Bank customer management system based on large model

Abstract

The invention relates to a bank customer management system based on a large model, and the system specifically comprises the following modules: a market research module which is a key part in a bank customer management system architecture and mainly aims at collecting and analyzing market data; the sales management module is responsible for formulating sales targets, managing business opportunities and executing sales activities; the customer service module is a core part of interaction between enterprises and customers; the customer satisfaction module is used for collecting, processing and analyzing customer feedback through a systematic method so as to improve service quality and customer experience; and a data integration and intelligent processing module.

Description

Technical Field

[0001] The present invention belongs to the technical field of fintech, and particularly relates to a bank customer management system based on a large model. Background Art

[0002] In today's highly competitive banking environment, achieving efficient customer management is of crucial importance for enhancing customer satisfaction, streamlining business processes, and strengthening market competitiveness. However, traditional customer management systems often struggle to quickly adapt to changing customer needs due to their excessive complexity, and are also accompanied by the phenomenon of information silos, which hinders effective communication between departments and thus reduces the overall customer experience. Summary of the Invention

[0003] (I) Object of the Invention

[0004] To overcome the above deficiencies, the object of the present invention is to provide a bank customer management system based on a large model to solve the above technical problems.

[0005] (II) Technical Solution

[0006] To achieve the above object, the technical solution provided by this application is as follows:

[0007] A bank customer management system based on a large model specifically includes the following modules:

[0008] A market research module, which is a key component in the architecture of the bank customer management system. Its main objective is to collect and analyze market data to gain a deep understanding of customer needs, competitive landscape, and market trends. Through this module, the bank can formulate more precise market strategies, enhancing customer satisfaction and market share.

[0009] A sales management module, responsible for setting sales targets, managing business opportunities, and executing sales activities. This module comprehensively monitors and manages the sales process through functions such as sales targets and plans, business opportunity management, and activity management. At the same time, this module is closely integrated with the marketing module and the customer service module to ensure the smooth and efficient operation of the sales process;

[0010] A customer service module, which is the core part of the interaction between the enterprise and customers, covering aspects such as customer interaction, problem-solving, information management, service delivery, customer feedback, and other related aspects of customer service, aiming to enhance customer satisfaction and loyalty;

[0011] A customer satisfaction module, which improves service quality and customer experience by systematically collecting, processing, and analyzing customer feedback;

[0012] Data integration and intelligent processing module. Data integration involves collecting, cleaning, transforming, and storing customer data from internal and external sources; intelligent processing includes preprocessing, customer segmentation, risk assessment, intelligent recommendation, and intelligent customer service, aiming to provide personalized services and optimize the customer experience.

[0013] Preferably, the market research module includes the following components:

[0014] Market activity management, responsible for planning and executing market research activities, including determining the research theme, designing the research plan, and organizing the research team, monitoring the progress and quality of the research activities to ensure the smooth progress of the research activities;

[0015] Target customer list, storing and managing information of potential customers, including basic information, purchase history, and preferences of customers, identifying potential target customer groups through data analysis and mining;

[0016] Marketing feedback and results, collecting and analyzing feedback data from market research activities, where the feedback data includes customer feedback and sales data, evaluating the effectiveness of market research activities, and proposing improvement suggestions or optimization strategies;

[0017] Recommended products and services, recommending suitable products and services according to the market research results to meet customer needs, and formulating personalized product recommendation plans by combining customer needs and market trends;

[0018] Intelligent marketing support system, using artificial intelligence technology to deeply analyze and mine market data, providing intelligent marketing strategy suggestions to help banks formulate more accurate market strategies;

[0019] Data analysis tools, providing data analysis functions, where the data analysis functions include data cleaning, data visualization, and data mining, supporting various data analysis methods and models to help banks deeply explore the value of market data.

[0020] Preferably, the market research module includes the following steps:

[0021] A1 Determine the research objective, clarify the purpose of market research, such as understanding customer needs, evaluating competitors, and analyzing market trends, setting specific research indicators and expected goals to be achieved;

[0022] A2 Design the research plan, select the research method, where the research methods include questionnaire surveys, interviews, and data analysis, design the research questionnaire or interview outline to ensure that the questions are comprehensive and targeted; determine the research objects, including individual customers, corporate customers, and industry experts.

[0023] Implement A3 research activities, distribute research questionnaires or conduct interviews with target objects, collect and analyze market data, including sales data, customer feedback, and industry reports, monitor the progress of the research to ensure that the research activities are carried out as planned;

[0024] A3 data analysis and interpretation, organize and analyze the collected data, extract valuable information, use statistical methods and data mining techniques to uncover the patterns and trends behind the data, write research reports, summarize the research results and findings, and put forward corresponding suggestions or strategies;

[0025] A4 application of research results, apply the research results to the formulation and adjustment of market strategies, optimize products or services according to the research results, improve customer satisfaction, share the research results with relevant departments, and promote cross-departmental cooperation and information sharing.

[0026] Preferably, the sales management module includes the following components:

[0027] Sales target and plan management system, used to formulate, track, and adjust sales targets and plans to ensure the orderly progress of sales activities;

[0028] Business opportunity management system, used to collect, evaluate, allocate, and track business opportunities to improve sales efficiency and customer satisfaction;

[0029] Activity management system, used to plan, execute, and provide feedback on sales activities to ensure the pertinence and effectiveness of sales activities;

[0030] Sales monitoring and evaluation system, used to monitor the progress and results of sales activities in real time, regularly evaluate the implementation of sales strategies, and provide adjustment suggestions;

[0031] Sales data analysis tool, used to conduct in-depth analysis of sales data, uncover potential business opportunities and market trends, and provide data support for the formulation of sales strategies;

[0032] Sales collaboration and communication tools, the communication tools include instant messaging, email system, and phone system, used for collaboration and communication between sales personnel and customers, internal teams, to improve sales efficiency and service quality.

[0033] Preferably, the sales management module specifically includes the following steps:

[0034] B1 Sales target and plan formulation, the sales management department formulates specific sales targets and plans based on the data provided by the market research module and the overall business strategy of the bank. These targets and plans include sales volume, number of new customers, and product promotion. The formulation of targets and plans should be based on historical sales data, market trends, and competitor analysis to ensure their feasibility and effectiveness;

[0035] B2 Business Opportunity Management. The core of business opportunity management lies in quick response and accurate judgment to ensure that no business opportunities are missed and at the same time improve sales efficiency;

[0036] B21 Business Opportunity Collection. Collect potential business opportunities through channels such as market research, customer feedback, and partner recommendations;

[0037] B22 Business Opportunity Evaluation. Conduct a preliminary evaluation of the collected business opportunities to determine their potential value and feasibility;

[0038] B23 Business Opportunity Allocation. Allocate the evaluated business opportunities to appropriate salespersons or teams for follow-up;

[0039] B3 Activity Management. Pay attention to the pertinence and effectiveness of activities. By continuously optimizing the activity content and methods, improve the sales success rate;

[0040] B31 Activity Planning. According to the sales target and business opportunity situation, plan specific sales activities, and the sales activities include product demonstrations, customer visits, and telemarketing;

[0041] B32 Activity Execution. Salespersons execute sales activities according to the plan and communicate with customers;

[0042] B33 Activity Feedback. After the activity ends, salespersons record the activity results and feedback for subsequent analysis and improvement.

[0043] B4 Sales Monitoring and Evaluation. It is an important part of sales management. Through real-time and regular analysis, problems can be discovered in a timely manner and measures can be taken to ensure the smooth realization of sales targets;

[0044] B41 Real-time Monitoring. Through the system, real-time monitor the progress and results of sales activities, including sales volume, the number of new customers, or other relevant key indicators.

[0045] B42 Regular Evaluation. Regularly evaluate the implementation of sales targets and plans, and analyze existing problems and reasons;

[0046] B43 Strategy Adjustment. According to the evaluation results, timely adjust sales strategies and targets to cope with market changes.

[0047] Preferably, the customer service module includes the following components:

[0048] Service Request Management: Responsible for receiving and recording various service requests from customers. The service requests include consultations, complaints, and suggestions. Through an automated receiving system, the receiving system includes online customer service and telephone customer service, to achieve instant capture and classification of service requests;

[0049] Product service configuration: According to the customer's needs and the bank's product lines, recommend suitable products and services to the customer, and provide detailed product information and service descriptions. This part needs to be closely integrated with the bank's marketing system and product database to ensure the accuracy and timeliness of information;

[0050] Business application monitoring: Real-time track and monitor the customer's business applications to ensure the timely processing and approval of applications. At the same time, provide a real-time query function for the application progress to facilitate the customer to understand the application status at any time;

[0051] Complaint management: Establish a complaint handling process, classify, record, and track the customer's complaints. Through an automated complaint analysis system, identify the root causes and hot issues of complaints to provide a basis for the bank to improve services;

[0052] Signing information management: Manage the customer's signing information, including contract content, signing date, and signing period. This part needs to ensure the confidentiality and security of information to prevent information leakage and abuse;

[0053] Organization and personnel management: Organize and manage the customer service team, including personnel recruitment, training, assessment, and motivation. By optimizing personnel allocation and improving personnel quality, improve the overall service level of the customer service team;

[0054] Knowledge base management: Establish and maintain a knowledge base for customer service, including frequently asked questions, solutions, and product materials. Through intelligent retrieval and query tools, facilitate customer service personnel to quickly obtain the required information and improve service efficiency.

[0055] Preferably, the customer service module includes the following steps:

[0056] C1 Receive service requests. The customer submits service requests through channels such as online customer service and telephone customer service. The system automatically captures the requests and classifies them to generate service work orders;

[0057] C2 Analyze service requirements. The customer service personnel analyze the specific requirements of the customer based on the content of the service work order and determine the required products and services;

[0058] C3 Recommend products and services. According to the customer's needs and the bank's product lines, the customer service personnel recommend suitable products and services through the system and provide detailed product information and service descriptions;

[0059] C4 Process business applications. After the customer submits a business application, the system real-time tracks and monitors the application. The customer service personnel communicate with the customer in a timely manner according to the application progress to ensure the timely processing and approval of the application;

[0060] C5 Handle complaints and suggestions. For customer complaints and suggestions, customer service staff need to record and classify them in a timely manner. Through an automated complaint analysis system, identify the root causes and hot issues of complaints, and propose improvement measures;

[0061] C6 Maintain signing information. Customer service staff need to regularly update and maintain customers' signing information to ensure the accuracy and integrity of the information;

[0062] C7 Knowledge base query and update. During the process of providing services to customers, customer service staff need to query the information in the knowledge base at any time. At the same time, for newly emerging problems and solutions, they need to update them to the knowledge base in a timely manner for subsequent use;

[0063] C8 Service evaluation and feedback. After the service is completed, the system automatically invites customers to evaluate the service. Customer service staff need to continuously improve the service quality based on customers' evaluations and suggestions.

[0064] The steps of combining the customer service module with the large model are as follows:

[0065] D1 Data collection and integration. Collect information such as customers' historical data, behavior patterns, and service requests, clean, label, and integrate the data to provide a reliable data basis for training the large model;

[0066] D2 Large model selection and training;

[0067] D3 System development and integration. Develop the relevant functions of the customer service module, including intelligent recognition, quick response, and personalized recommendation. Integrate the trained large model into the customer service module to achieve intelligent services;

[0068] D4 Testing and optimization. Conduct a comprehensive test on the customer service module, including understanding ability, answer accuracy, and user satisfaction. Optimize and adjust the model according to the test results to improve the service quality and efficiency;

[0069] D5 Go live and monitor. Put the customer service module into operation to provide intelligent services to customers, monitor the running status of the module and user feedback in real time, and promptly discover problems and make improvements.

[0070] Preferably, the large model selection and training are specifically divided into large model selection and large model training,

[0071] The large model selection specifically includes the following parts:

[0072] Model type. According to the requirements of the customer service module, select a large model based on the Transformer architecture. This type of model has achieved remarkable results in the field of natural language processing and is suitable for understanding and generating text;

[0073] Model scale: According to the computing resources and business requirements, select a large model with a corresponding scale. Generally, the larger the model scale, the more language features can be captured, but more computing resources are also required for training and inference.

[0074] Pre-training and fine-tuning: Select a model that has been pre-trained on a large-scale dataset, and then fine-tune it for the specific tasks of the customer service module. This can utilize the knowledge of the pre-trained model while adapting to the requirements of specific tasks.

[0075] The training of the large model specifically includes the following content:

[0076] Data preparation: Collect and organize data related to the customer service module, including customer consultation records and historical conversation data, and ensure the quality and diversity of the data to cover various possible customer questions and scenarios.

[0077] Data preprocessing: Clean, annotate, and format the collected data, including removing irrelevant information, correcting spelling mistakes, and annotating the intent and entities of the conversation.

[0078] Model training: Use the prepared dataset to train the selected large model. The training process usually includes the following steps:

[0079] a Initialize parameters: Use random initialization or the parameters of the pre-trained model as the starting point.

[0080] b Forward propagation: Propagate the input data forward through the model to calculate the output.

[0081] c Calculate loss: Calculate the value of the loss function based on the output and the true labels. Commonly used loss functions include cross-entropy loss.

[0082] d Backward propagation: Calculate the gradient of the loss function with respect to each parameter through the backward propagation algorithm.

[0083] e Parameter update: Use an optimizer to update the parameters of the model according to the gradient. Optimizers include Adam and SGD.

[0084] f Algorithm and formula

[0085] The formula for the gradient descent algorithm is:

[0086]

[0087] where θ represents the model parameters, η represents the learning rate, and L represents the loss function.

[0088] The update rule of the Adam optimizer includes calculating the first-order moment estimate and the second-order moment estimate of the gradient, and adjusting the learning rate of each parameter according to these estimates.

[0089] The h self-attention mechanism, the self-attention mechanism widely used in the Transformer model, can capture long-range dependencies in the sequence.

[0090] Its calculation formula is:

[0091] Attention(Q, K, V) = softmax(QKT / √dk)V

[0092] Among them, Q, K, and V respectively represent the query, key, and value matrices, and dk represents the dimension of the key vector.

[0093] Model evaluation and debugging. During the training process, regularly evaluate the performance of the model. The performance includes accuracy, recall, and F1 score. According to the evaluation results, adjust the model parameters, optimizer settings, or data preprocessing methods to improve the model performance.

[0094] Model fine-tuning. For the specific tasks of the customer service module, fine-tune the trained model, including adjusting the output layer of the model to adapt to specific classification tasks or generation tasks. During the fine-tuning process, use the learning rate and number of training epochs to avoid overfitting.

[0095] Deployment and monitoring. Deploy the trained model to the customer service module, and set up monitoring and logging to track the performance of the model. Regularly retrain the model to update its knowledge and adapt to the new data distribution.

[0096] Preferably, the customer satisfaction module includes the following steps:

[0097] E1 Data collection, including customer feedback channel setting, feedback content collection, and data format conversion.

[0098] The customer feedback channel setting includes:

[0099] Online channels. Set obvious "Customer Feedback" entrances on the bank website and mobile application platforms, including online questionnaires, message boards, and online customer service, to ensure that customers can provide feedback conveniently and quickly.

[0100] Phone channels. Establish a dedicated customer satisfaction survey phone hotline, and have professional customer service staff answer and record customer feedback.

[0101] On-site channels. Set up suggestion boxes and questionnaire distribution points at physical branches to ensure that on-site customers can also provide feedback conveniently.

[0102] The feedback content collection includes:

[0103] Clarify the collection content. When designing questionnaires or collecting feedback, clearly list the content to be collected, including customers' evaluations and opinions on bank products, services, personnel, and the environment.

[0104] Provide multiple forms of feedback. In addition to text feedback, feedback options such as ratings and satisfaction levels can also be provided to facilitate customer selection;

[0105] The data format conversion includes:

[0106] Unify the data format, convert the data collected from different channels and in different formats into a unified standard format, including converting text feedback into text format and converting ratings into numerical format;

[0107] Data cleaning and sorting, remove duplicate, invalid, and abnormal data to ensure the accuracy and reliability of the data;

[0108] E2 data processing and analysis, including data cleaning, data standardization, and data analysis;

[0109] The specific data cleaning includes:

[0110] Remove duplicate data, use data processing tools or software to screen out duplicate data and delete it;

[0111] Process abnormal data. For obviously abnormal data, abnormal data includes ratings beyond a reasonable range and unrecognizable text, and conduct manual verification or deletion;

[0112] The specific data standardization includes:

[0113] Unify the rating standard. For rating data, formulate a unified rating standard, and the rating standard includes converting a rating with a full score of 10 into a percentage-based rating;

[0114] Text data encoding. For text feedback data, conduct encoding processing to facilitate subsequent data analysis and mining;

[0115] The specific data analysis includes:

[0116] Quantitative analysis, use data analysis software to calculate indicators such as the average score and standard deviation of customer satisfaction to evaluate the overall level of customer satisfaction;

[0117] Qualitative analysis, summarize the text feedback data, extract key information and opinions, and understand the specific needs and expectations of customers for the bank;

[0118] Trend analysis, compare historical data, analyze the change trend of customer satisfaction, and identify potential problems and improvement directions;

[0119] E3 result presentation and decision support, including visual report production and decision support,

[0120] The specific visual report production includes:

[0121] Select a suitable visualization tool. Based on the characteristics and requirements of the data, select a suitable visualization tool, which includes bar charts, line charts, and pie charts;

[0122] Create a report. Present the data analysis results in a visual report, including customer satisfaction index, key information extraction, and trend analysis;

[0123] The decision-making support specifically includes:

[0124] Provide decision-making suggestions. Based on the data analysis results, provide decision-making suggestions for the bank's management, including optimizing service processes, improving product quality, and strengthening personnel training;

[0125] Formulate improvement measures. Clearly define the specific content, responsible person, and time node of the improvement measures to ensure the effective implementation of the improvement measures;

[0126] Implementation and feedback of E4 improvement measures, including project management, customer feedback mechanism, and continuous optimization;

[0127] The project management specifically includes:

[0128] Establish project management tools. Use project management tools, which include Trello and Jira, to track and manage the implementation progress of improvement measures;

[0129] Regularly evaluate the progress. Regularly evaluate the implementation progress and effect of the improvement measures to ensure that the improvement measures are carried out according to the plan;

[0130] The customer feedback mechanism specifically includes:

[0131] Establish a feedback mechanism. Establish a continuous customer feedback mechanism to ensure that customers can provide feedback and suggestions at any time;

[0132] Collect feedback and evaluate the effect. Collect customers' feedback on the improvement measures, evaluate the effect of the improvement measures, and form a closed-loop management;

[0133] The continuous optimization specifically includes:

[0134] Analyze the feedback data. Conduct in-depth analysis of the collected feedback data to understand customers' reactions and opinions on the improvement measures;

[0135] Adjust the improvement measures. According to the feedback data, adjust and improve the original improvement measures to ensure the continuous improvement of customer satisfaction;

[0136] The customer satisfaction module includes a data collection module, a data processing and analysis module, a result presentation and decision-making support module, and an improvement measure real-time and feedback module.

[0137] Preferably, the data integration in the data integration and intelligent processing module includes the following steps:

[0138] Data collection: Collect customer-related data from internal and external bank data sources to ensure the comprehensiveness and diversity of the data;

[0139] Data cleaning: Preprocess the collected data to ensure the accuracy and consistency of the data;

[0140] Data transformation and mapping: Convert data in different formats and standards into a unified format and standard, and establish data mapping relationships;

[0141] Data storage: Store the cleaned and transformed data in a unified database for subsequent analysis and processing;

[0142] The intelligent processing specifically includes the following steps:

[0143] Data preprocessing: Further preprocess the stored data to meet the requirements of intelligent analysis;

[0144] Customer segmentation: Use algorithms such as clustering analysis and decision trees to divide customers into different groups or market segments based on customers' basic information, transaction records, and consumption habits;

[0145] Risk assessment: Use machine learning algorithms to assess the credit status, transaction behavior, or other relevant data of customers;

[0146] Intelligent recommendation: Provide personalized product or service recommendations for customers based on their consumption habits and interests, using collaborative filtering and deep learning;

[0147] Intelligent customer service: Use technologies such as natural language processing and speech recognition to achieve intelligent interaction and Q&A with customers.

[0148] Beneficial effects:

[0149] 1. Improve customer satisfaction: Through integrated design and intelligent data processing, the system can quickly respond to customer needs and provide personalized services, thereby improving customer satisfaction.

[0150] 2. Optimize business processes: Through intelligent data processing and optimized business processes, the system can comprehensively improve the efficiency and effectiveness of bank customer management, reduce the problem of information silos, and improve the communication efficiency between departments.

[0151] 3. Enhance market competitiveness: Through accurate market strategies and personalized product recommendations, the system can help banks gain an advantage in the fierce market competition and increase market share.

[0152] 4. Improve service efficiency and quality: The customer service module supported by large model technology can achieve multi-channel access and unified management, improve service efficiency and quality, and continuously adjust service strategies through continuous learning and optimization.

[0153] 5. Reduce service error rate: By leveraging the deep learning and natural language processing capabilities of large models, the system can intelligently identify and understand various customer requests, accurately capture the real needs of customers, and reduce service errors caused by misunderstandings or poor communication. Specific implementation manners

[0154] To make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with specific implementation manners. It should be understood that these descriptions are exemplary and are not intended to limit the scope of the present invention. In addition, in the following descriptions, the descriptions of well-known structures and technologies are omitted to avoid unnecessarily confusing the concepts of the present invention.

[0155] A bank customer management system based on large models provided by the present invention specifically includes the following modules:

[0156] The market research module is a key component in the architecture of the bank customer management system. Its main objective is to collect and analyze market data to deeply understand customer needs, competitive situations and market trends. Through this module, the bank can formulate more accurate market strategies and improve customer satisfaction and market share.

[0157] The sales management module is responsible for formulating sales targets, managing business opportunities and executing sales activities. Through the functions of sales targets and plans, business opportunity management, and activity management, this module realizes the comprehensive monitoring and management of the sales process. At the same time, this module is closely integrated with the marketing module and the customer service module to ensure the smooth and efficient operation of the sales process;

[0158] The customer service module is the core part of the interaction between the enterprise and customers, involving customer interaction, problem solving, information management, service delivery, customer feedback and other related aspects of customer service, aiming to improve customer satisfaction and loyalty;

[0159] The customer satisfaction module improves service quality and customer experience by systematically collecting, processing and analyzing customer feedback;

[0160] The data integration and intelligent processing module. Data integration involves collecting, cleaning, transforming and storing customer data from internal and external sources; intelligent processing includes preprocessing, customer segmentation, risk assessment, intelligent recommendation and intelligent customer service, aiming to provide personalized services and optimize the customer experience.

[0161] Preferably, the market research module includes the following components:

[0162] Market activity management is responsible for planning and executing market research activities, including determining research topics, designing research plans, and organizing research teams, monitoring the progress and quality of research activities to ensure the smooth progress of research activities;

[0163] Target customer list stores and manages information of potential customers, including basic customer information, purchase history, and preferences. Through data analysis and mining, it identifies potential target customer groups;

[0164] Marketing feedback and results collect and analyze feedback data from market research activities. The feedback data includes customer feedback and sales data, evaluates the effectiveness of market research activities, and proposes improvement suggestions or optimization strategies;

[0165] Recommended products and services recommend suitable products and services based on market research results to meet customer needs, and develop personalized product recommendation plans by combining customer needs and market trends;

[0166] Intelligent marketing support system uses artificial intelligence technology to deeply analyze and mine market data, provides intelligent marketing strategy suggestions, and helps banks formulate more accurate market strategies;

[0167] Data analysis tools provide data analysis functions, including data cleaning, data visualization, and data mining, support various data analysis methods and models, and help banks deeply explore the value of market data.

[0168] Preferably, the market research module includes the following steps:

[0169] A1 Determine research objectives, clarify the purpose of market research, such as understanding customer needs, evaluating competitors, and analyzing market trends, set specific research indicators and expected goals to be achieved;

[0170] A2 Design a research plan, select research methods, including questionnaire surveys, interviews, and data analysis, design research questionnaires or interview outlines to ensure that the questions are comprehensive and targeted; determine research objects, including individual customers, corporate customers, and industry experts.

[0171] A3 Implement research activities, distribute research questionnaires or conduct interviews with target objects, collect and analyze market data, including sales data, customer feedback, and industry reports, monitor the research progress to ensure that the research activities are carried out as planned;

[0172] A3 Data analysis and interpretation, organize and analyze the collected data, extract valuable information, use statistical methods and data mining techniques to explore the laws and trends behind the data, write research reports, summarize research results and findings, and propose corresponding suggestions or strategies;

[0173] Apply the results of the A4 application research to the formulation and adjustment of market strategies, optimize products or services according to the research results, improve customer satisfaction, share the research results with relevant departments, and promote cross-departmental cooperation and information sharing.

[0174] Preferably, the sales management module includes the following components:

[0175] A sales target and plan management system for formulating, tracking, and adjusting sales targets and plans to ensure the orderly progress of sales activities;

[0176] A business opportunity management system for collecting, evaluating, allocating, and tracking business opportunities to improve sales efficiency and customer satisfaction;

[0177] An activity management system for planning, executing, and providing feedback on sales activities to ensure the pertinence and effectiveness of sales activities;

[0178] A sales monitoring and evaluation system for real-time monitoring of the progress and results of sales activities, regularly evaluating the implementation of sales strategies, and providing adjustment suggestions;

[0179] A sales data analysis tool for in-depth analysis of sales data, mining potential business opportunities and market trends, and providing data support for the formulation of sales strategies;

[0180] A sales collaboration and communication tool, including instant messaging, email system, and phone system, for collaboration and communication between sales personnel and customers and internal teams to improve sales efficiency and service quality.

[0181] Preferably, the sales management module specifically includes the following steps:

[0182] B1 Sales target and plan formulation: The sales management department formulates specific sales targets and plans based on the data provided by the market research module and the overall business strategy of the bank. These targets and plans include sales volume, number of new customers, and product promotion. The formulation of targets and plans should be based on historical sales data, market trends, and competitor analysis to ensure their feasibility and effectiveness;

[0183] B2 Business opportunity management: The core of business opportunity management lies in quick response and accurate judgment to ensure that business opportunities are not missed and at the same time improve sales efficiency;

[0184] B21 Business opportunity collection: Collect potential business opportunities through channels such as market research, customer feedback, and partner recommendations;

[0185] B22 Business opportunity evaluation: Conduct a preliminary evaluation of the collected business opportunities to determine their potential value and feasibility;

[0186] B23 Business opportunity allocation: Allocate the evaluated business opportunities to appropriate salespersons or teams for follow-up.

[0187] B3 Activity management: Focus on the pertinence and effectiveness of activities. By continuously optimizing activity content and methods, improve the sales success rate.

[0188] B31 Activity planning: According to sales targets and business opportunity situations, plan specific sales activities, which include product demonstrations, customer visits, and telemarketing.

[0189] B32 Activity execution: Salespersons execute sales activities according to the plan and communicate with customers.

[0190] B33 Activity feedback: After the activity, salespersons record the activity results and feedback for subsequent analysis and improvement.

[0191] B4 Sales monitoring and evaluation: It is an important part of sales management. Through real-time and regular analysis, problems can be discovered in a timely manner and measures can be taken to ensure the smooth realization of sales targets.

[0192] B41 Real-time monitoring: Through the system, monitor the progress and results of sales activities in real time, including sales volume, the number of new customers, or other relevant key indicators.

[0193] B42 Regular evaluation: Regularly evaluate the implementation of sales targets and plans, and analyze existing problems and reasons.

[0194] B43 Strategy adjustment: According to the evaluation results, timely adjust sales strategies and targets to cope with market changes.

[0195] Preferably, the customer service module includes the following components:

[0196] Service request management: Responsible for receiving and recording various service requests from customers. The service requests include consultations, complaints, and suggestions. Through an automated receiving system, which includes online customer service and telephone customer service, achieve the instant capture and classification of service requests.

[0197] Product service configuration: According to customer needs and the bank's product line, recommend suitable products and services to customers, and provide detailed product information and service descriptions. This part needs to be closely integrated with the bank's marketing system and product database to ensure the accuracy and timeliness of information.

[0198] Business application monitoring: Real-time track and monitor customers' business applications to ensure the timely processing and approval of applications. At the same time, provide a real-time query function for the application progress, facilitating customers to understand the application status at any time.

[0199] Complaint Management: Establish a complaint handling process to classify, record, and track customer complaints. Through an automated complaint analysis system, identify the root causes and hot issues of complaints, providing a basis for the bank to improve its services;

[0200] Signing Information Management: Manage customers' signing information, including contract content, signing date, and signing term. This part needs to ensure the confidentiality and security of information to prevent information leakage and abuse;

[0201] Organization and Personnel Management: Organize and manage the customer service team, including personnel recruitment, training, assessment, and motivation. By optimizing personnel allocation and improving personnel quality, enhance the overall service level of the customer service team;

[0202] Knowledge Base Management: Establish and maintain a knowledge base for customer service, including frequently asked questions, solutions, and product information. Through intelligent retrieval and query tools, facilitate customer service staff to quickly obtain the required information and improve service efficiency.

[0203] Preferably, the customer service module includes the following steps:

[0204] C1 Receive service requests. Customers submit service requests through channels such as online customer service and telephone customer service. The system automatically captures the requests and classifies them to generate service work orders;

[0205] C2 Analyze service requirements. Customer service staff analyze the specific requirements of customers based on the content of the service work order and determine the required products and services;

[0206] C3 Recommend products and services. According to customers' requirements and the bank's product lines, customer service staff recommend suitable products and services through the system and provide detailed product information and service descriptions;

[0207] C4 Process business applications. After customers submit business applications, the system tracks and monitors the applications in real time. Customer service staff communicate with customers in a timely manner according to the application progress to ensure the timely processing and approval of the applications;

[0208] C5 Handle complaints and suggestions. For customers' complaints and suggestions, customer service staff need to record and classify them in a timely manner. Through an automated complaint analysis system, identify the root causes and hot issues of complaints and propose improvement measures;

[0209] C6 Maintain signing information. Customer service staff need to regularly update and maintain customers' signing information to ensure the accuracy and completeness of the information;

[0210] C7 Query and update the knowledge base. During the process of providing services to customers, customer service staff need to query the information in the knowledge base at any time. At the same time, for newly emerging problems and solutions, they need to be updated to the knowledge base in a timely manner for subsequent use;

[0211] C8 Service evaluation and feedback. After the service is completed, the system automatically invites customers to evaluate the service. Customer service staff need to continuously improve service quality based on customer evaluations and suggestions.

[0212] The steps of combining the customer service module with the large model are as follows:

[0213] D1 Data collection and integration. Collect information such as customers' historical data, behavior patterns, and service requests, clean, label, and integrate the data to provide a reliable data foundation for training the large model.

[0214] D2 Large model selection and training;

[0215] D3 System development and integration. Develop relevant functions of the customer service module, including intelligent recognition, quick response, and personalized recommendation. Integrate the trained large model into the customer service module to achieve intelligent services.

[0216] D4 Testing and optimization. Conduct comprehensive testing on the customer service module, including understanding ability, answer accuracy, and user satisfaction. Optimize and adjust the model according to the test results to improve service quality and efficiency.

[0217] D5 Online operation and monitoring. Put the customer service module into online operation to provide intelligent services for customers. Real-time monitor the running status of the module and user feedback, and promptly discover and improve problems.

[0218] Preferably, the large model selection and training are specifically divided into large model selection and large model training.

[0219] The large model selection specifically includes the following parts:

[0220] Model type. According to the requirements of the customer service module, select a large model based on the Transformer architecture. Such models have achieved remarkable results in the field of natural language processing and are suitable for understanding and generating text.

[0221] Model scale. According to computing resources and business needs, select a large model of the corresponding scale. The larger the model scale, the more language features can usually be captured, but more computing resources are also required for training and inference.

[0222] Pre-training and fine-tuning. Select a model that has been pre-trained on a large-scale dataset, and then fine-tune it for the specific tasks of the customer service module. The knowledge of the pre-trained model can be utilized while adapting to the needs of specific tasks.

[0223] The large model training specifically includes the following contents:

[0224] Data Preparation: Collect and organize data related to the customer service module, including customer consultation records and historical conversation data, ensuring the quality and diversity of the data to cover various possible customer problems and scenarios;

[0225] Data Preprocessing: Clean, annotate, and format the collected data, including removing irrelevant information, correcting spelling mistakes, and annotating the intent and entities of the conversations;

[0226] Model Training: Use the prepared dataset to train the selected large model. The training process usually includes the following steps:

[0227] a Initialize parameters: Use random initialization or the parameters of a pre-trained model as the starting point;

[0228] b Forward propagation: Propagate the input data forward through the model to calculate the output;

[0229] c Calculate loss: Calculate the value of the loss function based on the output and the true labels. Commonly used loss functions include cross-entropy loss;

[0230] d Backward propagation: Calculate the gradient of the loss function with respect to each parameter through the backward propagation algorithm;

[0231] e Parameter update: Use an optimizer to update the parameters of the model according to the gradient. Optimizers include Adam and SGD;

[0232] f Algorithm and formula,

[0233] The formula for the gradient descent algorithm is:

[0234]

[0235] where θ represents the model parameters, η represents the learning rate, and L represents the loss function;

[0236] The update rule of the Adam optimizer includes calculating the first-order moment estimate and the second-order moment estimate of the gradient, and adjusting the learning rate of each parameter according to these estimates;

[0237] h Self-attention mechanism: The self-attention mechanism widely used in the Transformer model can capture long-range dependencies in the sequence,

[0238] Its calculation formula is:

[0239] Attention(Q,K,V)=softmax(QKT√dk)V

[0240] where Q, K, and V represent the query, key, and value matrices respectively, and dk represents the dimension of the key vector;

[0241] Model evaluation and debugging. During the training process, regularly evaluate the performance of the model. The performance includes accuracy, recall rate, and F1 score. Adjust the model parameters, optimizer settings, or data preprocessing methods according to the evaluation results to improve the model performance.

[0242] Model fine-tuning. For the specific tasks of the customer service module, fine-tune the trained model, including adjusting the output layer of the model to adapt to specific classification tasks or generation tasks. During the fine-tuning process, use the learning rate and the number of training epochs to avoid overfitting.

[0243] Deployment and monitoring. Deploy the trained model to the customer service module, and set up monitoring and logging to track the model performance. Regularly retrain the model to update its knowledge and adapt to the new data distribution.

[0244] The combination of the customer module and the large model has the following innovation points.

[0245] 1. Intelligent recognition and understanding

[0246] Utilize the deep learning and natural language processing capabilities of the large model to intelligently recognize and understand various forms of requests from customers, such as text, voice, and images. Accurately capture the real needs of customers and reduce service errors caused by misunderstandings or poor communication.

[0247] 2. Quick response and solution

[0248] The large model has powerful data processing and computing capabilities, and can quickly analyze and give solutions. Shorten the waiting time of customers, improve the problem-solving speed, and enhance customer satisfaction.

[0249] 3. Personalized service recommendation

[0250] Based on the historical data and behavior patterns of customers, the large model can provide personalized service suggestions and product recommendations for customers. Through intelligent recommendation algorithms, achieve precise marketing and improve customer conversion rate and retention rate.

[0251] 4. Sentiment analysis and emotion management

[0252] The large model can identify the emotional state of customers and reply based on preset coping strategies. Effectively relieve the emotions of customers and avoid conflicts and dissatisfaction during the service process.

[0253] 5. Omnichannel integration and unified management

[0254] The customer service module supported by the large model technology can achieve multi-channel access, including phone, network, social media, etc. Unifiedly manage and monitor service requests from various channels to improve service efficiency and quality.

[0255] 6. Continuous learning and optimization

[0256] Large models have strong self-learning and optimization capabilities. They can continuously adjust service strategies according to customer feedback and business scenarios to improve service levels.

[0257] Preferably, the customer satisfaction module includes the following steps:

[0258] E1 Data collection, including customer feedback channel setting, feedback content collection, and data format conversion;

[0259] The customer feedback channel setting includes:

[0260] Online channels, set obvious "customer feedback" entrances on the platforms of bank websites and mobile applications, including online questionnaires, message boards, and online customer service, to ensure that customers can provide feedback conveniently and quickly;

[0261] Phone channels, establish a dedicated customer satisfaction survey phone hotline, and have professional customer service staff answer and record customer feedback;

[0262] On-site channels, set up suggestion boxes and questionnaire distribution points at physical branches to ensure that on-site customers can also provide feedback conveniently;

[0263] The feedback content collection includes:

[0264] Clarify the collection content. When designing questionnaires or collecting feedback, clearly list the content to be collected, including customers' evaluations and opinions on bank products, services, personnel, and the environment;

[0265] Provide multiple feedback forms. In addition to text feedback, also provide feedback options such as ratings and satisfaction levels to facilitate customer selection;

[0266] The data format conversion includes:

[0267] Unify the data format, convert the data collected from different channels and in different formats into a unified standard format, including converting text feedback into text format and converting ratings into numerical format;

[0268] Data cleaning and sorting, remove duplicate, invalid, and abnormal data to ensure the accuracy and reliability of the data;

[0269] E2 Data processing and analysis, including data cleaning, data standardization, and data analysis;

[0270] The data cleaning specifically includes:

[0271] Remove duplicate data, use data processing tools or software to screen out duplicate data and delete it;

[0272] Process abnormal data. For data that is obviously abnormal, such as scores beyond a reasonable range and unrecognizable text, conduct manual verification or deletion.

[0273] The data standardization specifically includes:

[0274] Unify the scoring criteria. For scoring data, formulate a unified scoring criteria, which includes converting a score with a full mark of 10 into a percentage-based score.

[0275] Encode text data. For text feedback data, perform encoding processing to facilitate subsequent data analysis and mining.

[0276] The data analysis specifically includes:

[0277] Quantitative analysis. Use data analysis software to calculate indicators such as the average score and standard deviation of customer satisfaction to evaluate the overall level of customer satisfaction.

[0278] Qualitative analysis. Summarize and extract key information and opinions from text feedback data to understand the specific needs and expectations of customers for the bank.

[0279] Trend analysis. Compare historical data to analyze the change trend of customer satisfaction, identify potential problems and improvement directions.

[0280] E3 Result presentation and decision support, including visual report production and decision support.

[0281] The visual report production specifically includes:

[0282] Select appropriate visualization tools. According to the data characteristics and requirements, select appropriate visualization tools, including bar charts, line charts, and pie charts.

[0283] Produce a report. Present the data analysis results in a visual report, including the customer satisfaction index, key information extraction, and trend analysis.

[0284] The decision support specifically includes:

[0285] Provide decision-making suggestions. Based on the data analysis results, provide decision-making suggestions for the bank's management, including optimizing service processes, improving product quality, and strengthening personnel training.

[0286] Formulate improvement measures. Clearly define the specific content, responsible person, and time node of the improvement measures to ensure the effective implementation of the improvement measures.

[0287] E4 Implementation and feedback of improvement measures, including project management, customer feedback mechanism, and continuous optimization.

[0288] The project management specifically includes:

[0289] Establish a project management tool. Using the project management tool, which includes Trello and Jira, track and manage the implementation progress of improvement measures;

[0290] Regularly evaluate the progress. Regularly evaluate the implementation progress and effectiveness of improvement measures to ensure that the improvement measures are carried out according to the plan;

[0291] The specific customer feedback mechanism includes:

[0292] Establish a feedback mechanism. Establish a continuous customer feedback mechanism to ensure that customers can provide feedback and suggestions at any time;

[0293] Collect feedback and evaluate the effect. Collect customers' feedback on improvement measures, evaluate the effect of improvement measures, and form a closed-loop management;

[0294] The specific continuous optimization includes:

[0295] Analyze the feedback data. Conduct in-depth analysis on the collected feedback data to understand customers' reactions and opinions on improvement measures;

[0296] Adjust the improvement measures. According to the feedback data, adjust and improve the original improvement measures to ensure the continuous improvement of customer satisfaction;

[0297] The customer satisfaction module includes a data collection module, a data processing and analysis module, a result presentation and decision support module, and an improvement measure real-time and feedback module.

[0298] The data collection module includes:

[0299] Online surveys: Set up online questionnaires through platforms such as bank websites and mobile applications to facilitate customers to provide feedback at any time.

[0300] Telephone interviews: Conduct customer satisfaction surveys through telephone customer service to obtain more direct and in-depth feedback.

[0301] On-site surveys: Set up suggestion boxes, questionnaires, etc. at physical branches to collect feedback from on-site customers.

[0302] The data processing and analysis module includes:

[0303] Data processing tools, including data cleaning software and data conversion tools, for processing the collected customer feedback data.

[0304] Data analysis software, including SPSS, Excel, and Python, for data analysis, mining, and visualization.

[0305] The result presentation and decision support module includes:

[0306] Visualization tools, including Power BI and Tableau, are used to create charts, reports, and other visual reports.

[0307] Decision support system, based on the data analysis results, provides intelligent decision-making suggestions and improvement measures;

[0308] The improvement measure implementation and feedback module includes:

[0309] Project management tools, including Trello and Jira, are used to track and manage the implementation progress of improvement measures;

[0310] Customer feedback mechanism, establishing a continuous customer feedback mechanism to ensure the effectiveness of improvement measures and the continuous improvement of customer satisfaction.

[0311] Preferably, the data integration in the data integration and intelligent processing module includes the following steps:

[0312] S1 Data collection, collecting customer-related data from internal and external data sources of the bank to ensure the comprehensiveness and diversity of the data; internal data sources include the core business system, credit system, and risk control system, obtaining customer basic information, transaction records, and credit status.

[0313] External data sources include credit reference agencies, social media, and public databases, obtaining customer credit scores, social behaviors, and consumption habits;

[0314] Data format, supporting multiple data formats, including CSV, Excel, JSON, XML, to ensure data compatibility.

[0315] S2 Data cleaning, preprocessing the collected data to ensure the accuracy and consistency of the data; specifically including:

[0316] Removing duplicate data, identifying and deleting duplicate records by comparing key fields in the data;

[0317] Filling in missing values, according to the data distribution and context information, using appropriate filling methods to handle missing values, and the filling methods include mean filling, median filling, and interpolation method.

[0318] Correcting incorrect data, identifying and correcting incorrect data records through data verification and rule validation;

[0319] S3 Data conversion and mapping, converting data in different formats and standards into a unified format and standard, and establishing data mapping relationships; specifically including:

[0320] Data format conversion, converting the collected data into the corresponding format according to the format requirements of the target database;

[0321] Data standard mapping, establishing data mapping relationships between different data sources to ensure correct correspondence and association of data;

[0322] Data verification, during the conversion and mapping process, verifying and validating the data to ensure its accuracy and consistency;

[0323] S4 Data storage, storing the cleaned and transformed data in a unified database for subsequent analysis and processing; specifically including:

[0324] Database selection, selecting a suitable database system based on factors such as data scale, query performance, and security;

[0325] Table structure design, designing a reasonable database table structure, including customer information table, transaction record table, and risk assessment table, to ensure data organization and management;

[0326] Index optimization, creating indexes for frequently queried fields to improve query performance.

[0327] The intelligent processing specifically includes the following steps:

[0328] T1 Data preprocessing, further preprocessing the stored data to meet the requirements of intelligent analysis; specifically including:

[0329] Data cleaning, checking the accuracy and consistency of the data again to ensure data quality;

[0330] Data transformation, performing appropriate transformations on the data, including normalization, standardization, and discretization, for subsequent analysis;

[0331] T2 Customer segmentation, using algorithms such as clustering analysis and decision trees, and based on customers' basic information, transaction records, and consumption habits, dividing customers into different groups or market segments; specifically including:

[0332] Algorithm selection, selecting a suitable clustering algorithm or decision tree algorithm according to data characteristics and business requirements;

[0333] Feature selection, extracting key features from the data for customer segmentation;

[0334] Model training, using historical data for model training to obtain a customer segmentation model;

[0335] Result verification, verifying and evaluating the results of the model to ensure its accuracy and reliability.

[0336] T3 Risk assessment, using machine learning algorithms to assess the credit status, transaction behavior, or other relevant data of customers; specifically including:

[0337] Algorithm selection: Select appropriate machine learning algorithms according to the requirements of risk assessment, specifically including logistic regression and support vector machine;

[0338] Feature engineering: Extract features related to risk assessment from data;

[0339] Model training: Use historical data for model training to obtain a risk assessment model;

[0340] Result analysis: Analyze and interpret the results of the model to identify potential risk points and fraud behaviors.

[0341] T4 intelligent recommendation: Provide personalized product or service recommendations for customers based on their consumption habits and interests, using collaborative filtering and deep learning; specifically including:

[0342] Algorithm selection: Select appropriate algorithms according to the requirements of the recommendation system;

[0343] Feature extraction: Extract features related to recommendations from data, including user behavior data and interest preferences;

[0344] Model training: Use historical data for model training to obtain a recommendation system model;

[0345] Recommendation strategy: Develop personalized recommendation strategies based on the results of the model, including product recommendations and service recommendations;

[0346] Effect evaluation: Evaluate and monitor the effectiveness of the recommendation system and continuously optimize the recommendation strategy.

[0347] T5 intelligent customer service: Use natural language processing and speech recognition technologies to achieve intelligent interaction and question answering with customers. Specifically include the following:

[0348] Technology selection: Select appropriate natural language processing and speech recognition technologies according to the requirements of intelligent customer service.

[0349] Knowledge base construction: Establish a rich knowledge base, including frequently asked questions and product information.

[0350] Dialogue management: Design a reasonable dialogue process and interaction method to ensure smooth communication with customers.

[0351] Effect evaluation: Evaluate and monitor the effectiveness of intelligent customer service and continuously optimize the dialogue process and interaction method.

[0352] The large model-based bank customer management system integrates multiple modules such as market research, sales management, customer service, and customer satisfaction, achieving comprehensive coverage of bank customer management and improving management efficiency and customer experience. The system utilizes the deep learning and natural language processing capabilities of the large model to intelligently analyze and process customer data, realizing functions such as intelligent identification, rapid response, and personalized service recommendation, enhancing the accuracy and response speed of services. At the same time, the system integrates multiple service channels such as telephone, network, and social media, realizing unified management and monitoring of customer service requests and improving service efficiency and quality. In addition, the system provides data-driven decision support for the bank by collecting and analyzing customer feedback data, helping the bank optimize service processes, improve product quality, and strengthen personnel training. The system also has the ability of self-learning and optimization, and can continuously adjust service strategies according to customer feedback and business scenarios to improve service levels. The direct benefits brought by these innovation points are the improvement of customer satisfaction, the enhancement of market competitiveness, the improvement of operational efficiency, the reduction of service error rates, and the optimization of resource allocation, jointly promoting the continuous growth of banking business and the deepening of customer relationships.

[0353] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0354] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A bank customer management system based on a large model, characterized in that, Specifically, it includes the following modules: The market research module is a key component in the architecture of the bank's customer management system. Its main goal is to collect and analyze market data to gain in-depth understanding of customer needs, competitive landscape, and market trends. Through this module, the bank can formulate more accurate market strategies, enhance customer satisfaction, and increase market share; The sales management module is responsible for setting sales targets, managing business opportunities, and executing sales activities. Through functions such as sales targets and plans, business opportunity management, and activity management, this module enables comprehensive monitoring and management of the sales process. At the same time, this module is closely integrated with the marketing module and the customer service module to ensure the smooth and efficient operation of the sales process; The customer service module is the core part of the enterprise's interaction with customers, covering aspects such as customer interaction, problem-solving, information management, service delivery, customer feedback, and other related areas in customer service, aiming to enhance customer satisfaction and loyalty; The customer satisfaction module improves service quality and customer experience by systematically collecting, processing, and analyzing customer feedback; The data integration and intelligent processing module: Data integration involves collecting, cleaning, transforming, and storing customer data from internal and external sources; Intelligent processing includes preprocessing, customer segmentation, risk assessment, intelligent recommendation, and intelligent customer service, aiming to provide personalized services and optimize the customer experience.

2. The bank customer management system based on a large model according to claim 1, wherein The market research module includes the following components: Market activity management is responsible for planning and executing market research activities, including determining research topics, designing research plans, and organizing research teams, monitoring the progress and quality of research activities to ensure the smooth progress of research activities; The target customer list stores and manages information on potential customers, including customers' basic information, purchase history, and preferences. Through data analysis and mining, potential target customer groups are identified; Marketing feedback and results collect and analyze feedback data from market research activities. The feedback data includes customer feedback and sales data, evaluate the effectiveness of market research activities, and propose improvement suggestions or optimization strategies; Recommended products and services recommend suitable products and services based on market research results to meet customer needs, and formulate personalized product recommendation plans by combining customer needs and market trends; The intelligent marketing support system uses artificial intelligence technology to deeply analyze and mine market data, providing intelligent marketing strategy suggestions to help the bank formulate more accurate market strategies; Data analysis tools provide data analysis functions, including data cleaning, data visualization, and data mining, supporting various data analysis methods and models to help the bank deeply explore the value of market data.

3. The bank customer management system based on a large model according to claim 1, wherein The market research module includes the following steps: A1 Determine the research objective, clarify the purpose of market research, such as understanding customer needs, evaluating competitors, and analyzing market trends, and set specific research indicators and expected goals to be achieved; A2 Design the research plan, select research methods, including questionnaire surveys, interviews, and data analysis. Design research questionnaires or interview outlines to ensure that the questions are comprehensive and targeted; Determine the research objects, including individual customers, enterprise customers, and industry experts; Conduct research activities of A3, distribute research questionnaires or conduct interviews with target objects, collect and analyze market data, including sales data, customer feedback, and industry reports, monitor the progress of the research to ensure that the research activities are carried out as planned; Data analysis and interpretation of A3, organize and analyze the collected data, extract valuable information, use statistical methods and data mining techniques to explore the laws and trends behind the data, write research reports, summarize the research results and findings, and put forward corresponding suggestions or strategies; Apply the research results of A4, apply the research results to the formulation and adjustment of market strategies, optimize products or services according to the research results, improve customer satisfaction, share the research results with relevant departments, and promote cross-departmental cooperation and information sharing.

4. The bank customer management system based on a large model according to claim 1, characterized in that, The sales management module includes the following components: Sales target and plan management system, used to formulate, track, and adjust sales targets and plans to ensure the orderly progress of sales activities; Business opportunity management system, used to collect, evaluate, allocate, and track business opportunities to improve sales efficiency and customer satisfaction; Activity management system, used to plan, execute, and feedback sales activities to ensure the pertinence and effectiveness of sales activities; Sales monitoring and evaluation system, used to monitor the progress and results of sales activities in real time, regularly evaluate the implementation of sales strategies, and provide adjustment suggestions; Sales data analysis tool, used to deeply analyze sales data, explore potential business opportunities and market trends, and provide data support for the formulation of sales strategies; Sales collaboration and communication tools, the communication tools include instant messaging, email system, and telephone system, used for collaboration and communication between sales personnel and customers, internal teams, to improve sales efficiency and service quality.

5. A bank customer management system based on a large model according to claim 1, characterized in that, The sales management module specifically includes the following steps: Formulation of sales targets and plans of B1, the sales management department formulates specific sales targets and plans according to the data provided by the market research module and the overall business strategy of the bank. These targets and plans include sales volume, number of new customers, and product promotion. The formulation of targets and plans should be based on historical sales data, market trends, and competitor analysis to ensure their feasibility and effectiveness; Business opportunity management of B2, the core of business opportunity management lies in quick response and accurate judgment to ensure that business opportunities are not missed and at the same time improve sales efficiency; Collection of business opportunities of B21, collect potential business opportunities through channels such as market research, customer feedback, and partner recommendations; Evaluation of business opportunities of B22, conduct a preliminary evaluation of the collected business opportunities to determine their potential value and feasibility; Allocation of business opportunities of B23, allocate the evaluated business opportunities to suitable sales personnel or teams for follow-up; Activity management of B3, pay attention to the pertinence and effectiveness of activities, and improve the sales success rate by continuously optimizing the activity content and methods; Activity planning of B31, according to the sales targets and business opportunity situations, plan specific sales activities, and the sales activities include product demonstrations, customer visits, and telemarketing; Activity execution of B32, sales personnel execute sales activities according to the plan and communicate with customers; Activity feedback of B33, after the activity ends, sales personnel record the activity results and feedback for subsequent analysis and improvement; B4 Sales monitoring and evaluation is an important part of sales management. Through real-time and regular analysis, problems can be discovered in a timely manner and measures can be taken to ensure the smooth realization of sales targets; B41 Real-time monitoring: Through the system, the progress and results of sales activities are monitored in real time, including sales volume, the number of new customers or other relevant key indicators; B42 Regular evaluation: Regularly evaluate the implementation of sales targets and plans, and analyze existing problems and reasons; B43 Adjust strategies: According to the evaluation results, adjust sales strategies and targets in a timely manner to respond to market changes.

6. The bank customer management system based on a large model according to claim 1, wherein The customer service module includes the following components: Service request management: Responsible for receiving and recording various service requests from customers. The service requests include consultations, complaints, and suggestions. Through an automated receiving system, which includes online customer service and telephone customer service, instant capture and classification of service requests are achieved; Product service configuration: According to the needs of customers and the bank's product lines, recommend suitable products and services to customers, and provide detailed product information and service descriptions. This part needs to be closely integrated with the bank's marketing system and product database to ensure the accuracy and timeliness of information; Business application monitoring: Real-time track and monitor customers' business applications to ensure the timely processing and approval of applications. At the same time, provide a real-time query function for the application progress, so that customers can easily understand the application status at any time; Complaint management: Establish a complaint handling process, classify, record, and track customers' complaints. Through an automated complaint analysis system, identify the root causes and hot issues of complaints, and provide a basis for the bank to improve services; Contract signing information management: Manage customers' contract signing information, including contract content, signing date, and signing period. This part needs to ensure the confidentiality and security of information to prevent information leakage and abuse; Organization and personnel management: Organize and manage the customer service team, including personnel recruitment, training, assessment, and motivation. By optimizing personnel allocation and improving personnel quality, the overall service level of the customer service team is improved; Knowledge base management: Establish and maintain a knowledge base for customer service, including answers to common questions, solutions, and product materials. Through intelligent retrieval and query tools, it is convenient for customer service personnel to quickly obtain the required information and improve service efficiency.

7. A bank customer management system based on a large model according to claim 1, characterized in that, The customer service module includes the following steps: C1 Receive service requests: Customers submit service requests through channels such as online customer service and telephone customer service. The system automatically captures the requests and classifies them to generate service work orders; C2 Analyze service requirements: Customer service personnel analyze the specific requirements of customers according to the content of the service work order and determine the required products and services; C3 Recommend product services: According to the needs of customers and the bank's product lines, customer service personnel recommend suitable products and services through the system and provide detailed product information and service descriptions; C4 Process business applications: After customers submit business applications, the system conducts real-time tracking and monitoring of the applications. Customer service personnel communicate with customers in a timely manner according to the application progress to ensure the timely processing and approval of applications; C5 Handle complaints and suggestions. For customer complaints and suggestions, customer service staff need to record and classify them in a timely manner. Through an automated complaint analysis system, identify the root causes and hot issues of complaints, and propose improvement measures; C6 Maintain signing information. Customer service staff need to regularly update and maintain customers' signing information to ensure the accuracy and integrity of the information; C7 Query and update the knowledge base. During the process of providing services to customers, customer service staff need to query the information in the knowledge base at any time. At the same time, for newly emerging problems and solutions, they need to update them to the knowledge base in a timely manner for subsequent use; C8 Service evaluation and feedback. After the service is completed, the system automatically invites customers to evaluate the service. Customer service staff need to continuously improve the service quality based on customers' evaluations and suggestions; The steps of combining the customer service module with the large model are as follows: D1 Data collection and integration. Collect information such as customers' historical data, behavior patterns, and service requests, clean, label, and integrate the data to provide a reliable data basis for training the large model; D2 Large model selection and training; D3 System development and integration. Develop the relevant functions of the customer service module, including intelligent recognition, quick response, and personalized recommendation. Integrate the trained large model into the customer service module to achieve intelligent services; D4 Testing and optimization. Conduct a comprehensive test on the customer service module, including understanding ability, answer accuracy, and user satisfaction. Optimize and adjust the model according to the test results to improve service quality and efficiency; D5 Go live operation and monitoring. Put the customer service module into operation to provide intelligent services to customers, monitor the running status of the module and user feedback in real time, and discover and improve problems in a timely manner.

8. The bank customer management system based on a large model according to claim 7, characterized in that, The selection and training of the large model are specifically divided into the selection of the large model and the training of the large model. The selection of the large model specifically includes the following parts: Model type. According to the requirements of the customer service module, select a large model based on the Transformer architecture. This type of model has achieved remarkable results in the field of natural language processing and is suitable for understanding and generating text; Model scale. According to the computing resources and business requirements, select a large model of the corresponding scale. The larger the model scale, the more language features can usually be captured, but more computing resources are also required for training and inference; Pre-training and fine-tuning. Select a model that has been pre-trained on a large-scale dataset, and then fine-tune it for the specific tasks of the customer service module. The knowledge of the pre-trained model can be utilized while adapting to the requirements of specific tasks; The training of the large model specifically includes the following contents: Data preparation. Collect and organize data related to the customer service module, including customer consultation records and historical conversation data, to ensure the quality and diversity of the data to cover various possible customer problems and scenarios; Data preprocessing. Clean, label, and format the collected data, including removing irrelevant information, correcting spelling mistakes, and labeling the intent and entities of the conversation; Model training. Use the prepared dataset to train the selected large model. The training process usually includes the following steps: a Initialize parameters, using random initialization or the parameters of a pre-trained model as a starting point; b Forward propagation, passing the input data through the model for forward propagation to calculate the output; c Calculate the loss, calculating the value of the loss function based on the output and the true labels. Commonly used loss functions include cross-entropy loss; d Backward propagation, calculating the gradient of the loss function with respect to each parameter through the backward propagation algorithm; e Parameter update, using an optimizer to update the parameters of the model according to the gradient. Optimizers include Adam and SGD; f Algorithms and formulas, The formula for the gradient descent algorithm is: where θ represents the model parameters, η represents the learning rate, and L represents the loss function; The update rule of the Adam optimizer includes calculating the first-order moment estimate and the second-order moment estimate of the gradient, and adjusting the learning rate of each parameter according to these estimates; h Self-attention mechanism, the self-attention mechanism widely used in the Transformer model can capture long-range dependencies in the sequence, and its calculation formula is: Attention(Q, K, V) = softmax(QKT / √dk)V where Q, K, and V represent the query, key, and value matrices respectively, and dk represents the dimension of the key vector; Model evaluation and debugging, during the training process, regularly evaluate the performance of the model. The performance includes accuracy, recall, and F1 score. Adjust the model parameters, optimizer settings, or data preprocessing methods according to the evaluation results to improve the model performance; Model fine-tuning, for the specific tasks of the customer service module, fine-tune the trained model, including adjusting the output layer of the model to adapt to specific classification tasks or generation tasks. During fine-tuning, use the learning rate and the number of training epochs to avoid overfitting; Deployment and monitoring, deploy the trained model to the customer service module, and set up monitoring and logging to track the performance of the model. Regularly retrain the model to update its knowledge and adapt to the new data distribution.

9. A bank customer management system based on a large model according to claim 1, characterized in that, The customer satisfaction module includes the following steps: E1 Data collection, including customer feedback channel setting, feedback content collection, and data format conversion; The customer feedback channel setting includes: Online channels, set obvious "Customer Feedback" entrances on the platforms of bank websites and mobile applications, including online questionnaires, message boards, and online customer service, to ensure that customers can provide feedback conveniently and quickly; Phone channels, establish a dedicated customer satisfaction survey phone hotline, and have professional customer service staff answer and record customer feedback; On-site channels, set up suggestion boxes and questionnaire distribution points at physical branches to ensure that on-site customers can also provide feedback conveniently; The feedback content collection includes: Clarify the collection content. When designing questionnaires or collecting feedback, clearly list the content to be collected, including customers' evaluations and opinions on bank products, services, personnel, and the environment; Provide multiple feedback forms. In addition to text feedback, also provide feedback options such as ratings and satisfaction levels to facilitate customers' selection; The data format conversion includes: Unify the data format, convert the data collected from different channels and different formats into a unified standard format, including converting text feedback into text format and converting ratings into numerical formats; Data cleaning and sorting to remove duplicate, invalid, and abnormal data to ensure data accuracy and reliability; E2 Data processing and analysis, including data cleaning, data standardization, and data analysis; The data cleaning specifically includes: Removing duplicate data, using data processing tools or software to screen out and delete duplicate data; Handling abnormal data. For obviously abnormal data, which includes scores beyond a reasonable range and unrecognizable text, conduct manual verification or deletion; The data standardization specifically includes: Unifying the scoring criteria. For scoring data, formulate a unified scoring criteria, which includes converting a score with a full score of 10 points into a percentage-based score; Encoding text data. For text feedback data, perform encoding processing to facilitate subsequent data analysis and mining; The data analysis specifically includes: Quantitative analysis. Using data analysis software, calculate indicators such as the average score and standard deviation of customer satisfaction to evaluate the overall level of customer satisfaction; Qualitative analysis. Summarize and extract key information and opinions from text feedback data to understand customers' specific needs and expectations for the bank; Trend analysis. Compare historical data, analyze the changing trend of customer satisfaction, and identify potential problems and improvement directions; E3 Result presentation and decision support, including visual report production and decision support. The visual report production specifically includes: Selecting appropriate visualization tools. According to data characteristics and requirements, select appropriate visualization tools, which include bar charts, line charts, and pie charts; Producing reports. Present the data analysis results in a visual report, including customer satisfaction index, key information extraction, and trend analysis; The decision support specifically includes: Providing decision-making suggestions. Based on the data analysis results, provide decision-making suggestions for the bank's management, and the suggestions include optimizing service processes, improving product quality, and strengthening personnel training; Formulating improvement measures. Clearly define the specific content, responsible person, and time node of the improvement measures to ensure the effective implementation of the improvement measures; E4 Implementation and feedback of improvement measures, including project management, customer feedback mechanism, and continuous optimization; The project management specifically includes: Establishing project management tools. Using project management tools, which include Trello and Jira, to track and manage the implementation progress of improvement measures; Regularly evaluating the progress. Regularly evaluate the implementation progress and effect of improvement measures to ensure that the improvement measures are carried out according to the plan; The customer feedback mechanism specifically includes: Establishing a feedback mechanism. Establish a continuous customer feedback mechanism to ensure that customers can provide feedback and suggestions at any time; Collecting feedback and evaluating the effect. Collect customers' feedback on improvement measures, evaluate the effect of improvement measures, and form a closed-loop management; The continuous optimization specifically includes: Analyzing feedback data. Conduct in-depth analysis of the collected feedback data to understand customers' reactions and opinions on improvement measures; Adjusting improvement measures. According to the feedback data, adjust and improve the original improvement measures to ensure the continuous improvement of customer satisfaction; The customer satisfaction module includes a data collection module, a data processing and analysis module, a result presentation and decision support module, and an improvement measure implementation and feedback module.

10. A bank customer management system based on a large model according to claim 1, characterized in that, The data integration in the data integration and intelligent processing module includes the following steps: Data collection, collecting customer-related data from internal and external data sources of the bank to ensure the comprehensiveness and diversity of the data; Data cleaning, preprocessing the collected data to ensure the accuracy and consistency of the data; Data transformation and mapping, transforming data in different formats and standards into a unified format and standard and establishing data mapping relationships; Data storage, storing the cleaned and transformed data in a unified database for subsequent analysis and processing; The specific intelligent processing includes the following steps: Data preprocessing, further preprocessing the stored data to meet the requirements of intelligent analysis; Customer segmentation, using algorithms such as clustering analysis and decision trees to divide customers into different groups or market segments based on customers' basic information, transaction records, and consumption habits; Risk assessment, using machine learning algorithms to conduct risk assessments on customers' credit status, transaction behaviors, or other relevant data; Intelligent recommendation, providing personalized product or service recommendations for customers based on customers' consumption habits and interests using collaborative filtering and deep learning; Intelligent customer service, using technologies such as natural language processing and speech recognition to achieve intelligent interaction and Q&A with customers.

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