Method and system for improving service efficiency of intelligent financial management business, and electronic equipment

Abstract

The invention provides an intelligent financial management business service efficiency improvement method and system and electronic equipment, and the method comprises the steps: S1, collecting customer data, and carrying out the cleaning and preprocessing of the data; s2, performing deep mining on the customer data; s3, according to the customer portrait, market dynamic and customer behavior changes are monitored in real time, and a recommendation strategy is dynamically adjusted; s4, constructing a risk assessment model, performing risk assessment on the recommended financial products, and setting a risk early warning mechanism; s5, introducing an intelligent customer service system to carry out real-time conversation with the customer, solving customer questions, collecting customer feedback, and optimizing a recommendation algorithm and a risk assessment model according to the customer feedback and transaction behavior data; and S6, regularly evaluating system performance and service effect, collecting user feedback and market data, identifying potential problems and improving space, and optimizing and iterating the system according to an evaluation result. According to the invention, accurate capture of deep demands of customers is realized, and more personalized financial management suggestions are provided.

Description

Technical Field

[0001] The present invention relates to the field of the intersection of financial technology and artificial intelligence, and in particular to a method, system and electronic equipment for improving the service efficiency of intelligent financial management business. Background Art

[0002] With the rapid development of financial technology, intelligence has become an important direction for the transformation and upgrading of bank wealth management business. At present, there are many products and services on the market that use AI technology to improve the efficiency of wealth management services, such as smart investment advisors and risk assessment models. These technologies provide customers with preliminary investment advice and risk assessments by automatically processing large amounts of data, but most of them remain at a relatively basic level and fail to fully tap the potential of AI technology.

[0003] Existing technical solutions mainly include two categories: one is the intelligent investment advisory system based on fixed rules or simple machine learning algorithms. This type of system usually generates recommendations based on the customer's static information (such as age, income, etc.) and preset investment strategies, and lacks the ability to respond to market dynamics and changes in customer behavior in real time; the other is the intelligent financial management platform that integrates more complex machine learning models. Although it can process data in more dimensions, there is still room for improvement in personalized recommendations, the accuracy of risk assessment, and the optimization of user experience.

[0004] The existing technical solutions have the following problems:

[0005] Insufficient personalization: Existing systems often make recommendations based on standardized models, ignoring customers’ unique needs and preferences, resulting in a lack of personalization in recommendation results.

[0006] Limited dynamic adjustment capabilities: The market environment and customer needs are constantly changing, and existing systems often have difficulty adapting to these changes in real time;

[0007] Incomplete risk assessment: Traditional risk assessment models often focus on a single dimension or static factors, which makes it difficult to fully reflect investment risks;

[0008] User experience needs to be optimized: The existing smart financial management services still need to be improved in terms of user experience, such as complicated operating procedures and unnatural interactions. Summary of the invention

[0009] In response to the technical problems existing in the prior art, the present invention provides a method, system and electronic device for improving the service efficiency of intelligent financial management business. The system uses big data analysis, machine learning and deep learning technologies to deeply explore and analyze customers' financial management needs, risk preferences and market trends, thereby providing more personalized, accurate and efficient financial management services.

[0010] According to a first aspect of the present invention, the present invention provides a method for improving the service efficiency of intelligent financial management business, comprising the following steps:

[0011] Step S1. Collect customer data, clean and pre-process the collected data;

[0012] Step S2. Conduct in-depth mining on the pre-processed customer data to identify the customer's investment habits, consumption patterns, and risk preference characteristics, build a customer portrait model based on a machine learning algorithm, segment the customers into different groups, and label each group accordingly;

[0013] Step S3. Generate a personalized list of financial product recommendations based on customer profiles, market trends, and product characteristics, monitor market dynamics and customer behavior changes in real time, and dynamically adjust recommendation strategies;

[0014] Step S4. Construct a risk assessment model, comprehensively consider market risk, credit risk, and liquidity risk factors, conduct risk assessment on recommended financial products, and set up a risk warning mechanism;

[0015] Step S5. Introduce an intelligent customer service system to conduct real-time conversations with customers, answer customer questions, collect customer feedback, and continuously optimize the recommendation algorithm and risk assessment model based on customer feedback and transaction behavior data;

[0016] Step S6. Regularly evaluate system performance and service effects, collect user feedback and market data, identify potential problems and room for improvement, and optimize and iterate the system based on the evaluation results.

[0017] Based on the above technical solution, the present invention can also make the following improvements.

[0018] Optionally, the collecting of customer data refers to extracting customer-related data in the target data source system from the source data system; the customer data includes basic customer information, financial status, historical transaction records and risk preference questionnaire; the cleaning and preprocessing of the collected data includes:

[0019] Eliminate erroneous, duplicate, incomplete or irrelevant customer data, standardize, verify and deduplicate customer data, and finally, standardize and normalize the collected customer data to facilitate subsequent data analysis and model training.

[0020] Optionally, building a customer portrait model based on a machine learning algorithm includes:

[0021] Use big data analysis technology to conduct in-depth mining of pre-processed customer data to identify customers' investment habits, consumption patterns, and risk preference characteristics;

[0022] Based on machine learning algorithms, a customer portrait model is constructed to segment customers into different groups and label each group accordingly; the machine learning algorithms include cluster analysis, decision trees, and neural networks.

[0023] Optionally, the segmenting of customers into different groups includes: segmenting into high-income, middle-income and low-income groups according to income level or segmenting into aggressive, steady and conservative groups according to risk preference.

[0024] Optionally, generating a personalized recommendation list of financial products based on the customer portrait, market trends, and product characteristics includes:

[0025] Based on the financial status, risk appetite and customer label assessment of customers, and in light of current economic trends, we use deep learning and machine learning algorithms to provide customized investment advice and data analysis, and offer robust financial products and low-risk investment solutions, including time deposits and bond funds.

[0026] Optionally, the risk assessment model is constructed to comprehensively consider market risk, credit risk, and liquidity risk factors, and the risk assessment of the recommended financial products includes:

[0027] Build a multi-dimensional and multi-level risk assessment model to systematically evaluate customers' risk tolerance, covering investment experience and risk preferences; analyze market, credit, liquidity and legal risks, use customer questionnaires to understand product features, and ensure that the investment matches one's own risk tolerance; when the assessment result exceeds the preset threshold, it automatically triggers an early warning and sends risk warning information to customers and financial advisors.

[0028] Optionally, before introducing the intelligent customer service system to conduct a real-time conversation with the customer, the process further includes:

[0029] Design an intuitive and easy-to-use user interface, provide convenient operation processes and clear interactive guidance, and ensure that customers can easily complete the selection and transaction of financial products;

[0030] Design real-time feedback and dynamic adjustment mechanisms to promptly collect and process customer feedback through the intelligent customer service system and user interaction interface, and reflect market changes and customer needs in real time to the intelligent customer service system.

[0031] Optionally, optimizing and iterating the system according to the evaluation result includes:

[0032] First, we analyze the evaluation data in depth to identify the strengths and weaknesses of the marketing activities;

[0033] Subsequently, based on data feedback, the marketing strategy is optimized, the target audience positioning is adjusted, the content creativity is improved, or the channel delivery is optimized; at the same time, algorithms are used to improve the accuracy of personalized recommendations and enhance user experience;

[0034] Finally, conduct small-scale tests to verify the results, and perform iterative optimization based on feedback to ensure that the system is in the best condition to help achieve marketing goals.

[0035] According to a second aspect of the present invention, there is provided a system for improving the service efficiency of intelligent financial management business, comprising:

[0036] Data collection and processing module, used to collect customer data, clean and pre-process the collected data;

[0037] The customer portrait building module is used to conduct in-depth mining of pre-processed customer data, identify customers' investment habits, consumption patterns, and risk preference characteristics, build a customer portrait model based on machine learning algorithms, segment customers into different groups, and label each group accordingly;

[0038] The intelligent recommendation module is used to generate a personalized list of financial product recommendations based on customer portraits, market trends, product features and other factors, monitor market dynamics and customer behavior changes in real time, and dynamically adjust recommendation strategies;

[0039] The risk assessment and early warning module is used to build a risk assessment model, comprehensively consider market risk, credit risk, and liquidity risk factors, conduct risk assessments on recommended financial products, and set up a risk early warning mechanism;

[0040] User interaction and feedback module, which is used to introduce intelligent customer service system to conduct real-time dialogue with customers, answer customer questions, collect customer feedback, and continuously optimize recommendation algorithms and risk assessment models based on customer feedback and transaction behavior data;

[0041] The continuous optimization and iteration module is used to regularly evaluate system performance and service effects, collect user feedback and market data, identify potential problems and areas for improvement, and optimize and iterate the system based on the evaluation results.

[0042] According to a third aspect of the present invention, there is provided an electronic device comprising a memory and a processor, wherein the processor is configured to implement the steps of a method for improving the service efficiency of an intelligent financial management business when executing a computer program stored in the memory.

[0043] Technical effects and advantages of the present invention:

[0044] The present invention provides a method, system and electronic device for improving the service efficiency of intelligent financial management business. The present invention uses deep learning technology to accurately capture the deep-level needs of customers and provide more personalized financial management advice. The present invention introduces a real-time data analysis and feedback mechanism to ensure that investment strategies and risk assessment models can be dynamically adjusted as the market and customer behavior change. The present invention constructs a multi-dimensional and multi-level risk assessment system, comprehensively considers market, product, and customer factors, and improves the comprehensiveness and accuracy of risk assessment. The present invention improves the convenience and satisfaction of user experience by optimizing the user interface design and introducing intelligent customer service functions. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution in the present invention is described clearly and completely. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] It is understandable that based on the defects in the background technology, the embodiment of the present invention proposes a method for improving the service efficiency of intelligent financial management business, and the method includes the following steps:

[0047] Step S1. Collect customer data, clean and pre-process the collected data;

[0048] Collecting customer data refers to extracting customer-related data from the target data source system from the source data system. Its purpose is to integrate customer data from scattered and heterogeneous data sources and provide a basis for subsequent data processing, analysis or storage.

[0049] Customer data includes but is not limited to basic customer information (such as age, gender, occupation), financial status (such as income, assets, liabilities), historical transaction records and risk preference questionnaires.

[0050] Cleaning the collected data includes: removing erroneous, duplicate, incomplete or irrelevant customer data to ensure data quality. Through standardization, verification, deduplication and other steps, the accuracy and reliability of customer data are improved to ensure data quality.

[0051] Preprocessing of the collected data includes standardizing and normalizing the collected information to facilitate subsequent data analysis and model training.

[0052] Step S2. Conduct in-depth mining on the pre-processed customer data to identify the customer's investment habits, consumption patterns, and risk preference characteristics, build a customer portrait model based on a machine learning algorithm, segment the customers into different groups, and label each group accordingly;

[0053] In this embodiment, the construction of a customer portrait model based on a machine learning algorithm includes:

[0054] Using big data analysis technology, we conduct in-depth mining of pre-processed customer data to identify characteristics such as customers' investment habits, consumption patterns, and risk preferences. Based on machine learning algorithms (such as cluster analysis, decision trees, neural networks, etc.), we build a customer portrait model, segment customers into different groups, and label each group accordingly.

[0055] It should be noted that big data analysis technology includes distributed storage, parallel computing and other technical means, which can realize the rapid collection, cleaning, pre-processing and in-depth analysis of massive customer data. This highly integrated data processing and analysis capability enables the system to respond quickly to market changes, accurately capture customer needs, and provide solid data support for personalized financial services.

[0056] Segment customers into different groups, for example, divide them into high, medium and low income categories according to their income level; divide them into aggressive, steady and conservative according to their risk preferences; and label each group accordingly.

[0057] Step S3. Based on the customer portrait, combined with market trends and product characteristics, a recommendation algorithm is used to generate a personalized recommendation list of financial products, monitor market dynamics and customer behavior changes in real time, and dynamically adjust the recommendation strategy;

[0058] For example: When customers are in the bank wealth management area of ​​mobile banking, the system evaluates customer labels such as customer financial status and risk preference, and provides customized investment advice and data analysis based on current economic trends, and provides stable financial products and low-risk investment plans, including time deposits, bond funds, etc.

[0059] The recommendation algorithm includes: intelligent algorithms such as deep learning and machine learning. The embodiment of the present invention constructs an accurate customer portrait model, a personalized recommendation algorithm and a risk assessment model. These algorithm models can automatically learn and adapt to market changes, continuously optimize recommendation strategies and risk assessment results, and ensure the accuracy and effectiveness of services. At the same time, the application of intelligent algorithms also significantly improves the work efficiency and automation of the system, and reduces the cost and error rate of manual intervention.

[0060] Step S4. Construct a risk assessment model, comprehensively consider market risk, credit risk, and liquidity risk factors, conduct risk assessment on recommended financial products, and set up a risk warning mechanism;

[0061] In this embodiment, a multi-dimensional and multi-level risk assessment model is constructed to systematically assess the risk tolerance of customers, including investment experience, risk preference, etc. At the same time, multi-dimensional risks such as market, credit, liquidity and law are analyzed. Customers should fill out the questionnaire truthfully, understand the characteristics of the product, and ensure that the investment matches their own risk tolerance. When the assessment result exceeds the preset threshold, an early warning is automatically triggered, and risk warning information is sent to the customer and the financial advisor.

[0062] Step S5. Introduce an intelligent customer service system to conduct real-time conversations with customers, answer customer questions, collect customer feedback, and optimize the recommendation algorithm and risk assessment model based on customer feedback and transaction behavior data;

[0063] Before introducing the intelligent customer service system to conduct real-time conversations with customers, it also includes:

[0064] Design an intuitive and easy-to-use user interface, provide convenient operation processes and clear interactive guidance, and ensure that customers can easily complete the selection and transaction of financial products;

[0065] At the same time, a real-time feedback and dynamic adjustment mechanism is designed to collect and process customer feedback in a timely manner through the intelligent customer service system and user interaction interface, and reflect market changes and customer needs in real time to the system. The system can automatically adjust the recommendation strategy and risk assessment model based on the feedback results to ensure that the service is always close to customer needs and market trends. This real-time feedback and dynamic adjustment mechanism enables the system to continuously improve service efficiency and customer satisfaction.

[0066] Through the above implementation, accurate positioning, personalized recommendations and efficient interaction can be achieved. This embodiment uses big data to analyze customer needs and accurately match products; uses system algorithms to achieve personalized recommendations and improve purchase conversion rates; at the same time, 24-hour online service, quickly responds to customer inquiries, and enhances the shopping experience. In addition, it can also automatically process orders, track logistics, simplify sales processes, and improve overall efficiency. Improve service efficiency and customer satisfaction.

[0067] Step S6. Regularly evaluate system performance and service effects, collect user feedback and market data, identify potential problems and room for improvement, and optimize and iterate the system based on the evaluation results.

[0068] The optimization and iteration of the system according to the evaluation results include:

[0069] First, you need to analyze and evaluate data in depth to identify the strengths and weaknesses of your marketing activities;

[0070] Subsequently, based on data feedback, the marketing strategy is optimized, such as adjusting the target audience positioning, improving content creativity, or optimizing channel delivery. At the same time, the technical level also needs to follow up, using algorithms to improve the accuracy of personalized recommendations and enhance user experience.

[0071] Finally, we conduct small-scale tests to verify the results and iterate and optimize based on the feedback to ensure that the system continues to evolve and achieve marketing goals in the best possible state. We ensure that the system can continue to adapt to market changes and customer needs.

[0072] During data processing and transmission, strict data encryption, access control and privacy protection technologies are used to ensure the security and privacy of customer data. At the same time, the system also has data backup and disaster recovery capabilities to prevent data loss or damage from causing losses to customers. This comprehensive data security measure enhances customers' trust in the system and their willingness to use it.

[0073] In addition, the embodiments of the present invention fully consider the scalability and flexibility of the system when designing, and adopt a modular and loosely coupled architecture design, so that the system can easily cope with business expansion and change requirements. At the same time, the system also provides a wealth of interfaces and configuration options, which is convenient for users to customize and expand according to actual needs. This scalability and flexibility enable the system to adapt to financial institutions and customer groups of different sizes and different needs, and improve the applicability and market competitiveness of the system.

[0074] Through the above steps, the method for improving the service efficiency of intelligent financial management business proposed in the present invention can achieve accurate capture of customer needs, personalized recommendations, risk assessment and early warning, and all-round optimization of user interaction and feedback, thereby significantly improving the service efficiency and customer satisfaction of bank financial management business.

[0075] According to a second aspect of the present invention, there is provided a system for improving the service efficiency of intelligent financial management business, comprising:

[0076] Data collection and processing module, used to collect customer data, clean and pre-process the collected data;

[0077] The customer portrait building module is used to conduct in-depth mining of pre-processed customer data, identify customers' investment habits, consumption patterns, and risk preference characteristics, build a customer portrait model based on machine learning algorithms, segment customers into different groups, and label each group accordingly;

[0078] The intelligent recommendation module is used to generate a personalized list of financial product recommendations based on customer portraits, market trends, product features and other factors, monitor market dynamics and customer behavior changes in real time, and dynamically adjust recommendation strategies;

[0079] The risk assessment and early warning module is used to build a risk assessment model, comprehensively consider market risk, credit risk, and liquidity risk factors, conduct risk assessments on recommended financial products, and set up a risk early warning mechanism;

[0080] User interaction and feedback module, which is used to introduce intelligent customer service system to conduct real-time dialogue with customers, answer customer questions, collect customer feedback, and continuously optimize recommendation algorithms and risk assessment models based on customer feedback and transaction behavior data;

[0081] The continuous optimization and iteration module is used to regularly evaluate system performance and service effects, collect user feedback and market data, identify potential problems and areas for improvement, and optimize and iterate the system based on the evaluation results.

[0082] It can be understood that the intelligent financial management business service efficiency improvement system provided by the present invention corresponds to the intelligent financial management business service efficiency improvement method provided by the aforementioned embodiments. The relevant technical features of the intelligent financial management business service efficiency improvement system can refer to the relevant technical features of the intelligent financial management business service efficiency improvement method, which will not be repeated here.

[0083] An embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned method for improving the service efficiency of intelligent financial management business are implemented.

[0084] Compared with the closest prior art, the embodiments of the present invention have:

[0085] More accurate personalized recommendations: The embodiment of the present invention uses a deep learning algorithm to build a customer profile. Compared with traditional rule-based or simple machine learning methods, it can more deeply understand customer needs and achieve more accurate personalized recommendations.

[0086] Personalized financial services: This application proposal uses deep learning and big data analysis technology to build a refined customer profile and achieve highly personalized financial service recommendations, which is the key to improving service efficiency and customer satisfaction. The point to be protected is the customer profile construction method and the personalized recommendation algorithm based on it.

[0087] Real-time dynamic risk assessment: The risk assessment model in the proposal can capture market dynamics and changes in customer behavior in real time, adjust risk assessment results in a timely manner, and effectively reduce investment risks. However, existing technologies often lack this real-time performance. Dynamic risk assessment and early warning: The multi-dimensional, real-time risk assessment model and early warning mechanism introduced in the proposal can effectively reduce investment risks and ensure the safety of customer assets. The point to be protected is the construction method of the risk assessment model and its real-time update strategy.

[0088] Enhanced user experience: Through the intelligent interactive interface and intelligent customer service system, this proposal provides a more convenient and intuitive user operation experience, and can respond to customer needs in a timely manner to improve user satisfaction. Intelligent interaction and feedback system: Through natural language processing technology and intelligent customer service system, it provides a smooth user interaction experience, collects and processes customer feedback in a timely manner, and optimizes service processes. The point to be protected is the design method of the intelligent interactive interface and the user feedback processing mechanism.

[0089] Continuous optimization capability: The system has self-learning and optimization mechanisms, and can continuously optimize service processes and algorithm models based on market feedback and user behavior data to ensure continuous improvement of service efficiency. Existing technologies often lack this continuous optimization capability. Continuous optimization and iteration mechanism: The proposed system has self-learning and optimization capabilities, and can continuously iterate and upgrade based on market changes and user feedback to maintain continuous improvement of service efficiency. The point to be protected is the system's continuous optimization strategy and iteration method.

[0090] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for improving the service efficiency of intelligent financial management business, characterized in that: The following steps are involved: Step S1. Collect customer data, clean and pre-process the collected data; Step S2. Conduct in-depth mining on the pre-processed customer data to identify the customer's investment habits, consumption patterns, and risk preference characteristics, build a customer portrait model based on a machine learning algorithm, segment the customers into different groups, and label each group accordingly; Step S3. Generate a personalized list of financial product recommendations based on customer profiles, market trends, and product characteristics, monitor market dynamics and customer behavior changes in real time, and dynamically adjust recommendation strategies; Step S4. Construct a risk assessment model, comprehensively consider market risk, credit risk, and liquidity risk factors, conduct risk assessment on the recommended financial products, and set up a risk warning mechanism; Step S5. Introduce an intelligent customer service system to conduct real-time conversations with customers, answer customer questions, collect customer feedback, and optimize the recommendation algorithm and risk assessment model based on customer feedback and transaction behavior data; Step S6. Regularly evaluate system performance and service effects, collect user feedback and market data, identify potential problems and room for improvement, and optimize and iterate the system based on the evaluation results.

2. A method for improving the service efficiency of intelligent financial management business according to claim 1, characterized in that: Collecting customer data refers to extracting customer-related data from the source data system in the target data source system; The customer data includes basic customer information, financial status, historical transaction records and risk preference questionnaire; the cleaning and preprocessing of the collected data includes: Eliminate erroneous, duplicate, incomplete or irrelevant customer data, standardize, verify and deduplicate customer data, and finally, standardize and normalize the collected customer data to facilitate subsequent data analysis and model training.

3. The method for improving the service efficiency of intelligent financial management business according to claim 1, characterized in that: The customer portrait model constructed based on the machine learning algorithm includes: Use big data analysis technology to conduct in-depth mining of pre-processed customer data to identify customers' investment habits, consumption patterns, and risk preference characteristics; Based on machine learning algorithms, a customer portrait model is constructed to segment customers into different groups and label each group accordingly; the machine learning algorithms include cluster analysis, decision trees or neural networks.

4. The method for improving the service efficiency of intelligent financial management business according to claim 3, characterized in that: The customer segments are divided into different groups including: high-income, middle-income and low-income groups according to income level or aggressive, steady and conservative groups according to risk preference.

5. The method for improving the service efficiency of intelligent financial management business according to claim 1, characterized in that: The personalized financial product recommendation list generated based on customer portraits, combined with market trends and product characteristics includes: Based on the financial status, risk appetite and customer label assessment of customers, and in light of current economic trends, we use deep learning and machine learning algorithms to provide customized investment advice and data analysis, and offer robust financial products and low-risk investment solutions, including time deposits and bond funds.

6. The method for improving the service efficiency of intelligent financial management business according to claim 1, characterized in that: The risk assessment model is constructed to comprehensively consider market risk, credit risk, and liquidity risk factors, and the risk assessment of the recommended financial products includes: Build a multi-dimensional and multi-level risk assessment model to systematically evaluate customers' risk tolerance, covering investment experience and risk preferences; analyze market, credit, liquidity and legal risks, use customer questionnaires to understand product features, and ensure that the investment matches one's own risk tolerance; when the assessment result exceeds the preset threshold, it automatically triggers an early warning and sends risk warning information to customers and financial advisors.

7. The method for improving the service efficiency of intelligent financial management business according to claim 1, characterized in that: Before introducing the intelligent customer service system to conduct real-time conversations with customers, it also includes: Design an intuitive and easy-to-use user interface, provide convenient operation processes and clear interactive guidance, and ensure that customers can easily complete the selection and transaction of financial products; Design real-time feedback and dynamic adjustment mechanisms to promptly collect and process customer feedback through the intelligent customer service system and user interaction interface, and reflect market changes and customer needs in real time to the intelligent customer service system.

8. The method for improving the service efficiency of intelligent financial management business according to claim 1, characterized in that: The optimization and iteration of the system according to the evaluation results include: First, we analyze the evaluation data in depth to identify the strengths and weaknesses of the marketing activities; Subsequently, based on data feedback, the marketing strategy is optimized, the target audience positioning is adjusted, the content creativity is improved, or the channel delivery is optimized; at the same time, algorithms are used to improve the accuracy of personalized recommendations and enhance user experience; Finally, conduct small-scale tests to verify the results, and perform iterative optimization based on feedback to ensure that the system is in the best condition to help achieve marketing goals.

9. An intelligent financial management service efficiency improvement system, characterized in that: include: Data collection and processing module, used to collect customer data, clean and pre-process the collected data; The customer portrait building module is used to conduct in-depth mining of pre-processed customer data, identify customers' investment habits, consumption patterns, and risk preference characteristics, build a customer portrait model based on machine learning algorithms, segment customers into different groups, and label each group accordingly; The intelligent recommendation module is used to generate a personalized list of financial product recommendations based on customer portraits, market trends, and product characteristics, monitor market dynamics and customer behavior changes in real time, and dynamically adjust recommendation strategies; The risk assessment and early warning module is used to build a risk assessment model, comprehensively consider market risk, credit risk, and liquidity risk factors, conduct risk assessments on recommended financial products, and set up a risk early warning mechanism; The user interaction and feedback module is used to introduce the intelligent customer service system to conduct real-time dialogue with customers, answer customer questions, collect customer feedback, and continuously optimize the recommendation algorithm and risk assessment model based on customer feedback and transaction behavior data; The continuous optimization and iteration module is used to regularly evaluate system performance and service effects, collect user feedback and market data, identify potential problems and areas for improvement, and optimize and iterate the system based on the evaluation results.

10. An electronic device, characterized in that: It includes a memory and a processor, and the processor is used to implement a method for improving the service efficiency of intelligent financial management business as described in any one of claims 1 to 8 when executing a computer program stored in the memory.