An AI-driven intelligent investment banking service system and method

Abstract

The present invention discloses an AI-driven intelligent investment banking service system and method, comprising the following steps: the system collects and processes structured and unstructured data from financial markets, industry reports, social media and customer transaction records through an information release and interpretation module to generate a standardized data set; the AI ​​intelligent search module retrieves and extracts features from the data set to generate a high-dimensional feature representation. Based on these features, the system generates investment advice based on customer historical behavior and investment preferences, and optimizes investment strategies through self-evolving algorithms. The industrial brain module monitors key nodes in the industrial chain in real time, identifies bottlenecks and risks; the system also supports the development, configuration and deployment of AI assistants through the AI ​​application supermarket module, and provides assistant recommendations to comprehensively improve the intelligence and automation level of investment banking business. The present invention aims to optimize investment banking business processes and enhance data analysis, decision support and automated trading capabilities.

Description

Technical Field

[0001] The present invention relates to the field of financial technology, and in particular to an AI-driven intelligent investment banking service system and method. Background Art

[0002] With the rapid development of financial markets and the advancement of technology, traditional property transactions and investment banking are facing unprecedented challenges. The traditional business model relies on manual processing, analysis and decision-making, but in today's rapidly changing market environment, this approach has become inefficient and difficult to meet market needs.

[0003] Inefficient information acquisition and processing is the main bottleneck of current property rights transactions and investment banking business. Existing technical means are often unable to efficiently process large amounts of heterogeneous data obtained from multiple sources. These data include dynamic changes in financial markets, social media opinions, adjustments to policies and regulations, industry reports, and customer transaction records. Traditional systems usually rely on manual collection, screening, and organization of data. Especially when faced with unstructured data, such as policy documents and news reports, existing technologies are difficult to quickly and effectively perform semantic analysis and standardized processing of data, resulting in insufficient information utilization and affecting the timeliness and accuracy of decision-making.

[0004] Insufficient decision-making support capabilities are also a major flaw in existing technologies. Traditional investment decision support systems are mostly based on basic data analysis and are unable to cope with complex and rapidly changing market environments. Such systems can often only provide simple historical data reviews or basic market analysis, and lack in-depth understanding of the market and forward-looking forecasting capabilities. When major changes or emergencies occur in the market, existing systems find it difficult to quickly adjust investment strategies and cannot optimize decisions in real time. The accuracy and effectiveness of investment decisions cannot be guaranteed in a complex market environment.

[0005] Incomplete analysis of the industrial chain is also a common defect in traditional investment banking services. Existing technologies are unable to fully and dynamically display and analyze complex industrial chain relationships, especially the flow of upstream and downstream supply chains, real-time monitoring of key nodes, and dynamic changes in regional industrial layout. This limitation causes investment banks to lack a global perspective and in-depth understanding of the entire industrial ecosystem when making investment decisions, thus affecting the accuracy and timeliness of decisions. The low level of intelligence is also a key issue restricting property rights transactions and investment banking services. The current property rights trading platform has obvious deficiencies in services, automated transactions, and intelligence. Users need to face a large amount of complex information and diversified needs in investment banking business, and existing systems often cannot provide solutions for different business needs. Systems with low levels of automation find it difficult to respond to market changes in real time, resulting in low business execution efficiency. This defect is particularly evident in high-frequency trading and complex decision support scenarios.

[0006] Low efficiency in knowledge management and sharing is also an important factor restricting the quality of investment banking services. In the current investment banking business, a large amount of industry knowledge, market experience and investment strategies are difficult to accumulate and share effectively, resulting in inefficient knowledge utilization. The existing system lacks an efficient knowledge management mechanism, and is unable to generate and update knowledge graphs through automated means, nor can it dynamically adjust the knowledge management system according to market dynamics and user behavior, resulting in low efficiency in knowledge utilization. Especially in the face of a complex market environment, the system finds it difficult to provide users with accurate and comprehensive knowledge support, further affecting the business's decision-making ability and response speed.

[0007] Therefore, how to provide an AI-driven intelligent investment banking service system and method is an issue that technical personnel in this field urgently need to solve. Summary of the invention

[0008] One purpose of the present invention is to propose an AI-driven intelligent investment banking service system and method. The present invention utilizes bio-inspired swarm optimization technology, AI intelligent search, dynamic panoramic display algorithm, self-evolving algorithm decision technology and large language model technology, and describes in detail how to realize intelligent information collection, investment decision optimization, industrial chain analysis and knowledge management sharing in investment banking business. The method effectively improves the efficiency of information acquisition and processing, enhances the accuracy and foresight of decision-making, and has the advantages of high intelligence, dynamic adjustment of investment strategies, panoramic display of industrial chains and knowledge management optimization.

[0009] An AI-driven intelligent investment banking service system and method according to an embodiment of the present invention includes the following steps:

[0010] S1. Through the information release and interpretation module, the system collects structured and unstructured data from financial markets, industry reports, social media, news and customer transaction records, uses bio-inspired swarm optimization technology to dynamically adjust the data collection path and frequency, performs semantic analysis, formatting, standardization and deduplication on the data, and generates a standardized data set;

[0011] S2. Utilization The intelligent search module retrieves and extracts features from the generated standardized data set, combining structured and unstructured data to generate high-dimensional feature representations;

[0012] S3. Based on the generated high-dimensional feature representation, combined with the customer's historical trading behavior and investment preferences, generate investment recommendations. Through self-evolving algorithmic decision-making technology, the system continuously optimizes investment strategies so that decisions can adapt to the dynamic market environment and achieve the best results.

[0013] S4. Through the industrial brain module, the system uses a dynamic panoramic display algorithm to generate and update the global view of the industrial chain in real time, showing upstream and downstream relationships, supply chain flows and regional layouts, and monitors key nodes in the industrial chain through real-time data to identify potential bottlenecks and risks;

[0014] S5. Through the large language model module and the intelligent knowledge mapping algorithm, the system automatically generates and adjusts the knowledge graph, provides dynamic knowledge management and sharing functions based on user behavior and market changes, and optimizes knowledge acquisition efficiency;

[0015] S6. Based on the optimized investment portfolio, the trading strategy is executed through the automated trading module, and the trading behavior is reviewed in real time through the compliance monitoring module, and compliance reports are generated and compliance warnings are provided;

[0016] S7. Based on investment strategies and knowledge management data, the system automatically generates investment reports through the intelligent report writing module. The report content covers market trend analysis, investment advice and risk warning, and is pushed to customers through email and mobile applications to provide decision-making feedback;

[0017] S8, through Application supermarket module, supporting modularization The development and deployment of assistants allows for the Assistant to configure and expand, using Use automated recommendation mechanisms to provide users with appropriate Assistant, optimize business efficiency.

[0018] Optionally, the S1 specifically includes:

[0019] S11. The system collects structured and unstructured data from multiple data sources, allocates data sources to corresponding collection nodes according to the type and importance of data sources, and reasonably schedules and executes data collection tasks;

[0020] S12. Use bio-inspired swarm optimization technology to dynamically adjust data collection paths and frequency , optimize the path and frequency according to the collection cost and information redundancy:

[0021] ;

[0022] in, represents the path distance, The effective amount of information collected is and is the weight coefficient;

[0023] S13, perform semantic analysis on the collected unstructured data, extract key information therein, and convert the information into structured data;

[0024] S14. Format and standardize the parsed data to convert data from different sources into a unified format;

[0025] S15, through the deduplication algorithm, using the similarity function Identify and remove duplicate data to further optimize the quality and efficiency of the data set;

[0026] S16. After data processing is completed, the system generates the final standardized data set ,The standardized dataset covers all processed multi-source heterogeneous data.

[0027] Optionally, the S2 specifically includes:

[0028] S21. The system receives the generated standardized data set , and enter it into Intelligent search module performs retrieval by building an index Data Unit Carry out optimization processing;

[0029] S22. Using index For each data unit Perform feature extraction and generate preliminary feature representation for each data unit , combining structured and unstructured data for feature extraction:

[0030] ;

[0031] in, represents the structured data feature extraction function, represents the unstructured data feature extraction function, For fusion operation, is the initial feature representation;

[0032] S23, based on this, the system introduces a hierarchical feature representation learning algorithm. Perform multi-level feature extraction and gradually generate higher-dimensional feature representations. For each layer , generating Layer feature representation:

[0033] ;

[0034] in, For the The weight matrix of the layer, is the bias term, is a nonlinear activation function, For the Feature representation of the layer;

[0035] S24, hierarchical feature representation After multi-level processing, the system accumulates and fuses the features of all levels to generate the final high-dimensional feature representation :

[0036] ;

[0037] in, The weights of each layer of features;

[0038] S25, the system represents the high-dimensional features of all data units Fusion is performed to generate the final high-dimensional feature representation matrix .

[0039] Optionally, the S3 specifically includes:

[0040] S31, the system receives the generated high-dimensional feature representation matrix , combined with the customer's historical transaction behavior data and investment preference data , generate preliminary investment recommendations;

[0041] S32. Iteratively optimize the initially generated investment recommendations through a self-evolving algorithmic decision module, and dynamically adjust the strategy based on the current market environment;

[0042] S33. Introducing a multi-layer risk management weight matrix , used to adjust investment strategies to cope with market fluctuations:

[0043] ;

[0044] in, is the risk adjustment factor, is the risk weight matrix, Dynamically modify parameters for strategies;

[0045] S34. In the iteration cycle In the process of adjusting strategies based on market environment and customer transaction feedback, a final optimized investment proposal is generated. .

[0046] Optionally, the S4 specifically includes:

[0047] S41. The system receives real-time data from multiple data sources, including supply chain logistics information, enterprise production data, market changes and regional layout data, and generates a preliminary data structure ;

[0048] S42. Using the generated preliminary data structure Generate a global view through a dynamic panoramic display algorithm , which shows the upstream and downstream relationships , Supply Chain Flow and regional layout :

[0049] ;

[0050] in, represents a function that combines multiple inputs to generate a global view;

[0051] S43. Integrate supply chain flows , Upstream and downstream relationships and data structures Key node data in , computing bottlenecks in the industry chain and risks :

[0052] ;

[0053] ;

[0054] in, For time The moment Key node data, For the The weight of the key nodes in bottleneck identification, is the risk assessment function;

[0055] S44. Based on the identified bottlenecks and risks , the system dynamically updates the global view , generate warning information and trigger response mechanisms, and adjust industry chain strategies to deal with real-time risks;

[0056] S45, the system in each time period Continuously monitor, update and optimize the global view, combined with real-time data structure Adjusting supply chain flows and regional layout , showing the dynamic changes of the global view of the industrial chain.

[0057] Optionally, the S5 specifically includes:

[0058] S51. The system receives user behavior data and market change data , generate preliminary knowledge structure data ;

[0059] S52: The received knowledge structure data is processed by the large language model module. Perform semantic analysis and feature extraction to generate a preliminary knowledge graph ;

[0060] S53, combined with intelligent knowledge mapping algorithm, the generated preliminary knowledge graph Make dynamic adjustments based on user behavior data Changes and market dynamics Changes to optimize the knowledge graph:

[0061] ;

[0062] in, is the adjustment factor, and Respectively User behavior characteristics and The weight of each market change characteristic;

[0063] S54, based on the adjusted knowledge graph , generate dynamic knowledge management and sharing functions, and provide knowledge recommendations through knowledge recommendation functions;

[0064] S55, system in time period Continuously monitor user behavior and market trends, and update and optimize the knowledge graph.

[0065] Optionally, the S8 specifically includes:

[0066] S81. The system receives business demand data from users and investment banking business environment data , generate a preliminary configuration plan ;

[0067] S82, based on the preliminary configuration plan , the system allows users to Application supermarket module selection modularization The functions of the assistant, combined with business demand data and modular functional combination , generating the final Assistant Configuration Scheme ;

[0068] S83. Use the automated recommendation mechanism to determine the final configuration plan based on the user's and investment banking environment , recommend the best Assistant Program :

[0069] ;

[0070] in, For different The weight of the helper function module, is the matching score function, For the final selection Assistant configuration scheme, Indicates Functional modules, Indicates business environment characteristics;

[0071] S84, according to the recommended Assistant Program ,pass The application supermarket module has been deployed, and the system monitors changes in the user's business environment and demand changes , and adjust according to these changes The assistant's functions should remain adaptable to business needs;

[0072] S85, the system continues to respond to user feedback and environmental data right The assistant's configuration is dynamically adjusted and new recommendations are generated , push new functional modules to adapt to changing business needs.

[0073] Optional modules include:

[0074] 1. Information release and interpretation module: including policy and regulation analysis, industry dynamics analysis, enterprise information mining, market trend interpretation and public opinion analysis, automatic analysis of policies and regulations, analysis of industry dynamics, construction of enterprise portraits, interpretation of market trends, monitoring of public opinion and assessment of risks;

[0075] 2. Intelligent search module: includes multi-dimensional retrieval, semantic analysis, sorting, real-time search suggestions and visual display, supports multi-source data search, provides intelligent search experience and visual result display;

[0076] 3. Intelligent report writing module: including report template library, data collection and organization, intelligent writing assistance and automatic generation of charts, providing intelligent report templates, automatically processing data, assisting in writing and generating visual reports;

[0077] 4. Industry Brain Module: including industrial chain map, industrial prosperity assessment, risk warning, regional layout analysis and enterprise query, building industrial chain map, assessing prosperity, warning risks, analyzing regional layout and querying enterprise information;

[0078] 5. Application supermarket module: including Assistant classification, search and screening, user feedback and learning, knowledge base management, intelligent interaction and system management, support Assistant development and deployment, providing recommendation and optimization functions;

[0079] 6. Large language model module: includes data collection, pre-training, fine-tuning, model optimization, deployment configuration and interface development, collects and processes data, pre-trains and optimizes models, and supports system integration and deployment.

[0080] The beneficial effects of the present invention are:

[0081] The present invention uses an information release and interpretation module and an AI intelligent search module. The system utilizes bio-inspired bee colony optimization technology and a multi-dimensional intelligent retrieval engine to quickly collect, process and analyze multi-source data from financial markets, social media, industry reports, etc., significantly improving the efficiency of information acquisition and processing, and ensuring that investment decisions are based on more comprehensive, timely and accurate information; it also generates investment advice through high-dimensional feature extraction and self-evolution algorithm decision-making technology, combined with the customer's historical trading behavior and investment preferences. The system can optimize decisions in a dynamic market environment and help users make more accurate investment decisions. The integration of the industrial brain module and the AI ​​application supermarket module enables the platform to provide more in-depth and comprehensive decision support. Especially in complex industrial chain analysis, the system generates a real-time updated global view through a dynamic panoramic display algorithm, showing upstream and downstream relationships, supply chain flows and regional layouts, helping users identify and monitor key nodes and potential bottlenecks in the industrial chain, and providing an important basis for investment decisions.

[0082] The present invention supports AI assistant configuration and dynamic knowledge management through the AI ​​application supermarket module and the large language model module. Users can choose different AI assistants according to their own business needs. At the same time, the system dynamically adjusts the knowledge graph according to market changes and user behavior to provide intelligent services. This service can meet the specific needs of different users and improve the user experience.

[0083] In terms of knowledge management, the system can automatically generate and adjust knowledge graphs through large language model modules and intelligent knowledge mapping algorithms, and combine knowledge base management and AI assistant functions to promote the accumulation and sharing of industry knowledge and improve knowledge utilization efficiency. The knowledge graph is continuously optimized with market dynamics and user needs, providing users with knowledge recommendation services, greatly improving the effect of knowledge acquisition and utilization. The system adopts a modular design, especially the openness and flexibility of the AI ​​application supermarket module, which makes the system highly scalable and can continuously expand new functional modules and services according to different investment banking business needs. While meeting diversified business needs, it reduces system maintenance and upgrade costs. Through highly automated functions, it effectively reduces the operating costs of investment banking business, reduces the need for manual intervention, and significantly improves the overall operating efficiency of the platform. It also effectively enhances the risk management capabilities of investment banking business through intelligent risk warning systems and multi-dimensional data analysis technologies. The system can monitor market dynamics and potential risks in the industrial chain in real time, identify and warn of market fluctuations in a timely manner, help users cope with complex market environments, and ensure the security of investment decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0085] Figure 1 A flowchart of an AI-driven intelligent investment banking service system and method proposed by the present invention;

[0086] Figure 2 A flowchart for data retrieval and high-dimensional feature representation generation by the AI ​​intelligent search module of the present invention;

[0087] Figure 3 Flow chart of optimizing investment recommendations for the self-evolving algorithm decision-making technology of the present invention. DETAILED DESCRIPTION

[0088] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0089] refer to Figure 1-3 , an AI-driven intelligent investment banking service system and method, comprising the following steps:

[0090] S1. Through the information release and interpretation module, the system collects structured and unstructured data from financial markets, industry reports, social media, news and customer transaction records, uses bio-inspired swarm optimization technology to dynamically adjust the data collection path and frequency, performs semantic analysis, formatting, standardization and deduplication on the data, and generates a standardized data set;

[0091] S2. Utilization The intelligent search module retrieves and extracts features from the generated standardized data set, combining structured and unstructured data to generate high-dimensional feature representations;

[0092] S3. Based on the generated high-dimensional feature representation, combined with the customer's historical trading behavior and investment preferences, generate investment recommendations. Through self-evolving algorithmic decision-making technology, the system continuously optimizes investment strategies so that decisions can adapt to the dynamic market environment and achieve the best results.

[0093] S4. Through the industrial brain module, the system uses a dynamic panoramic display algorithm to generate and update the global view of the industrial chain in real time, showing upstream and downstream relationships, supply chain flows and regional layouts, and monitors key nodes in the industrial chain through real-time data to identify potential bottlenecks and risks;

[0094] S5. Through the large language model module and the intelligent knowledge mapping algorithm, the system automatically generates and adjusts the knowledge graph, provides dynamic knowledge management and sharing functions based on user behavior and market changes, and optimizes knowledge acquisition efficiency;

[0095] S6. Based on the optimized investment portfolio, the trading strategy is executed through the automated trading module, and the trading behavior is reviewed in real time through the compliance monitoring module, and compliance reports are generated and compliance warnings are provided;

[0096] S7. Based on investment strategies and knowledge management data, the system automatically generates investment reports through the intelligent report writing module. The report content covers market trend analysis, investment advice and risk warning, and is pushed to customers through email and mobile applications to provide decision-making feedback;

[0097] S8, through Application supermarket module, supporting modularization The development and deployment of assistants allows for the Assistant to configure and expand, using Use automated recommendation mechanisms to provide users with appropriate Assistant, optimize business efficiency.

[0098] In this implementation, S1 specifically includes:

[0099] S11. The system collects structured and unstructured data from multiple data sources, allocates data sources to corresponding collection nodes according to the type and importance of data sources, and reasonably schedules and executes data collection tasks;

[0100] S12. Use bio-inspired swarm optimization technology to dynamically adjust data collection paths and frequency , optimize the path and frequency according to the collection cost and information redundancy:

[0101] ;

[0102] in, represents the path distance, The effective amount of information collected is and is the weight coefficient;

[0103] S13, perform semantic analysis on the collected unstructured data, extract key information therein, and convert the information into structured data;

[0104] S14. Format and standardize the parsed data to convert data from different sources into a unified format;

[0105] S15, through the deduplication algorithm, using the similarity function Identify and remove duplicate data to further optimize the quality and efficiency of the data set;

[0106] S16. After data processing is completed, the system generates the final standardized data set ,The standardized dataset covers all processed multi-source heterogeneous data.

[0107] In this implementation, S2 specifically includes:

[0108] S21. The system receives the generated standardized data set , and enter it into Intelligent search module performs retrieval by building an index Data Unit Carry out optimization processing;

[0109] S22. Using index For each data unit Perform feature extraction and generate preliminary feature representation for each data unit , combining structured and unstructured data for feature extraction:

[0110] ;

[0111] in, represents the structured data feature extraction function, represents the unstructured data feature extraction function, For fusion operation, is the initial feature representation;

[0112] S23, based on this, the system introduces a hierarchical feature representation learning algorithm. Perform multi-level feature extraction and gradually generate higher-dimensional feature representations. For each layer , generating Layer feature representation:

[0113] ;

[0114] in, For the The weight matrix of the layer, is the bias term, is a nonlinear activation function, For the Feature representation of the layer;

[0115] S24, hierarchical feature representation After multi-level processing, the system accumulates and fuses the features of all levels to generate the final high-dimensional feature representation :

[0116] ;

[0117] in, The weights of each layer of features;

[0118] S25, the system represents the high-dimensional features of all data units Fusion is performed to generate the final high-dimensional feature representation matrix .

[0119] In this implementation, S3 specifically includes:

[0120] S31, the system receives the generated high-dimensional feature representation matrix , combined with the customer's historical transaction behavior data and investment preference data , generate preliminary investment recommendations;

[0121] S32. Iteratively optimize the initially generated investment recommendations through a self-evolving algorithmic decision module, and dynamically adjust the strategy based on the current market environment;

[0122] S33. Introducing a multi-layer risk management weight matrix , used to adjust investment strategies to cope with market fluctuations:

[0123] ;

[0124] in, is the risk adjustment factor, is the risk weight matrix, Dynamically modify parameters for strategies;

[0125] S34. In the iteration cycle In the process of adjusting strategies based on market environment and customer transaction feedback, a final optimized investment proposal is generated. .

[0126] In this implementation, S4 specifically includes:

[0127] S41. The system receives real-time data from multiple data sources, including supply chain logistics information, enterprise production data, market changes and regional layout data, and generates a preliminary data structure ;

[0128] S42. Using the generated preliminary data structure Generate a global view through a dynamic panoramic display algorithm , which shows the upstream and downstream relationships , Supply Chain Flow and regional layout :

[0129] ;

[0130] in, represents a function that combines multiple inputs to generate a global view;

[0131] S43. Integrate supply chain flows , Upstream and downstream relationships and data structures Key node data in , bottlenecks in the computing industry chain and risks :

[0132] ;

[0133] ;

[0134] in, For time The moment Key node data, For the The weight of the key nodes in bottleneck identification, is the risk assessment function;

[0135] S44. Based on the identified bottlenecks and risks , the system dynamically updates the global view , generate warning information and trigger response mechanisms, and adjust industry chain strategies to deal with real-time risks;

[0136] S45, the system in each time period Continuously monitor, update and optimize the global view, combined with real-time data structure Adjusting supply chain flows and regional layout , showing the dynamic changes of the global view of the industrial chain.

[0137] In this implementation manner, S5 specifically includes:

[0138] S51. The system receives user behavior data and market change data , generate preliminary knowledge structure data ;

[0139] S52: The received knowledge structure data is processed by the large language model module. Perform semantic analysis and feature extraction to generate a preliminary knowledge graph ;

[0140] S53, combined with intelligent knowledge mapping algorithm, the generated preliminary knowledge graph Make dynamic adjustments based on user behavior data Changes and market dynamics Changes to optimize the knowledge graph:

[0141] ;

[0142] in, is the adjustment factor, and Respectively User behavior characteristics and The weight of each market change characteristic;

[0143] S54, based on the adjusted knowledge graph , generate dynamic knowledge management and sharing functions, and provide knowledge recommendations through knowledge recommendation functions;

[0144] S55, system in time period Continuously monitor user behavior and market trends, and update and optimize the knowledge graph.

[0145] In this implementation manner, S8 specifically includes:

[0146] S81. The system receives business demand data from users and investment banking business environment data , generate a preliminary configuration plan ;

[0147] S82, based on the preliminary configuration plan , the system allows users to Application supermarket module selection modularization The functions of the assistant, combined with business demand data and modular functional combination , generating the final Assistant Configuration Scheme ;

[0148] S83. Use the automated recommendation mechanism to determine the final configuration plan based on the user's and investment banking environment , recommend the best Assistant Program :

[0149] ;

[0150] in, For different The weight of the helper function module, is the matching score function, For the final selection Assistant configuration scheme, Indicates Functional modules, Indicates business environment characteristics;

[0151] S84, according to the recommended Assistant Program ,pass The application supermarket module has been deployed, and the system monitors changes in the user's business environment and demand changes , and adjust according to these changes The assistant's functions should remain adaptable to business needs;

[0152] S85, the system continues to respond to user feedback and environmental data right The assistant's configuration is dynamically adjusted and new recommendations are generated , push new functional modules to adapt to changing business needs.

[0153] In this embodiment, the following modules are included:

[0154] 1. Information release and interpretation module: including policy and regulation analysis, industry dynamics analysis, enterprise information mining, market trend interpretation and public opinion analysis, automatic analysis of policies and regulations, analysis of industry dynamics, construction of enterprise portraits, interpretation of market trends, monitoring of public opinion and assessment of risks;

[0155] 2. Intelligent search module: includes multi-dimensional retrieval, semantic analysis, sorting, real-time search suggestions and visual display, supports multi-source data search, provides intelligent search experience and visual result display;

[0156] 3. Intelligent report writing module: including report template library, data collection and organization, intelligent writing assistance and automatic generation of charts, providing intelligent report templates, automatically processing data, assisting in writing and generating visual reports;

[0157] 4. Industry Brain Module: including industrial chain map, industrial prosperity assessment, risk warning, regional layout analysis and enterprise query, building industrial chain map, assessing prosperity, warning risks, analyzing regional layout and querying enterprise information;

[0158] 5. Application supermarket module: including Assistant classification, search and screening, user feedback and learning, knowledge base management, intelligent interaction and system management, support Assistant development and deployment, providing recommendation and optimization functions;

[0159] 6. Large language model module: includes data collection, pre-training, fine-tuning, model optimization, deployment configuration and interface development, collects and processes data, pre-trains and optimizes models, and supports system integration and deployment.

[0160] Embodiment 1:

[0161] In order to verify the feasibility of the present invention in implementation, the present invention is applied to a large investment bank that plans to provide its customers with a brand-new investment banking business support system, with the goal of further improving customers' investment returns and reducing investment risks by improving information processing efficiency, optimizing investment decisions, and enhancing risk management capabilities. The investment bank previously relied on traditional manual information processing and decision-making methods, resulting in slow information acquisition, difficulty in data integration, and lagging investment strategies, and customers' satisfaction with its services gradually decreased; for this reason, the investment bank decided to introduce the AI-driven intelligent investment banking service system of the present invention to address the above problems.

[0162] The investment bank needs to provide its clients with market analysis reports on a regular basis, covering financial market dynamics, client historical transaction data, policy and regulatory updates, and other aspects. The information collection work of the investment bank has always been inefficient, especially the unstructured data from industry reports and social media are difficult to process quickly. By introducing the AI-driven intelligent investment banking service system of the present invention, the investment bank uses an information release and interpretation module to automatically collect multi-source data such as financial markets, industry reports, social media, and especially unstructured data such as policies and regulations. Through the bio-inspired swarm optimization technology, the data collection path and frequency are dynamically adjusted to reduce the collection of redundant information. The system obtains market change data from the financial market in real time and generates a structured data set through standardized processing.

[0163] After implementing this technology, the information processing time of the investment bank was shortened from the original average of 24 hours to 6 hours, greatly improving the efficiency of data collection and processing. For example, in June 2023, the system collected about 5 million data from more than 100 data sources, including unstructured data such as industry reports, social media comments, and policy updates. The processed standardized data set size is 30GB, and about 90% of redundant data is effectively removed; the investment bank's customers need personalized investment advice, but the traditional investment decision support system lacks adaptability to complex market environments and is difficult to respond to market changes in real time; by using the AI ​​intelligent search module of the present invention, the investment bank can quickly retrieve the generated standardized data set, combine the customer's historical transaction behavior data and investment preferences, generate high-dimensional feature representations, and analyze and optimize these features with self-evolving algorithmic decision technology to dynamically generate personalized investment advice. In the case of large market fluctuations, the system adjusts the strategy through real-time data updates to ensure that decisions can quickly adapt to changes in the market environment and achieve optimal results.

[0164] During the investment service from July to September 2023, the system optimized clients' investment portfolios in real time according to the sharp fluctuations in the financial markets, and the average investment return rate increased from the original 8% to 12%. For example, in a certain client's investment portfolio adjustment, the system generated optimized investment advice in less than 10 minutes through a self-evolution algorithm based on the client's historical trading behavior and current market dynamics, thus avoiding the client's potential loss of approximately 5% due to market fluctuations. During this period, the system processed approximately 100 million pieces of market dynamics data every day and generated thousands of personalized investment advice.

[0165] Traditional investment banking systems find it difficult to fully display complex industrial chain relationships, which affects the accuracy of investment decisions. By using the industrial brain module of the present invention, investment banks can generate and update a panoramic view of the industrial chain in real time, display upstream and downstream relationships, supply chain flows and regional layouts, and identify bottlenecks and risks in the industrial chain. This module uses a dynamic panoramic display algorithm to help investment banks monitor key nodes in the industrial chain in real time, promptly discover potential supply chain problems, and provide corresponding risk warnings. For example, when a customer invested in the automotive manufacturing industry, the system discovered that there was a risk of supply chain disruption in a specific area of ​​the industry by analyzing industrial chain data, and issued a risk warning in a timely manner.

[0166] In August 2023, the system successfully monitored the industrial chain of a certain automobile manufacturer. The upstream suppliers might face shortages of raw materials due to geopolitical conflicts. The system issued a warning signal within 24 hours and provided suggestions for industrial chain adjustments. Based on the suggestions, the investment bank helped the client adjust the investment portfolio and avoided approximately RMB 5 million in losses due to supply chain disruptions. The system processes approximately 5TB of industrial chain data every day, covering the supply chain information of more than 1,000 companies worldwide, and classifies and warns of potential risks with an accuracy of 95%.

[0167] Through the large language model module, investment banks can generate knowledge graphs based on users' trading behaviors and market dynamics, and provide personalized knowledge recommendation services in combination with intelligent knowledge mapping algorithms. Users can choose different AI assistants through the AI ​​application supermarket module. The system will dynamically adjust the knowledge graph according to user needs and provide real-time decision support; for example, when a user needs to understand a new market dynamic, the system can not only provide relevant analysis reports, but also recommend the latest industry information and legal and regulatory adjustment information in real time to ensure that users obtain the latest knowledge support.

[0168] In the second half of 2023, the investment bank's system generated personalized knowledge graphs for more than 500 users, and dynamically adjusted them based on market changes and user behavior. User feedback showed that about 80% of users believed that the knowledge recommendations and market analysis provided by the system were highly relevant, helping them to better understand market trends and make more informed investment decisions. The accuracy of the knowledge recommendation system reached 92%, and the system processed more than 200GB of market change data and user behavior data every day.

[0169] Table 1 Data table of the implementation effect of the intelligent investment banking service system

[0170] According to the data summary in the above table, the application of the intelligent investment banking service system of the present invention in multiple key business scenarios has achieved remarkable results. In terms of information processing, the system shortens the information processing time from the original 24 hours to 6 hours by introducing intelligent information release and interpretation modules, and effectively removes 90% of redundant data. This shows that the system has shown extremely high efficiency in the face of multi-source data collection and processing, ensuring that investment banking decisions are based on more comprehensive, timely and accurate data information; in terms of the generation and optimization of investment strategies, the system can dynamically adjust investment strategies based on real-time market dynamics and customers' historical trading behaviors. Through self-evolving algorithm technology, the system has increased customers' investment returns from 8% to 12%, and avoided potential losses of about 5% in the event of large market fluctuations. This shows that the system has strong investment strategy optimization and risk avoidance capabilities in a rapidly changing market environment. The system has also demonstrated outstanding capabilities in industrial chain analysis and risk management. Through real-time monitoring of industrial chain data, the system can identify potential bottlenecks in the supply chain and help customers avoid investment losses of 5 million yuan in actual applications. The system has also achieved remarkable results in knowledge management and personalized services. With the support of large language models and AI assistants, the system can provide users with personalized knowledge recommendations with an accuracy rate of 92% and a user satisfaction rate of 80%. This shows that the system can effectively help users understand market dynamics, optimize knowledge acquisition and utilization efficiency, and provide a highly personalized service experience.

[0171] The intelligent investment banking service system of the present invention has demonstrated outstanding performance in improving information processing efficiency, optimizing investment strategies, enhancing industrial chain analysis capabilities, and providing personalized services. It has successfully solved multiple bottlenecks in traditional technologies and brought significant advantages to the intelligence and automation of investment banking business.

[0172] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. An AI-driven intelligent investment banking service method, characterized in that: The steps include: S1. Through the information release and interpretation module, the system collects structured and unstructured data from financial markets, industry reports, social media, news and customer transaction records, uses bio-inspired swarm optimization technology to dynamically adjust the data collection path and frequency, performs semantic analysis, formatting, standardization and deduplication on the data, and generates a standardized data set; S2. Use the AI ​​intelligent search module to retrieve and extract features from the generated standardized data set, and generate high-dimensional feature representations by combining structured and unstructured data; S3. Based on the generated high-dimensional feature representation, combined with the customer's historical trading behavior and investment preferences, generate investment recommendations. Through self-evolving algorithmic decision-making technology, the system continuously optimizes investment strategies so that decisions can adapt to the dynamic market environment and achieve the best results. S4. Through the industrial brain module, the system uses a dynamic panoramic display algorithm to generate and update the global view of the industrial chain in real time, showing upstream and downstream relationships, supply chain flows and regional layouts, and monitors key nodes in the industrial chain through real-time data to identify potential bottlenecks and risks; S5. Through the large language model module and the intelligent knowledge mapping algorithm, the system automatically generates and adjusts the knowledge graph, provides dynamic knowledge management and sharing functions based on user behavior and market changes, and optimizes knowledge acquisition efficiency; S6. Based on the optimized investment portfolio, the trading strategy is executed through the automated trading module, and the trading behavior is reviewed in real time through the compliance monitoring module, and compliance reports are generated and compliance warnings are provided; S7. Based on investment strategies and knowledge management data, the system automatically generates investment reports through the intelligent report writing module. The report content covers market trend analysis, investment advice and risk warning, and is pushed to customers through emails and mobile applications to provide decision-making feedback; S8. Support the development and deployment of modular AI assistants through the AI ​​application supermarket module, allowing the configuration and expansion of AI assistants according to different investment banking business needs, and using the automated recommendation mechanism to provide users with suitable AI assistants to optimize business efficiency; The S1 specifically includes: S11. The system collects structured and unstructured data from multiple data sources, allocates data sources to corresponding collection nodes according to the type and importance of data sources, and reasonably schedules and executes data collection tasks; S12, using bio-inspired swarm optimization technology to dynamically adjust the data collection path P i (t) and frequency f i (t), optimize the path and frequency according to the acquisition cost and information redundancy: Among them, d(P i (t),P j (t)) represents the path distance, E i (t) is the effective information amount of the collected data, α i and β i is the weight coefficient; S13, perform semantic analysis on the collected unstructured data, extract key information therein, and convert the information into structured data; S14. Format and standardize the parsed data to convert data from different sources into a unified format; S15, through the deduplication algorithm, using the similarity function Sim(x i ,x j ) Identify and remove duplicate data to further optimize the quality and efficiency of the data set; S16. After the data processing is completed, the system generates a final standardized data set D, which covers all processed multi-source heterogeneous data; The S3 specifically includes: S31, the system receives the generated high-dimensional feature representation matrix H, combined with the customer's historical transaction behavior data T c and investment preference data P c , generate preliminary investment recommendations; S32. Iteratively optimize the initially generated investment recommendations through a self-evolving algorithmic decision module, and dynamically adjust the strategy based on the current market environment; S33. Introducing the multi-layer risk management weight matrix W r , used to adjust investment strategies to cope with market fluctuations: Among them, α is the risk adjustment coefficient, W r is the risk weight matrix, γ is the strategy dynamic correction parameter; S34. In the iteration cycle t, adjust the strategy based on the market environment and customer transaction feedback to generate the final optimized investment recommendations The S4 specifically includes: S41. The system receives real-time data from multiple data sources, including supply chain logistics information, enterprise production data, market changes and regional layout data, and generates a preliminary data structure D t ; S42. Using the generated preliminary data structure D t Generate a global view V through a dynamic panoramic display algorithm t , this view shows the upstream and downstream relationship U t , Supply Chain Flow t and regional layout L t : V t =f(U t ,S t ,L t ,D t ); Where f(·) represents a function that combines multiple inputs to generate a global view; S43. Combined with supply chain flow S t , Upstream and downstream relationships t and data structure D t The key node data K in t , bottleneck B in computing industry chain t and risk R t : Among them, K t,k is the kth key node data at time t, β k is the weight of the kth key node in bottleneck identification, h(·) is the risk assessment function; S44, based on the identified bottleneck B t and risk R t , the system dynamically updates the global view V t , generate warning information and trigger response mechanisms, and adjust industry chain strategies to deal with real-time risks; S45, the system continuously monitors, updates and optimizes the global view in each time period t, combined with the real-time data structure D t Adjusting supply chain flows t and regional layout L t , showing the dynamic changes of the global view of the industrial chain; The S5 specifically includes: S51, the system receives user behavior data U b and market change data M t , generate preliminary knowledge structure data K t ; S52: The received knowledge structure data K is processed by the large language model module. t Perform semantic analysis and feature extraction to generate a preliminary knowledge graph G t ; S53, combined with intelligent knowledge mapping algorithm, the generated preliminary knowledge graph G t Make dynamic adjustments based on user behavior data U b Changes and market dynamics t Changes to optimize the knowledge graph: Among them, α is the adjustment coefficient, γ j and δ k are the weights of the j-th user behavior feature and the k-th market change feature respectively; S54, based on the adjusted knowledge graph Generate dynamic knowledge management and sharing functions, and provide knowledge recommendations through knowledge recommendation functions; S55. The system continuously monitors user behavior and market trends within the time period t, and updates and optimizes the knowledge graph.

2. The AI-driven intelligent investment banking service method according to claim 1, characterized in that: The S2 specifically includes: S21, the system receives the generated standardized data set D and inputs it into the AI ​​intelligent search module for retrieval, and constructs an index I(D) to search for the data unit D k Carry out optimization processing; S22, use index I(D) to identify each data unit D k Perform feature extraction and generate preliminary feature representation. For each data unit D k , combining structured and unstructured data for feature extraction: Among them, g1(D k ) represents the structured data feature extraction function, g2(D k ) represents the unstructured data feature extraction function, For fusion operation, is the initial feature representation; S23, the system introduces a hierarchical feature representation learning algorithm based on this. Perform multi-level feature extraction and gradually generate higher-dimensional feature representations. For each layer l, generate the l-th layer feature representation: Among them, W l is the weight matrix of the lth layer, b l is the bias term, σ is the nonlinear activation function, is the feature representation of the lth layer; S24, hierarchical feature representation After multi-level processing, the system accumulates and fuses the features of all levels to generate the final high-dimensional feature representation Among them, α l The weights of each layer of features; S25, the system represents the high-dimensional features of all data units The fusion is performed to generate the final high-dimensional feature representation matrix H.

3. The AI-driven intelligent investment banking service method according to claim 1, characterized in that: The S8 specifically includes: S81. The system receives business demand data R from the user u and investment banking business environment data E b , generate a preliminary configuration plan C a ; S82, based on preliminary configuration plan C a The system allows users to select the functions of modular AI assistants through the AI ​​application supermarket module, combined with business demand data R u and modular functional combination M f , generate the final AI assistant configuration plan S83. Use the automated recommendation mechanism to determine the final configuration plan based on the user's and investment banking business environment b , recommend the best AI assistant solution A r : Among them, α i is the weight of different AI assistant function modules, S(·) is the matching score function, For the final selected AI assistant configuration solution, M f,i represents the i-th functional module, E b Indicates business environment characteristics; S84. According to the recommended AI assistant solution A r , the deployment is completed through the AI ​​application supermarket module, and the system monitors the changes in the user's business environment ΔE b and demand change ΔR u , and adjust the functions of the AI ​​assistant according to these changes to maintain adaptability to business needs; S85, the system continues to use user feedback and environmental data E b Dynamically adjust the configuration of the AI ​​assistant and generate new recommended solutions A r new , push new functional modules to adapt to changing business needs.

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