Financial innovation business incubation management system based on big data

Abstract

The invention discloses a financial innovation business incubation management system based on big data, which adopts a real-time streaming data processing framework to ensure that data is reliable and instantly available. The market demand analysis module integrates deep learning and knowledge graph technologies, combines reinforcement learning optimization prediction, and accurately grasps market trends and customer demands. The business planning and design module improves the design rationality by means of a quantum computing optimization scheme and VR / AR simulation. And the risk assessment and management and control module realizes multi-mechanism cooperative assessment through federated learning, and adopts an adaptive early warning technology to cope with market changes. And the project management and cooperation module drives cooperation by using a block chain smart contract and assists in decision making by means of artificial intelligence. The business incubation and monitoring module adopts edge computing to accelerate development and deployment, and realizes real-time monitoring and optimization through digital twinning. The system comprehensively improves the incubation efficiency and success rate of financial innovation services, effectively reduces the risk, and enhances the competitiveness of financial institutions.

Description

Technical Field

[0001] The present invention belongs to the technical field related to financial technology, and specifically relates to a financial innovation business incubation management system based on big data. Background Art

[0002] With the rapid development of the financial industry and increasingly fierce market competition, financial institutions need to continuously launch innovative businesses to meet diverse customer needs and enhance their competitiveness. However, traditional financial business incubation models present numerous problems. First, market demand analysis is often inaccurate, often based on limited data and experience. This leads to discrepancies between new businesses and actual market demand, making it difficult to achieve the desired results. Second, during the business incubation process, risk assessments are not comprehensive and timely, making it difficult to effectively address potential financial risks. Furthermore, the traditional model suffers from inefficient collaboration between different departments and poor information flow, severely impacting the speed and quality of business incubation.

[0003] The rise of big data technology offers a new approach to addressing these issues. By collecting, organizing, and analyzing massive amounts of financial data, we can more accurately grasp market trends and customer needs, while also enabling more effective risk assessment and management. However, there is currently no comprehensive, big data-based financial innovation business incubation management system that fully integrates big data technology to achieve efficient management of the entire process, from market analysis and business planning to risk assessment and incubation. Summary of the Invention

[0004] The purpose of the present invention is to provide a financial innovation business incubation management system based on big data to solve the problems of inaccurate market analysis, incomplete risk assessment and low departmental collaboration efficiency in the existing financial business incubation model proposed in the above background technology.

[0005] In order to achieve the above-mentioned purpose, the present invention provides the following technical solutions:.

[0006] A financial innovation business incubation management system based on big data, including:

[0007] The data collection and integration module is used to collect financial market transaction data, customer behavior data, and macroeconomic data from multiple data sources such as financial exchanges, internal bank systems, and third-party data providers. It also cleans, converts, and integrates the collected data. It uses blockchain technology to trace the source of data in real time, attaches a unique blockchain identifier to each piece of collected data, and records the entire process from data generation to entry into the system. At the same time, it uses a real-time streaming data processing framework to process incoming data immediately.

[0008] The market demand analysis module uses big data analysis algorithms to deeply mine the integrated data. It combines deep learning with knowledge graph fusion technology to build a financial knowledge graph to understand market structure and customer behavior patterns. It uses reinforcement learning algorithms to optimize the prediction model through interaction with the market environment, thereby analyzing market trends, customer demand preferences, and predicting changes in market demand.

[0009] The business planning and design module plans and designs innovative financial businesses based on market demand analysis and the strategic goals and resource availability of financial institutions. It uses quantum computing to assist in optimizing the design of business models, product structures, and service processes. It also leverages virtual reality / augmented reality simulation experience technology to provide the team with an immersive simulation experience environment to identify and optimize design issues.

[0010] The risk assessment and control module establishes a multi-dimensional risk assessment model that includes market risk, credit risk, and operational risk. The model combines machine learning algorithms such as neural networks and decision trees to analyze data, introduces federated learning technology to jointly train risk assessment models among different financial institutions, and builds an adaptive risk warning system to automatically adjust risk warning indicators and thresholds based on business and market changes.

[0011] The project management and collaboration module is responsible for the full-process management of financial innovation business incubation projects. It uses blockchain smart contracts to automate task allocation, progress tracking, and results acceptance. It also introduces an artificial intelligence assistant to analyze project data in real time and provide decision-making advice to project managers.

[0012] The business incubation and monitoring module actually incubates financial innovation businesses based on business planning schemes, including system development, product testing, and market promotion. It uses edge computing technology to accelerate system development and deployment, and creates a digital twin model for the business. By collecting business operation data in real time, it builds a corresponding model in a virtual environment to monitor and optimize business operations.

[0013] Preferably, in the data collection and integration module, when cleaning the collected data, duplicate records in the transaction data are removed through a data duplication detection algorithm, error fields in the customer information data are checked according to data validation rules, and missing data are supplemented by data interpolation or data filling methods, and data in different formats are uniformly converted into a format that is convenient for analysis and processing.

[0014] Preferably, in the market demand analysis module, data mining algorithms such as association rule mining, cluster analysis, and classification analysis are used to mine data in the data warehouse, and text mining technology and sentiment analysis algorithms are used to extract valuable information from unstructured data such as customer feedback data and industry research reports. Time series analysis algorithms and neural network algorithms are used to construct market demand forecasting models and optimize model parameters through cross-validation.

[0015] Preferably, in the business planning and design module, a cost-benefit analysis is conducted when designing a business model to evaluate feasibility and sustainability; the product structure design determines the investment target, profit distribution method, risk level and adopts a financial pricing model for pricing; the service process design simplifies the process of customer account opening and formulates service standards and specifications; different market and customer scenarios are set during simulation and the business plan is optimized based on the simulation results.

[0016] Preferably, in the risk assessment and control module, market risk assessment uses the VaR model to calculate the maximum loss that the business may suffer under a certain confidence level, credit risk assessment uses machine learning algorithms such as logistic regression and support vector machines to analyze customer credit data to predict the probability of default, and operational risk assessment uses the risk list method and flowchart method to identify and classify risks, and uses a combination of qualitative and quantitative methods to evaluate the probability of occurrence and the extent of loss.

[0017] Preferably, in the project management and collaboration module, project planning uses project management tools to clarify critical paths and task time nodes, task allocation clarifies information about the person in charge, participants, and task objectives, and tracks progress through project weekly reports and meetings. Resource allocation rationally arranges resources based on progress and demand and establishes a resource management database for record management. The communication and coordination mechanism achieves information flow and collaborative work by holding regular coordination meetings and establishing a shared document platform.

[0018] Preferably, in the business incubation and monitoring module, system development is carried out in iterative cycles using an agile development approach. Product testing includes comprehensive testing of functional testing, performance testing, security testing, and compatibility testing. Edge computing is used for rapid testing and debugging during the system development phase, and rapid launch and elastic expansion are achieved during the deployment phase. The digital twin model reflects the business operation status in real time and is used to discover and solve problems in advance.

[0019] Compared with the existing technology, the present invention provides a financial innovation business incubation management system based on big data, which has the following beneficial effects:

[0020] This invention uses big data technology to achieve accurate analysis and prediction of market demand, which can help financial institutions better grasp market opportunities and improve the pertinence and success rate of financial innovation business;

[0021] Multi-dimensional risk assessment models and effective risk management strategies can comprehensively assess and control various risks of financial innovation businesses, reduce the risk of business failure, and ensure the stable operation of financial institutions;

[0022] The project management and collaboration module improves the collaboration efficiency of the project team, promotes communication and cooperation between different departments, shortens the business incubation cycle, and improves the market response speed of financial institutions;

[0023] The business incubation and monitoring module monitors and adjusts the business incubation process in real time to ensure that the business can be launched smoothly and achieve the expected results, thereby improving the quality and stability of financial innovation business. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a system diagram of the present invention. DETAILED DESCRIPTION

[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0026] The present invention provides Figure 1 The financial innovation business incubation management system shown here, based on big data, includes:

[0027] The data collection and integration module is used to collect financial market transaction data, customer behavior data, and macroeconomic data from multiple data sources such as financial exchanges, internal bank systems, and third-party data providers. It also cleans, converts, and integrates the collected data. It uses blockchain technology to trace the source of data in real time, attaches a unique blockchain identifier to each piece of collected data, and records the entire process from data generation to entry into the system. At the same time, it uses a real-time streaming data processing framework to process incoming data immediately.

[0028] The market demand analysis module uses big data analysis algorithms to deeply mine the integrated data. It combines deep learning with knowledge graph fusion technology to build a financial knowledge graph to understand market structure and customer behavior patterns. It uses reinforcement learning algorithms to optimize the prediction model through interaction with the market environment, thereby analyzing market trends, customer demand preferences, and predicting changes in market demand.

[0029] The business planning and design module plans and designs innovative financial businesses based on market demand analysis and the strategic goals and resource availability of financial institutions. It uses quantum computing to assist in optimizing the design of business models, product structures, and service processes. It also leverages virtual reality / augmented reality simulation experience technology to provide the team with an immersive simulation experience environment to identify and optimize design issues.

[0030] The risk assessment and control module establishes a multi-dimensional risk assessment model that includes market risk, credit risk, and operational risk. The model combines machine learning algorithms such as neural networks and decision trees to analyze data, introduces federated learning technology to jointly train risk assessment models among different financial institutions, and builds an adaptive risk warning system to automatically adjust risk warning indicators and thresholds based on business and market changes.

[0031] The project management and collaboration module is responsible for the full-process management of financial innovation business incubation projects. It uses blockchain smart contracts to automate task allocation, progress tracking, and results acceptance. It also introduces an artificial intelligence assistant to analyze project data in real time and provide decision-making advice to project managers.

[0032] The business incubation and monitoring module actually incubates financial innovation businesses based on business planning schemes, including system development, product testing, and market promotion. It uses edge computing technology to accelerate system development and deployment, and creates a digital twin model for the business. By collecting business operation data in real time, it builds a corresponding model in a virtual environment to monitor and optimize business operations.

[0033] Data collection and integration module implementation

[0034] Blockchain data traceability is implemented: When establishing a data interface with a financial exchange, a blockchain node is deployed simultaneously. Each time transaction data, such as real-time stock prices and trading volume, is obtained from the exchange, a unique blockchain identifier is instantly generated for that data at the data collection end. The creation time of the data associated with this identifier is accurate to the millisecond, and the collection time is also recorded simultaneously, clearly indicating which exchange server the data originated from (recording the collection device information). If data undergoes encryption or other operations during transmission, these processing steps and timestamps are recorded on the blockchain. Once the data reaches the system database, the entire data process can be traced back at any time through a blockchain browser, ensuring the data source is reliable and preventing malicious tampering.

[0035] Real-time streaming data processing practice: Apache Flink was selected to build a real-time streaming data processing framework. Customer transaction flow data extracted from the bank's internal systems was connected to the Flink system, and corresponding data cleaning rules were set up, such as filtering out obviously erroneous transaction amounts (such as negative amounts and amounts far exceeding the normal range). Regarding data conversion, customer transaction times were converted from different formats to a standard time format. Using Flink's Complex Event Processing (CEP) capabilities, real-time monitoring of foreign exchange market transaction data was performed. If an exchange rate fluctuated beyond a preset threshold within a short period of time, such as a fluctuation of more than 0.5% within a minute, an alert mechanism was immediately triggered, rapidly transmitting this abnormal information to the market risk assessment module, providing timely information for subsequent decision-making.

[0036] Market Demand Analysis Module Implementation Method

[0037] Deep learning and knowledge graph integration: Models are built using the TensorFlow deep learning framework to process massive amounts of collected financial data. Using neural network algorithms, we analyze customer purchase history for various financial products and their browsing behavior for financial information, exploring potential relationships between customers, products, and industries. For example, we found that customers who purchased technology funds frequently viewed research reports on the artificial intelligence industry. This led to the establishment of a correlation between customers, technology funds, and the artificial intelligence industry within the knowledge graph. By continuously updating new financial market data and regularly training deep learning models, we continuously refine the knowledge graph, accurately grasping changes in market structure, such as the impact of emerging financial products on the market landscape, and providing comprehensive insights for business planning.

[0038] Reinforcement Learning Forecast Optimization: Build a Python-based reinforcement learning environment, using the market demand forecasting model as an intelligent agent. Set the model's forecasting strategy space, such as a combination of different time series forecasting algorithms. When actual market demand data is fed back, the model is rewarded or penalized based on the degree of deviation between the forecast and actual values. If the deviation between the forecast and actual market demand is within 5%, a higher reward is awarded; if the deviation exceeds 10%, a penalty is imposed. Based on this feedback, the model continuously adjusts its forecasting strategy. When predicting the market demand trend for financial products in the next cycle, it dynamically selects the optimal forecasting parameters and methods, such as adjusting the smoothing coefficient of the time series model, to improve forecast accuracy.

[0039] Business Planning and Design Module Implementation Method

[0040] Quantum computing-assisted business optimization: Partner with quantum computing service providers to access quantum computing resources. When designing investment portfolios, parameters such as available asset classes, expected returns, and risk factors are input into the quantum computing model. This model rapidly traverses a vast array of asset allocation combinations, calculating millions of different stock, bond, and fund allocation scenarios within a second, to identify combinations with the optimal risk-return ratio. The results are fed back to the business design team, who then optimize the portfolio structure, such as determining the specific allocation ratios for each asset class, to enhance product competitiveness.

[0041] Virtual Reality / Augmented Reality Simulation Experience Development: VR / AR simulation scenarios were developed using the Unity 3D game engine. Simulating a customer's new banking process at a virtual bank branch, customers donned VR headsets to enter a virtual banking environment and experience the entire process, from entering the bank, receiving consultation and guidance, completing their transaction, and leaving. The business planning and design team monitored customer behavior, dwell time, and other data in real time. If a customer remained in a particular transaction for an extended period or made frequent operational errors, such as repeatedly returning to modify information during a financial product signing process, the team could optimize the process, simplify the signing process, or add clearer operational prompts to enhance the rationale of the business design.

[0042] Implementation Methods of Risk Assessment and Control Module

[0043] Federated Learning Risk Assessment Collaboration: Banks, securities firms, and insurance companies are participating in the Federated Learning Alliance. Each institution deploys a local Federated Learning node and uses encrypted customer risk data—such as customer credit records for banks, customer investment risk preference data for securities firms, and customer claims records for insurance firms—to train models without leaving their local area. Using a federated learning algorithm, the model parameters of each institution are exchanged and aggregated in an encrypted state, jointly training a comprehensive risk assessment model. For example, when assessing the risk of a customer applying for cross-institutional financial services, this model can comprehensively consider the customer's risk profile in different financial sectors, providing a more comprehensive and accurate risk assessment, avoiding the biased nature of a single institution's assessment.

[0044] Adaptive Risk Warning Technology Implementation: An adaptive risk warning system is built based on Python's Scikit-learn machine learning library. The system collects real-time transaction data and market volatility data from innovative financial businesses. Decision tree algorithms are used to analyze the relationship between business data characteristics and risk occurrence, dynamically adjusting risk warning indicators. For example, when market volatility increases, the warning thresholds for certain business risk indicators are automatically lowered, such as lowering the Value at Risk (VaR) warning threshold for investment portfolio products from 5% to 3%. This ensures timely and accurate risk warning signals in complex and volatile market environments, supporting risk management decisions.

[0045] Project Management and Collaboration Module Implementation Method

[0046] Blockchain smart contracts drive project collaboration: Smart contracts are deployed on the Ethereum blockchain platform. During the project launch phase, project team members' task assignments, deadlines, and acceptance criteria are compiled into smart contract code and deployed to the blockchain. For example, developers are required to complete the development of a specific system module within one month. Upon completion, the code must be submitted and tested. The smart contract automatically verifies the test results. If the test passes, the smart contract will implement a pre-set reward mechanism, such as increasing the developer's performance score or issuing a bonus. This automates task assignment, progress tracking, and acceptance of results, improving the efficiency and fairness of project collaboration.

[0047] AI-assisted project decision-making applications: Introducing AI assistants such as IBM Watson. Project progress data, resource usage data, and risk assessment data are integrated into the AI assistant. When a project is delayed, the AI assistant analyzes the progress data and identifies a task delayed due to insufficient resource allocation. It then queries the resource management database for available resources and, based on the overall project goals, provides project managers with recommendations for adjusting resource allocation. For example, this can involve reallocating temporarily idled personnel from other tasks to this task. This helps project managers make timely and informed decisions to ensure smooth project progress.

[0048] Business incubation and monitoring module implementation

[0049] Edge computing accelerates system development and deployment: During the system development phase, the development team deploys edge computing devices, such as small servers, in local offices. Developers quickly test and debug the written code on the edge computing devices, reducing the time required to upload it to the central server. During system deployment, edge computing servers are deployed at network nodes close to the user end based on the expected traffic volume of the business. When traffic is low in the early stages of the business launch, the edge computing servers allocate a small amount of computing resources on demand. As the business expands and traffic increases, the edge computing servers elastically scale computing resources in real time, such as increasing server memory and CPU core count, to ensure that the business system can quickly respond to market changes and operate efficiently.

[0050] Digital twin monitoring and optimization implementation: Leveraging digital twin technology platforms such as ANSYS TwinBuilder, digital twin models are created for innovative financial businesses. Real-time data from the marketing process of financial products, such as advertising effectiveness data, customer inquiries, and purchase data, is collected and synchronized into the digital twin model. Different marketing strategies, such as changing advertising channels and adjusting product pricing, are simulated in a virtual environment. Changes in business indicators such as sales and customer conversion rates are observed in the digital twin model. Simulation results inform actual business decisions, enabling the selection of optimal marketing strategies and improving marketing success rates.

[0051] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein. Any modification, replacement, and improvement made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A financial innovation business incubation management system based on big data, characterized by: include: The data collection and integration module is used to collect financial market transaction data, customer behavior data, and macroeconomic data from multiple data sources such as financial exchanges, internal bank systems, and third-party data providers. It also cleans, converts, and integrates the collected data. It uses blockchain technology to trace the source of data in real time, attaches a unique blockchain identifier to each piece of collected data, and records the entire process from data generation to entry into the system. At the same time, it uses a real-time streaming data processing framework to process incoming data immediately. The market demand analysis module uses big data analysis algorithms to deeply mine the integrated data. It combines deep learning with knowledge graph fusion technology to build a financial knowledge graph to understand market structure and customer behavior patterns. It uses reinforcement learning algorithms to optimize the prediction model through interaction with the market environment, thereby analyzing market trends, customer demand preferences, and predicting changes in market demand. The business planning and design module plans and designs innovative financial businesses based on market demand analysis and the strategic goals and resource availability of financial institutions. It uses quantum computing to assist in optimizing the design of business models, product structures, and service processes. It also leverages virtual reality / augmented reality simulation experience technology to provide the team with an immersive simulation experience environment to identify and optimize design issues. The risk assessment and control module establishes a multi-dimensional risk assessment model that includes market risk, credit risk, and operational risk. The model combines machine learning algorithms such as neural networks and decision trees to analyze data, introduces federated learning technology to jointly train risk assessment models among different financial institutions, and builds an adaptive risk warning system to automatically adjust risk warning indicators and thresholds based on business and market changes. The project management and collaboration module is responsible for the full-process management of financial innovation business incubation projects. It uses blockchain smart contracts to automate task allocation, progress tracking, and results acceptance. It also introduces an artificial intelligence assistant to analyze project data in real time and provide decision-making advice to project managers. The business incubation and monitoring module actually incubates financial innovation businesses based on business planning schemes, including system development, product testing, and market promotion. It uses edge computing technology to accelerate system development and deployment, and creates a digital twin model for the business. By collecting business operation data in real time, it builds a corresponding model in a virtual environment to monitor and optimize business operations.

2. The financial innovation business incubation management system based on big data according to claim 1, characterized in that: In the data collection and integration module, when cleaning the collected data, duplicate records in the transaction data are removed through a data duplication detection algorithm, error fields in the customer information data are checked according to data validation rules, and missing data are supplemented by data interpolation or data filling methods, and data in different formats are uniformly converted into a format that is convenient for analysis and processing.

3. The financial innovation business incubation management system based on big data according to claim 1, characterized in that: In the market demand analysis module, data mining algorithms such as association rule mining, cluster analysis, and classification analysis are used to mine data in the data warehouse. Text mining technology and sentiment analysis algorithms are used to extract valuable information from unstructured data such as customer feedback data and industry research reports. Time series analysis algorithms and neural network algorithms are used to construct a market demand forecasting model and optimize model parameters through cross-validation.

4. The financial innovation business incubation management system based on big data according to claim 1, characterized in that: In the business planning and design module, a cost-benefit analysis is conducted when designing the business model to evaluate feasibility and sustainability. The product structure design determines the investment target, profit distribution method, risk level and adopts the financial pricing model for pricing. The service process design simplifies the process of customer account opening and formulates service standards and specifications. Different market and customer scenarios are set during simulation and the business plan is optimized according to the simulation results.

5. The financial innovation business incubation management system based on big data according to claim 1, characterized in that: In the risk assessment and control module, market risk assessment uses the VaR model to calculate the maximum loss that the business may suffer under a certain confidence level. Credit risk assessment uses machine learning algorithms such as logistic regression and support vector machines to analyze customer credit data and predict the probability of default. Operational risk assessment uses risk checklist and flowchart methods to identify and classify risks, and uses a combination of qualitative and quantitative methods to assess the probability of occurrence and the extent of loss.

6. The financial innovation business incubation management system based on big data according to claim 1, characterized in that: In the project management and collaboration module, project planning uses project management tools to clarify critical paths and task time nodes, task allocation clarifies information about responsible persons, participants, and task objectives, and progress is tracked through project weekly reports and meetings. Resource allocation rationally arranges resources based on progress and demand and establishes a resource management database for record management. The communication and coordination mechanism achieves information flow and collaborative work through regular coordination meetings and the establishment of a shared document platform.

7. The financial innovation business incubation management system based on big data according to claim 1, characterized in that: In the business incubation and monitoring module, system development is carried out in iterative cycles using an agile development approach. Product testing includes comprehensive functional testing, performance testing, security testing, and compatibility testing. Edge computing is used for rapid testing and debugging during the system development phase, and for rapid rollout and elastic expansion during the deployment phase. The digital twin model reflects the business operation status in real time and is used to discover and solve problems in advance.

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