Method and system for individually judging contract risk based on decision engine

Abstract

The invention provides a personalized contract risk judgment method and system based on a decision engine, and the system comprises the steps: recognizing basic terms in an ISDA protocol, and marking an open term set which allows supplement and modification in the ISDA protocol; identifying a reference statement in the supplementary protocol, calculating a semantic deviation value between the supplementary protocol and an original reference clause, and establishing a clause relation tree; accessing a market data interface, calling market data, inputting the market data into a risk open simulator, and calculating a risk open index; and generating an interaction report. According to the personalized contract risk judgment method based on the decision engine, the dependence of a traditional method on a fixed template structure is broken through, and the clause relation tree is constructed, so that a system can accurately describe dynamic reference and modification paths between a main protocol and a supplementary protocol. And market data is accessed, so that the limitation of static evaluation of a traditional method is overcome. And the generated interaction report enables a user with a non-legal background to master a decision of an actual contract service.

Description

Technical Field

[0001] The present application relates to the field of computer contract decision-making technology, and in particular to a method and system for personalized contract risk judgment based on a decision engine. Background Art

[0002] In the current field of financial transaction compliance management, especially in the processing of ISDA agreement texts widely used in derivatives transactions, contract risk assessment is gradually shifting from manual review to automated and intelligent decision-making engines.

[0003] Existing contract risk assessment methods mostly rely on static rule engines and feature matching algorithms. These technologies use predefined rules or trained models to identify high-risk clauses in contracts, such as payment defaults, force majeure, and termination triggers. They then score these clauses based on text features like word frequency statistics and syntactic structure, and generate risk classification reports based on the scoring results. Some more advanced systems also incorporate simple semantic analysis models to identify cross-clause references.

[0004] However, compared to general contracts, ISDA agreements feature complex structures, highly dynamic content, and multi-layered nested clause dependency chains. Traditional judgment methods based on static rules and feature matching remain based on fixed template structures and lack the ability to respond to dynamic market parameters and regulatory policies. Traditional judgment methods cannot accurately identify the complex reference and modification relationships between the master agreement and its ancillary agreements, and do not consider the dynamic impact of derivative risk parameters such as CVA. Existing systems only provide text-based risk output and lack visual simulation chart reports for non-legal users, making it difficult to assist in actual business decision-making. Summary of the Invention

[0005] In view of this, it is necessary to provide a method and system for personalized contract risk judgment based on a decision engine that can combine market dynamic parameters and can directly target non-legal users to solve the above problems.

[0006] An embodiment of the present application provides a method for personalized contract risk determination based on a decision engine, characterized by comprising the steps of: Identify the irrevocable foundational clauses in the ISDA Master Agreement and mark the set of open clauses in the ISDA Master Agreement that are subject to supplemental amendment; Identify the reference statement in the supplemental agreement to the open clause set, and establish a clause relationship tree between the main agreement and the supplemental agreement; Calculating a semantic deviation value between the supplementary agreement and the referenced clause in conjunction with the clause relationship tree, and calculating a risk factor based on the semantic deviation value, wherein the referenced clause is one of the open clauses referenced in the reference statement; Accessing the market data interface, retrieving market data and inputting it into the risk exposure simulator, and calculating potential risk exposure indicators based on the termination clause in the master agreement and the risk factors; Based on the clause relationship tree and the potential risk exposure indicators, an interactive report is generated to assist non-legal users in making actual contract business decisions.

[0007] In at least one embodiment of the present application, the step of "calculating the semantic deviation value between the supplementary agreement and the referenced clause" further includes the following steps: Allocate risk weights between the master agreement and the supplemental agreement, where the risk weight of the master agreement is denoted as a and the risk weight of the supplemental agreement is denoted as b, satisfying a ≥ b; Calculate the incremental risk weight in the supplementary agreement based on the semantic deviation between each supplementary agreement and the referenced clause, where the incremental risk weight is denoted as Δt; If b+Δt>a, the risk warning mechanism is triggered, and the supplementary agreement is marked as a warning node in the interaction report.

[0008] In at least one embodiment of the present application, the step of “calculating a potential risk exposure indicator based on the termination clause in the master agreement and the risk factor” includes the following specific steps: Based on the terms of the credit support annex in the supplementary agreement and combined with market data, the credit exposure assessment value of the parties to the agreement is dynamically estimated. The credit exposure assessment value is combined with the termination terms and the risk factors and input into the risk exposure simulator to calculate the potential risk exposure indicator.

[0009] In at least one embodiment of the present application, the step of “calculating a semantic deviation value between the supplementary agreement and the referenced clause, and calculating a risk factor based on the semantic deviation value” includes the following specific steps: The semantic deviation value includes a first semantic deviation value, and the risk factor includes a first risk factor; The supplementary agreement is semantically compared with the referenced clause, a first semantic deviation is calculated, and a first risk factor of the supplementary agreement is output. The first risk factor is combined with the standard clause library to generate the potential risk exposure indicator of the current agreement.

[0010] In at least one embodiment of the present application, the step of "combining the first semantic deviation with the standard library clauses to generate the potential risk exposure indicator of the current agreement" includes the following specific steps: The semantic deviation value includes a second semantic deviation degree, and the risk factor includes a second risk factor; Based on the semantic comparison between the open clause set in the master agreement and the corresponding clauses in the standard clause library, the second semantic deviation is calculated, and the second risk factor of the master agreement is output. The first risk factor and the second risk factor are combined to perform a double verification mechanism to generate the potential risk exposure indicator of the current agreement.

[0011] In at least one embodiment of the present application, the step of “calculating the incremental risk weight in the supplemental agreement” further includes the steps of: Combined with the derivative contract terms and forecasting model in the supplementary agreement, the potential deviation in the impact of the modification of the supplementary agreement on cash flow or performance path is evaluated, and the incremental risk weight is calculated based on the semantic deviation value.

[0012] In at least one embodiment of the present application, the credit support annex includes derivatives, and the step of “dynamically estimating the credit exposure assessment value of the parties to the agreement based on the terms of the credit support annex in the supplemental agreement and in combination with market data” specifically includes the following steps: Based on the standard model stipulated by the international derivatives market, the SIMM compliance module is called to calculate the standardized initial margin of the derivatives transaction and generate the credit guarantee index required for the derivatives transaction; Simulate multiple market data, call the Monte Carlo path generation module, evaluate the credit risk exposure of the derivative transaction, and dynamically estimate the credit exposure assessment value based on the credit guarantee indicator and credit risk exposure.

[0013] In at least one embodiment of the present application, the market data includes market extreme data.

[0014] In at least one embodiment of the present application, the interaction report is displayed in a tree structure, with the original terms of the main agreement as the root node and the supplementary agreement as the child node.

[0015] In one embodiment of a system for personalized contract risk determination based on a decision engine of the present application, the above-mentioned method for personalized contract risk determination based on a decision engine is applied.

[0016] The above-mentioned method and system for personalized contract risk assessment based on a decision engine breaks through the traditional method's reliance on fixed template structures by identifying the basic clauses in the ISDA agreement and marking the open clause sets. It distinguishes the basic clauses and open clause sets in advance, can accurately identify the variable and immutable parts of the contract, effectively improve the efficiency of clause analysis, and avoid the limitation of the traditional method of statically identifying risks based on the full text of the contract. By identifying reference statements in supplemental agreements, calculating semantic deviations from the referenced clauses, and constructing a clause relationship tree, the system accurately depicts the dynamic reference and modification paths between the main agreement and the supplemental agreement. The calculation of semantic deviations takes into account changes in clause content and their potential impact on the contract performance path, ensuring the ability to identify subtle changes between different clauses and conditions in the contract. This avoids the misjudgment or oversight of clause relationships that can occur with traditional methods, significantly improving the accuracy and real-time nature of contract risk identification.

[0017] It also connects to a real-time market data interface, inputting market parameters such as interest rate curves and CDS spreads into the risk exposure simulator. This dynamically calculates risk exposure indicators based on the agreement terms, overcoming the limitations of traditional static assessment methods. Based on the clause relationship tree and simulation results, it generates an interactive report containing a risk tracing diagram and market sensitivity analysis matrix, enabling even non-legal business personnel to intuitively grasp the potential risks of a contract. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flowchart of a method for personalized contract risk judgment based on a decision engine in one embodiment of the present application.

[0019] Figure 2 for Figure 1 A flowchart of the interactive report output of the method for personalized contract risk judgment based on a decision engine.

[0020] Figure 3 for Figure 1 A flowchart of the potential risk exposure indicator output process of the method for personalized contract risk judgment based on a decision engine.

[0021] Figure 4 for Figure 1 A flowchart of the credit exposure assessment value output process of the method for personalized contract risk judgment based on a decision engine. DETAILED DESCRIPTION

[0022] The embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments.

[0023] It should be noted that when a component is considered to be "connected to" another component, it can be directly connected to the other component or there may be an intermediate component. When a component is considered to be "disposed on" another component, it can be directly disposed on the other component or there may be an intermediate component. The terms "top", "bottom", "upper", "lower", "left", "right", "front", "back", and similar expressions used herein are for illustrative purposes only.

[0024] An embodiment of the present application provides a method for personalized contract risk determination based on a decision engine, characterized by comprising the steps of: Identify the irrevocable foundational clauses in the ISDA Master Agreement and mark the set of open clauses in the ISDA Master Agreement that are subject to supplemental amendment; Identify the reference statement in the supplemental agreement to the open clause set, and establish a clause relationship tree between the main agreement and the supplemental agreement; Calculating a semantic deviation value between the supplementary agreement and the referenced clause, and calculating a risk factor based on the semantic deviation value, wherein the referenced clause is the clause cited in the reference statement; Accessing the market data interface, retrieving market data and inputting it into the risk exposure simulator, and calculating potential risk exposure indicators based on the termination clause in the master agreement and the risk factors; Based on the clause relationship tree and the potential risk exposure indicators, an interactive report is generated to assist non-legal users in making actual contract business decisions.

[0025] The above-mentioned method and system for personalized contract risk assessment based on a decision engine breaks through the traditional method's reliance on fixed template structures by identifying the basic clauses in the ISDA agreement and marking the open clause sets. It distinguishes the basic clauses and open clause sets in advance, can accurately identify the variable and immutable parts of the contract, effectively improve the efficiency of clause analysis, and avoid the limitation of the traditional method of statically identifying risks based on the full text of the contract. By identifying reference statements in supplemental agreements, calculating semantic deviations from the referenced clauses, and constructing a clause relationship tree, the system accurately depicts the dynamic reference and modification paths between the main agreement and the supplemental agreement. The calculation of semantic deviations takes into account changes in clause content and their potential impact on the contract performance path, ensuring the ability to identify subtle changes between different clauses and conditions in the contract. This avoids the misjudgment or oversight of clause relationships that can occur with traditional methods, significantly improving the accuracy and real-time nature of contract risk identification.

[0026] It also connects to a real-time market data interface, inputting market parameters such as interest rate curves and CDS spreads into the risk exposure simulator. This dynamically calculates risk exposure indicators based on the agreement terms, overcoming the limitations of traditional static assessment methods. Based on the clause relationship tree and simulation results, it generates an interactive report containing a risk tracing diagram and market sensitivity analysis matrix, enabling even non-legal business personnel to intuitively grasp the potential risks of a contract.

[0027] The following embodiments of the present application are described in detail with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features therein may be combined with each other.

[0028] See also Figures 1-4An embodiment of the present application provides a method for personalized contract risk determination based on a decision engine, characterized by comprising the steps of: S10 identifies the irrevocable basic clauses in the ISDA Master Agreement and marks the set of open clauses in the ISDA Master Agreement that are allowed to be supplemented and modified; S20 identifies a reference statement in the supplementary agreement to the open clause set, and establishes a clause relationship tree between the main agreement and the supplementary agreement; S30, combining the clause relationship tree, calculating a semantic deviation value between the supplementary agreement and the referenced clause, and calculating a risk factor based on the semantic deviation value, wherein the referenced clause is one of the open clauses referenced in the reference statement; S40 accesses the market data interface, retrieves market data and inputs it into the risk exposure simulator, and calculates potential risk exposure indicators based on the termination clause in the master agreement and the risk factors; S50 generates an interactive report to assist non-legal users in making actual contractual business decisions based on the clause relationship tree and the potential risk exposure indicator.

[0029] Specifically, the ISDA agreement is a standard master agreement provided by the International Swaps and Derivatives Association (ISDA) that regulates over-the-counter derivatives trading. The system uses a natural language processing (NLP) model to segment and semantically cluster the ISDA master agreement text. Based on the standard ISDA structure (e.g., the 2002 / 2016 versions), the content is divided into a base clause set and an open clause set. Base clauses refer to the general and unchangeable clauses in the master agreement, such as definitions, default obligations, and notice clauses. Open clauses refer to clauses that can be supplemented, modified, or replaced in trading practice, such as margin, default definitions, and termination rights.

[0030] Preferably, the system utilizes NER entity recognition and rule template matching to identify references to open clauses in the main agreement (e.g., "Modify Article 5(a) of the Main Agreement") in the supplemental agreement through rule template and entity recognition. The main agreement clauses and the supplemental agreement content are then bound together through reference paths, constructing a "clause relationship tree" between the main and supplemental agreements. This tree records the clause reference path, modification content, modification type, and corresponding main clause node, supporting subsequent structured analysis and semantic positioning, and clearly visualizing the hierarchical dependencies of the contract clauses.

[0031] Preferably, a semantic matching model is used to quantify the semantic similarity between each modification and the original clause, generating a semantic deviation index. A sentence embedding model (such as SBERT or BART) is used to calculate the semantic similarity between the referenced clause (the open clause referenced in the main agreement) and the modified clause in the supplemental agreement, with similarity S ∈ [0,1]. The semantic deviation value D is set to 1- S. A larger D indicates a more drastic change and is positively correlated with the risk factor, reflecting the level of risk.

[0032] Based on the identified principal and supplementary clause structure, the system accesses external or internal market data interfaces, including but not limited to interest rate curves, CDS spreads, and HQLA haircuts. Using the clause risk factors along with market data as input, the system drives financial risk models and simulates potential risk exposure (PFE) indicators under different market scenarios.

[0033] Furthermore, the risk exposure simulator uses financial models such as Monte Carlo simulation, VaR, and expected exposure to simulate potential contract losses under various market fluctuations. The termination clause in the master agreement serves as a contract default trigger. The simulator uses the termination clause logic as a boundary condition in the simulation path to dynamically adjust the risk exposure window.

[0034] In one specific embodiment, the risk exposure simulator uses Monte Carlo simulation to simulate market paths over the next N days (e.g., 10 or 30 days). Combining the contractual cash flow paths with risk factors, the maximum potential future exposure (PFE) is calculated for each path and the PFE value is output at a confidence level (e.g., 95%). Example formula: PFE = max{E[L(t)]}, t ∈ T, where L(t) is the expected loss at time t.

[0035] Furthermore, the interactive report uses a clause relationship tree as its navigation structure, allowing users to click on each clause node to view its modification path, semantic deviation value, risk factor, market correlation data, etc. The visualization module displays PFE curves, sensitivity analysis (such as sensitivity to interest rate or exchange rate changes), risk level scores, and other content.

[0036] In a specific embodiment, the step of “calculating the semantic deviation value between the supplementary agreement and the referenced clause” further includes the following steps: Allocate risk weights between the master agreement and the supplemental agreement, where the risk weight of the master agreement is denoted as a and the risk weight of the supplemental agreement is denoted as b, satisfying a ≥ b; Calculate the incremental risk weight in the supplementary agreement based on the semantic deviation between each supplementary agreement and the referenced clause, where the incremental risk weight is denoted as Δt; If b+Δt>a, the risk warning mechanism is triggered, and the supplementary agreement is marked as a warning node in the interaction report.

[0037] Specifically, the system first uses a clause classifier to automatically identify the category of each master agreement clause, such as "termination clause," "breach of contract clause," "margin arrangement," "notice clause," etc. Each clause category is assigned a different base risk weight value a in the standard clause weight library based on its impact on the overall risk exposure of the contract.

[0038] For example, a termination clause, because it directly determines the termination point of a transaction and may carry the risk of extreme losses, is assigned a higher base weight (e.g., a = 0.9). A notice clause, on the other hand, is merely a procedural obligation and may be assigned only a = 0.2. When a supplemental agreement references a clause in the main agreement (e.g., a termination right), the initial assigned risk weight b for the supplemental clause is no higher than the weight a of the referenced main clause, i.e., a ≥ b.

[0039] Furthermore, the system uses a semantic deviation value, D, to measure the extent to which a supplemental agreement modifies the terms of the main agreement. To further characterize the amplifying effect of this change on risk, an incremental risk weight, Δt, is introduced. The greater the supplemental agreement's modification of the original terms, the greater the increase in its risk rating.

[0040] Furthermore, if b + Δt > a, the supplemental agreement clause has been significantly modified, and its overall risk level has exceeded the original risk level of the referenced master agreement clause, thus constituting a "risk boundary breach." The system automatically triggers the risk warning mechanism, highlighting the clause node as a "warning node" in the interactive report interface and displaying its semantic deviation value D, risk factor, Δt value, and the referenced master clause.

[0041] In a specific embodiment, the step of “calculating a potential risk exposure indicator based on the termination clause in the master agreement and the risk factor” includes the following specific steps: Based on the terms of the credit support annex in the supplementary agreement and combined with market data, the credit exposure assessment value of the parties to the agreement is dynamically estimated. The credit exposure assessment value is combined with the termination terms and the risk factors and input into the risk exposure simulator to calculate the potential risk exposure indicator.

[0042] Specifically, the system identifies the terms of the Credit Support Annex (CSA) in the supplemental agreement, such as additional initial margin requirements, adjustments to collateral types, and modifications to repurchase trigger conditions. It then combines current market data (such as HQLA haircuts, counterparty CDS spreads, and exchange rate fluctuations) to estimate credit exposures under different market scenarios. By linking textual semantic risk with actual market risk, the system simulates and predicts the maximum potential losses under different future market volatility paths.

[0043] Preferably, the key trigger conditions for the termination clause in the master agreement are extracted and used as simulation breakpoints. The risk factors and credit exposure assessments generated from the aforementioned semantic deviations are linked with market data and fed into the risk exposure simulator. The simulator generates a large number of future market paths and calculates potential losses for each path based on the termination mechanism, credit exposure, and risk factors. The resulting PFE indicator curve is then output with a confidence interval.

[0044] Furthermore, if the semantic deviation value increases (i.e., the difference between the supplementary agreement and the main agreement becomes greater), the risk factor will increase, which will ultimately amplify the potential risk exposure indicator (PFE value); If the market CDS spread rises or the rating falls (i.e. the credit exposure increases), the PFE value will also rise; If the supplementary agreement relaxes the termination terms (such as delaying the rating trigger conditions), combined with market data, it may increase the exposure duration in the simulation path and increase the potential losses.

[0045] In a specific embodiment, the step of “calculating the semantic deviation value between the supplementary agreement and the referenced clause, and calculating the risk factor based on the semantic deviation value” includes the following specific steps: The semantic deviation value includes a first semantic deviation value, and the risk factor includes a first risk factor; The supplementary agreement is semantically compared with the referenced clause, a first semantic deviation is calculated, and a first risk factor of the supplementary agreement is output. The first risk factor is combined with the standard clause library to generate the potential risk exposure indicator of the current agreement.

[0046] Specifically, a semantic comparison between the supplemental agreement and the referenced clauses is performed to generate a first semantic deviation, outputting a first risk factor. This is then combined with the industry benchmark standard clause library to calculate the potential risk exposure indicator for the current agreement. To avoid misjudging the risk of "minor deviations from industry norms in form," the standard clause library is used to determine the deviation rate reference range of the first risk factor within the standard clause library to generate the potential risk exposure indicator for the current agreement.

[0047] In a specific embodiment, the step of "combining the first semantic deviation with the standard library clauses to generate the potential risk exposure indicator of the current agreement" includes the following specific steps: The semantic deviation value includes a second semantic deviation degree, and the risk factor includes a second risk factor; Based on the semantic comparison between the open clause set in the master agreement and the corresponding clauses in the standard clause library, the second semantic deviation is calculated, and the second risk factor of the master agreement is output. The first risk factor and the second risk factor are combined to perform a double verification mechanism to generate the potential risk exposure indicator of the current agreement.

[0048] Specifically, the system identifies each clause in the master agreement's open clause set and performs a semantic comparison with the corresponding standard clauses in the standard clause library. Using sentence embedding models (such as SBERT), the system quantifies the degree of semantic deviation, generating a second semantic deviation measure. This deviation measure reflects the extent to which the master agreement as a whole deviates from industry standards, either structurally or in terms of expression. Based on this second semantic deviation measure, a second risk factor for the master agreement is generated, representing the inherent risk level of the master agreement relative to the standard structure.

[0049] Furthermore, a "double validation mechanism" is introduced, fusing the primary risk factor generated by the supplemental agreement with the secondary risk factor generated by the master agreement to form a more comprehensive potential risk exposure indicator. This fusion calculation can employ a linear weighting function, a cascading scoring model, or a rule-based decision function to reflect the risk coupling effect between "changes in the risk of the supplemental clause" and "deviations from the master agreement structure." This process not only assesses the marginal incremental impact of the supplemental agreement on the overall contract risk but also considers the fundamental differences between the master agreement structure and the standard template, ensuring that the system outputs more comprehensive, objective, and dynamic potential risk exposure results.

[0050] In a specific embodiment, the step of “calculating the incremental risk weight in the supplemental agreement” further includes the steps of: Combined with the derivative contract terms and forecasting model in the supplementary agreement, the potential deviation in the impact of the modification of the supplementary agreement on cash flow or performance path is evaluated, and the incremental risk weight is calculated based on the semantic deviation value.

[0051] Specifically, and preferably, the derivatives contract terms involved in the supplemental agreement are thoroughly analyzed to identify the specific content and scope of the changes (e.g., price adjustment mechanism, payment frequency, performance period, margin ratio, credit event triggering criteria, etc.). A set of predictive models driven by historical data and current market conditions (e.g., cash flow forecasting models, performance probability simulators, etc.) is integrated to simulate the contract performance path and cash flow performance after the supplemental agreement is modified. By comparing the contract execution results before and after the modification, the potential deviation of the contract performance process caused by the change in terms is systematically quantified, with particular attention to its impact on cash flow stability, performance reliability, and the probability of triggering risk events.

[0052] Furthermore, based on the performance assessment, the system also conducts a semantic deviation analysis between the supplemental agreement amendments and the referenced and standard clauses in the master agreement, quantitatively characterizing the degree of semantic divergence between the amendments and industry consensus. The system integrates the performance / cash flow deviations output by the forecasting model with the semantic deviation results and calculates the final incremental risk weight using a weighting factor or rule-driven model. This weighting metric reflects the incremental risk contribution brought about by the supplemental agreement amendments and is both interpretable and dynamic.

[0053] Furthermore, the incremental risk weights can be dynamically adjusted as contract terms and market conditions change. Automatic calculation of incremental risk weights will help to quickly identify risk points in supplementary agreements, shorten contract risk review time, and improve risk management efficiency.

[0054] In a specific embodiment, the credit support annex includes derivatives, and the step of "dynamically estimating the credit exposure assessment value of the parties to the agreement based on the terms of the credit support annex in the supplemental agreement and in combination with market data" specifically includes the following steps: Based on the standard model stipulated by the international derivatives market, the SIMM compliance module is called to calculate the standardized initial margin of the derivatives transaction and generate the credit guarantee index required for the derivatives transaction; Simulate multiple market data, call the Monte Carlo path generation module, evaluate the credit risk exposure of the derivative transaction, and dynamically estimate the credit exposure assessment value based on the credit guarantee indicator and credit risk exposure.

[0055] Specifically, the SIMM module is invoked to map risk factors for each derivative transaction covered by the supplemental agreement, calculate the initial margin required based on current market conditions, and output a "credit guarantee index." The SIMM module is specifically designed to calculate the initial margin required between counterparties in uncleared derivatives transactions. Monte Carlo simulations can be used to generate potential market trends at multiple points in the future and along multiple paths, thereby assessing the potential credit exposure of a transaction over a specific period.

[0056] Furthermore, the two aforementioned results are integrated, comparing the guarantee capacity output from SIMM with the exposure risk output from Monte Carlo. If the potential risk exposure exceeds the guarantee coverage level, a net risk exposure value is calculated as the final credit exposure assessment. This assessment is input into the main risk decision engine and, along with the semantic deviation risk factor, contributes to the overall risk score and early warning logic. Using a standard model as the basis for guarantee calculations enhances the objectivity and audit traceability of the assessment results. Scenario simulations cover uncertain future market paths, enhancing the forward-looking nature of risk assessments.

[0057] In a specific embodiment, the market conditions include extreme market data.

[0058] Specifically, the system simulates extreme market events, such as financial crises and stock market crashes, to assess the potential risks of derivatives in these extreme situations and provide timely warnings and adjustments. All simulation results are exported to help financial institutions assess the potential risks of derivatives transactions and make appropriate funding arrangements and risk control decisions based on this data.

[0059] Specifically, extreme market conditions refer to market scenarios characterized by unusual volatility or unexpected events, such as financial crises, market crashes, and political risk events. Incorporating extreme market data into Monte Carlo simulations can help systematically assess the potentially high risk exposure of derivatives trading under extreme market volatility.

[0060] Furthermore, derivatives trading can face significant risk exposure in extreme market conditions. Incorporating extreme market data into simulations can help traders anticipate these unique circumstances, preparing for and responding to risks. Simulating extreme market scenarios can help financial institutions improve their crisis response capabilities when extreme events occur, thereby reducing losses caused by unexpected events and maintaining market stability.

[0061] In a specific embodiment, the interaction report is displayed in a tree structure, with the original clauses of the main agreement as the root node and the supplementary agreement as the child nodes.

[0062] Specifically, a tree structure is used here to display the relationships between agreement clauses. The root node represents the original clauses of the main agreement, while the child nodes represent the clauses in the supplemental agreements. This structure clearly and intuitively presents the clause hierarchy, facilitating a quick understanding of the agreement's logical relationships and interdependencies. In semantic analysis of the agreement, the original clauses of the main agreement as the root node represent these clauses as the core content of the agreement, while the supplemental agreements, as child nodes, clearly indicate how they supplement, modify, or expand upon the main agreement's terms.

[0063] In a specific embodiment, the interactive report includes a risk tracing diagram and a market sensitivity analysis matrix.

[0064] Specifically, the risk traceability diagram presents the evolution of contract terms in a tree-like structure. The original terms of the master agreement serve as the root node, while references or amendments to the supplemental agreements are attached as child nodes, clearly demonstrating the hierarchical relationships and reference paths between terms. Each node displays the clause's semantic deviation, risk factor, incremental risk weight, and whether a risk warning has been triggered. Users can expand any clause node to view its detailed modification trajectory and structural impact, thereby tracing the source of risk and supporting rapid compliance review and interpretation.

[0065] Furthermore, the market sensitivity analysis matrix, based on the termination clauses and credit support arrangements in the derivative contract terms and master agreement, incorporates market disturbance parameters (such as interest rates, exchange rates, and credit spreads) and invokes simulation engines (such as Monte Carlo path generation and VaR / Expected Exposure models) to construct a term-market variable mapping matrix. This sensitivity matrix quantifies the impact of changes in various market variables on the contract's risk exposure, allowing users to simulate changes in risk exposure under different market scenarios and determine which terms are most sensitive to financial risk in current or future scenarios.

[0066] A system for personalized contract risk judgment based on a decision engine includes the above steps applied to the system. Since this embodiment includes all the features of the above embodiments, the above embodiment has all the beneficial effects of the above embodiments and will not be repeated here.

[0067] The above is only an implementation method of the present application. It should be pointed out that for ordinary technicians in this field, improvements can be made without departing from the creative concept of the present application, but these all fall within the scope of protection of the present application.

Claims

1. A method for personalized contract risk judgment based on a decision engine, characterized in that: Including steps: Identify the irrevocable foundational clauses in the ISDA Master Agreement and mark the set of open clauses in the ISDA Master Agreement that are subject to supplemental amendment; Identifying a reference statement to the open clause set in the supplemental agreement, and establishing a clause relationship tree between the main agreement and the supplemental agreement; Calculating a semantic deviation value between the supplementary agreement and the referenced clause in conjunction with the clause relationship tree, and calculating a risk factor based on the semantic deviation value, wherein the referenced clause is one of the open clauses referenced in the reference statement; Accessing the market data interface, retrieving market data and inputting it into the risk exposure simulator, and calculating potential risk exposure indicators based on the termination clause in the master agreement and the risk factors; Based on the clause relationship tree and the potential risk exposure indicators, an interactive report is generated to assist non-legal users in making actual contract business decisions.

2. The method for personalized contract risk judgment based on a decision engine according to claim 1, characterized in that: The step of "calculating the semantic deviation value between the supplementary agreement and the referenced clause" also includes the following steps: Allocate risk weights between the master agreement and the supplemental agreement, where the risk weight of the master agreement is denoted as a and the risk weight of the supplemental agreement is denoted as b, satisfying a ≥ b; Calculate the incremental risk weight in the supplementary agreement based on the semantic deviation between each supplementary agreement and the referenced clause, where the incremental risk weight is denoted as Δt; If b+Δt>a, the risk warning mechanism is triggered, and the supplementary agreement is marked as a warning node in the interaction report.

3. The method for determining contract risk based on a personalized decision engine according to claim 1, characterized in that: The step "calculating the potential risk exposure indicator based on the termination clause in the master agreement and the risk factors" includes the following specific steps: Based on the terms of the credit support annex in the supplementary agreement and combined with market data, the credit exposure assessment value of the parties to the agreement is dynamically estimated. The credit exposure assessment value is combined with the termination terms and the risk factors and input into the risk exposure simulator to calculate the potential risk exposure indicator.

4. The method for determining contract risk based on a personalized decision engine according to claim 1, characterized in that: The step of "calculating the semantic deviation value between the supplementary agreement and the referenced clause, and calculating the risk factor based on the semantic deviation value" includes the following specific steps: The semantic deviation value includes a first semantic deviation value, and the risk factor includes a first risk factor; The supplementary agreement is semantically compared with the referenced clause, a first semantic deviation is calculated, and a first risk factor of the supplementary agreement is output. The first risk factor is combined with the standard clause library to generate the potential risk exposure indicator of the current agreement.

5. The method for personalized contract risk judgment based on a decision engine according to claim 4, characterized in that: The step of "combining the first semantic deviation with the standard library clauses to generate the potential risk exposure indicator of the current agreement" includes the following specific steps: The semantic deviation value includes a second semantic deviation degree, and the risk factor includes a second risk factor; Based on the semantic comparison between the open clause set in the master agreement and the corresponding clauses in the standard clause library, the second semantic deviation is calculated, and the second risk factor of the master agreement is output. The first risk factor and the second risk factor are combined to perform a double verification mechanism to generate the potential risk exposure indicator of the current agreement.

6. The method for personalized contract risk judgment based on a decision engine according to claim 2, characterized in that: The step of "calculating the incremental risk weight in the supplemental agreement" further includes the steps of: Combined with the derivative contract terms and forecasting model in the supplementary agreement, the potential deviation in the impact of the modification of the supplementary agreement on cash flow or performance path is evaluated, and the incremental risk weight is calculated based on the semantic deviation value.

7. The method for personalized contract risk judgment based on a decision engine according to claim 3, characterized in that: The credit support annex includes derivatives. The step "dynamically estimating the credit exposure assessment value of the parties to the agreement based on the terms of the credit support annex in the supplemental agreement and in combination with market data" specifically includes the following steps: Based on the standard model stipulated by the international derivatives market, the SIMM compliance module is called to calculate the standardized initial margin of the derivatives transaction and generate the credit guarantee index required for the derivatives transaction; Simulate multiple market data, call the Monte Carlo path generation module, evaluate the credit risk exposure of the derivative transaction, and dynamically estimate the credit exposure assessment value based on the credit guarantee indicator and credit risk exposure.

8. The method for determining contract risk based on a personalized decision engine according to claim 7, characterized in that: The market data includes market extreme data.

9. The method for personalized contract risk judgment based on a decision engine according to claim 1, characterized in that: The interaction report is displayed in a tree structure, with the original terms of the main agreement as the root node and the supplementary agreement as the child node.

10. A system for personalized contract risk assessment based on a decision engine, characterized in that: A method for personalized contract risk judgment based on a decision engine, including any one of claims 1 to 9 applied to the system.

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