Post-investment risk early warning and exit decision auxiliary method and system for private equity investment projects based on stream data processing
By employing streaming data processing technology and multi-objective optimization algorithms, the real-time and quantification issues of data in post-investment management and exit decisions in private equity investments have been resolved. This has enabled real-time assessment of post-investment risks and more scientific exit decisions, improving the timeliness of risk warnings and the accuracy of decision-making.
Patent Information
- Application Number
- CN202610473656.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Filing Date
- 2026-04-10
- Publication Date
- 2026-07-10
AI Technical Summary
In the current post-investment management and exit decision-making of private equity investments, insufficient data timeliness, inability to integrate and process multi-source heterogeneous data in real time, and lack of quantitative technical support for exit decisions lead to delayed risk warnings and strong subjectivity in decision-making.
Employing streaming data processing technology, we can access and process multi-source heterogeneous data in real time, construct a multi-dimensional dynamic risk assessment system, and generate quantitative exit decision schemes by combining multi-objective optimization algorithms. This includes real-time access and preprocessing of multi-source heterogeneous data, feature engineering under the sliding window mechanism, hierarchical risk assessment, and quantitative assistance for exit decisions under multiple constraints.
It enables real-time management of post-investment risks and provides quantitative support for exit decisions, improving the timeliness of risk warnings and the scientific nature of exit decisions, while reducing the subjectivity and error rate of decision-making.
Smart Images
Figure CN122367631A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the fields of artificial intelligence and financial technology, and in particular to a method and system for post-investment risk warning and exit decision support for private equity investment projects based on streaming data processing. Background Technology
[0002] The core business loop of the private equity investment industry covers four major stages: fundraising, investment, management, and exit. Most existing intelligent technology solutions for private equity investment focus on LP-GP qualification matching, project due diligence, and access risk control before investment, and have formed relatively mature technology implementation paths.
[0003] However, existing technical solutions have three major flaws regarding the "post-investment management" and "exit decision-making" stages, which account for more than 70% of the investment cycle:
[0004] 1. Severe lack of data timeliness: Existing post-investment risk control solutions rely on static financial statements and operating reports submitted by investee companies quarterly / semi-annually. Data updates are delayed by 3-6 months, making it impossible to capture real-time changes in business operations, legal public opinion, and industry policies. Risk warnings are severely delayed, making it easy to miss risk disposal and optimal exit opportunities.
[0005] 2. Multi-source heterogeneous data cannot be integrated and processed in real time: Post-investment risk covers multiple dimensions of data, including operations, finance, judiciary, public opinion, industry, and policy. The data formats include structured log data, unstructured text data, and time-series event data. Existing solutions cannot achieve real-time access, cleaning, standardization, and feature extraction of multi-source heterogeneous data, making it difficult to build a full-dimensional dynamic risk assessment system.
[0006] 3. Lack of quantitative technical support for exit decisions: Existing exit decisions rely heavily on the personal experience of investment managers and cannot generate quantitative optimal exit timing, exit methods and return calculation schemes based on multi-dimensional constraints such as real-time risk level, market liquidity, changes in project valuation, LP exit intentions, and industry cycles. The decision-making is highly subjective and has a low tolerance for error.
[0007] Streaming data processing technology enables real-time access, millisecond-level computation, and continuous updates of massive dynamic data. It has been successfully applied in real-time risk control of bank credit and anti-fraud scenarios on the Internet. However, there is currently no customized streaming data processing solution for post-investment management and exit decision-making scenarios in private equity investment, which cannot meet the industry's core needs for real-time post-investment risk control and quantitative assistance for exit decisions. Summary of the Invention
[0008] The purpose of this invention is to address the aforementioned deficiencies in the prior art by providing a method and system for post-investment risk warning and exit decision support for private equity investment projects based on streaming data processing. This invention achieves real-time processing of multi-source dynamic data after investment through streaming computing technology, constructs a multi-dimensional dynamic risk assessment system, and generates quantitative exit decision schemes by combining multi-objective optimization algorithms. This solves the core problems of lagging risk warning, insufficient data fusion capabilities, and lack of quantitative support for decision-making in the prior art.
[0009] Technical solution
[0010] The first aspect of the core technical solution of this invention is: This invention provides a method for post-investment risk warning and exit decision support for private equity investment projects based on streaming data processing, comprising the following steps:
[0011] S1 Multi-source heterogeneous post-investment streaming data real-time access and preprocessing: Build a distributed streaming data access framework to connect in real time with six major data sources: business data of invested companies, industrial and commercial judicial data, public opinion data, industry policy data, capital market data, and manually supplemented data. Perform real-time cleaning, deduplication, standardization and format unification of heterogeneous data, and output standardized streaming data.
[0012] S2 Sliding Window Mechanism Real-time Feature Engineering: Based on the time sliding window, real-time feature extraction and feature updating are performed on standardized streaming data. Three major feature systems are constructed: static basic features, dynamic time-series features, and event-triggered features. At the same time, feature selection is performed by ranking feature importance, and a post-investment risk assessment feature set is output.
[0013] S3 Tiered Post-Investment Risk Dynamic Assessment and Real-time Early Warning: Based on the post-investment risk assessment feature set, a post-investment risk tiered assessment model is constructed by integrating the time-series anomaly detection model and the gradient boosting tree model. The model outputs the risk level and risk score of the invested project in real time and triggers corresponding early warning notifications and handling suggestions for different risk levels.
[0014] S4 Quantitative Assistance for Exit Decisions under Multiple Constraints: Based on real-time project risk level, valuation change data, LP exit intentions, capital market liquidity, and industry cycle data, a multi-objective optimized exit decision model is constructed to output the optimal exit timing, exit method, expected return calculation, and risk warning, forming a complete exit decision assistance solution.
[0015] Furthermore, in step S1, the distributed streaming data access framework adopts the Flink streaming computing framework, and uses the Kafka message queue to realize asynchronous access and caching of multi-source data, wherein:
[0016] The invested company's operating data includes: real-time operating flow, revenue data, cost data, cash flow data, and core team change data, which are connected to the company's ERP system and financial system in real time through API interface;
[0017] The aforementioned industrial and commercial judicial data includes: industrial and commercial change information, equity pledge information, judicial litigation information, information on persons subject to enforcement, and administrative penalty information, which are pushed in real time through a third-party compliance data interface;
[0018] The public opinion data includes: positive / negative public opinion about the company and its core team, industry reputation, and media reports. It is collected in real time through web crawlers and sentiment identification is performed using NLP technology.
[0019] The industry policy data includes: regulatory policies, industry support policies, and changes in industry access rules for the industries to which the investee companies belong. These are collected in real time through official channels and classified according to their compliance impact.
[0020] The capital market data includes: valuation data of listed companies in the same industry, data on mergers and acquisitions, changes in IPO review policies, and secondary market liquidity data, which are connected in real time through financial data terminals;
[0021] The manually supplemented data includes: investment manager's on-site due diligence notes, core team interview minutes, industry expert opinions, and internal investment decision-making meeting records, which are supplemented in real time through preset manual input interfaces and file upload interfaces.
[0022] Further, in step S1, the preprocessing specifically includes:
[0023] Missing values are filled in real time using the mean / median within a sliding window, and outliers are removed in real time using the 3σ principle;
[0024] Unstructured text data is transformed into structured feature indicators through named entity recognition, relation extraction, and sentiment classification techniques.
[0025] Min-max standardization and z-score standardization are performed on feature data of different magnitudes to unify the data magnitude.
[0026] Further, in step S2, the time sliding window includes a fixed-duration sliding window and an event-triggered sliding window, wherein:
[0027] The fixed-duration sliding window uses four window sizes: 1 hour, 24 hours, 7 days, and 30 days, which correspond to the real-time calculation of ultra-short-term, short-term, medium-term, and long-term characteristics, respectively.
[0028] The event-triggered sliding window is used to trigger temporary sliding windows for sudden events such as judicial litigation, core team departures, and major policy changes, and to extract specific features related to the event.
[0029] Furthermore, in step S2, the three major categories of feature systems specifically include:
[0030] Static basic features: company registration information, establishment time, core team background, initial investment valuation, equity structure, which are fixed and updated basic features;
[0031] Dynamic time-series characteristics: changes in revenue growth rate, cash flow fluctuations, changes in gross profit margin, changes in public opinion sentiment index, and valuation deviation of peers are time-series characteristics that are updated in real time based on a sliding window;
[0032] Event triggering characteristics: legal litigation events, equity pledge events, core team changes events, and major policy changes events are characteristics generated in real time after the event is triggered.
[0033] Furthermore, in step S3, the specific steps for constructing the post-investment risk grading and assessment model are as follows:
[0034] S31 Construct a training dataset by collecting post-investment full-cycle data of historical private equity investment projects, labeling the corresponding risk levels and risk events, and dividing the dataset into training, validation and test sets.
[0035] S32 is based on the Isolation Forest model to identify anomalies in real-time time-series features, output anomaly scores, and capture sudden abnormal fluctuations in business data.
[0036] S33 is based on the LightGBM gradient boosting tree model, which trains on the full feature set and outputs the basic risk score of the project. At the same time, it outputs the contribution of each feature to the risk score through the SHAP value, so as to realize risk attribution.
[0037] S34 performs a weighted fusion of the anomaly score and the basic risk score to obtain the final total risk score, and classifies the risk into four levels: low risk, medium risk, high risk, and major risk according to a preset threshold.
[0038] S35 provides pre-set corresponding handling suggestions for different risk levels, including: continuous monitoring, risk verification, post-investment intervention, and activation of exit contingency plans.
[0039] Further, in step S4, the multi-objective optimization exit decision model takes maximizing exit returns, minimizing exit risks, and minimizing the exit cycle as its three optimization objectives, and uses project risk level, LP exit intention, market liquidity, and lock-up period restrictions as constraints. It solves for the Pareto optimal solution using the NSGA-II multi-objective optimization algorithm, and outputs the corresponding exit decision scheme, wherein:
[0040] The exit methods include: IPO exit, M&A exit, equity transfer exit, buyback exit, and liquidation exit;
[0041] The exit decision-making scheme includes: the optimal exit time window, the recommended exit method, the calculation of the expected internal rate of return (IRR), the warning of potential risks, and the operation path suggestion.
[0042] The second aspect of the core technical solution of this invention is: This invention provides a post-investment risk warning and exit decision support system for private equity investment projects based on streaming data processing, comprising:
[0043] The multi-source streaming data processing module is used to receive multi-source heterogeneous post-projection data in real time, complete data cleaning, standardization and preprocessing, and output standardized streaming data.
[0044] The real-time feature engineering module communicates with the multi-source streaming data processing module and is used to complete real-time feature extraction, feature filtering and feature updating based on the sliding window mechanism, and output the post-investment risk assessment feature set.
[0045] The dynamic risk warning module communicates with the real-time feature engineering module and is used to complete the real-time assessment of the project risk level through the fusion model, triggering the corresponding level of warning and handling suggestions.
[0046] The exit decision support module communicates with the dynamic risk warning module to generate quantitative exit decision schemes based on multi-objective optimization algorithms, and completes profit calculation and risk warning.
[0047] The visualization and interaction module communicates and connects with all the above modules to display real-time risk profiles, early warning information, and exit decision-making schemes for the project, while also supporting manual intervention and parameter configuration. Attached Figure Description
[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:
[0049] Figure 1 This is a schematic diagram of the overall architecture of the private equity investment project post-investment risk warning and exit decision support system based on streaming data processing as described in this invention.
[0050] Figure 2 This is a schematic diagram of the overall steps of the method for post-investment risk warning and exit decision support for private equity investment projects based on streaming data processing, as described in this invention.
[0051] Figure 3 This is a schematic diagram of the execution process for post-investment risk warning and exit decision-making as described in this invention. Detailed Implementation
[0052] To enable those skilled in the art to better understand the present invention, the present invention will be further described in detail below with reference to specific embodiments. The specific embodiments described herein are only for explaining the present invention and are not intended to limit the present invention.
[0053] Example 1: This example provides a method for post-investment risk warning and exit decision support for private equity investment projects based on streaming data processing. The specific implementation steps are as follows:
[0054] 1. Real-time access and preprocessing of multi-source heterogeneous post-transmission streaming data
[0055] A distributed streaming data access framework based on Apache Flink+Kafka was built, with 6 partitions per Kafka topic, corresponding to six major data sources: operational data of invested companies, industrial and commercial judicial data, public opinion data, industry policy data, capital market data, and manually supplemented data. This enables low-latency real-time data access and meets the business needs for real-time early warning of post-investment risks.
[0056] For the real-time data received, preprocessing operations are performed synchronously: for structured data such as business transaction records, missing values are filled using the median of a 30-day sliding window, and outliers are removed using the 3σ principle; for public opinion text data, named entity recognition and negative sentiment recognition are performed using a BERT pre-trained model, and unstructured text is transformed into a negative public opinion index in the 0-1 range; for feature data of different magnitudes, min-max standardization is used to unify them to the [0,1] range, and standardized streaming data is output.
[0057] Among them, the data is supplemented manually in real time through a preset online input window and an Excel file upload interface. Investment managers can upload on-site due diligence notes and interview minutes in real time. After the relevant text content is structured, the core features are extracted and incorporated into the post-investment risk assessment feature set.
[0058] 2. Real-time feature engineering under the sliding window mechanism
[0059] Four fixed-duration sliding windows are set up: a 1-hour window is used to calculate ultra-short-term characteristics such as the frequency of negative public opinion outbreaks and sudden fluctuations in operating revenue; a 24-hour window is used to calculate short-term characteristics such as daily revenue changes and the average public opinion sentiment index; a 7-day window is used to calculate medium-term characteristics such as the growth rate of weekly operating indicators and the impact of judicial events; and a 30-day window is used to calculate long-term characteristics such as monthly financial indicator changes and industry valuation deviations.
[0060] At the same time, an event-triggered sliding window is set up. When an unexpected event such as legal proceedings, departure of the core team, or major administrative penalty is detected in the invested company, a temporary sliding window for 7 days before and after the event is automatically triggered to extract the characteristics of indicator changes before and after the event.
[0061] Finally, a post-investment risk assessment feature set containing 32 dimensions was constructed, including 8 static basic features, 18 dynamic time-series features, and 6 event-triggered features. The feature importance was ranked using a random forest model, and redundant features with an importance lower than 0.01 were removed to ensure the effectiveness of the feature set.
[0062] 3. Tiered post-investment risk dynamic assessment and real-time early warning
[0063] Construct a risk assessment model that integrates temporal anomaly detection and gradient boosting trees:
[0064] An anomaly detection was performed on the 18-dimensional dynamic temporal features using the Isolation Forest model, and an anomaly score of 0-100 was output. The higher the score, the greater the degree of abnormal fluctuation in the data.
[0065] The LightGBM model is used to train the full feature set. The training dataset collects historical data of domestic private equity investment projects throughout the entire post-investment cycle, and labels the corresponding risk events and final exit returns. The model achieves a high level of accuracy in identifying risk events on the test set and outputs a basic risk score of 0-100.
[0066] The final total risk score is obtained by weighting and merging the scores according to the abnormality score weight of 0.4 and the basic risk score weight of 0.6.
[0067] The risk score is used to set up a classification rule: 0-30 points is low risk, 30-60 points is medium risk, 60-80 points is high risk, and 80 points or above is major risk.
[0068] Corresponding actions are triggered based on different risk levels: low-risk projects are tracked daily and post-investment tracking reports are generated weekly; medium-risk projects trigger risk verification notices, requiring investment managers to complete on-site verification and submit risk disposal plans within 3 working days; high-risk projects trigger post-investment intervention warnings and activate post-investment management plans; major risk projects trigger emergency warnings and simultaneously activate exit plans.
[0069] 4. Quantitative Assistance for Exit Decisions under Multiple Constraints
[0070] A multi-objective optimization exit decision model is constructed, with three main optimization objectives: maximizing exit IRR, minimizing exit risk, and shortening the exit cycle; constraints include: project lock-up period restrictions, minimum return requirements for LPs, real-time risk level of the project, IPO / M&A policy environment of the capital market, and industry liquidity level.
[0071] The Pareto optimal solution is obtained by using the NSGA-II algorithm, and 3-5 alternative exit decision schemes are output. Each scheme includes: recommended exit time window, optimal exit method, expected IRR calculation, maximum drawdown risk, and operation path suggestion.
[0072] For example, for a high-risk growth project, the optimal solution output by the model is: recommend exiting in the short term through the transfer of existing shares, with the expected IRR reaching a reasonable level and the maximum drawdown risk controlled within a manageable range. At the same time, alternative exit options such as mergers and acquisitions and buybacks are provided for the investment team's decision-making reference.
[0073] Example 2: This example provides a post-investment risk warning and exit decision support system for private equity investment projects based on streaming data processing, including:
[0074] Multi-source streaming data processing module: Supports multiple data access methods such as API interface, message queue, file upload, and manual entry. It has built-in standardized data cleaning, format conversion, and preprocessing operators, enabling real-time processing of massive amounts of data.
[0075] Real-time feature engineering module: Built-in sliding window configuration, feature extraction operators, and feature filtering functions, supporting user-defined feature rules and window parameters, enabling real-time feature updates and online iteration;
[0076] Dynamic risk warning module: It has a built-in pre-trained risk assessment model, supports online fine-tuning of the model, and can realize real-time updates of project risk levels. It also supports multiple warning channels such as email, SMS, and in-system notifications.
[0077] Exit Decision Support Module: Built-in multi-objective optimization algorithm, supports user-defined optimization objectives and constraints, and can automatically generate quantitative exit decision schemes and benefit calculation reports;
[0078] Visual interactive module: Includes project risk dashboard, single project risk profile, early warning information dashboard, exit decision analysis panel, supports drag-and-drop configuration and interactive query, and also supports manual annotation and model feedback functions.
[0079] This system can be deployed on the local server of a private equity investment institution, or it can be provided as a SaaS service via cloud services. It can be seamlessly integrated with the institution's existing investment research system, OA system, and financial system.
Claims
1. A method for post-investment risk warning and exit decision support in private equity investment projects based on streaming data processing, characterized in that, Includes the following steps: S1 Multi-source heterogeneous post-investment streaming data real-time access and preprocessing: Build a distributed streaming data access framework to connect in real time with six major data sources: business data of invested companies, industrial and commercial judicial data, public opinion data, industry policy data, capital market data, and manually supplemented data. Perform real-time cleaning, deduplication, standardization and format unification of heterogeneous data, and output standardized streaming data. S2 Sliding Window Mechanism Real-time Feature Engineering: Based on the time sliding window, real-time feature extraction and feature updating are performed on standardized streaming data. Three major feature systems are constructed: static basic features, dynamic time-series features, and event-triggered features. At the same time, feature selection is performed by ranking feature importance, and a post-investment risk assessment feature set is output. S3 Tiered Post-Investment Risk Dynamic Assessment and Real-time Early Warning: Based on the post-investment risk assessment feature set, a post-investment risk tiered assessment model is constructed by integrating the time-series anomaly detection model and the gradient boosting tree model. The model outputs the risk level and risk score of the invested project in real time and triggers corresponding early warning notifications and handling suggestions for different risk levels. S4 Quantitative Assistance for Exit Decisions under Multiple Constraints: Based on real-time project risk level, valuation change data, LP exit intentions, capital market liquidity, and industry cycle data, a multi-objective optimized exit decision model is constructed to output the optimal exit timing, exit method, expected return calculation, and risk warning, forming a complete exit decision assistance solution.
2. The method according to claim 1, characterized in that, In step S1, the distributed streaming data access framework adopts the Flink streaming computing framework and uses the Kafka message queue to achieve asynchronous access and caching of multi-source data. The invested company's operating data includes real-time operating flow, revenue data, cost data, cash flow data, and core team change data, which are connected to the company's ERP system and financial system in real time through API interfaces. The industrial and commercial judicial data includes industrial and commercial change information, equity pledge information, judicial litigation information, information on persons subject to enforcement, and administrative penalty information, which are pushed in real time through third-party compliance data interfaces. The manually supplemented data includes investment manager's on-site due diligence notes, core team interview minutes, industry expert opinions, and internal investment decision meeting records, which are supplemented in real time through preset manual input interfaces and file upload interfaces.
3. The method according to claim 1, characterized in that, In step S1, the preprocessing specifically includes: filling missing values in real time using the mean / median within a sliding window, and removing outliers in real time using the 3σ principle; converting unstructured text data into structured feature indicators through named entity recognition, relation extraction, and sentiment classification techniques; and performing min-max standardization and z-score standardization on feature data of different magnitudes to unify the data magnitude.
4. The method according to claim 1, characterized in that, In step S2, the time sliding window includes a fixed duration sliding window and an event-triggered sliding window. The fixed duration sliding window uses four window sizes: 1 hour, 24 hours, 7 days, and 30 days, which correspond to the real-time calculation of ultra-short-term, short-term, medium-term, and long-term characteristics, respectively. The event-triggered sliding window is used to trigger temporary sliding windows for sudden events such as judicial litigation, core team departures, and major policy changes, and to extract specific features related to the event.
5. The method according to claim 1, characterized in that, In step S2, the three major categories of feature systems specifically include: static basic features, including enterprise registration information, establishment time, core team background, initial investment valuation, and equity structure, which are fixed and updated basic features; dynamic time-series features, including changes in revenue growth rate, cash flow fluctuations, gross profit margin changes, changes in public opinion sentiment index, and valuation deviation of peers, which are time-series features updated in real time based on a sliding window; and event-triggered features, including judicial litigation events, equity pledge events, core team change events, and major policy change events, which are features generated in real time after the event is triggered.
6. The method according to claim 1, characterized in that, In step S3, the specific steps for constructing the post-investment risk grading assessment model are as follows: S31 Construct a training dataset by collecting post-investment full-cycle data of historical private equity investment projects, labeling the corresponding risk levels and risk events, and dividing the dataset into training, validation and test sets. S32 is based on the Isolation Forest model to identify anomalies in real-time time-series features, output anomaly scores, and capture sudden abnormal fluctuations in business data. S33 is based on the LightGBM gradient boosting tree model, which trains on the full feature set and outputs the basic risk score of the project. At the same time, it outputs the contribution of each feature to the risk score through the SHAP value, so as to realize risk attribution. S34 performs a weighted fusion of the anomaly score and the basic risk score to obtain the final total risk score, and classifies the risk into four levels: low risk, medium risk, high risk, and major risk according to a preset threshold. S35 provides pre-set corresponding handling suggestions for different risk levels, including: continuous monitoring, risk verification, post-investment intervention, and activation of exit contingency plans.
7. The method according to claim 1, characterized in that, In step S4, the multi-objective optimization exit decision model takes maximizing exit returns, minimizing exit risks, and minimizing the exit cycle as three optimization objectives, and uses project risk level, LP exit intention, market liquidity, and lock-up period restrictions as constraints. It solves for Pareto optimal solutions using the NSGA-II multi-objective optimization algorithm and outputs corresponding exit decision schemes. The exit methods include IPO exit, M&A exit, equity transfer exit, repurchase exit, and liquidation exit. The exit decision scheme includes the optimal exit time window, recommended exit method, expected internal rate of return calculation, potential risk warning, and operation path suggestion.
8. A post-investment risk warning and exit decision support system for private equity investment projects based on streaming data processing, characterized in that, include: The multi-source streaming data processing module is used to receive multi-source heterogeneous post-projection data in real time, complete data cleaning, standardization and preprocessing, and output standardized streaming data. The real-time feature engineering module communicates with the multi-source streaming data processing module and is used to complete real-time feature extraction, feature filtering and feature updating based on the sliding window mechanism, and output the post-investment risk assessment feature set. The dynamic risk warning module communicates with the real-time feature engineering module and is used to complete the real-time assessment of the project risk level through the fusion model, triggering the corresponding level of warning and handling suggestions. The exit decision support module communicates with the dynamic risk warning module to generate quantitative exit decision schemes based on multi-objective optimization algorithms, and completes profit calculation and risk warning. The visualization and interaction module communicates and connects with all the above modules to display real-time risk profiles, early warning information, and exit decision-making schemes for the project, while also supporting manual intervention and parameter configuration.