Financial business approval and risk management system based on direct data connection

Abstract

This application involves the field of financial technology, and discloses a financial business approval and risk management system based on direct data connection, including: data integration module, risk analysis module, decision support module and feedback optimization module; the data integration module is used to collect and clean multi-source data in real time through direct data connection to generate key risk feature data; the risk analysis module uses multi-dimensional modeling and random fluctuation modeling based on the above data to dynamically monitor the risk factors of customers; the decision support module uses Markov decision process and game theory analysis to generate optimal approval decisions based on risk scores, balancing customer needs and risk control. The system realizes multi-source data integration, dynamic risk assessment and intelligent decision support, improves the approval efficiency and risk management level of financial institutions, and can be widely used in various financial service scenarios such as vehicle finance, corporate credit, and consumer finance.

Description

Technical Field

[0001] The present invention relates to the field of financial technology, and specifically to a financial business approval and risk management system based on direct data connection. Background Art

[0002] In the existing financial business approval and risk management system, with the diversification of data sources and the rapid changes in the market environment, traditional approval processes and risk management methods are difficult to meet the requirements of real-time and precision. Usually, the approval system of financial institutions needs to rely on a variety of external and internal data sources to complete risk assessment, but there is a lack of effective real-time integration mechanism between these data sources, resulting in data lag and incompleteness in the approval process. In addition, traditional risk management is mostly based on static data and relies on simple rules or scoring models of historical data. These methods show great limitations when facing dynamically changing markets and customer behaviors, and it is difficult to update and accurately predict customers' risk status in real time.

[0003] In the risk assessment process, traditional systems mainly rely on fixed scorecards or static rule bases, which are difficult to adapt to real-time changes in customer credit status and market fluctuations. For the volatility factors in the market environment and the randomness in customer behavior, the existing system lacks effective mathematical modeling methods to capture these dynamic characteristics, which often leads to delayed approval decisions or deviations in risk assessment. Due to the lack of support for dynamic modeling and real-time updates, the existing system has obvious deficiencies in identifying high-risk customers or potential risk transitions, and cannot effectively carry out risk warning and real-time monitoring.

[0004] In addition, traditional approval decisions rely on manual intervention or fixed decision rules, and fail to fully utilize intelligent decision support technology to automatically generate the optimal approval path and strategy. Decisions in the approval process are usually not personalized and dynamic, and it is difficult to find a reasonable solution between balancing customer credit needs and risk control. This lack of intelligent decision support not only reduces approval efficiency, but also makes it difficult to effectively meet the complex needs of diversified financial business scenarios.

[0005] Therefore, the existing financial business approval and risk management systems have many shortcomings in data integration, risk analysis, intelligent decision-making and real-time optimization. There is an urgent need for a system that can acquire and integrate multi-source data in real time, realize dynamic risk modeling, automate decision generation and adaptive optimization, so as to enhance the risk control capabilities and approval efficiency of financial institutions in complex business environments. Summary of the invention

[0006] In view of the shortcomings of the existing technology, the present invention provides a financial business approval and risk management system based on direct data connection, which realizes real-time integration of multi-source data, dynamic risk analysis and intelligent decision support through direct data connection, thereby improving the approval efficiency of financial business and the accuracy of risk management.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a financial business approval and risk management system based on direct data connection, comprising:

[0008] Data integration module, which is used to collect and clean external and internal customer data, market data, and industry data in real time, and to perform dimensionality reduction and feature extraction on high-dimensional data to generate key risk feature data;

[0009] A risk analysis module, which is used to perform multi-dimensional modeling and real-time monitoring of the risk factors of the customer based on the risk feature data generated by the data integration module, and generate a risk score for the customer;

[0010] The decision support module is used to generate the optimal approval decision based on the risk score of the risk analysis module, balancing the customer's credit demand and the risk preference of the financial institution.

[0011] Preferably, the data integration module includes:

[0012] Data collection unit, used to collect customer data, market data and industry data from external and internal data platforms in real time;

[0013] Data cleaning unit, used to remove missing values, outliers, and standardize data formats;

[0014] Feature dimension reduction unit, used to reduce the dimension of high-dimensional data using manifold learning technology;

[0015] The feature selection unit is used to select key feature data through the information gain method to generate dimension-reduced data for subsequent risk analysis.

[0016] Preferably, the risk analysis module includes:

[0017] Multidimensional modeling unit, used to model the risk factors of customers based on partial differential equations, describing the changes of risk factors in time and market space;

[0018] Stochastic Volatility Modeling Unit, which is used to model the volatility of clients’ risk factors under uncertain market conditions through stochastic differential equations;

[0019] The critical point monitoring unit is used to treat the customer's risk status as a phase change system and analyze the critical points of customer risk based on bifurcation theory to provide early warning of the customer's high-risk status.

[0020] Preferably, the multidimensional modeling unit models the changes of the risk factors of the client in time and market space through partial differential equations, specifically:

[0021] The partial differential equation is used to describe the change of risk indicator R(x,t) with time t and market status x, and its mathematical expression is:

[0022]

[0023] Among them, R represents the risk index, α is the impact coefficient of market fluctuation, β is the natural growth rate of risk factor, M is the market impact factor, and γ is the weight coefficient of market impact;

[0024] The multidimensional modeling unit discretizes and solves the above partial differential equation by finite difference method, decomposes the change of risk index into increments of discrete time steps, so as to approximately calculate the change trend of customer risk factor.

[0025] Preferably, the stochastic volatility modeling unit models the volatility of the client's risk factor under uncertain market conditions through a stochastic differential equation, specifically:

[0026] The stochastic differential equation represents the risk variable X t The change over time t is affected by the expected growth rate and volatility, and its mathematical expression is:

[0027] dX t =μX t dt+σX t dW t

[0028] Among them, X t represents the risk variable, μ is the expected growth rate of the risk variable, σ is the volatility of the risk variable, and W t is the standard Brownian motion;

[0029] The stochastic volatility modeling unit uses the Ito integral method to solve the above stochastic differential equation, and generates the probability distribution of the customer risk variable at a future time point through the Monte Carlo simulation method, so as to predict the customer's future risk status.

[0030] Preferably, the critical point monitoring unit performs critical point analysis on the risk status of the customer based on phase change system theory, specifically:

[0031] The critical point monitoring unit regards the risk status of the customer as a phase transition system between a low risk state and a high risk state, defines the control parameters of the customer risk status as credit score, debt ratio and liquidity index, and sets the critical values ​​of these control parameters to identify the conditions for the customer risk to transition from low risk to high risk;

[0032] The critical point monitoring unit analyzes the changes in control parameters based on bifurcation theory. When the control parameters are close to a preset critical value, the system triggers a risk warning, indicating that the customer's risk state may undergo a phase change, thereby entering a high-risk state.

[0033] Preferably, the decision support module includes:

[0034] The decision generation unit is used to generate the optimal decision strategy for the approval process based on the Markov decision process to maximize the approval benefits;

[0035] The Balance Analysis Unit, which analyses the interaction between customers and financial institutions through game theory to balance credit needs with risk appetite;

[0036] The path selection unit is used to map the risks and benefits in the approval process into energy values ​​and select the approval plan with the risk-minimizing path.

[0037] Preferably, the decision generation unit generates an optimal decision strategy for the approval process based on a Markov decision process, specifically:

[0038] The decision generation unit models the approval process as a multi-stage Markov decision process, in which the credit status of the customer is represented as a state space S, and each state s in the state space contains the customer's credit score, debt situation, financial data and loan history;

[0039] For each state s, the decision generation unit can choose a set of actions A, and each action a corresponds to a different gain or loss;

[0040] The decision generation unit defines the reward function R(s,a), which represents the benefit or risk reduction obtained by taking action a in state s, and solves the optimal strategy based on the Bellman equation to maximize the value function V(s) of each state:

[0041]

[0042] Among them, γ is the discount factor used to balance short-term benefits and long-term benefits, and P(s′|s,a) is the probability of transitioning from state s to state s′ after taking action a.

[0043] Preferably, the balance analysis unit determines the balance point between the credit demand and risk preference between the customer and the financial institution based on game theory equilibrium analysis, specifically:

[0044] The equilibrium analysis unit models the interaction process between the customer and the financial institution as a non-cooperative game process, in which the customer and the financial institution are the two parties of the game, the customer's goal is to obtain the highest possible credit limit, and the financial institution's goal is to control the credit risk within an acceptable range;

[0045] The balance analysis unit constructs the benefit matrix of both parties. The customer's strategy set includes different credit behavior choices, and the financial institution's strategy set includes different approval decisions;

[0046] The balance analysis unit calculates the return and risk values ​​under each strategy combination based on the profit matrix, and searches for the Nash equilibrium point, at which neither the customer nor the financial institution can unilaterally obtain higher returns by changing their own strategies, thereby determining an approval plan that balances the interests of both parties, in order to meet the customer's credit needs while avoiding high risks to the greatest extent possible.

[0047] The path selection unit is used to select a path that balances risk and benefits in the approval process according to the energy minimization principle, specifically:

[0048] The path selection unit maps each possible approval path in the approval process into an energy value, regards the risk factor as the energy source of the system, and assigns a weight to each risk factor to calculate the overall energy;

[0049] The path selection unit calculates the energy value of each approval path through the energy function, and the calculation formula of the total energy E is:

[0050]

[0051] Among them, r i is the i-th risk factor, w i is the weight coefficient associated with the risk factor;

[0052] The path selection unit selects the path with the smallest total energy from multiple approval paths as the optimal approval path to achieve an approval plan with minimized risk, thereby effectively balancing risks and benefits in the approval process.

[0053] The present invention provides a financial business approval and risk management system based on direct data connection. It has the following beneficial effects:

[0054] 1. The present invention collects and processes multi-source data in real time through direct data connection, can quickly obtain the latest information of customers and the market, automatically perform data cleaning, dimensionality reduction and feature extraction, thereby improving the accuracy of risk analysis, making the approval process more efficient and accurate, and realizing efficient automation from application to approval for customers.

[0055] 2. The risk analysis module of the present invention combines multi-dimensional modeling and random fluctuation modeling to conduct real-time dynamic monitoring of customer risk factors, and warns of possible risk transitions through critical point monitoring. This design enables the system to identify potential high-risk customers in a timely manner, respond quickly when the market environment or customer credit status changes, and effectively prevent and manage risks.

[0056] 3. The decision support module of the present invention adopts Markov decision process and game theory analysis, which not only generates the optimal approval decision, but also achieves a dynamic balance between risk control and customer needs. Through the energy minimization method of the path selection unit, the system can select the optimal path among multiple approval paths, ensuring the minimization of risks and maximization of benefits in the approval process.

[0057] 4. The system of the present invention includes a feedback optimization module, which continuously adjusts and optimizes model parameters by analyzing the approval results and the actual effect of risk management, so that risk analysis and approval decisions are more realistic. This closed-loop optimization mechanism enables the system to adapt to changes in the market environment and customer risks, thereby continuously improving the stability and accuracy of the system.

[0058] 5. This system can be applied to a variety of financial service scenarios, including vehicle financing, corporate credit, consumer finance and travel reimbursement, etc. It can flexibly connect to different data sources through direct data connection to achieve intelligent risk analysis and approval support. The modular design of the system can be customized according to different business needs, making it have strong application scalability and market adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 Schematic diagram of the system architecture of the present invention. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0061] Please see attached Figure 1 The present invention provides a financial business approval and risk management system based on direct data connection. By constructing multiple functional modules, the system can collect and process data from external and internal data sources in real time, perform multi-dimensional risk modeling, generate approval decisions, and optimize risk management strategies in combination with feedback mechanisms. The system solves the problems of cumbersome manual operations, poor real-time performance, and low risk identification accuracy in traditional financial approval processes, and effectively improves approval efficiency and risk control capabilities. The following is a detailed description of each module.

[0062] Data integration module

[0063] The data integration module realizes the real-time collection, cleaning, dimensionality reduction and feature extraction of multi-source data, ensuring that the system can carry out subsequent risk assessment and approval decisions with efficient and accurate data support. The data integration module connects with the external data platform in real time through direct data connection, can quickly respond to changes in customer and market conditions, and dynamically update the latest data status of customers, thereby providing accurate risk feature data for subsequent modules.

[0064] In this embodiment, the data integration module specifically includes a data acquisition unit, a data cleaning unit, a feature dimension reduction unit and a feature selection unit, which acquire, process and extract key data features through direct data connection.

[0065] The data collection unit is responsible for acquiring multi-source data through the data direct connection interface and achieving real-time connection with multiple external and internal data platforms. Specifically, the data collection unit establishes direct connections with data sources such as credit reporting platforms, market databases, and industry databases through API interfaces, and acquires a variety of information including customer financial data, market data, and industry data in real time. This information includes customer income, expenditure, debt status, credit score, as well as market interest rates, industry growth rates, etc.

[0066] Through direct data connection, the system can respond to changes in external data sources instantly. For example, when market interest rates or customer credit scores change, the system can immediately update the relevant data and provide the latest data support for the risk analysis module.

[0067] Generally speaking, the collection frequency of the data collection unit can be configured according to the update frequency of the data source to ensure the timeliness and accuracy of the data, especially when the customer's risk status changes rapidly, the latest data can be provided in a timely manner.

[0068] As a possible implementation method, after acquiring the data, the data acquisition unit transfers it to the data cleaning unit for data cleaning and standardization. The main task of the data cleaning unit is to remove missing values, abnormal values ​​and noise data in the data.

[0069] Under the premise of direct data connection, some external data may be missing or abnormal due to network interruption or incomplete data transmission. Therefore, the data cleaning unit further processes the data collected by direct data connection, including filling missing values, removing abnormal data points and unifying data formats, etc., to ensure the integrity and consistency of the data. Specifically, the data cleaning unit can use statistical analysis methods to detect outliers, such as by setting a reasonable value range, identifying and removing abnormal data points that exceed the range.

[0070] In addition, the data cleaning unit also converts the format and standardizes the units of data from different sources to facilitate subsequent dimensionality reduction and feature extraction.

[0071] The data after data cleaning enters the feature dimension reduction unit, which reduces the dimension of the data through the manifold learning method to reduce the dimension and redundancy of the data, thereby reducing the computational complexity.

[0072] In some embodiments, the feature dimension reduction unit may use the Isomap algorithm to reduce the dimension of high-dimensional data. For example, for a high-dimensional data matrix containing various financial information of a customer (where m represents the number of samples and n represents the feature dimension), the Isomap algorithm maps the original data to a low-dimensional space through nonlinear mapping. Where d<<n.

[0073] The Isomap algorithm can retain the main structural information of the data and remove redundant information, ensuring that the key risk information of the feature data will not be lost during the dimensionality reduction process. In general, dimensionality reduction processing can effectively reduce the amount of calculation for subsequent risk analysis and decision support, and improve the system response speed.

[0074] Based on the dimensionality reduction process, the data integration module further extracts key risk feature data through the feature selection unit. The feature selection unit evaluates the importance of each feature to risk prediction by calculating the information gain, and selects the most representative features based on the size of the information gain.

[0075] Specifically, the feature selection unit calculates the contribution of each feature to the risk factor Y. Let the feature variable be X i , information gain IG(X i ; The calculation formula of Y) is:

[0076] IG(X i ; Y) = H(Y) - H(Y|X i )

[0077] Among them, H(Y) is the total entropy of the system risk factor, H(Y|X i ) is a given feature X i In general, the feature selection unit will give priority to features with higher information gain and eliminate redundant features with lower information gain, thereby generating optimized key risk feature data as the input of the risk analysis module.

[0078] In some embodiments, the feature selection unit may be combined with other feature selection methods, such as L1 regularization method, to select the sparsity of different features, further ensuring that the retained feature data can cover all aspects of the customer's risk status.

[0079] In summary, the data integration module acquires, cleans and extracts key feature information from multi-source data in real time through direct data connection, providing a high-quality data foundation for the system's risk analysis and approval decisions, ensuring that the system can respond to changes in customers and the market in real time, and achieve dynamic risk management and efficient approval.

[0080] Risk Analysis Module

[0081] The risk analysis module is designed to perform multi-dimensional modeling and real-time monitoring of the risk feature data provided by the data integration module to generate the customer's risk score. The risk analysis module comprehensively depicts the dynamic changes of customer risk factors through multi-level modeling techniques, including multi-dimensional modeling, random fluctuation modeling, and critical point monitoring. This module can accurately quantify the customer's credit risk status and provide a scientific data basis for the subsequent decision support module.

[0082] In this embodiment, the risk analysis module includes a multi-dimensional modeling unit, a random fluctuation modeling unit and a critical point monitoring unit. Each unit cooperates with each other to form a complete risk analysis system, and the specific technical implementation is as follows.

[0083] The multidimensional modeling unit is based on partial differential equations (PDE) for modeling, and constructs a multidimensional risk profile of the customer by dynamically analyzing the changes in the time and market status of the customer's risk factors.

[0084] Specifically, let the customer's risk index be R(x,t), where t represents the time variable and x represents the market state variable. Generally, the customer's risk index will be affected by both time and market changes, so the multidimensional modeling unit uses partial differential equations to describe the changing trend of risk indicators in time and market dimensions.

[0085] Alternatively, the change in risk for a client can be expressed as a partial differential equation as follows:

[0086]

[0087] Among them, R represents the customer's risk indicator, α is the impact coefficient of market fluctuations on risk, β represents the natural growth rate of risk factors, M represents the market impact factor, and γ is the weight coefficient of market impact.

[0088] In a possible implementation, the multidimensional modeling unit uses the finite difference method to numerically solve the above partial differential equation. Specifically, the time t and the market state x are discretized into equally spaced steps, and then the continuous partial differential equation is discretized into a difference equation, and the risk change value of each time step is gradually solved.

[0089] The stochastic volatility modeling unit models the fluctuation of customer risk under uncertain market conditions through stochastic differential equations (SDE). Generally speaking, the risk status of customers is highly random under the influence of market uncertainty, so the stochastic volatility modeling unit describes the random fluctuation process of risk based on the Brownian motion model.

[0090] As an alternative, let the risk variable of the customer be X t , then its stochastic fluctuation model can be expressed as the following stochastic differential equation:

[0091] dX t =μX t dt+σX t dW t

[0092] Among them, X t represents the risk variable of the customer, μ represents the expected growth rate of risk, σ represents the volatility of risk, and W t is a standard Brownian motion. In some embodiments, the stochastic fluctuation modeling unit uses Ito integral method to solve the stochastic differential equation.

[0093] As an implementation method, the stochastic volatility modeling unit generates the probability distribution of customer risk variables at future time points through Monte Carlo simulation. Specifically, the Monte Carlo method calculates possible risk change paths through multiple simulations to obtain the future distribution of customer risks.

[0094] The critical point monitoring unit conducts critical point analysis on the customer's risk status based on phase transition theory to identify the mutation point of the customer's risk status. Generally speaking, the customer's risk status can be regarded as a phase transition system, which may transition from a low risk state to a high risk state under certain conditions.

[0095] As a possible implementation method, the critical point monitoring unit identifies the risk transition by setting the critical value of key control parameters, including credit score, debt ratio and liquidity index. Specifically, the control parameter of customer risk is set to C. When the control parameter is close to the preset critical value, it indicates that the customer risk state may undergo a phase change, and the critical point monitoring unit will issue a risk warning.

[0096] In some embodiments, the critical point monitoring unit analyzes the changes in the control parameters based on the bifurcation theory, and predicts the sudden change of risks by monitoring the change trend of the control parameters. For example, when the debt ratio of a customer gradually increases and approaches the critical value, the system can automatically generate an early warning signal to indicate possible high risk.

[0097] In summary, the risk analysis module in this embodiment realizes a multi-level analysis of customer risks through the coordinated operation of multi-dimensional modeling, random fluctuation modeling and critical point monitoring.

[0098] Decision support module

[0099] The decision support module generates the best approval decision by comprehensively analyzing the customer's risk score data to effectively balance the customer's credit demand and the risk tolerance of the financial institution. This module uses dynamic decision-making processes and game theory analysis, combined with multi-path selection optimization methods, to ensure that the generated approval plan takes into account both risk control and profit maximization.

[0100] In this embodiment, the decision support module includes a decision generation unit, a balance analysis unit and a path selection unit. Each unit cooperates with each other through data input, analysis and output to form a complete decision support framework. The specific technical implementation is as follows.

[0101] In the decision generation unit, the system is modeled based on the Markov decision process (MDP) to make the optimal approval decision under different credit statuses of customers.

[0102] In general, the approval decisions of financial institutions need to be constantly adjusted according to the dynamic risk status of customers to balance short-term benefits and long-term risks. Therefore, this unit uses the MDP model to model the approval process in stages. Specifically, the credit status of a customer is defined as a state space S, where each state s∈S contains information such as the customer's credit score, financial data, and debt ratio.

[0103] As an option, the decision generation unit may select an action set A including three operations of "approval", "further review" and "rejection" according to different statuses of the customer, and each operation has different effects on risk control and benefits.

[0104] In a possible implementation, the decision generation unit defines a reward function R(s,a) to represent the benefit or risk control effect of executing operation a in state s, and solves the optimal strategy based on the Bellman equation to maximize the value function V(s) in each state s. Specifically, the calculation formula of the Bellman equation is:

[0105]

[0106] Among them, γ is the discount factor, which is used to balance the weights of current benefits and future benefits, and P(s′|s,a) represents the probability of transitioning to state s′ after taking action a in state s.

[0107] In some embodiments, the decision generation unit iteratively solves the above-mentioned Bellman equation to obtain the optimal decision under each state, thereby selecting the most favorable operation path in the approval process.

[0108] The equilibrium analysis unit further models the interaction between customers and financial institutions based on game theory, and determines an equilibrium point that meets the needs of both parties by analyzing the strategy combination of both parties.

[0109] Generally speaking, customers tend to obtain more credit limits when applying for loans, while financial institutions hope to control credit risks within a reasonable range. Therefore, the interaction between customers and financial institutions can be regarded as a non-cooperative game.

[0110] As an option, the balance analysis unit constructs a profit matrix for the customer and the financial institution. The customer's strategy set includes different credit application methods, and the financial institution's strategy set includes different approval decisions. Specifically, the balance analysis unit calculates the profit and risk values ​​under different strategy combinations and uses Nash equilibrium analysis to determine the balance plan.

[0111] In one possible implementation, the balance analysis unit calculates the equilibrium point of both parties based on the profit matrix, that is, at this equilibrium point, neither the customer nor the financial institution can obtain a better result by changing their strategies, thereby forming a dynamic balance between risk and return. Through this equilibrium analysis, the balance analysis unit can effectively avoid high risks while meeting customer needs.

[0112] The path selection unit is responsible for selecting the best path among multiple possible approval paths to minimize risk and maximize benefits. In the approval process, different decision paths involve different risk factors and benefits, so the path selection unit optimizes path selection through the energy minimization principle.

[0113] As an implementation method, the path selection unit maps each possible path in the approval process to an energy value, regards the risk factor as the "energy source" in the path, and assigns a weight to each risk factor to calculate the total energy of the path. Specifically, the calculation formula for the total energy E is:

[0114]

[0115] Among them, r i represents the value of the ith risk factor, w i In a possible implementation, the path selection unit selects the path with the minimum energy as the optimal approval path, so as to ensure that the path selected in the approval process has the lowest risk level.

[0116] In summary, the decision support module achieves the risk control objectives of financial institutions while meeting customers' credit needs through the coordinated operation of decision generation, game theory analysis and path optimization.

[0117] In general, the present invention realizes the automation of the entire process from data collection, risk assessment to intelligent approval through the collaborative work of modules such as data integration, risk analysis and decision support. The data integration module uses data direct connection to collect and clean multi-source data in real time and generate risk characteristics; the risk analysis module dynamically monitors the customer's risk factors through multi-dimensional modeling and random fluctuation modeling, and conducts risk warning in combination with critical point analysis; the decision support module generates the optimal approval plan and optimizes the approval path based on the Markov decision process and game theory, thereby effectively controlling the risks of financial institutions while meeting customer credit needs. The system improves the real-time and intelligent level of approval, and provides financial institutions with more accurate and efficient risk management methods.

[0118] The financial business approval and risk management system based on direct data connection of the present invention has wide applicability in multiple practical application scenarios, and provides intelligent data processing and risk management support for various financial service projects, especially showing significant advantages in diversified and dynamic business needs.

[0119] In the vehicle financial service scenario, the system can connect vehicle transaction data, customer credit information and third-party evaluation data, analyze the risks of vehicle financing applicants in real time, automatically generate approval decision plans, and achieve fast and accurate credit approval to meet the efficient approval needs of the vehicle financing service platform. At the same time, the system can combine the volatility of the vehicle market to model risk fluctuations, provide a more accurate risk warning mechanism for vehicle financial projects, and further ensure the robustness of the business.

[0120] In the customer acquisition and credit support projects for small and medium-sized enterprises, this system integrates enterprise financial data, industry credit records, taxation and other multi-source information through direct data connection, establishes enterprise risk profiles, and automatically generates enterprise risk scores. Based on these data, the system can dynamically optimize the credit approval process, improve the accuracy of credit support, and combine the Nash equilibrium analysis method to find a balance between business risk control and customer needs, providing convenient and efficient customer acquisition and credit service support for a wide range of small and medium-sized enterprise credit needs.

[0121] In the application of travel and consumer financial services, this system can be connected with travel consumption data platforms, reimbursement systems, credit card bills, etc. in real time, integrating customers' consumption records, travel expenses and financial status, and comprehensively analyzing customers' consumer financial risks. The system implements dynamic risk assessment of travel consumption customers based on multi-dimensional modeling, and provides intelligent approval solutions, thereby improving the risk control accuracy and business service efficiency of consumer finance scenarios.

[0122] In addition, in the financial empowerment platform, the system provides technical support for the comprehensive data integration and risk management of corporate customers, connects with corporate financial flow, market dynamics and business performance data in real time, and intelligently generates corporate risk scores and credit decision-making plans. The system's multi-dimensional risk analysis and intelligent approval strategies can flexibly adapt to the changing needs of different corporate customers, effectively promote the service level of financial institutions in credit empowerment and risk control, and help financial empowerment platforms efficiently support the development needs of enterprises.

[0123] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A financial business approval and risk management system based on direct data connection, characterized by: include: Data integration module, which is used to collect and clean external and internal customer data, market data, and industry data in real time, and to perform dimensionality reduction and feature extraction on high-dimensional data to generate key risk feature data; A risk analysis module, which is used to perform multi-dimensional modeling and real-time monitoring of the risk factors of the customer based on the risk feature data generated by the data integration module, and generate a risk score for the customer; The decision support module is used to generate the optimal approval decision based on the risk score of the risk analysis module, balancing the customer's credit demand and the risk preference of the financial institution.

2. The financial business approval and risk management system based on direct data connection according to claim 1 is characterized in that: The data integration module includes: Data collection unit, used to collect customer data, market data and industry data from external and internal data platforms in real time; Data cleaning unit, used to remove missing values, outliers, and standardize data formats; Feature dimension reduction unit, used to reduce the dimension of high-dimensional data using manifold learning technology; The feature selection unit is used to select key feature data through the information gain method to generate dimension-reduced data for subsequent risk analysis.

3. The financial business approval and risk management system based on direct data connection according to claim 1 is characterized in that: The risk analysis module includes: Multidimensional modeling unit, used to model the risk factors of customers based on partial differential equations, describing the changes of risk factors in time and market space; Stochastic Volatility Modeling Unit, which is used to model the volatility of clients’ risk factors under uncertain market conditions through stochastic differential equations; The critical point monitoring unit is used to treat the customer's risk status as a phase change system and analyze the critical points of customer risk based on bifurcation theory to provide early warning of the customer's high-risk status.

4. The financial business approval and risk management system based on direct data connection according to claim 3 is characterized in that: The multidimensional modeling unit models the changes of the risk factors of the client in time and market space through partial differential equations, specifically: The partial differential equation is used to describe the change of risk indicator R(x,t) with time t and market status x, and its mathematical expression is: Among them, R represents the risk index, α is the impact coefficient of market fluctuations, β is the natural growth rate of risk factors, M is the market impact factor, and γ is the weight coefficient of market impact; The multidimensional modeling unit discretizes and solves the above partial differential equation by finite difference method, decomposes the change of risk index into increments of discrete time steps, so as to approximately calculate the change trend of customer risk factor.

5. The financial business approval and risk management system based on direct data connection according to claim 3 is characterized in that: The stochastic volatility modeling unit models the volatility of the client's risk factor under uncertain market conditions through stochastic differential equations, specifically: The stochastic differential equation represents the risk variable X t The change over time t is affected by the expected growth rate and volatility, and its mathematical expression is: dX t =μX t dt+σX t dW t Among them, X t represents the risk variable, μ is the expected growth rate of the risk variable, σ is the volatility of the risk variable, and W t is the standard Brownian motion; The stochastic volatility modeling unit uses the Ito integral method to solve the above stochastic differential equation, and generates the probability distribution of the customer risk variable at a future time point through the Monte Carlo simulation method, so as to predict the customer's future risk status.

6. The financial business approval and risk management system based on direct data connection according to claim 3 is characterized in that: The critical point monitoring unit performs critical point analysis on the risk status of the customer based on the phase change system theory, specifically: The critical point monitoring unit regards the risk status of the customer as a phase transition system between a low risk state and a high risk state, defines the control parameters of the customer risk status as credit score, debt ratio and liquidity index, and sets the critical values ​​of these control parameters to identify the conditions for the customer risk to transition from low risk to high risk; The critical point monitoring unit analyzes the changes in control parameters based on bifurcation theory. When the control parameters are close to a preset critical value, the system triggers a risk warning, indicating that the customer's risk state may undergo a phase change, thereby entering a high-risk state.

7. The financial business approval and risk management system based on direct data connection according to claim 1 is characterized in that: The decision support module includes: The decision generation unit is used to generate the optimal decision strategy for the approval process based on the Markov decision process to maximize the approval benefits; The Balance Analysis Unit, which analyses the interaction between customers and financial institutions through game theory to balance credit needs and risk appetite; The path selection unit is used to map the risks and benefits in the approval process into energy values ​​and select the approval plan with the risk-minimizing path.

8. The financial business approval and risk management system based on direct data connection according to claim 7 is characterized in that: The decision generation unit generates the optimal decision strategy for the approval process based on the Markov decision process, specifically: The decision generation unit models the approval process as a multi-stage Markov decision process, in which the credit status of the customer is represented as a state space S, and each state s in the state space contains the customer's credit score, debt situation, financial data and loan history; For each state s, the decision generation unit can choose a set of actions A, each action a corresponds to a different gain or loss; The decision generation unit defines the reward function R(s,a), which represents the benefit or risk reduction obtained by taking action a in state s, and solves the optimal strategy based on the Bellman equation to maximize the value function V(s) of each state: Among them, γ is the discount factor used to balance short-term benefits and long-term benefits, and P(s′|s,a) is the probability of transitioning from state s to state s′ after taking action a.

9. The financial business approval and risk management system based on direct data connection according to claim 7 is characterized in that: The balance analysis unit determines the balance point between the credit demand and risk preference between the customer and the financial institution based on the game theory equilibrium analysis, specifically: The equilibrium analysis unit models the interaction process between the customer and the financial institution as a non-cooperative game process, in which the customer and the financial institution are the two parties of the game, the customer's goal is to obtain the highest possible credit limit, and the financial institution's goal is to control the credit risk within an acceptable range; The balance analysis unit constructs the benefit matrix of both parties. The customer's strategy set includes different credit behavior choices, and the financial institution's strategy set includes different approval decisions; The balance analysis unit calculates the return and risk values ​​under each strategy combination based on the profit matrix, and searches for the Nash equilibrium point, at which neither the customer nor the financial institution can unilaterally obtain higher returns by changing their own strategies, thereby determining an approval plan that balances the interests of both parties, in order to meet the customer's credit needs while avoiding high risks to the greatest extent possible.

10. The financial business approval and risk management system based on direct data connection according to claim 7, characterized in that: The path selection unit is used to select a path that balances risk and benefits in the approval process according to the energy minimization principle, specifically: The path selection unit maps each possible approval path in the approval process into an energy value, regards the risk factor as the energy source of the system, and assigns a weight to each risk factor to calculate the overall energy; The path selection unit calculates the energy value of each approval path through the energy function, and the calculation formula of the total energy E is: Among them, r i is the i-th risk factor, w i is the weight coefficient associated with the risk factor; The path selection unit selects the path with the smallest total energy from multiple approval paths as the optimal approval path to achieve an approval plan with minimized risk, thereby effectively balancing risks and benefits in the approval process.

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