A method and system for personalized contract risk assessment based on a decision engine

Abstract

This application provides a method and system for personalized contract risk assessment based on a decision engine. The system includes the following steps: identifying the basic clauses in the ISDA agreement and marking the set of open clauses in the ISDA agreement that allow for supplementary modifications; identifying reference statements in the supplementary agreement, calculating the semantic deviation value between the supplementary agreement and the original referenced clauses, and establishing a clause relationship tree; accessing a market data interface, retrieving market data input to a risk exposure simulator, calculating risk exposure indicators; and generating an interactive report. The aforementioned method for personalized contract risk assessment based on a decision engine breaks through the dependence of traditional methods on fixed template structures. By constructing a clause relationship tree, the system can accurately depict the dynamic reference and modification paths between the main agreement and supplementary agreements. Accessing market data overcomes the limitations of static evaluation in traditional methods. The generated interactive report enables users without a legal background to make decisions regarding actual contractual business.

Description

Technical Field

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

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

[0003] Most existing contract risk assessment methods rely on static rule engines and feature matching algorithms. In these technologies, the system identifies high-risk clauses in contracts, such as payment defaults, force majeure, and termination trigger conditions, using predefined rules or trained models. It then scores these clauses based on textual features such as word frequency statistics and syntactic structure, generating a risk classification report based on the scoring results. Some more advanced systems also incorporate simplified semantic analysis models to identify cross-clause references.

[0004] However, compared to general contracts, ISDA agreements are characterized by complex structures, highly dynamic content, and multi-layered nested dependency chains. Traditional judgment methods based on static rules and feature matching still rely 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 referencing and modification relationships between the main agreement and its subsidiary agreements, nor do they consider the dynamic impact of derivative risk parameters such as CVA. Furthermore, existing systems only provide text-level risk output and lack visual simulation 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 assessment of contract risk based on a decision engine that can combine dynamic market parameters and be directly accessible to non-legal users, in order to solve the above problems.

[0006] Embodiments of this application provide a method for personalized contract risk assessment based on a decision engine, characterized by the following steps:

[0007] Identify the immutable basic clauses in the ISDA master agreement and mark the set of open clauses in the ISDA master agreement that allow for supplementary modifications;

[0008] Identify the reference statements to the open terms set in the supplementary agreement, and establish a terms relationship tree between the main agreement and the supplementary agreement;

[0009] Based on the aforementioned clause relationship tree, the semantic deviation value between the supplementary agreement and the referenced clause is calculated, and a risk factor is calculated based on the semantic deviation value, wherein the referenced clause is one of the clauses in the set of open clauses referenced in the reference statement;

[0010] Access the market data interface, retrieve market data and input it into the risk exposure simulator, and calculate the potential risk exposure index based on the termination clause in the main agreement and the risk factors.

[0011] Based on the aforementioned clause relationship tree and the aforementioned potential risk exposure indicators, an interactive report is generated to assist non-legal users in making actual contractual business decisions.

[0012] In at least one embodiment of this application, the step of "calculating the semantic deviation value between the supplementary agreement and the referenced terms" further includes the following step:

[0013] Assign risk weights between the main protocol and the supplementary protocol, wherein the risk weight of the main protocol is denoted as a, and the risk weight of the supplementary protocol is denoted as b, satisfying a≥b;

[0014] Based on the semantic deviation value between each supplementary agreement and the referenced clause, the incremental risk weight in the supplementary agreement is calculated, and the incremental risk weight is denoted as Δt;

[0015] If b+Δt>a, a risk warning mechanism is triggered, and the supplementary protocol is marked as a warning node in the interactive report.

[0016] In at least one embodiment of this application, the step "calculating the potential risk exposure index based on the termination clause in the master agreement and the risk factor" includes the following specific steps:

[0017] Based on the credit support annex terms in the supplementary agreement and in conjunction with market data, the credit exposure assessment value of the parties to the agreement is dynamically estimated. The credit exposure assessment value, together with the termination clause and the risk factors, is input into the risk exposure simulator to calculate the potential risk exposure index.

[0018] In at least one embodiment of this application, the step "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:

[0019] The semantic deviation value includes the first semantic deviation degree, and the risk factor includes the first risk factor;

[0020] The supplementary agreement is semantically compared with the referenced terms to calculate the first semantic deviation and output the first risk factor of the supplementary agreement. The first risk factor is combined with the standard terms library to generate the potential risk exposure index of the current agreement.

[0021] In at least one embodiment of this application, the step "generating the potential risk exposure index of the current protocol by combining the first semantic deviation with standard library terms" includes the following specific steps:

[0022] Semantic deviation value includes second semantic deviation degree, and risk factor includes second risk factor;

[0023] Based on the semantic comparison between the open terms set in the main protocol and the corresponding terms in the standard terms library, the second semantic deviation is calculated, and the second risk factor of the main protocol is output. The first risk factor and the second risk factor are combined to perform a dual verification mechanism to generate the potential risk exposure index of the current protocol.

[0024] In at least one embodiment of this application, the step "calculating the incremental risk weights in the supplementary agreement" further includes the step of:

[0025] By combining the derivative contract terms in the supplementary agreement with the forecasting model, the potential impact deviation of the amendments to the supplementary agreement on cash flow or performance path is assessed, and the incremental risk weight is calculated based on the semantic deviation value.

[0026] In at least one embodiment of this 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 supplementary agreement and in conjunction with market data" specifically includes the following steps:

[0027] Based on the standard model stipulated by the international derivatives market, the SIMM compliance module is called to calculate the standardized initial margin for the derivatives transaction and generate the credit guarantee indicators required for the derivatives transaction.

[0028] By simulating various market data and calling the Monte Carlo path generation module, the credit risk exposure of the derivatives transaction is assessed. Based on the credit guarantee indicator and credit risk exposure, the credit exposure assessment value is dynamically estimated.

[0029] In at least one embodiment of this application, the market data includes extreme market data.

[0030] In at least one embodiment of this application, the interaction report is displayed in a tree structure, with the original terms of the main protocol as the root node and the supplementary protocols as child nodes.

[0031] In one embodiment of a system for personalized contract risk assessment based on a decision engine, the aforementioned method for personalized contract risk assessment based on a decision engine is applied.

[0032] The aforementioned method and system for personalized contract risk assessment based on a decision engine overcomes the reliance on fixed template structures in traditional methods by identifying basic clauses and marking open clause sets in ISDA agreements. It distinguishes between basic clauses and open clause sets in advance, accurately identifying variable and immutable parts of the contract, effectively improving clause analysis efficiency, and avoiding the limitations of traditional methods that can only statically identify risks based on the full text of the contract.

[0033] By identifying reference statements in supplementary agreements, calculating semantic deviation values ​​between referenced clauses, and constructing a clause relationship tree, the system can accurately depict the dynamic reference and modification paths between the main agreement and supplementary agreements. The calculation of semantic deviation values ​​considers changes in clause content and their potential impact on the contract performance path, ensuring the identification of subtle changes between different clauses and conditions in the contract. This avoids misjudgments or omissions of clause relationships by traditional methods, significantly improving the accuracy and real-time performance of contract risk identification.

[0034] Furthermore, it integrates with real-time market data interfaces, inputting market parameters such as interest rate curves and CDS spreads into the risk exposure simulator. This allows for dynamic calculation of risk exposure indicators based on the terms of the agreement, overcoming the limitations of traditional static assessment methods. Based on the clause relationship tree and simulation results, an interactive report is generated, including a risk sourcing diagram and a market sensitivity analysis matrix, enabling business personnel without a legal background to intuitively grasp the potential risks of the contract. Attached Figure Description

[0035] Figure 1 This is a flowchart illustrating the steps of a personalized contract risk assessment method based on a decision engine, according to one embodiment of this application.

[0036] Figure 2 for Figure 1 The flowchart of the interactive report output process for a personalized contract risk assessment method based on a decision engine.

[0037] Figure 3 for Figure 1 A flowchart illustrating the output of potential risk exposure indicators for a personalized contract risk assessment method based on a decision engine.

[0038] Figure 4 for Figure 1 The flowchart of the credit exposure assessment value output process of the personalized contract risk assessment method based on decision engine described above. Detailed Implementation

[0039] The embodiments of this application will now be described with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0040] 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 may also have an intervening component. When a component is considered to be "placed" on another component, it can be directly placed on the other component or may also have an intervening component. The terms "top," "bottom," "upper," "lower," "left," "right," "front," "back," and similar expressions used in this article are for illustrative purposes only.

[0041] Embodiments of this application provide a method for personalized contract risk assessment based on a decision engine, characterized by the following steps:

[0042] Identify the immutable basic clauses in the ISDA master agreement and mark the set of open clauses in the ISDA master agreement that allow for supplementary modifications;

[0043] Identify the reference statements to the open terms set in the supplementary agreement, and establish a terms relationship tree between the main agreement and the supplementary agreement;

[0044] Calculate the semantic deviation value between the supplementary agreement and the referenced terms, and calculate the risk factor based on the semantic deviation value, wherein the referenced terms are the terms cited in the reference statement;

[0045] Access the market data interface, retrieve market data and input it into the risk exposure simulator, and calculate the potential risk exposure index based on the termination clause in the main agreement and the risk factors.

[0046] Based on the aforementioned clause relationship tree and the aforementioned potential risk exposure indicators, an interactive report is generated to assist non-legal users in making actual contractual business decisions.

[0047] The aforementioned method and system for personalized contract risk assessment based on a decision engine overcomes the reliance on fixed template structures in traditional methods by identifying basic clauses and marking open clause sets in ISDA agreements. It distinguishes between basic clauses and open clause sets in advance, accurately identifying variable and immutable parts of the contract, effectively improving clause analysis efficiency, and avoiding the limitations of traditional methods that can only statically identify risks based on the full text of the contract.

[0048] By identifying reference statements in supplementary agreements, calculating semantic deviation values ​​between referenced clauses, and constructing a clause relationship tree, the system can accurately depict the dynamic reference and modification paths between the main agreement and supplementary agreements. The calculation of semantic deviation values ​​considers changes in clause content and their potential impact on the contract performance path, ensuring the identification of subtle changes between different clauses and conditions in the contract. This avoids misjudgments or omissions of clause relationships by traditional methods, significantly improving the accuracy and real-time performance of contract risk identification.

[0049] Furthermore, it integrates with real-time market data interfaces, inputting market parameters such as interest rate curves and CDS spreads into the risk exposure simulator. This allows for dynamic calculation of risk exposure indicators based on the terms of the agreement, overcoming the limitations of traditional static assessment methods. Based on the clause relationship tree and simulation results, an interactive report is generated, including a risk sourcing diagram and a market sensitivity analysis matrix, enabling business personnel without a legal background to intuitively grasp the potential risks of the contract.

[0050] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0051] Please see Figures 1-4 Embodiments of this application provide a method for personalized contract risk assessment based on a decision engine, characterized by the following steps:

[0052] S10 identifies the immutable basic clauses in the ISDA master agreement and marks the set of open clauses in the ISDA master agreement that allow for supplementary modifications;

[0053] S20 identifies references to the open terms set in the supplementary agreement and establishes a terms relationship tree between the main agreement and the supplementary agreement;

[0054] S30 Combines the aforementioned clause relationship tree to calculate the semantic deviation value between the supplementary agreement and the referenced clause, and calculates a risk factor based on the semantic deviation value, wherein the referenced clause is one of the clauses in the set of open clauses referenced in the reference statement;

[0055] The S40 accesses the market data interface, retrieves market data, inputs it into the risk exposure simulator, and calculates the potential risk exposure index based on the termination clause in the main agreement and the risk factors.

[0056] Based on the aforementioned clause relationship tree and the aforementioned potential risk exposure indicators, S50 generates an interactive report to assist non-legal users in making actual contractual business decisions.

[0057] Specifically, the ISDA protocol is a set of standard master protocols provided by the International Swaps and Derivatives Association (ISDA) to regulate over-the-counter derivatives trading. The system uses a Natural Language Processing (NLP) model to segment and semantically cluster the ISDA master protocol text. Based on the ISDA standard structure (such as the 2002 / 2016 versions), the content is divided into a basic clause set and an open clause set. Basic clauses refer to the general and unchangeable clauses in the master protocol, such as definitions, default obligations, and notification clauses. Open clauses refer to clauses that can be supplemented, modified, or replaced in trading practice, such as margin requirements, definitions of default, and termination rights.

[0058] Preferably, the NER entity recognition and rule template matching method system uses rule templates and entity recognition to identify reference statements in the supplementary agreement to the open clause set in the main agreement (such as "modify Article 5(a) of the main agreement"). The main agreement clauses and the supplementary agreement content are bound together through reference paths to construct a "clause relationship tree" between the main agreement and the supplementary agreement. This tree structure records the reference path, modification content, modification type of the clauses, and their corresponding main clause nodes, supporting subsequent structured analysis and semantic localization, making the hierarchical dependency relationship of contract clauses clearly visible.

[0059] Preferably, a semantic matching model is used to quantify the semantic similarity between each modification and the original clause, resulting in a semantic deviation value index. A sentence vector model (such as SBERT, BART, etc.) is used to calculate the semantic similarity between the cited clause (the open clause referenced by the main agreement) and the modified clause in the supplementary agreement, where the similarity S ∈ [0,1]. The semantic deviation value D = 1 - S is set; a larger D indicates a more drastic change, is positively correlated with risk factors, and reflects the level of risk.

[0060] Based on the identified principal and supplementary clause structures, the system connects to external or internal market data interfaces, including but not limited to: interest rate curves, CDS spreads, and HQLA discount rates. The system uses clause risk factors and market data as input to drive the financial risk model, simulating potential risk exposure (PFE) indicators under different market scenarios.

[0061] Furthermore, the risk exposure simulator, based on financial models such as Monte Carlo simulation, VaR, and Expected Exposure, simulates potential contract losses under various market fluctuations. The termination clause in the master agreement serves as a contract default trigger mechanism; the simulator uses the termination clause logic as boundary conditions in the simulation path to dynamically adjust the risk exposure window.

[0062] In one specific embodiment, the risk exposure simulator uses the Monte Carlo simulation method to simulate market paths over the next N days (e.g., 10 days, 30 days). Combining 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.

[0063] 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 factors, market correlation data, and more. The visualization module displays PFE curves, sensitivity analysis (such as sensitivity to interest rate or exchange rate changes), risk level scores, and other content.

[0064] In one specific embodiment, the step of "calculating the semantic deviation between the supplementary agreement and the referenced terms" further includes the following step:

[0065] Assign risk weights between the main protocol and the supplementary protocol, wherein the risk weight of the main protocol is denoted as a, and the risk weight of the supplementary protocol is denoted as b, satisfying a≥b;

[0066] Based on the semantic deviation value between each supplementary agreement and the referenced clause, the incremental risk weight in the supplementary agreement is calculated, and the incremental risk weight is denoted as Δt;

[0067] If b+Δt>a, a risk warning mechanism is triggered, and the supplementary protocol is marked as a warning node in the interactive report.

[0068] Specifically, the system first uses a clause classifier to automatically identify the category to which each main agreement clause belongs, such as "termination clause," "default clause," "margin arrangement," and "notification clause." Each type of clause is configured with a different basic risk weight value 'a' in the standard clause weight library based on its impact on the overall risk exposure of the contract.

[0069] For example, termination clauses, because they directly determine the timing of transaction termination and may bring extreme loss risks, are assigned a higher base weight (e.g., a = 0.9); while notification clauses are merely procedural obligations and may only be assigned a = 0.2. When a supplementary agreement references a clause of the main agreement (such as the right to terminate), the initially assigned risk weight b of the supplementary clause is no higher than the weight a of the main clause it references, i.e., a ≥ b.

[0070] Furthermore, the system uses the semantic deviation value D to measure the extent to which the supplementary 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 modification of the original terms by the supplementary agreement, the greater the increase in its risk level.

[0071] Furthermore, if b + Δt > a, it indicates that the supplementary agreement clause has undergone excessive modifications, and its overall risk level has exceeded the original risk level of the main agreement clause it references, which is considered a "risk boundary breach". The system will automatically trigger a 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 content of the main agreement clause it references.

[0072] In one specific embodiment, the step "calculating the potential risk exposure index based on the termination clause in the master agreement and the risk factor" includes the following specific steps:

[0073] Based on the credit support annex terms in the supplementary agreement and in conjunction with market data, the credit exposure assessment value of the parties to the agreement is dynamically estimated. The credit exposure assessment value, together with the termination clause and the risk factors, is input into the risk exposure simulator to calculate the potential risk exposure index.

[0074] Specifically, the system identifies the Credit Support Annex (CSA) clauses in the supplemental agreement, such as additional initial margin requirements, adjustments to collateral types, and modifications to repurchase option trigger conditions. Combined with current market environment data (such as HQLA discount rates, counterparty CDS spreads, and exchange rate fluctuations), it estimates credit exposure values ​​under different market scenarios. By linking textual semantic risk with actual market risk, it simulates and predicts the maximum possible loss under different future market volatility paths.

[0075] Preferably, key triggering conditions of the termination clause in the master agreement are extracted and set as simulation interruption points. The risk factors generated by the aforementioned semantic deviations, credit exposure assessment values, and market data are linked and input into the risk exposure simulator. The simulator generates a large number of future market paths and calculates potential losses on each path based on the termination mechanism, credit exposure, and risk factors. The PFE (Peak Exposure Factor) curve under the confidence interval is output.

[0076] Furthermore, if the semantic deviation value increases (i.e., the difference between the supplementary agreement and the main agreement is greater), the risk factor will increase, which will eventually amplify the potential risk exposure indicator (PFE value).

[0077] If the market CDS spread increases or the rating is downgraded (i.e., credit exposure increases), the PFE value will increase accordingly.

[0078] If the supplemental agreement relaxes the termination terms (such as delaying the rating trigger conditions), combined with market data, it may increase the duration of exposure in the simulated path, thereby increasing potential losses.

[0079] In one specific embodiment, the step "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:

[0080] The semantic deviation value includes the first semantic deviation degree, and the risk factor includes the first risk factor;

[0081] The supplementary agreement is semantically compared with the referenced terms to calculate the first semantic deviation and output the first risk factor of the supplementary agreement. The first risk factor is combined with the standard terms library to generate the potential risk exposure index of the current agreement.

[0082] Specifically, a first semantic deviation is generated by semantically comparing the supplementary agreement with the referenced clauses, and a first risk factor is output. This, combined with a standard clause library based on industry benchmarks, is used to calculate the potential risk exposure index for the current agreement. To avoid misjudging risk scenarios where "the changes are only minor in form but substantially deviate from industry norms," ​​the deviation rate reference range of the first risk factor within the standard clause library is determined based on the standard clause library, and the potential risk exposure index for the current agreement is generated.

[0083] In one specific embodiment, the step "generating the potential risk exposure index of the current protocol by combining the first semantic deviation with standard library clauses" includes the following specific steps:

[0084] Semantic deviation value includes second semantic deviation degree, and risk factor includes second risk factor;

[0085] Based on the semantic comparison between the open terms set in the main protocol and the corresponding terms in the standard terms library, the second semantic deviation is calculated, and the second risk factor of the main protocol is output. The first risk factor and the second risk factor are combined to perform a dual verification mechanism to generate the potential risk exposure index of the current protocol.

[0086] Specifically, the system identifies each clause in the open clause set of the main protocol and performs a semantic comparison with the corresponding standard clauses in the standard clause library. It then uses a sentence vector model (such as SBERT) to quantify the degree of semantic deviation, obtaining a second semantic deviation. This deviation reflects the extent to which the main protocol deviates from industry-standard practices at the structural or expressive level. Based on this second semantic deviation, a second risk factor for the main protocol is generated to characterize the inherent risk level of the main protocol compared to the standard structure.

[0087] Furthermore, a "dual verification mechanism" is introduced, which integrates the first risk factor generated by the supplementary agreement with the second risk factor generated by the main agreement to form a more comprehensive potential risk exposure index. This integrated calculation can employ a linear weighted function, a cascaded scoring model, or a rule-based judgment function to reflect the risk coupling effect between "changes in the risk of supplementary clauses" and "deviations from the structure of the main agreement." This process not only assesses the marginal increment of the supplementary agreement on the overall contract risk but also considers the fundamental differences between the structure of the main agreement and the standard template, thereby ensuring that the system can output more comprehensive, objective, and dynamic potential risk exposure results.

[0088] In one specific embodiment, the step "calculating the incremental risk weight in the supplementary agreement" further includes the step of:

[0089] By combining the derivative contract terms in the supplementary agreement with the forecasting model, the potential impact deviation of the amendments to the supplementary agreement on cash flow or performance path is assessed, and the incremental risk weight is calculated based on the semantic deviation value.

[0090] Specifically, the system conducts a thorough and optimized analysis of the derivative contract terms involved in the supplementary agreement, identifying the specific content and scope of the changes (e.g., price adjustment mechanisms, payment frequency, performance period, margin ratio, credit event triggering criteria, etc.). It then integrates a predictive model driven by historical data and current market conditions (such as a cash flow forecasting model and a performance probability simulator) to simulate the contract performance path and cash flow performance after the supplementary agreement modifications. By comparing the contract execution results before and after the modifications, the system quantifies the potential deviation of the terms changes from the contract performance process, paying particular attention to their impact on cash flow stability, performance reliability, and the probability of triggering risk events.

[0091] Furthermore, based on the performance assessment, the system conducts a parallel semantic deviation analysis between the supplementary agreement's modified clauses and the main agreement's referenced clauses and standard clauses, quantitatively characterizing the degree of semantic difference between the modified content and industry consensus. The system integrates the performance / cash flow deviation output from the prediction model with the semantic deviation results, using a weighted factor or rule-driven model to calculate the final incremental risk weight. This weight indicator reflects the new risk contribution brought about by the supplementary agreement modification, possessing interpretability and dynamism.

[0092] Furthermore, the incremental risk weights can be dynamically adjusted according to changes in contract terms and market conditions. The automated calculation of incremental risk weights helps to quickly identify risk points in supplementary agreements, shorten contract risk review time, and improve risk management efficiency.

[0093] In one specific embodiment, the credit support annex includes derivatives, and 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 supplementary agreement and in conjunction with market data" specifically includes the following steps:

[0094] Based on the standard model stipulated by the international derivatives market, the SIMM compliance module is called to calculate the standardized initial margin for the derivatives transaction and generate the credit guarantee indicators required for the derivatives transaction.

[0095] By simulating various market data and calling the Monte Carlo path generation module, the credit risk exposure of the derivatives transaction is assessed. Based on the credit guarantee indicator and credit risk exposure, the credit exposure assessment value is dynamically estimated.

[0096] Specifically, the SIMM module can be used to map risk factors to each derivative transaction involved in the supplementary agreement, calculate its initial margin value based on current market conditions, and output a "credit guarantee index". The SIMM module is specifically used to calculate the initial margin required between counterparties in open derivative transactions. Monte Carlo simulation methods can be used to generate potential market trends at multiple points in time and along multiple paths in the future, thereby assessing the degree of credit exposure that a transaction may have in a future period.

[0097] Furthermore, the two types of results are integrated, and the guarantee capacity output by SIMM is compared with the exposure risk output by Monte Carlo. If the potential risk exposure exceeds the guarantee coverage level, the net risk exposure value is calculated as the final credit exposure assessment value. This assessment value is input into the main risk decision engine, participating in the overall risk scoring and early warning logic together with the semantic deviation risk factor. Using a standard model to provide the basis for guarantee calculation enhances the objectivity and audit traceability of the assessment results. Scenario simulations cover uncertain future market paths, enhancing the forward-looking nature of the risk assessment.

[0098] In one specific embodiment, the market situation includes extreme market data.

[0099] Specifically, it simulates extreme market events, such as financial crises and stock market crashes, to assess the potential risks of derivatives under these extreme conditions, enabling timely warnings and adjustments. All simulation results are output to help financial institutions assess the potential risks of derivatives trading and make corresponding funding arrangements and risk control decisions based on the assessment data.

[0100] Specifically, extreme market scenarios refer to market conditions that occur during periods of abnormal volatility or unexpected events, such as financial crises, market crashes, and political risk events. By incorporating extreme market data into Monte Carlo simulations, it is possible to systematically assess the extremely high risk exposure that derivatives trading may face under extreme market volatility.

[0101] Furthermore, in extreme market conditions, derivatives trading can expose traders to significant risks. By incorporating extreme market data into simulations, traders can make advance predictions and prepare risk mitigation strategies for these special circumstances. Simulating extreme market scenarios helps financial institutions improve their crisis response capabilities during extreme events, thereby reducing losses caused by unforeseen events and maintaining market stability.

[0102] In one specific embodiment, the interaction report is displayed in a tree structure, with the original terms of the main protocol as the root node and the supplementary protocols as child nodes.

[0103] Specifically, the tree structure is used here to represent the relationships between the agreement's clauses. The root node represents the original clauses of the main agreement, while the child nodes represent clauses in supplementary agreements. This structure clearly and intuitively presents the hierarchical structure of the clauses, making it easy for users to quickly understand the logical relationships within the agreement and the interdependencies between the clauses. In the semantic analysis of the agreement, the original clauses of the main agreement as root nodes signify that these clauses are the core content of the agreement, while the supplementary agreements as child nodes clearly show their supplementation, modification, or expansion of the main agreement's clauses.

[0104] In one specific embodiment, the interactive report includes a risk attribution map and a market sensitivity analysis matrix.

[0105] Specifically, the risk sourcing diagram presents the evolution of contract terms in a tree structure. The original terms of the main agreement serve as the root node, with references or modifications in supplementary agreements appearing as child nodes, clearly demonstrating the hierarchical relationship and reference path between terms. Each node displays the semantic deviation, risk factor, incremental risk weight, and whether a risk warning has been triggered. Users can expand any term node to view its detailed modification trajectory and structural impact, thereby tracing the source of risk and supporting rapid compliance review and interpretation.

[0106] Furthermore, the market sensitivity analysis matrix, based on the derivative contract terms and the termination clauses and credit support arrangements involved in the master agreement, incorporates market disturbance parameters (such as interest rates, exchange rates, and credit spreads) and calls simulation engines (such as Monte Carlo path generation and VaR / Expected Exposure models) to construct a clause-market variable correspondence matrix. The sensitivity matrix quantifies the impact of changes in various market variables on the agreement's risk exposure, allowing users to simulate changes in risk exposure under different market scenarios and determine which clauses are most sensitive to financial risk in the current or future context.

[0107] A personalized contract risk assessment system based on a decision engine includes the steps described above. Since this embodiment includes all the features of the above embodiments, it has all the beneficial effects of the above embodiments, and will not be repeated here.

[0108] The above description is merely an embodiment of this application. It should be noted that those skilled in the art can make improvements without departing from the inventive concept of this application, but these improvements all fall within the protection scope of this application.

Claims

1. A method for personalized contract risk assessment based on a decision engine, characterized in that, Including steps: Identify the immutable basic clauses in the ISDA master agreement and mark the set of open clauses in the ISDA master agreement that allow for supplementary modifications; Identify the reference statements to the open terms set in the supplementary agreement and establish a terms relationship tree between the main agreement and the supplementary agreement; Based on the aforementioned clause relationship tree, the semantic deviation value between the supplementary agreement and the referenced clause is calculated, and a risk factor is calculated based on the semantic deviation value. The risk factor includes a first risk factor and a second risk factor, and the referenced clause is one of the clauses in the set of open clauses referenced in the reference statement. The semantic deviation value includes a first semantic deviation degree. The supplementary agreement is semantically compared with the referenced terms to calculate the first semantic deviation degree. Based on the first semantic deviation degree, a first risk factor of the supplementary agreement is output. The semantic deviation value also includes a second semantic deviation degree. The second semantic deviation degree is calculated by semantically comparing the open terms set in the main protocol with the terms corresponding to the standard terms library. The second risk factor of the main protocol is output based on the second semantic deviation degree. The risk factor is obtained by combining the first risk factor and the second risk factor. Access the market data interface to retrieve market data, and dynamically estimate the credit exposure assessment value of the contracting party based on the credit support annex terms in the supplementary agreement and the market data. The credit exposure assessment value is combined with the termination clause and the risk factors are input into the risk exposure simulator to calculate the potential risk exposure index. Based on the aforementioned clause relationship tree and the aforementioned potential risk exposure indicators, an interactive report is generated to assist non-legal users in making actual contractual business decisions.

2. The method for personalized contract risk assessment based on a decision engine according to claim 1, characterized in that, The step "Calculate the semantic deviation between the supplementary agreement and the referenced terms" is followed by the following step: Assign risk weights between the main protocol and the supplementary protocol, wherein the risk weight of the main protocol is denoted as a, and the risk weight of the supplementary protocol is denoted as b, satisfying a≥b; Based on the semantic deviation value between each supplementary agreement and the referenced clause, the incremental risk weight in the supplementary agreement is calculated, and the incremental risk weight is denoted as Δt; If b+Δt>a, a risk warning mechanism is triggered, and the supplementary protocol is marked as a warning node in the interactive report.

3. The method for personalized contract risk assessment based on a decision engine according to claim 2, characterized in that, The step "Calculate the incremental risk weights in the supplementary agreement" further includes the following steps: By combining the derivative contract terms in the supplementary agreement with the forecasting model, assess the potential impact deviation of the amendments to the supplementary agreement on cash flow or performance path, and calculate the incremental risk weight based on the semantic deviation value. The prediction model is a cash flow prediction model or a performance probability simulator.

4. The method for personalized contract risk assessment based on a decision engine according to claim 1, characterized in that, The credit support annex includes derivatives, and the step "dynamically estimate the credit exposure of the parties to the agreement based on the terms of the credit support annex in the supplementary agreement and in conjunction 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 for the derivatives transaction and generate the credit guarantee indicators required for the derivatives transaction. By simulating various market data and calling the Monte Carlo path generation module, the credit risk exposure of the derivatives transaction is assessed. Based on the credit guarantee indicator and credit risk exposure, the credit exposure assessment value is dynamically estimated.

5. The method for personalized contract risk assessment based on a decision engine according to claim 4, characterized in that, The market data includes extreme market data.

6. The method for personalized contract risk assessment 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 protocol as the root node and the supplementary protocols as child nodes.

7. A system for personalized contract risk assessment based on a decision engine, characterized in that, This includes a personalized method for assessing contract risk based on a decision engine, applicable to any one of claims 1-6 of the preceding claims, used in the system described above.

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