital. The cost-benefit ratio is the ratio of revenue to total cost. Assess the matching degree between costs and benefits and identify cost optimization opportunities. At the same time, analyze the capital occupation of the plan. Capital occupation includes the impact of risk-weighted asset size and capital adequacy ratio. Calculate the capital turnover rate and economic value added. The capital turnover rate is the ratio of operating revenue to average capital occupation. Compare with capital regulatory requirements and internal capital allocation efficiency standards to assess capital utilization efficiency and value-added capacity. Step 9: Conduct a strategic value and risk-return balance assessment. In conjunction with the market environment and the institution's strategic objectives, analyze the contribution of the plan to customer structure optimization, business structure upgrade, and market competitiveness enhancement. Customer structure optimization includes an increase in the proportion of high-net-worth customers, business structure upgrade includes an increase in the proportion of intermediary business revenue, and market competitiveness enhancement includes product differentiation advantages. Assess the fit between the plan and the long-term strategy and the potential synergistic value. Establish a risk-return matrix and couple risk indicators with return indicators for analysis. Risk indicators include value at risk and default rate, while return indicators include rate of return and net present value. Calculate the Sharpe ratio and information ratio. The Sharpe ratio is the ratio of excess return to risk volatility, and the information ratio is the ratio of excess return to tracking error. Evaluate the cost-effectiveness of returns at a specific risk level and clarify the rationality of the risk compensation of the plan. Step 10: Generate a financial solution based on the set of calculation formulas, financial risk calculation results, financial risk simulation results, financial risk response strategies, and financial value assessment. The financial solution includes a financial product or business plan, corresponding calculation formulas, risk assessment report, risk simulation analysis report, risk response plan, and value assessment report. All parts are interconnected and together constitute a complete solution system. Step 11: Conduct compliance verification of the generated financial plan. Based on financial regulatory laws and internal management systems, comprehensively check whether the terms of the plan comply with regulatory and internal regulations, whether the risk indicators are within the scope of compliance, and whether there are any illegal assumptions or calculations in the value assessment logic. Generate a compliance verification report and identify compliant and non-compliant items. Step 12: Adjust and optimize the financial plan based on the compliance verification report, formulate specific adjustment measures for non-compliant items, modify the plan terms, adjust the calculation method of risk indicators or revise the value assessment logic to ensure that the plan fully complies with compliance requirements. Step 13: Conduct a second verification of the adjusted and optimized financial plan. Check again whether the plan fully complies with the compliance verification rules. If there are still non-compliant items, return to Step 12 to continue the adjustment until the plan is fully compliant. Step Fourteen: Output the final financial solution, and store various data and reports generated during the solution generation process, including calculation formula set, risk calculation results, simulation data, response strategies, value assessment content, compliance verification records, etc., to provide data support for subsequent solution traceability, analysis and optimization.

2. A system for determining a financial scheme, employing the method for determining a financial scheme as described in any one of claims 1-9, characterized in that, The system includes an application platform server, a data storage module connected to the application platform server (the data storage module may be located inside the application platform server or set up independently), and an application platform server connected to a terminal module via the Internet. The terminal module is used to send data request conditions to the backend application platform server and receive financial solutions returned by the application platform server. The data request conditions include financial business requirements, risk preferences, customer information, and value assessment requirements. The application platform server has built-in computing engine modules, risk engine modules, value assessment modules, solution generation modules, and compliance verification modules. The application platform server is used to store financial template sets and templates. The system includes parameters, a risk assessment model set, a value assessment indicator library, a value evaluation algorithm, and a compliance verification rule library. The risk assessment model set includes credit risk assessment models, market risk assessment models, liquidity risk assessment models, operational risk assessment models, etc., covering various risk measurement algorithms and model parameters. The financial template set includes at least one financial template covering various financial businesses such as credit, investment, wealth management, and financing; each financial template includes at least one query tag. The value assessment indicator library includes return on investment indicators, cost indicators, efficiency indicators, and strategic value indicators. Return on investment indicators include annualized return and internal rate of return; cost indicators include cost of capital ratio and risk reserve ratio; efficiency indicators include... The system includes capital turnover rate and return on capital; strategic value indicators include market share growth rate and customer lifetime value; the compliance verification rule base includes financial regulatory provisions and internal management system provisions; product attributes are obtained through the solution generation module, and based on these product attributes, target templates, target parameters, applicable risk assessment models, and value assessment indicators are determined. The product attributes include template attributes, parameter attributes, risk characteristic attributes, and value characteristic attributes. The target template is a financial template whose query tags match the template attributes; the target parameters are template parameters that match the parameter attributes; and the applicable risk assessment model is a risk assessment model that matches the risk characteristic attributes. The model comprises: a value assessment index library matching the value characteristic attributes; the target parameters are injected into the target template through the calculation engine module to generate a set of calculation formulas; the applicable risk assessment model is called through the risk engine module, and financial risk is calculated in combination with the set of calculation formulas, outputting quantitative results of various risk indicators; financial risk simulation is performed based on the financial risk calculation results through the risk engine module, including multi-scenario simulation, stress testing, sensitivity analysis, dynamic simulation, etc., generating a simulation analysis report; and risk response strategies are formulated based on the financial risk calculation and simulation results through the risk engine module, including risk avoidance conditions, risk reduction measures, risk transfer plans, risk tolerance plans, etc.The value assessment module utilizes a value evaluation indicator library and algorithm to conduct financial value assessments based on a set of calculation formulas, risk calculation results, and risk response strategies. It outputs profitability, cost-effectiveness, capital efficiency, strategic value, and risk-return balance assessments. The solution generation module integrates the calculation formulas, risk calculation results, risk simulation analysis reports, risk response plans, and value assessment results to generate preliminary financial solutions. These preliminary financial solutions include financial product or business plans, corresponding calculation formulas, risk assessment reports, simulation analysis reports, response plans, and value assessment reports. Finally, a compliance verification module... The module invokes the compliance verification rule library to perform compliance verification on the preliminary financial plan and generates a compliance verification report. The plan generation module then adjusts and optimizes the preliminary financial plan based on the compliance verification report, outputting the final financial plan. The data storage module stores relevant data, including financial template sets, template parameters, risk assessment model sets and algorithm parameters, value assessment indicator library, compliance verification rule library, product attribute database, market data, customer information database, risk calculation results, simulation process data, value evaluation data, compliance verification data, and historical financial plan archives. Market data includes interest rates, exchange rates, and stock prices.

3. The system for determining financial schemes according to claim 2, characterized in that, The computing engine module is further configured to: acquire an input information set, the input information set including at least one input information, wherein the input information is a calculation formula corresponding to a financial product or business with embedded relevant information, the calculation formula of the financial product or business is configured with a wrapper and a parameter expression; traverse the input information set, parse the input information respectively, and generate parsed data; and calculate the parsing result based on the parsed data.

4. The system for determining financial schemes according to claim 3, characterized in that, The computing engine module is also used for: traversing the input information in the input information set; determining whether the input information has parameters; if the input information has parameters, continuing to track the input information in the input information set; if the input information does not have parameters, calculating the input information to obtain the calculated input information, storing the calculated input information to obtain temporary data; and parsing the temporary data to generate parsed data.

5. The system for determining financial schemes according to claim 3, characterized in that, The computing engine module is further configured to: traverse the input information in the input information set; determine whether the wrapper of the input information is a preset wrapper; when the wrapper of the input information is a preset wrapper, parse out the Boolean value of the input information and the result branch of the input information; and generate the parsed result according to the ternary operation rules, the Boolean value, and the result branch.

6. The system for determining a financial scheme according to any one of claims 3-5, characterized in that, The calculation engine module is further configured to: round the parsed data according to preset rounding rules to obtain rounded data; and calculate the parsing result based on the rounded data. The risk engine module is further configured to: use the rounded data as input parameters in financial risk calculations, and combine it with a risk assessment model for indicator quantification; combine the rounded data with scenario parameters in financial risk simulations to improve the reliability of simulation results; and set response thresholds based on the rounded risk data when formulating risk response strategies. The value assessment module is further configured to: use the rounded data as the basis for calculating value assessment indicators, ensuring the accuracy of calculations for indicators such as returns and costs, and improving the credibility of the value assessment results.

7. The system for determining financial schemes according to claim 2, characterized in that, The risk engine module also includes a risk monitoring submodule, which is used to collect dynamic data in real time during the execution of financial solutions. The dynamic data includes changes in market data, customer performance, and capital flow information. The data is compared with the risk calculation and simulation results. When the actual data exceeds the preset warning range, a risk warning is automatically triggered, and risk response adjustment suggestions are pushed to the terminal module.

8. The system for determining financial schemes according to claim 2, characterized in that, The application platform server also includes a model optimization module, which is used to periodically analyze the deviation between historical risk data and model prediction data, and the deviation between the actual value of historical value indicators and the predicted value of the evaluation, to evaluate the effectiveness of the risk assessment model and the value evaluation algorithm. When the deviation exceeds a set threshold, the model parameters and algorithm coefficients are optimized and adjusted, and the risk assessment model and value evaluation algorithm are updated to improve the accuracy of risk assessment and value evaluation.

9. The system for determining financial schemes according to claim 2, characterized in that, The compliance verification module is also used to: regularly update the compliance verification rule base, synchronize the latest financial regulatory laws and internal management systems; mark the violations and provide compliance adjustment suggestions for preliminary financial solutions that fail the compliance verification; and store the compliance verification history to provide data support for compliance audits.

10. The system for determining financial schemes according to claim 2, characterized in that, The terminal module also includes an interaction submodule, which is used to receive user instructions to adjust the financial plan, transmit the adjustment instructions to the application platform server, receive the final financial plan and related reports returned by the application platform server, and support users to query, download and print the plan content.

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