Risk control method and system for identifying greening risk of transformed financial service

Abstract

The invention relates to the technical field of financial risk management, and discloses a risk control method and system for identifying a transition financial business greening risk, and the method comprises the steps: deconstructing an enterprise commitment into an expected economic behavior vector through a transition knowledge graph, capturing an actual economic behavior data flow, and carrying out the logic consistency matching; according to the method, environmental benefit verification is converted into economic behavior logic chain review, so that a financial institution can avoid high-cost environmental auditing to return to business essence judgment, and meanwhile, bogus transactions are effectively identified through business activeness analysis of a transaction counterparty; and the risk identification capability of the transformed financial business is obviously improved.

Description

Technical Field

[0001] The present invention relates to a risk control method and system for identifying greenwashing risks in transitional financial services, belonging to the technical field of financial risk management. Background Art

[0002] In the field of risk management for transitional financial services, the current mainstream risk control approach relies on an environmental outcome audit strategy, which involves assessing greenwashing risks by verifying terminal indicators such as a company's carbon emission intensity. For example, when a cement company applies for a loan to low-carbonize its kilns, the bank is required to review its energy consumption monitoring reports or third-party environmental audit data. However, this model has gradually exposed three systemic limitations during implementation: First, when companies falsify equipment procurement flows or tamper with environmental data through related-party transactions, traditional methods struggle to penetrate the data authenticity barrier, resulting in risk identification lagging behind fund disbursement. Second, the cycle for environmental benefits to manifest is typically 12-18 months, while risk exposure often occurs after funds have been consumed, leaving financial institutions without a window for intervention. Third, the underlying issue lies in the fact that existing technologies place financial institutions in the role of environmental engineering auditors, exceeding their professional capabilities while neglecting their inherent expertise in verifying business logic.

[0003] Despite the recent emergence of improvement solutions such as Internet of Things monitoring, which attempt to improve verification reliability through real-time sensor data, they are still limited by bottlenecks such as high hardware deployment costs and the easy avoidance of key node data. More importantly, these improvements have failed to break through the result-oriented mindset and cannot resolve the core contradiction between the logical disconnect between economic behavior and environmental protection commitments.

[0004] Specifically, existing technologies suffer from the following major flaws: 1. They equate physical environmental monitoring with financial risk control, ignoring the inherent logic of economic behavior. This results-based assessment model causes risk identification to lag behind the point at which funds are disbursed. 2. They fail to transform existing economic behavior data, such as bank transaction flows, into a basis for risk assessment. Therefore, the technical challenge addressed by this invention is how to replace environmental audits with verification of the logical chain of economic behavior, thereby identifying the logical breakpoints between transformation commitments and actual business activities before funds are disbursed. Summary of the Invention

[0005] The present invention provides a risk control method and system for identifying greenwashing risks in transitional financial services. The main purpose of the method and system is to solve the problem of how to achieve forward-looking identification of greenwashing risks through verification of a logical chain of economic behavior.

[0006] To achieve the above objectives, the present invention provides a risk control method for identifying greenwashing risks in transitional financial services, the method comprising the following steps:

[0007] Step 1: Based on a transformation knowledge graph constructed from a defined set of rules, the transformation project proposal submitted by the enterprise is structurally deconstructed to generate an expected economic behavior vector that includes time nodes, behavior types, counterparty types, and amount ranges. The expected economic behavior vector includes at least one of key asset procurement behavior or key service procurement behavior. The actual economic behavior data stream of the enterprise is non-invasively obtained, and the actual economic behavior data stream includes at least the enterprise's transaction payment data at the bank.

[0008] Step 2: Dynamically match and compare the actual economic behavior data stream with the expected economic behavior vector to verify the logical consistency between the actual economic behavior and the expected economic behavior. Logical consistency verification includes checking whether the payee of the actual transaction meets the expected counterparty type restrictions, whether the actual transaction amount and occurrence time are within the expected amount and time node range, and whether there are any key economic behavior omissions that are inconsistent with the expected economic behavior vector;

[0009] Step 3: When the logical consistency is lower than the preset compliance threshold, a greenwashing risk warning signal is output. The greenwashing risk warning signal indicates that there is a logical break in the economic behavior trajectory of the transformation project plan.

[0010] Preferably, the transformation knowledge graph is an extensible rule base and associative database built based on the experience of industry experts, which is used to associate transformation commitments with specific economic behavior sequences; structured deconstruction is to deconstruct the transformation project plan into an expected economic behavior vector containing at least one of procurement behavior, service behavior, asset disposal behavior and supply chain change behavior according to the rule base.

[0011] Preferably, the actual economic behavior data stream also includes at least one of government public bidding information, corporate industrial and commercial change information, customs import and export data, and authorized tax invoice information summaries obtained through open interfaces.

[0012] Preferably, the logical consistency verification step specifically includes one of the following operations: detecting whether there is transaction payment data in the actual economic behavior data stream corresponding to the key asset procurement behavior in the expected economic behavior vector, or detecting whether there is transaction payment data in the actual economic behavior data stream corresponding to the key service procurement behavior in the expected economic behavior vector; detecting whether there is abnormal economic behavior in the actual economic behavior data stream that is contrary to the goals of the transformation project plan, and abnormal economic behavior refers to behavior that is defined as contrary to the transformation goals, such as purchasing spare parts for obsolete old equipment models while claiming to upgrade equipment.

[0013] Preferably, in the step of structurally deconstructing the transformation project plan based on the transformation knowledge graph, when the transformation knowledge graph cannot effectively deconstruct the transformation project plan, the method further includes the following steps: performing natural language processing on the transformation project plan to extract the core technical elements contained therein; using the core technical elements as query probes, initiating parallel queries to at least one independent public information database to verify the objective existence or logical relevance of the core technical elements; the public information database includes at least one of an academic paper database, a patent database, an industrial product B2B platform, a professional technical forum, and an industry news portal; and generating a plan logic credibility value based on the verification result, and the plan logic credibility value is used to assist in generating a greenwashing risk warning signal.

[0014] Preferably, after the step of dynamically matching and comparing the actual economic behavior data stream with the expected economic behavior vector, the method further includes the following steps: determining the counterparty of an actual transaction that matches the key economic behavior in the expected economic behavior vector; generating a correlation network snapshot of the counterparty's recent business activity based on anonymous transaction data within the bank, with the determined counterparty as the center; and analyzing the correlation network snapshot to evaluate the counterparty's business enthusiasm and behavior pattern, the evaluation including determining one or more of the following: determining the customer diversity ratio R of the counterparty's transactions with other customers within a preset analysis period, the customer diversity ratio R being calculated using the following formula: Among them, N c Represents the number of different customers who have transactions with the counterparty, N t Represents the total number of bank customers. If the customer diversity ratio R is lower than the preset ratio threshold, it is judged that the interaction diversity is low; judge the distribution uniformity of all transaction time points of the counterparty. If the distribution uniformity is lower than the preset uniformity threshold, it is judged that the behavior pattern is abnormal; judge the average sedimentation period of large funds flowing into the counterparty. If the average sedimentation period is less than the preset sedimentation period threshold, it is judged that the capital flow is abnormal; when the business heat and behavior pattern judgment results indicate abnormality, a transaction hollowing risk labeling signal is generated.

[0015] Preferably, the transaction hollowing risk labeling signal is presented in association with the greenwashing risk warning signal, indicating to the risk manager possible commercial substance problems in the actual transaction.

[0016] Preferably, the greenwashing risk warning signal divides the risk into at least one of low logical inconsistency risk, key behavior missing risk, and contradictory behavior risk based on the degree to which the logical consistency is lower than a preset value and the absence of key economic behaviors, so as to guide banks to take targeted due diligence measures.

[0017] Preferably, structured deconstruction uses semantic analysis technology to identify and extract core semantic elements and quantitative information corresponding to the rules in the transformation knowledge graph from the transformation project plan text, and map them to the corresponding fields of the expected economic behavior vector. The preset numerical compliance threshold is based on the analysis of the actual economic behavior pattern of historical transformation financial business, determined by statistical methods and dynamically adjusted to ensure the sensitivity and specificity of the greenwashing risk warning signal. The system corresponding to the method includes: a deconstruction module, configured to perform structured deconstruction on the transformation project plan based on the transformation knowledge graph to generate a predicted economic behavior vector; a data acquisition module, configured to non-invasively acquire the actual economic behavior data stream; a comparison module, configured to continuously dynamically match and compare the actual economic behavior data stream with the expected economic behavior vector to verify logical consistency; and an early warning module, configured to output a greenwashing risk warning signal when the logical consistency is lower than the preset numerical compliance threshold.

[0018] Compared with the prior art, the present invention has the following beneficial effects:

[0019] 1. By transforming the knowledge graph, corporate commitments are deconstructed into time-series economic behavior vectors, and actual behavioral data streams such as bank transaction flows are simultaneously captured. During the matching process, the focus is on the logical consistency between key procurement behaviors and counterparties. This shift from environmental outcome audits to economic behavior trajectory verification allows financial institutions to return to the original purpose of business logic review, avoiding the dilemma of physical environment verification with high professional barriers, and significantly reducing verification costs while maintaining risk control professionalism.

[0020] 2. When the knowledge graph is unable to deconstruct an unconventional transformation plan, the system automatically extracts its core technical elements and initiates cross-verification with independent sources such as academic databases and industrial product platforms. This mechanism of reversely constructing a technology credibility portrait through public information not only avoids the risk of misjudgment of unconventional transformation plans, but also retains financial support channels for the project, making the risk control system both rigorous and inclusive.

[0021] 3. For key transactions that match the expected vector, the system locks on the counterparty and analyzes its recent business activity characteristics. By capturing implicit indicators such as transaction time distribution entropy and capital sedimentation cycle, it effectively identifies hollow transactions that appear to be compliant but lack real business background. This ability to perceive the temperature of economic behavior supplements traditional payment data verification with an unforgeable audit dimension. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a timing diagram of the greenwashing risk control process of the present invention;

[0023] Figure 2 A radar chart comparing the economic behavior of enterprises in the present invention;

[0024] Figure 3 This is a statistical diagram of the greenwashing type identification effect of the present invention;

[0025] Figure 4 This is a dynamic matching and early warning flow chart of the present invention.

[0026] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0027] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0028] The present invention discloses a risk control method and system for identifying greenwashing risks in transitional financial services. In a specific business application, a large chemical company applies for a transitional financial loan from a financial institution, aiming to carry out energy-saving and carbon-reduction transformation of its high-energy-consuming ethylene cracking unit. In response to the potential greenwashing risks in such businesses, the system first initiates the structural deconstruction of the transformation project plan submitted by the company. The core of this step is to call a pre-built transformation knowledge graph based on historical experience. The knowledge graph is technically implemented as a dynamically expandable graph database, which internally defines the technical path and business logic that a specific industry should follow when implementing green transformation; the system's deconstruction module uses semantic analysis technology to identify and extract core semantic elements from the plan text. It combines elements and quantitative information and automatically maps them to the rules in the knowledge graph, thereby generating a time-series and quantified expected economic behavior vector. For example, for the chemical enterprise's project, this vector will structuredly list a series of expected activities such as key service procurement behavior: energy-saving transformation project design and evaluation, key asset procurement behavior: core oxygen-enriched burner procurement, and so on. It will also clearly define the time node, counterparty type and amount range for each activity. At the same time, the data acquisition module captures the actual economic behavior data flow of the enterprise account in real time through the bank's internal core system interface in a non-invasive manner. This data flow contains at least all its transaction payment data, and can integrate external public information such as taxation, bidding and industrial and commercial changes through the compliance interface to form a comprehensive data view.

[0029] Next, the comparison module will continuously and dynamically match the real-time actual economic behavior data stream with the generated expected economic behavior vector, and verify the logical consistency between the two. This verification is not an isolated transaction check, but a continuous review of the entire economic behavior logic chain. Specifically, it includes: verifying whether the payee of the actual transaction meets the expected counterparty type restriction; verifying whether the amount and occurrence time of the actual transaction are within the expected amount range and time node; retrospectively checking whether there are any missing key economic behaviors that should have logically occurred earlier; and actively searching for abnormal economic behaviors that are inconsistent with the project objectives, such as purchasing spare parts for obsolete equipment while claiming to upgrade equipment. Within the comparison module, the system performs a one-to-one verification of each actual transaction data with the corresponding node in the expected economic behavior vector. If the payee's corporate type fails to match the set of legal types listed in the expected counterparty type restriction, or the transaction amount is not within the closed interval of the amount range of the corresponding behavior node, or the transaction time is earlier than the earliest allowable time point defined by the behavior node, or later than the latest allowable time point, the system will determine that the transaction has failed to match. Based on the results of the above multi-dimensional verification, the system calculates a comprehensive compliance score. When this score falls below a preset compliance threshold, the early warning module outputs a greenwashing risk warning signal. The determination of this threshold is to achieve an optimal balance between sensitivity and specificity in risk identification. Its value is determined based on statistical modeling of big data of historical similar businesses and can be dynamically optimized. This warning signal will clearly indicate that there is a logical break in the economic behavior trajectory of the transformation project plan. According to the severity of the non-compliance, the risk is subdivided into low-level logical non-compliance risk, key behavior missing risk, or contradictory behavior risk.

[0030] At the same time, after completing the initial match or issuing an early warning, the system will automatically initiate an in-depth penetration analysis of the counterparty. After confirming that a key payment matches the expected behavior, the system immediately locks the payee and, based on the bank's massive internal, strictly anonymized transaction data, instantly generates a snapshot of the associated network of business activity centered on this payee. The analysis engine then scans the snapshot to assess its business enthusiasm and behavior patterns and determine whether there is a risk of transaction hollowing out. This assessment includes several key judgments: First, calculate the customer diversity ratio If the ratio falls below a preset threshold, the interaction diversity is determined to be low. Secondly, the uniformity of the distribution of all transaction times is analyzed. If it falls below a preset uniformity threshold, the behavior pattern is determined to be abnormal. Thirdly, the average period of large funds flowing into the account is tracked. If it falls below a preset period threshold, an abnormal fund flow is determined. If any of the above assessments indicate an anomaly, the system generates a transaction hollowing risk signal. This signal is correlated with the aforementioned greenwashing risk warning signal, not only highlighting logical deviations at the project execution level but also raising profound questions about the commercial nature of key economic activities from the perspective of the counterparty's authenticity. Furthermore, when dealing with unconventional scenarios and the knowledge graph cannot be effectively deconstructed, the system activates a backup verification process. Using natural language processing technology, it extracts the core technical elements of the solution as query probes, initiates parallel queries against independent public information databases, such as academic papers, patent libraries, and industrial product B2B platforms, and generates a logical credibility value for the solution based on the returned results to assist in the final risk assessment, ensuring the rigor of the risk control system.

[0031] At the same time, in the specific implementation, taking the customer diversity ratio R in the counterparty business activity analysis as an example, its calculation formula is In the specific system implementation, the denominator N t Rather than simply using the total number of all bank customers, the system defines a more business-relevant benchmark customer group based on the analysis scenario. For example, in a preferred embodiment, the system will use N t It is defined as the total number of active corporate customers of the bank in the past fiscal year, excluding a large number of personal savings accounts to make the benchmark more meaningful. In a more refined scenario, the system can also use the industry of the analyzed counterparty as a screening dimension to select N t The R-value is limited to the total number of active corporate clients of a bank within the industry. This design allows the R-value to standardize the customer breadth of an individual counterparty within the real competitive environment of its industry or market, significantly improving the indicator's signal-to-noise ratio and horizontal comparability. Therefore, the R-value's application logic does not rely on comparison with a fixed absolute threshold, but rather identifies anomalies by determining its relative position within the statistical distribution of its peer group. The system compares the target counterparty's R-value with key quantiles such as the 10th and 25th percentiles of its peer group's R-value. This dynamic evaluation logic, based on relative ranking, ensures that the model can accurately identify shell companies with unusually narrow customer networks while effectively avoiding misjudgments of legitimate suppliers with small but highly specialized customer bases. This fully demonstrates the intelligent and adaptable nature of the risk control system, and the quantitative evaluation of technical effectiveness also follows this principle.

[0032] Furthermore, when processing the actual economic activity data streams of enterprises, all data undergoes a mandatory preprocessing phase before entering the comparison module. During this preprocessing phase, the system does not simply transmit raw transaction records. Instead, it uses structured extraction techniques to remove unnecessary sensitive descriptive text, retaining only the core elements necessary for comparison with the expected economic activity vector, such as transaction timestamps, amounts, and typed counterparty identifiers. This reduces the exposure of sensitive information at the source. More crucially, when performing counterparty activity analysis, to completely eliminate the risk of re-identification, the system employs an anonymization process, essentially a composite pseudonymization scheme combining hashing and tokenization. Specifically, the counterparty's real identity information is replaced with a one-time, irreversible cryptographic hash value, valid only for the lifetime of the analysis. Simultaneously, all participating entities in the surrounding transaction data used to construct the associated network snapshot are replaced with meaningless tokens. In this way, the system can completely retain the network topology and transaction pattern information required to calculate indicators such as the customer diversity ratio R and distribution uniformity, while fundamentally cutting off any possibility of reversely deducing the true identity of any single entity from the analysis results. This design ensures that the commercial substance review of the counterparty is carried out in a pure and irreversible data sandbox environment, thereby technically achieving the dual goals of privacy protection and risk insight.

[0033] Example 1: To obtain a transition finance loan for the low-carbon transformation of its core production line kilns, a cement manufacturer submitted a transformation project proposal to a financial institution, including a specific technical path and expected emission reduction benefits. For the financial institution, this business scenario presents an inherent technical dilemma: if the financial institution relies on a third-party environmental benefit audit report after the project is completed as the key judgment basis, risk identification will lag significantly behind the actual investment of funds, thereby losing the ability to intervene in the process. If an attempt is made to conduct real-time physical monitoring during project execution, not only will the deployment cost be high, but the institution itself lacks the technical capabilities to conduct professional environmental engineering verification.

[0034] The present invention first uses its built-in transformation knowledge graph to structurally deconstruct the transformation project proposal submitted by the cement company. This process transforms a textual commitment centered on physical engineering and environmental benefits into an expected economic behavior vector with time as the sequence, transactions as nodes, and the type and amount range of commercial counterparties as constraints. The company's kiln transformation plan is thus accurately mapped into a series of expected payment behaviors with inherent temporal logic, such as the procurement of design services in the early stages of the project, the procurement of key equipment in the mid-term, and the procurement of installation engineering services in the later stages. The direct effect of this transformation is to redefine a high-cost, long-term environmental engineering audit problem into a business logic consistency problem that financial institutions can perform high-frequency, low-cost verification in their core business data streams. The verification focus of financial institutions is no longer the complex and difficult-to-penetrate kiln process, but rather whether the actual economic behavior data streams fully recorded within their systems are consistent with the business logic trajectory depicted by this expected economic behavior vector.

[0035] On the basis of this logical redefinition, the system's internal operating mechanism demonstrates its synergistic effect, thereby resolving the aforementioned contradiction between efficiency and rigor. When the actual economic behavior data stream of the cement enterprise is continuously captured, the comparison module first performs high-speed, automated logical consistency verification. This ensures continuous monitoring of corporate behavior and meets the requirements of business efficiency. At the same time, any deviation from the expected economic behavior vector, such as the absence of key behaviors or inconsistency in the counterparty type, will immediately trigger a greenwashing risk warning signal, ensuring the timeliness of risk identification. This logical consistency verification process also provides precise triggering conditions for subsequent in-depth risk penetration. When the system detects When a large payment matches the key asset procurement behavior in the expected vector, the matching event itself becomes a high-value analysis trigger point and deterministically activates the commercial activity analysis of the counterparty of the transaction. At this time, a synergistic effect is formed between the expected economic behavior vector and the transaction hollowing risk analysis mechanism: the former solves the targeting problem of which transaction should be monitored, and the latter solves the deep penetration problem of whether this seemingly compliant transaction has real commercial substance; even if a shell company perfectly forges the procurement payment in terms of amount and time, it cannot simultaneously forge the customer diversity ratio R and the average fund sedimentation period that conform to normal business logic in the bank's global anonymous data.

[0036] This architecture, which combines macro-verification of business logic trajectories with micro-substantive review of key counterparties, enables financial institutions to make judgments on the authenticity and compliance of transformation projects based on a complete chain of evidence through their core financial data analysis capabilities without getting involved in specific engineering details. This fundamental shift in verification method, from a lagging audit of the results of an external physical world to a real-time review of an endogenous financial data logic chain, provides an implementation method with internal logical consistency and high operational feasibility for risk management in the emerging field of transformational finance.

[0037] Example 2: In this example, to address the deep technical challenges of delayed greenwashing risk identification, high verification costs, and the inability of existing technologies to effectively utilize financial institutions' own data in transitional financial services, we developed and verified a risk control solution based on the review of the logical chain of economic behavior. The core of this solution is to transform the company's vague transformation commitments into quantifiable and verifiable expected economic behavior vectors, and identify potential greenwashing risks through dynamic comparison with actual economic behavior data streams.

[0038] The experiment was conducted on a risk control platform that simulates the internal business environment of a financial institution. The platform integrates a data simulation generator, a transformation knowledge graph module, an expected economic behavior vector generation module, an actual economic behavior data capture and preprocessing module, a dynamic comparison and early warning module, and a counterparty business activity analysis module. The construction of the transformation knowledge graph is based on a specific industrial field, combined with the industry's historical public technology roadmap, environmental protection policy orientation and typical engineering cases, to construct a transformation knowledge graph containing approximately 3,000 rules and 50,000 associated entities. The knowledge graph is implemented using graph database technology and internally defines the mapping relationship from macro-transformation commitments to specific economic behaviors, such as key asset procurement behaviors, key service procurement behaviors, and supply chain change behaviors. For example, for the blast furnace oxygen-enriched combustion technology transformation, the rules contained in the knowledge graph can deconstruct it into a series of expected economic behavior sequences such as purchasing oxygen-enriched burners, purchasing supporting oxygen supply equipment, and paying system integration service fees, and are associated with typical supplier types, procurement amount ranges and time windows. The expected economic behavior vector contains time nodes, behavior types, counterparty types and amount ranges. The time nodes are connected through By analyzing historical project cycle data and industry benchmarks, reasonable time windows for key behaviors are set. For example, for a large-scale equipment procurement, from contract signing to first payment, it is usually set within 15-30 working days; and the payment for installation and commissioning services is set within 30-60 working days after the equipment arrives and completes the initial acceptance. The fundamental technical consideration in setting these time windows is to achieve a technical optimization balance between the actual timeliness of project execution and the effectiveness of financial institutions' fund supervision. Specifically, if the time window is set too wide, it will tend to excessively sacrifice risk identification. The timeliness of the timeframe will cause risk exposure to lag behind fund disbursement, resulting in the loss of the window for financial institutions to intervene. Conversely, if the timeframe is set too narrowly, the flexibility of project execution will be excessively sacrificed, leading to the system's misjudgment of normal business fluctuations and excessive false alarms. Therefore, in specific engineering practice, the determination of this time node is not an isolated absolute value, but needs to be based on the typical cycle data and inherent characteristics of the industry projects to which it is applied, combined with the core performance indicators required for the forward-looking risk identification that this invention aims to solve, to set it within a reasonable engineering range that can optimize the overall technical effect.Behavior types include key asset procurement (such as equipment purchase), key service procurement such as engineering design, technical consulting, installation and commissioning, asset disposal (such as elimination of old equipment) and supply chain change (such as switching to green raw material suppliers). The counterparty type is based on industry experience and market data, and presets typical legal counterparty portraits corresponding to various expected economic behaviors, such as large equipment manufacturers, certified engineering service providers, professional recycling companies, etc., and clarifies their typical payee name patterns and corporate nature in bank transaction data. The amount range is based on market public quotations, historical project data and industry average costs, and a reasonable amount range is set for various key procurement behaviors. For example, the purchase amount of an oxygen-enriched burner is set between RMB 5 million and RMB 15 million. The actual economic activity data stream simulates real bank transaction payment data and combines it with government public bidding information, business registration change information, customs import and export data, and authorized tax invoice information summaries obtained through open interfaces. The simulated data includes normal business transactions, normal transformation-related transactions, and various types of greenwashing behavior data, such as fraudulent procurement, related-party transactions, amount manipulation, and time misalignment. The total simulated data volume is set at approximately 200,000 transaction records from a medium-sized enterprise over an 18-month period, including 2,000 transactions directly related to the transformation project. The experiment uses a control group design to compare the risk control method of the present invention with traditional risk identification methods based on environmental outcome indicators such as carbon emission intensity.

[0039] We set up three typical transformation finance business scenarios and simulated three different corporate behavior patterns for each scenario. Scenario one is a low-carbon transformation project for kilns, which involves the transformation of high-energy-consuming traditional industries and involves large-scale equipment procurement and engineering services. Its models include real transformation, that is, the company strictly implements the transformation plan, purchases real equipment, pays real service fees, and the counterparties are all legal and compliant entities; greenwashing of misappropriation of funds, that is, part of the company's funds are used to purchase non-critical equipment spare parts that are inconsistent with the transformation goals, or the funds are transferred to shell companies with no substantial connection with them; and greenwashing of time dislocation, that is, after the funds are allocated, the company delays or advances key procurement in an attempt to evade supervision, or creates false transaction flows through short-term capital bridges; scenario two is a green supply chain upgrade project, which involves switching raw material suppliers and small, high-frequency transactions. Its models include real transformation, that is, the company gradually replaces the original high-pollution and dyeing raw material suppliers, and Establish stable cooperation with certified green suppliers; greenwashing with false suppliers, that is, enterprises create the illusion of green procurement by establishing or conducting false transactions with related shell companies; and greenwashing with abnormal behavior patterns, that is, the transaction amount and frequency between enterprises and green suppliers are seriously inconsistent with the normal level of the industry, or there are abnormal capital flows, such as the rapid return of large amounts of funds; scenario three is an industrial wastewater treatment system upgrade project, which involves the procurement of technical services and phased payments. Its models include real transformation, that is, enterprises purchase professional wastewater treatment technical services as planned and make phased payments according to the progress of the project; greenwashing with missing key behaviors, that is, after obtaining loans, enterprises do not actually purchase key technical services, or the procurement volume is far lower than expected, and only a small amount of superficial expenditure is made; and greenwashing with obstructive behavior, that is, while enterprises claim to be carrying out environmental upgrades, they actually purchase spare parts for old models of sewage treatment equipment that have been eliminated, or carry out production expansion that is contrary to environmental protection goals.

[0040] In the experimental process, first, for the enterprise transformation project plans under the above scenarios and models, the transformation knowledge graph is used for structured deconstruction to generate expected economic behavior vectors containing time nodes, behavior types, counterparty types and amount ranges. This step uses semantic analysis technology to identify and extract core semantic elements and quantitative information from the plan text, and automatically map them to the rules in the knowledge graph. When the knowledge graph cannot effectively deconstruct non-standard or new transformation plans, the system will automatically start the backup verification process: natural language processing is performed on the transformation project plan to extract the core technical elements it contains, and these elements are used as query probes to initiate parallel queries to at least one independent public information database, including academic paper databases, patent databases, industrial product B2B platforms, professional technical forums and industry news portals, to verify the objective existence or logical relevance of the core technical elements. Based on the returned results, the system will generate a plan logic credibility value to assist in the final risk judgment. Secondly, simulate the acquisition of the company's transaction payment data in the bank and integrate simulated external public information, such as bidding, industrial and commercial changes, and tax invoice summaries; perform pre-processing operations such as standardization, deduplication, and timestamp alignment on the captured data to form a unified actual economic behavior data stream. Then, the actual economic behavior data stream is continuously and dynamically matched and compared with the expected economic behavior vector. This verification process focuses on the following core judgment dimensions: payee compliance detection, that is, detecting whether the payee of the actual transaction meets the expected counterparty type restriction; amount and time range detection, that is, detecting whether the actual transaction amount and occurrence time are within the expected amount range and time node range; key economic behavior missing detection, that is, retrospectively checking whether there are key economic behavior missing that are inconsistent with the expected economic behavior vector; and abnormal economic behavior detection, that is, actively searching for abnormal economic behavior that is contrary to the goals of the transformation project, such as the behavior of purchasing spare parts for obsolete old equipment models while claiming to upgrade equipment.Based on the results of the above multi-dimensional verification, the system calculates a comprehensive compliance score. Subsequently, when this compliance score is lower than the preset compliance threshold, the system outputs a greenwashing risk warning signal, which indicates that there is a logical break in the economic behavior trajectory of the transformation project plan. The preset value of this compliance threshold is based on big data statistical modeling of the actual economic behavior pattern of historical transformation financial business. It is determined by analyzing a large number of real transformation cases (positive samples) and known greenwashing cases (negative samples) combined with ROC curve optimization, and can be dynamically adjusted according to actual operational results. The fundamental technical consideration for setting this threshold is to achieve a balance between the sensitivity (recall rate) and specificity (precision rate) of risk identification. A technical optimization balance; specifically, if the threshold is set too high, that is, the consistency requirement is too loose, it will tend to over-sacrifice sensitivity, resulting in the omission of real greenwashing risks; conversely, if it is set too low, that is, the consistency requirement is too strict, it will over-sacrifice specificity, thereby causing the system to misjudge normal business fluctuations and generate too many false alarms; therefore, in specific engineering practice, the determination of this threshold is not an isolated absolute value, but needs to be based on the actual data characteristics of the industry projects to which it is applied, the frequency of potential greenwashing risks, and the core performance indicators required for the forward-looking risk identification that this invention aims to solve, so as to set it within a reasonable engineering range that can optimize the overall technical effect.

[0041] Early warning signals are subdivided into low logic mismatch risk, key behavior mismatch risk, and contradictory behavior risk based on the degree of logical mismatch and the lack of key behaviors. Finally, for actual transactions that match the key economic behaviors in the expected economic behavior vector, the system automatically determines its counterparty and generates a snapshot of its recent business activity network centered on the counterparty based on simulated internal bank anonymous transaction data. The analysis engine evaluates the snapshot to determine whether there is a risk of transaction hollowing out. The evaluation dimensions include: customer diversity ratio where N c Represents the number of different customers who have transactions with the counterparty, N tThe ratio represents the total number of bank customers. If this ratio is lower than the preset ratio threshold, it is judged that the interaction diversity is low. The fundamental technical consideration for setting this threshold is to achieve an optimal balance between the general law of counterparty diversity in normal business activities and the effectiveness of identifying shell companies or related transactions. Specifically, if the threshold is set too high, it will tend to over-sacrifice sensitivity, resulting in the omission of truly small-scale fraudulent transactions; conversely, if it is set too low, it will over-sacrifice specificity, thereby causing the system to misjudge normal small and micro enterprises or professional suppliers. The uniformity of transaction time distribution is measured by calculating the entropy value of the transaction time point. If the entropy value is lower than the preset uniformity threshold, it is judged that the behavior pattern is abnormal. The fundamental technical consideration for setting this threshold is to achieve an optimal balance between the randomness of the transaction time distribution in normal business activities and the effectiveness of identifying abnormal sudden transactions or concentrated transaction patterns. Specifically, if the threshold is set too high, it will tend to over-sacrifice sensitivity, resulting in the omission of abnormal behaviors with abnormal transaction time distribution; conversely, if it is set too low, it will over-sacrifice specificity. , which can lead to the system misjudging normal periodic or large-value transactions. The average settlement period of large funds monitors the average residence time of large funds flowing into the counterparty's account. If the average settlement period is less than the preset settlement period threshold, it is judged as an abnormal fund flow. In calculating the average fund settlement period, the system first screens all large single payment records entering the counterparty's account and constructs a fund life cycle trajectory for each payment. Specifically, starting from the arrival time of the funds, the system continuously tracks the fund outflow records of the account in the subsequent time window and calculates the number of natural days between the first complete transfer of the funds. All eligible settlement times constitute a set of observation samples. The system calculates the mean of this sample set and uses the result as the average fund settlement period for the counterparty. If this value is less than the preset settlement period threshold, which is the risk-sensitive quantile selected from the industry distribution statistics constructed based on the bank's historical full transaction account data, the system marks the counterparty as having abnormal fund flow and generates a transaction hollowing risk labeling signal. The fundamental technical consideration in setting this threshold is to achieve an optimal balance between the general law of capital turnover efficiency in normal business transactions and the effectiveness of identifying abnormal behaviors such as rapid capital bridging and cashing out. Specifically, if the threshold is set too short, it will tend to over-sacrifice sensitivity, resulting in the omission of some rapidly transferred funds; conversely, if it is set too long, it will over-sacrifice specificity, thereby causing the system to misjudge normal high-turnover companies. When any of the above assessments indicates an abnormality, the system generates a transaction hollowing risk labeling signal and presents it in association with the greenwashing risk warning signal.

[0042] After running simulations for each of the above scenarios and models, the present invention demonstrated extremely high sensitivity in identifying various greenwashing risks. The detection rate for various complex greenwashing methods, such as misappropriation of funds, false suppliers, missing key behaviors, and contradictory behaviors, reached over 95%. The average warning lag of the present invention is 3-10 days, which means that a warning can be issued very soon after the risk behavior occurs, allowing financial institutions to intervene before funds are consumed or at an early stage. In comparison, traditional methods, due to their over-reliance on post-environmental audit results, have significantly lower detection rates and warning lags of up to 90-210 days. This delays the timing of risk exposure and essentially eliminates the intervention window. The present invention can effectively identify transactions in greenwashing involving false suppliers. It can effectively identify hollowing-out risks (detection rate is as high as 98.7%) and mark some related transactions in greenwashing involving misappropriation of funds. This shows that through the analysis of the commercial activity of counterparties, it can effectively identify false transactions, significantly improve the risk identification ability of transitional financial services, and penetrate superficially compliant transaction behaviors from the commercial essence level. In the real transition mode, the system will occasionally issue a very low proportion of false alarms (0.1%). This usually occurs when some small, legitimate suppliers with a single business model conduct small, concentrated transactions with enterprises, and their customer diversity ratio may be low, but these situations can be eliminated through manual review, which shows that the module can control the false alarm rate within an acceptable range while maintaining high sensitivity.

[0043] In the low-carbon transformation scenario of kilns, the transformation knowledge graph can accurately deconstruct the macro goal of energy-saving and carbon-reduction transformation into specific expected economic behavior vectors, such as purchasing high-efficiency burners, with an amount range of 8 million to 12 million yuan and a time period of D+30-D+90 days, hiring engineering design and construction services, with an amount range of 1.5 million to 3 million yuan and a time period of D+15-D+180 days. In the green supply chain upgrade scenario, it can be refined to establish long-term supply contracts with certified green raw material suppliers, with an amount range of 200,000 to 500,000 yuan per month and continuous transactions. This structural deconstruction is the basis for achieving subsequent precise matching. It enables financial institutions to return to substantive commercial judgment by transforming environmental benefit verification into a review of the logical chain of economic behavior. In the time-displaced greenwashing model, even if the company finally completes the purchase, this solution can quickly identify the company because its payment time point deviates seriously from the expected time window. In the case of contradictory behavior greenwashing, for example, a company claims to upgrade its wastewater treatment system but actually purchases spare parts for old equipment, the system can capture key economic behaviors that are contrary to the transformation goals in the actual economic behavior data stream through abnormal economic behavior detection rules, thereby triggering an early warning. For false supplier greenwashing, the system found that the customer diversity ratio R of a green supplier was far below the preset threshold, and the average sedimentation period of funds for most of its transaction flows was less than 24 hours, and they were quickly returned to other related accounts. At the same time, the time points of its transactions were extremely concentrated, showing an unnatural uniform distribution. After aggregating these abnormal indicators, the system immediately generated a high-confidence transaction hollowing risk labeling signal, and associated it with the greenwashing risk warning signal, indicating to the risk manager the possible commercial substance problems of the actual transaction, which effectively avoided the high-cost environmental audit and returned to the commercial substance judgment.

[0044] Example 3: This example combines Figures 1 to 4 , a risk control method and system for identifying greenwashing risks in transitional financial business is described. Figure 1As shown in the figure, first, the enterprise module submits the transformation project plan to the deconstruction module; the deconstruction module obtains relevant graph rules from the transformation knowledge graph by querying industry knowledge and related data, and completes the processing of the project text based on semantic analysis and structured deconstruction technology to generate a structured expected economic behavior vector. Subsequently, the vector is used in the continuous monitoring process. The data acquisition module obtains the actual economic behavior data stream of the enterprise by importing or obtaining bank transaction data and integrating external public information. Based on this data, the comparison module conducts dynamic matching and comparison, verifies logical consistency, and calculates the compliance score. If the compliance score is lower than the threshold, the early warning module is triggered to trigger risk identification and perform risk grading, and finally outputs a greenwashing risk warning signal. The warning information is synchronously transmitted to the risk manager module, which conducts targeted due diligence based on the risk level. If the judgment result indicates that the compliance score is normal, the comparison module returns to continue the continuous monitoring process to ensure that the subsequent behavior of the project is continuously controlled.

[0045] like Figure 2 As shown in the figure, normal enterprises are represented by solid dots, corresponding to the label "normal enterprises" in the figure. Their customer diversity ratio R is close to 80 points, indicating that they have real transactions with multiple customers; they perform well in indicators such as transaction time distribution uniformity, average capital sedimentation period, transaction frequency stability and reasonable amount fluctuation, and the values ​​are significantly higher than the dotted circle shown in the risk threshold; suspicious enterprises are represented by dotted boxes, corresponding to the label "suspicious enterprises" in the figure. They are significantly below the risk threshold in all indicators, especially in the two dimensions of customer diversity ratio R and average capital sedimentation period. Their performance is extremely poor, reflecting that their transactions may be concentrated in a few related accounts and there is abnormal short-term capital repatriation. In addition, the low industry matching degree indicates that the company's trading behavior is inconsistent with the typical model of its industry, further increasing its suspicion of greenwashing risk.

[0046] like Figure 3 As shown in the table, five typical types of greenwashing are listed: misappropriation of funds, false suppliers, missing key behaviors, time misalignment, and contradictory behaviors. The corresponding detection rates are 95.2%, 98.7%, 96.5%, 93.8%, and 97.3%, respectively. The false alarm rates are 0.12, 0.08, 0.15, 0.18, and 0.10, respectively, and the confidence levels are 95%, 98%, 96%, 94%, and 97%, respectively. The figure visualizes the confidence level of each greenwashing type in the form of a bar chart, reflecting the system's ability to accurately identify and trustworthiness of each type of risk. At the bottom of the table, the comprehensive detection rate, comprehensive false alarm rate, and comprehensive confidence level are further summarized and calculated, with values ​​of 96.3%, 0.13, and 96%, respectively.

[0047] like Figure 4As shown, the system first extracts each behavior node from the expected economic behavior vector, which are behavior 1: D+30, equipment procurement, manufacturer A, 8 million, behavior 2: D+45, engineering service, service provider B, 1.5 million, behavior 3: D+90, equipment procurement, manufacturer C, 5 million, and behavior 4: D+120, installation service, service provider D, 2 million; at the same time, it accesses the actual economic behavior data stream, which includes transaction 1: D+35, payment to XX manufacturing, 8.2 million, which is successfully matched; transaction 2: D+50, payment to an affiliated company, 1.4 million, which is successfully matched; Easy verification failed; Transaction 3: D+95, YY Technology, 4.8 million, successfully matched; and Transaction 4: ---Missing---, failed to match due to missing data. The system uses the dynamic matching verification logic module to sequentially check multiple verification dimensions, including time window matching, payee type verification, amount range verification, key behavior missing detection, and abnormal behavior identification. It then enters the consistency calculation module. In this example, the system calculates a match score of 75%, while the preset threshold is 85%, resulting in a logical inconsistency. Based on the verification dimension scores and consistency judgment, the system generates a greenwashing risk warning signal, clearly stating the cause of the warning is the missing key behavior and the transaction anomaly.

[0048] Example 4: In this example, to ensure the stability and accuracy of the entire risk identification framework, a key preprocessing stage ensures the quality and availability of the input data before the aforementioned step of feeding the actual economic behavior data stream into the comparison module. This stage includes: first, standardizing and cleaning the original transaction flow, and mapping transaction descriptions of different sources and formats into structured records through preset entity recognition and normalization algorithms, and interpolating or marking missing or abnormal values ​​of key fields according to preset rules; second, performing entity resolution of the counterparty, that is, by comparing external auxiliary data such as industrial and commercial information and tax summaries, the seemingly different names of the payee are accurately associated with a unique business entity, providing a solid foundation for the calculation of subsequent indicators such as customer diversity ratios; finally, when generating the association network snapshot for business activity analysis, the anonymization processing used is intended to fully retain and calculate the topological characteristics of the transaction network centered on any anonymous node while complying with privacy regulations, rather than simply hiding identity information.

[0049] In the aforementioned logic of calculating compliance scores and generating greenwashing risk warning signals, the internal quantitative integration and judgment mechanism is embodied in a specific implementation as a multi-layer weighted scoring and dynamic adjustment model. Specifically, the compliance score is derived through a preset weighted summation model, rather than a simple arithmetic average of the scores of each verification dimension. In this model, the matching score with key economic behaviors has the highest weight, followed by the compliance of the counterparty type, and then the matching of the amount and time. Once a contradictory behavior that is contrary to the transformation goal is identified, it will be treated as a fixed factor in the model. Furthermore, the scoring model integrates the output signals of other modules as dynamic adjustment factors: when the system generates a hollowing-out risk labeling signal for a key transaction, the compliance score of the transaction will be multiplied by a preset penalty coefficient lower than 1.0; if the project plan itself is assessed to have a low logical credibility value during the deconstruction phase, a preset negative offset will be applied to the overall compliance score of the project at the beginning of the calculation. This design ensures that the final risk judgment is a multi-dimensional logical result that integrates the behavioral trajectory, the essence of the transaction, and the feasibility of the plan.

[0050] In addition, the core business thresholds are not set as isolated empirical values, but follow a set of systematic, data-driven engineering calibration procedures. Taking the determination of compliance thresholds as an example, a feasible standard process includes: the first step is to collect a labeled data set containing a large number of historical real transformation cases (positive samples) and known greenwashing cases (negative samples); the second step is to run the aforementioned compliance score calculation model on the data set to obtain the score distribution of each case; the third step is to draw the receiver operating characteristic (ROC) curve and determine a value on the curve that can achieve the preset business goals, for example, while maximizing the recognition sensitivity of high-risk scenarios, the false alarm rate is controlled within a certain percentage. The optimal balance point under the above conditions is determined and used as the initial threshold. The dynamic adjustment mechanism of this threshold is reflected in the fact that the system regularly adds newly confirmed risk cases and successful transformation cases to the benchmark data set and automatically repeats the above calibration process. Similarly, the various sub-thresholds used to judge transaction hollowing, such as the threshold of the customer diversity ratio R and the threshold of the average fund deposit cycle, are also set based on the statistical distribution analysis of the corresponding indicators of the bank's full set of anonymous corporate customers. Usually, a specific quantile of its statistical distribution, such as the 5% or 10% quantile, is used as the initial baseline for defining normal and abnormal behaviors, thereby ensuring the objectivity and statistical significance of the judgment benchmark. These are all extended implementation methods known to ordinary technicians in this field.

[0051] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0052] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A risk control method for identifying greenwashing risks in transitional financial services, characterized by: The method comprises the following steps: Step 1: Based on a transformation knowledge graph constructed from a defined set of rules, the transformation project proposal submitted by the enterprise is structurally deconstructed to generate an expected economic behavior vector that includes time nodes, behavior types, counterparty types, and amount ranges. The expected economic behavior vector includes at least one of key asset procurement behavior or key service procurement behavior. The actual economic behavior data stream of the enterprise is non-invasively obtained, and the actual economic behavior data stream includes at least the enterprise's transaction payment data at the bank. Step 2: Dynamically match and compare the actual economic behavior data stream with the expected economic behavior vector to verify the logical consistency between the actual economic behavior and the expected economic behavior. Logical consistency verification includes checking whether the payee of the actual transaction meets the expected counterparty type restrictions, whether the actual transaction amount and occurrence time are within the expected amount and time node range, and whether there are any key economic behavior omissions that are inconsistent with the expected economic behavior vector; Step 3: When the logical consistency is lower than the preset compliance threshold, a greenwashing risk warning signal is output. The greenwashing risk warning signal indicates that there is a logical break in the economic behavior trajectory of the transformation project plan.

2. The risk control method for identifying greenwashing risks in transitional financial services according to claim 1, characterized in that: The transformation knowledge graph is an extensible rule base and associative database built based on the experience of industry experts, which is used to associate transformation commitments with specific economic behavior sequences; structured deconstruction is to deconstruct the transformation project plan into an expected economic behavior vector that includes at least one of procurement behavior, service behavior, asset disposal behavior and supply chain change behavior based on the rule base.

3. The risk control method for identifying greenwashing risks in transitional financial services according to claim 1, characterized in that: The actual economic behavior data flow also includes at least one of government public bidding information, corporate industrial and commercial change information, customs import and export data, and authorized tax invoice information summaries obtained through open interfaces.

4. The risk control method for identifying greenwashing risks in transitional financial services according to claim 1, characterized in that: The logical consistency verification step specifically includes one of the following operations: detecting whether there is transaction payment data in the actual economic behavior data stream that corresponds to the key asset procurement behavior in the expected economic behavior vector, or detecting whether there is transaction payment data in the actual economic behavior data stream that corresponds to the key service procurement behavior in the expected economic behavior vector; detecting whether there is abnormal economic behavior in the actual economic behavior data stream that is contrary to the goals of the transformation project plan. Abnormal economic behavior refers to behavior that is defined as contrary to the transformation goals, such as purchasing spare parts for obsolete old equipment models while claiming to upgrade equipment.

5. The risk control method for identifying greenwashing risks in transitional financial services according to claim 1, characterized in that: In the step of structurally deconstructing the transformation project plan based on the transformation knowledge graph, when the transformation knowledge graph cannot effectively deconstruct the transformation project plan, the method further includes the following steps: performing natural language processing on the transformation project plan to extract the core technical elements contained therein; using the core technical elements as query probes, initiating parallel queries to at least one independent public information database to verify the objective existence or logical relevance of the core technical elements; the public information database includes at least one of an academic paper database, a patent database, an industrial product B2B platform, a professional technical forum, and an industry news portal; and generating a plan logic credibility value based on the verification result, and the plan logic credibility value is used to assist in generating a greenwashing risk warning signal.

6. The risk control method for identifying greenwashing risks in transitional financial services according to claim 1, characterized in that: After the step of dynamically matching and comparing the actual economic behavior data stream with the expected economic behavior vector, the method further includes the following steps: determining the counterparty of the actual transaction that matches the key economic behavior in the expected economic behavior vector; generating a snapshot of the association network of the counterparty's recent business activity based on the anonymous transaction data within the bank, with the determined counterparty as the center; and analyzing the snapshot of the association network to evaluate the counterparty's business enthusiasm and behavior pattern, the evaluation including one or more of the following judgments: judging the customer diversity ratio of the counterparty to other customers in the preset analysis period; , customer diversity ratio Calculated by the following formula: ,in, Represents the number of different customers who have transactions with the counterparty, Represents the total number of bank customers. If the customer diversity ratio If it is lower than the preset ratio threshold, it is judged that the interaction diversity is low; judge the distribution uniformity of all transaction time points of the counterparty. If the distribution uniformity is lower than the preset uniformity threshold, it is judged that the behavior pattern is abnormal; judge the average sedimentation period of large funds flowing into the counterparty. If the average sedimentation period is less than the preset sedimentation period threshold, it is judged that the capital flow is abnormal; when the business heat and behavior pattern judgment results indicate abnormality, a transaction hollowing risk labeling signal is generated.

7. The risk control method for identifying greenwashing risks in transitional financial services according to claim 6, characterized in that: The transaction hollowing risk marking signal is associated with the greenwashing risk warning signal, indicating to risk managers possible commercial substance problems in actual transactions.

8. The risk control method for identifying greenwashing risks in transitional financial services according to claim 1, characterized in that: The greenwash risk warning signal classifies the risk into at least one of low logical inconsistency risk, key behavior omission risk, and contradictory behavior risk, based on the degree to which the logical consistency is lower than the preset value and the absence of key economic behaviors, to guide banks to take targeted due diligence measures.

9. The risk control method for identifying greenwashing risks in transitional financial services according to claim 1, characterized in that: Structured deconstruction uses semantic analysis technology to identify and extract core semantic elements and quantitative information corresponding to the rules in the transformation knowledge graph from the transformation project plan text, and map them to the corresponding fields of the expected economic behavior vector. The preset numerical compliance threshold is based on the analysis of the actual economic behavior patterns of historical transformation financial business, determined through statistical methods and dynamically adjusted to ensure the sensitivity and specificity of the greenwashing risk warning signal.

10. The risk control method for identifying greenwashing risks in transitional financial services according to any one of claims 1 to 9, characterized in that: The system corresponding to the method includes: a deconstruction module, configured to structurally deconstruct the transformation project plan based on the transformation knowledge graph to generate an expected economic behavior vector; a data acquisition module, configured to non-invasively acquire the actual economic behavior data stream; a comparison module, configured to continuously and dynamically match and compare the actual economic behavior data stream with the expected economic behavior vector to verify logical consistency; and an early warning module, configured to output a greenwashing risk warning signal when the logical consistency falls below a preset numerical compliance threshold.

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