A consumer risk tolerance assessment system

Abstract

The application discloses a kind of consumer risk tolerance assessment systems, including data acquisition module: the age of collection consumer, family annual income, disposable income ratio, debt condition, to assess its financial stability;Record consumer investment history, investment product type, investment cycle, past investment return rate, to assess its investment experience and market familiarity;Through questionnaire, test mode collection consumer's tolerance to loss, the expectation to income, investment preference information, reflect its risk preference;Understand consumer's demand for investment liquidity, expected investment cycle, investment purpose;The beneficial effects of the application are: the application integrates multiple data sources, comprehensively assesses the risk tolerance of consumers from multiple angles, improves the accuracy and comprehensiveness of the assessment;Using machine learning technology, an adaptive risk tolerance assessment model is constructed, which can dynamically adjust as the consumer's situation changes, ensuring the timeliness of the assessment results.

Description

Technical Field

[0001] This invention belongs to the field of risk tolerance assessment technology, specifically relating to a consumer risk tolerance assessment system. Background Technology

[0002] Risk tolerance, also known as risk endurance, refers to the degree to which a company can accept differences in the process of achieving its goals. It is the limit set by a company based on its risk appetite to tolerate differences that may arise during the achievement of relevant goals. A higher risk tolerance indicates that the company has a stronger ability to withstand risks, and minor risks within the tolerance range can be addressed through routine measures. Internal auditors can determine the risk tolerance based on the company's operating environment, operating standards, capital structure, etc., and take different measures to deal with risk events when they occur. In a market environment, companies will face various risks. Internal auditors should combine the internal and external environments of the company to identify all risks that may threaten the company, identify the most important risks, and then conduct a quantitative analysis of the tolerance of these key risks to determine the acceptable risk range.

[0003] Risk tolerance assessment is crucial for consumers, as it helps them make more rational investment decisions. Through assessment, consumers can clarify the lower limit of investment losses they can accept, thus avoiding excessive anxiety or panic selling due to market fluctuations. In addition, the assessment results can help financial institutions such as banks better understand their customers' needs and recommend suitable investment products.

[0004] A method and system for assessing network service quality risk tolerance, disclosed in patent CN109327322B, includes a data acquisition module, a factor risk tolerance calculation module, and a service quality risk tolerance calculation module. The data acquisition module collects historical sample data of service operation according to factors affecting service quality risk. The factor risk tolerance calculation module processes the historical sample data according to the factors affecting service quality risk to obtain the risk tolerance of each factor. The service quality risk tolerance calculation module comprehensively calculates the risk tolerance of each factor to obtain the service quality risk tolerance. The data acquisition module includes a status data sub-unit. The patent includes an operation data subunit; the status data subunit collects historical data of multiple factors at a fixed period, and collects business status information at the same time according to whether the business is normal; the operation data subunit collects the risk factor values ​​and business status information corresponding to the operation time of operation and maintenance personnel. Although this patent has proposed a specific tolerance assessment method for network business quality risks, it still lacks a systematic and comprehensive solution for the comprehensive assessment of individual consumer risk tolerance. Especially in today's increasingly complex and volatile financial market, accurately assessing consumer risk tolerance is of great significance for protecting consumer rights and promoting the healthy development of the financial market. Summary of the Invention

[0005] The purpose of this invention is to provide a consumer risk tolerance assessment system. This system collects and analyzes multi-dimensional information about consumers and uses mathematical models and algorithms to achieve a precise quantitative assessment of consumers' risk tolerance.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a consumer risk tolerance assessment system, comprising...

[0007] Data collection module: Collects consumers' age, annual household income, disposable income ratio, and debt situation to assess their financial stability; records consumers' investment history, types of investment products, investment periods, and past investment returns to assess their investment experience and market familiarity; collects information on consumers' tolerance for losses, expectations for returns, and investment preferences through questionnaires and tests to reflect their risk appetite; understands consumers' need for investment liquidity, expected investment periods, and investment objectives.

[0008] Data Analysis and Evaluation Module: Employing machine learning algorithms, this module performs in-depth analysis of the data collected by the data acquisition module, identifying the correlation and weight between various factors and risk tolerance; based on the analysis results, it constructs a personalized risk tolerance assessment model that can dynamically adjust parameters to adapt to changes in different consumers; and it sets risk tolerance thresholds to classify consumers into different risk tolerance levels.

[0009] Results Application and Feedback Module: Based on the assessment results, provide consumers with personalized investment advice and asset allocation plans to ensure that their investment portfolio matches their risk tolerance; establish a continuous tracking mechanism to reassess the risk tolerance level of consumers as needed and adjust investment strategies in a timely manner; and provide an interactive interface to allow consumers to understand their own risk level.

[0010] As a preferred technical solution of the present invention, consumers are divided into different risk tolerance levels, including aggressive, stable, balanced, growth-oriented, and resilient.

[0011] As a preferred technical solution of the present invention, data on consumers' age, annual household income, disposable income ratio, and debt situation are collected to assess their financial stability. The method for achieving this is as follows:

[0012] Design a data collection questionnaire or form that includes the consumer's age, annual household income, proportion of disposable income, and debt status.

[0013] Data can be collected through online or offline channels. Online platforms can be used to publish questionnaires for consumers to fill out online. Offline channels can be used to distribute paper questionnaires or collect data face-to-face.

[0014] Verify the collected data to ensure its authenticity and accuracy; clean the data to remove duplicates, invalid or outliers.

[0015] The validated and cleaned data is organized into a structured format to facilitate subsequent analysis, and the data is stored in a secure and reliable database to ensure data integrity and security.

[0016] As a preferred technical solution of the present invention, a machine learning algorithm is used to perform in-depth analysis on the data collected by the data acquisition module to identify the correlation and weight between various factors and risk tolerance. The implementation method is as follows:

[0017] The collected data is preprocessed;

[0018] Choose a machine learning algorithm based on the nature of the problem and the characteristics of the data;

[0019] Features related to risk tolerance are extracted from the raw data, and feature selection methods are used to further filter and optimize the feature set.

[0020] The machine learning model is trained using preprocessed data and a selected feature set. The model's performance is evaluated using cross-validation, and the model parameters are adjusted to improve accuracy.

[0021] By analyzing the model's output, we can identify the correlation and weight between each factor and risk tolerance. Based on the magnitude of the correlation and weight, we can rank and classify the factors to better understand their impact on risk tolerance.

[0022] As a preferred technical solution of the present invention, the collected data is preprocessed, including data standardization and normalization; machine learning algorithms, including decision trees, random forests, and support vector machines.

[0023] As a preferred technical solution of the present invention, the feature selection method includes filtering, wrapping, and embedding.

[0024] As a preferred technical solution of the present invention, the method for constructing a personalized risk tolerance assessment model is as follows:

[0025] Based on the analysis results of machine learning algorithms, a framework for a personalized risk tolerance assessment model is constructed, and the model's input and output are determined.

[0026] Based on the distribution and importance of the data characteristics, set the parameters and thresholds of the model to ensure that the settings of the parameters and thresholds can reflect the actual situation and risk tolerance of consumers.

[0027] The model is validated using a validation dataset to assess its accuracy and reliability, and the model is optimized based on the validation results.

[0028] Implement a dynamic adjustment mechanism in the model to adapt to changes in different consumers and fluctuations in risk tolerance.

[0029] As a preferred technical solution of the present invention, the model is optimized based on the verification results, including adjusting parameters and adding features.

[0030] Compared with the prior art, the beneficial effects of the present invention are:

[0031] This invention integrates multiple data sources to comprehensively assess consumers' risk tolerance from multiple perspectives, thereby improving the accuracy and comprehensiveness of the assessment.

[0032] By utilizing machine learning technology, an adaptive risk tolerance assessment model was constructed, which can dynamically adjust as consumer circumstances change, ensuring the timeliness of the assessment results.

[0033] Based on the assessment results, investment strategies are tailored for each consumer, which not only improves investment efficiency but also enhances consumers' investment confidence and satisfaction. Attached Figure Description

[0034] Figure 1 This is a schematic diagram of the consumer risk tolerance assessment system of the present invention. Detailed Implementation

[0035] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] Example 1

[0037] Please see Figure 1 This is the first embodiment of the present invention, which provides a consumer risk tolerance assessment system, including...

[0038] Data collection module: Collects consumers' age, annual household income, disposable income ratio, and debt situation to assess their financial stability; records consumers' investment history, types of investment products, investment periods, and past investment returns to assess their investment experience and market familiarity; collects information on consumers' tolerance for losses, expectations for returns, and investment preferences through questionnaires and tests to reflect their risk appetite; understands consumers' need for investment liquidity, expected investment periods, and investment objectives.

[0039] Data Analysis and Evaluation Module: Employing machine learning algorithms, this module performs in-depth analysis of the data collected by the data acquisition module, identifying the correlation and weight between various factors and risk tolerance; based on the analysis results, it constructs a personalized risk tolerance assessment model that can dynamically adjust parameters to adapt to changes in different consumers; and it sets risk tolerance thresholds to classify consumers into different risk tolerance levels, including aggressive, stable, balanced, growth-oriented, and resilient.

[0040] Results Application and Feedback Module: Based on the assessment results, provide consumers with personalized investment advice and asset allocation plans to ensure that their investment portfolio matches their risk tolerance; establish a continuous tracking mechanism to reassess the risk tolerance level of consumers as needed and adjust investment strategies in a timely manner; and provide an interactive interface to allow consumers to understand their own risk level.

[0041] In this embodiment, preferably, data such as the consumer's age, annual household income, proportion of disposable income, and debt situation are collected to assess their financial stability. The method for achieving this is as follows:

[0042] Design a data collection questionnaire or form that includes the consumer's age, annual household income, proportion of disposable income, and debt status.

[0043] Data can be collected through online or offline channels. Online platforms can be used to publish questionnaires for consumers to fill out online. Offline channels can be used to distribute paper questionnaires or collect data face-to-face.

[0044] Verify the collected data to ensure its authenticity and accuracy; clean the data to remove duplicates, invalid or outliers.

[0045] The validated and cleaned data is organized into a structured format to facilitate subsequent analysis, and the data is stored in a secure and reliable database to ensure data integrity and security.

[0046] In this embodiment, preferably, a machine learning algorithm is used to perform in-depth analysis on the data collected by the data acquisition module to identify the correlation and weight between various factors and risk tolerance. The implementation method is as follows:

[0047] The collected data is preprocessed, including data standardization and normalization, to improve the efficiency and accuracy of the algorithm.

[0048] Choose an appropriate machine learning algorithm based on the nature of the problem and the characteristics of the data, such as decision tree, random forest, support vector machine, etc.

[0049] Extract risk tolerance-related features from the raw data, such as age, annual household income, disposable income ratio, and debt situation; further screen and optimize the feature set using feature selection methods (such as filtering, wrapping, and embedding).

[0050] Train a machine learning model using preprocessed data and a selected feature set; evaluate the model's performance using methods such as cross-validation; and adjust the model parameters to improve accuracy.

[0051] Use visualization tools or interpretive algorithms (such as LIME, SHAP, etc.) to interpret the model's output; validate the model to ensure its stability and reliability on different datasets;

[0052] By analyzing the model's output, we can identify the correlation and weight between each factor and risk tolerance. Based on the magnitude of the correlation and weight, we can rank and classify the factors to better understand their impact on risk tolerance.

[0053] In this embodiment, the preferred method for constructing a personalized risk tolerance assessment model is as follows:

[0054] Based on the analysis results of machine learning algorithms, a framework for a personalized risk tolerance assessment model is constructed, and the model's input and output are determined.

[0055] Based on the distribution and importance of the data characteristics, set the parameters and thresholds of the model; ensure that the setting of parameters and thresholds can reflect the actual situation and risk tolerance of consumers;

[0056] The model is validated using a validation dataset to assess its accuracy and reliability; the model is then optimized based on the validation results, such as by adjusting parameters or adding features.

[0057] Implementing a dynamic adjustment mechanism in the model to adapt to changes in different consumers and fluctuations in risk tolerance can be achieved by periodically updating data and retraining the model.

[0058] Example 2

[0059] Please see Figure 1 This is the second embodiment of the present invention, which is based on the previous embodiment, but differs in that:

[0060] The specific methods for establishing a continuous tracking mechanism are as follows:

[0061] Based on the rate of change in consumers' investment cycles and risk tolerance, set reasonable tracking cycles and frequencies; ensure that the tracking cycles and frequencies can promptly detect changes in consumers and make corresponding adjustments.

[0062] Regularly collect new consumer data during the tracking period, such as age increases and changes in annual household income; ensure the accuracy and completeness of the new data so that it can be used to update the evaluation model;

[0063] Use the updated data to reassess consumers’ risk tolerance; adjust investment recommendations and asset allocation strategies based on the results of the reassessment.

[0064] The results of the reassessment and corresponding investment recommendations will be fed back to consumers, and personalized investment and risk management advice will be provided based on consumer feedback and needs.

[0065] Continuously monitor changes in consumers' risk tolerance and investment portfolios, and update assessment models and investment strategies in a timely manner based on changes in market conditions and consumer needs.

[0066] Although embodiments of the invention have been shown and described in detail above, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A consumer risk tolerance assessment system, characterized in that: include Data collection module: Collects consumers' age, annual household income, disposable income ratio, and debt situation to assess their financial stability; Record consumers' investment history, types of investment products, investment periods, and past investment returns to assess their investment experience and market familiarity; collect information on consumers' tolerance for losses, expectations for returns, and investment preferences through questionnaires and tests to reflect their risk appetite. Understand consumers' needs for investment liquidity, expected investment period, and investment objectives; Data Analysis and Evaluation Module: Employing machine learning algorithms, this module performs in-depth analysis of the data collected by the data acquisition module, identifying the correlation and weight between various factors and risk tolerance; based on the analysis results, it constructs a personalized risk tolerance assessment model that can dynamically adjust parameters to adapt to changes in different consumers; Set risk tolerance thresholds to classify consumers into different risk tolerance levels; Results Application and Feedback Module: Based on the evaluation results, provide consumers with personalized investment advice and asset allocation solutions to ensure that their investment portfolio matches their risk tolerance. Establish a continuous tracking mechanism to reassess the risk tolerance of consumers as needed and adjust investment strategies in a timely manner; provide an interactive interface to allow consumers to understand their own risk level.

2. The consumer risk tolerance assessment system according to claim 1, characterized in that: Consumers are categorized into different risk tolerance levels, including aggressive, moderate, balanced, growth-oriented, and resilient.

3. The consumer risk tolerance assessment system according to claim 1, characterized in that: To assess a consumer's financial stability, data such as age, annual household income, percentage of disposable income, and debt status are collected. The method is as follows: Design a data collection questionnaire or form that includes the consumer's age, annual household income, proportion of disposable income, and debt status. Data can be collected through online or offline channels. Online platforms can be used to publish questionnaires for consumers to fill out online. Offline channels can be used to distribute paper questionnaires or collect data face-to-face. Verify the collected data to ensure its authenticity and accuracy; clean the data to remove duplicates, invalid or outliers. The validated and cleaned data is organized into a structured format to facilitate subsequent analysis, and the data is stored in a secure and reliable database to ensure data integrity and security.

4. The consumer risk tolerance assessment system according to claim 1, characterized in that: Machine learning algorithms are used to perform in-depth analysis on the data collected by the data acquisition module to identify the correlation and weight between various factors and risk tolerance. The implementation method is as follows: The collected data is preprocessed; Choose a machine learning algorithm based on the nature of the problem and the characteristics of the data; Features related to risk tolerance are extracted from the raw data, and feature selection methods are used to further filter and optimize the feature set. The machine learning model is trained using preprocessed data and a selected feature set. The model's performance is evaluated using cross-validation, and the model parameters are adjusted to improve accuracy. By analyzing the model's output, we can identify the correlation and weight between each factor and risk tolerance. Based on the magnitude of the correlation and weight, we can rank and classify the factors to better understand their impact on risk tolerance.

5. A consumer risk tolerance assessment system according to claim 4, characterized in that: The collected data is preprocessed, including data standardization and normalization; Machine learning algorithms, including decision trees, random forests, and support vector machines.

6. A consumer risk tolerance assessment system according to claim 4, characterized in that: Feature selection methods include filtering, wrapping, and embedding.

7. A consumer risk tolerance assessment system according to claim 1, characterized in that: The method for constructing a personalized risk tolerance assessment model is as follows: Based on the analysis results of machine learning algorithms, a framework for a personalized risk tolerance assessment model is constructed, and the model's input and output are determined. Based on the distribution and importance of the data characteristics, set the parameters and thresholds of the model to ensure that the settings of the parameters and thresholds can reflect the actual situation and risk tolerance of consumers. The model is validated using a validation dataset to assess its accuracy and reliability, and the model is optimized based on the validation results. Implement a dynamic adjustment mechanism in the model to adapt to changes in different consumers and fluctuations in risk tolerance.

8. A consumer risk tolerance assessment system according to claim 7, characterized in that: The model is optimized based on the validation results, including adjusting parameters and adding features.

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