Reverse risk management investigation method and system

Abstract

This disclosure relates to the field of computer data processing technology, and provides a reverse risk management investigation method and system. The method includes: importing risk case data, constructing a standard clue matrix and an investigation action matrix; receiving the trainee's selection of investigation actions or interview questions, releasing corresponding clues, and writing the clue types of the released clues into the acquired clue set; matching the acquired clue set with any and all trigger conditions of the pending investigation actions or interview questions, dynamically unlocking the pending investigation actions or interview questions; recording the trainee's investigation path, and calculating risk identification ability, risk investigation ability, and investigation and interview skills based on the investigation path. This technical solution can reversely identify the risk management capability structure of the trainee through the investigation path, realizing a process-oriented and quantifiable capability evaluation.

Description

This disclosure pertains to the field of computer data processing technology, specifically to a reverse risk management investigation method and system. Background: In business scenarios such as financial risk management, anti-fraud, anti-money laundering, credit risk disposal, and identification of abnormal over-the-counter transactions, staff often need to gradually form risk judgments through interviews, document review, off-site analysis, and on-site investigations, under conditions of insufficient information, unstable customer statements, difficulty in verifying the authenticity of external materials, and limited time resources. Therefore, training and evaluating the risk identification ability, risk investigation ability, and interview skills of relevant staff is a crucial aspect of building a compliance and risk control system for financial institutions. Training and evaluation schemes provided by related technologies often focus on static case explanations, rule presentations, or single-question quizzes. Specifically, online examination systems primarily score based on the final answer, failing to identify comprehension biases that trainees may exhibit during the investigation process; case study platforms typically present linear materials, lacking dynamic progression mechanisms based on triggering clues; traditional risk control rule engines tend to automatically identify business data, unsuitable for training investigators' investigative skills; and while gamified training systems generally increase engagement, they lack calculable risk clue structures, investigative action trigger logic, and competency assessment models. Therefore, these technologies struggle to replicate the dynamic process of real-world investigations—the gradual release of clues, action selection, interview strategies, risk assessment, and competency evaluation—resulting in poor accuracy in evaluating trainees' competencies and an inability to provide a process-oriented and quantifiable characterization of their risk management capabilities. It should be noted that the information disclosed in the above background technology section is solely for enhancing understanding of the background of this disclosure. The purpose of this disclosure is to provide a reverse risk management investigation method and a reverse risk management investigation system, thereby at least partially replicating the dynamic process of real risk investigation, and identifying the risk management capability structure of trained users through their investigation paths, achieving a process-oriented and quantifiable evaluation of risk identification ability, risk investigation ability, and investigation and interview skills. Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part through practice of this disclosure.According to one aspect of this disclosure, a reverse risk management investigation method is provided, executed by an electronic device. This method, as described in 1 HK 30137921 A specification, includes: importing risk case data; constructing a standard clue matrix and an investigation action matrix; wherein each clue in the standard clue matrix is ​​configured with a clue type, clue importance, and key clue identifier; and each investigation action or interview question in the investigation action matrix is ​​configured with any trigger condition and all trigger conditions; wherein the values ​​of the any trigger condition and all trigger conditions are all clue types pre-registered in the standard clue matrix; and the acquired clue set is used to store the clue types already acquired by the trained user during the investigation process. This serves as the basis for dynamically unlocking survey actions or interview questions; it receives the trainee's selection of open survey actions or interview questions, releases clues corresponding to the selected survey actions or interview questions, and writes the clue type of the released clues into the acquired clue set; it matches the acquired clue set with any and all trigger conditions of the survey actions or interview questions to be opened, and dynamically unlocks the survey actions or interview questions to be opened based on the matching results; it records the trainee's survey path, which includes the survey action selection sequence, clue acquisition order, and resource consumption; it calculates the trainee's risk identification ability, risk investigation ability, and survey interview skills based on the survey path, and outputs the ability evaluation results. In some embodiments of this disclosure, based on the foregoing scheme, the step of matching the acquired set of clues with any and all triggering conditions of the investigation action or interview question to be opened, and dynamically unlocking the investigation action or interview question to be opened according to the matching result, includes: if any and all triggering conditions are empty, then the investigation action or interview question to be opened is determined as an investigation action or interview question without a prerequisite triggering condition and is directly opened; if any triggering condition is not empty, then if the acquired set of clues contains any type of clue indicated by any triggering condition, the investigation action or interview question to be opened is unlocked; if all triggering conditions are not empty, then if the acquired set of clues contains all types of clues indicated by all triggering conditions, the investigation action or interview question to be opened is unlocked; wherein, the unlocked investigation actions include medium-in-depth investigation actions and re-triggered investigation actions. In some embodiments of this disclosure, based on the foregoing scheme, each clue in the standard clue matrix is ​​further configured with a clue release quality, which includes one of clarity, ambiguity, and attenuation; the release of the clue corresponding to the selected investigation action or interview question includes: determining the presentation form of the clue according to the clue release quality of the clue, and releasing the clue according to the presentation form.In some embodiments of this disclosure, based on the foregoing scheme, calculating the risk identification capability of the trained user according to the investigation path includes: obtaining the subsequent response actions of the trained user after obtaining clues; matching the preset response actions pointed to by the clue type with the subsequent response actions to obtain the response matching degree of the clues; and performing a weighted calculation based on the response matching degree of each clue and the corresponding clue importance to obtain the risk identification capability of the trained user. In some embodiments of this disclosure, based on the foregoing scheme, calculating the risk investigation capability of the trained user according to the investigation path includes: statistically analyzing the proportion of key clues reached by the trained user based on the key clue identifiers to obtain the key clue reach rate; obtaining the investigation focus degree based on the proportion of investigation actions related to the target investigation object in the investigation action selection sequence; obtaining the investigation efficiency based on the ratio of resource consumption to the total configured investigation resources; and performing a weighted calculation on the key clue reach rate, the investigation focus degree, and the investigation efficiency to obtain the risk investigation capability of the trained user. In some embodiments of this disclosure, based on the foregoing scheme, the interview questions are configured with interview strategies, and the survey subjects in the risk case data are configured with object status parameters, which include one or more of psychological state, conflict level, age group, and job position; the calculation of the survey interview skills of the trained user according to the survey path includes: determining the matching probability between the interview strategy selected by the trained user and the object status parameters of the survey subject according to a preset strategy probability table, and calculating the survey interview skills of the trained user according to the matching probability of each interview round; and if the interview strategy selected by the trained user triggers a high-risk interview rule, the survey interview skills are deducted according to a preset deduction constraint to obtain the interview risk control result. In some embodiments of this disclosure, based on the foregoing scheme, the method further includes: aggregating the survey path logs and corresponding capability evaluation results of multiple trained users to form a survey behavior dataset with capability annotations; statistically analyzing the correct response rate of each clue, the unlocking rate of each survey action, and the distribution of survey resource consumption based on the survey behavior dataset; calibrating the importance of clues and the quality of clue release in the standard clue matrix, as well as any and all triggering conditions in the survey action matrix, based on the statistical results; updating the calibrated parameters to the standard clue matrix and the survey action matrix, and iteratively updating the capability evaluation model used to calculate the risk identification capability, the risk investigation capability, and the survey interview skills. According to one aspect of this disclosure, a reverse risk management survey system is provided, comprising: a survey interaction module, a data processing module, a survey path recording module, a capability evaluation module, and a debriefing report output module.The above-mentioned survey interaction module is used to present the open survey actions and interview questions to the user terminal of the trainee and receive the trainee's selection; the above-mentioned data processing module includes a case import unit, a standard clue matrix unit, a survey action matrix unit, a trigger condition judgment unit, and a clue release unit; the above-mentioned survey path recording module is used to record the survey path of the trainee; the above-mentioned capability evaluation module includes a risk identification capability evaluation unit, a risk investigation capability evaluation unit, a survey interview skills evaluation unit, and an interview risk control evaluation unit; the above-mentioned debriefing report output module is used to generate a capability profile based on the calculation results of the capability evaluation module and output a debriefing report. In some embodiments of this disclosure, based on the foregoing scheme, each clue in the above-mentioned standard clue matrix is ​​also configured with a clue release quality, the clue release quality including one of clarity, ambiguity, and attenuation; the above-mentioned clue release unit is also configured to: determine the presentation form of the clue according to the clue release quality of the clue, and release the clue according to the presentation form. In some embodiments of this disclosure, based on the foregoing scheme, the aforementioned reverse risk management investigation system is further configured to: aggregate the investigation path logs and corresponding capability evaluation results of multiple trained users to form a dataset of investigation behaviors with capability annotations; statistically analyze the correct response rate of each clue, the unlocking rate of each investigation action, and the distribution of investigation resource consumption based on the investigation behavior dataset; calibrate the importance of clues and the quality of clue release in the standard clue matrix, as well as any triggering condition and all triggering conditions in the investigation action matrix, based on the statistical results; update the calibrated parameters to the standard clue matrix and the investigation action matrix, and iteratively update the capability evaluation model used by the capability evaluation module. As can be seen from the above technical solutions, the reverse risk management investigation method and system in the exemplary embodiments of this disclosure have at least the following advantages and positive effects: On the one hand, by decomposing complex risk cases into a computable data table structure through the standard clue matrix and the investigation action matrix, and dynamically unlocking investigation actions and interview questions based on a dual matching mechanism of clue type and any triggering condition and all triggering conditions, the clue release process closely resembles the gradual emergence of evidence in real investigations, reproducing the dynamic investigation process where clues, actions, and interviews are intertwined, and improving the fidelity of the training process to real risk investigations. On the other hand, by recording the survey paths of trained users and calculating risk identification ability, risk investigation ability, and survey and interview skills in reverse based on the survey paths, the scoring is no longer based solely on the final conclusion. This achieves a process-oriented, structured, and quantifiable evaluation of risk management capability structure, thereby improving the accuracy of capability evaluation.Furthermore, by aggregating the survey path logs of multiple trained users to form a capability-annotated survey behavior dataset, and using this dataset to calibrate the clue matrix and trigger condition parameters, and iteratively update the capability evaluation model, the case difficulty and evaluation criteria can be automatically optimized as training data accumulates. Simultaneously, the anonymized survey behavior dataset can also be used to assist in the training of risk management models, improving the system's adaptability and data reuse value. It should be understood that the above general description and the following detailed description are merely exemplary and explanatory, and do not limit this disclosure. The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with this disclosure and, together with the specification, serve to explain the principles of this disclosure. Obviously, the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without creative effort. In the accompanying drawings: Figure 1 schematically illustrates a flowchart of a reverse risk management investigation method according to an embodiment of the present disclosure; Figure 2 schematically illustrates a flowchart of a trigger condition determination method according to an embodiment of the present disclosure; Figure 3 schematically illustrates a structural diagram of a capability evaluation model according to an embodiment of the present disclosure; Figure 4 schematically illustrates a structural block diagram of a reverse risk management investigation system according to an embodiment of the present disclosure; Figure 5 schematically illustrates a flowchart of a closed-loop calibration method based on investigation path logs according to an embodiment of the present disclosure; Figure 6 schematically illustrates a flowchart of an embodiment of a personal business loan case investigation according to an embodiment of the present disclosure; and Figure 7 shows a structural diagram of an electronic device in an exemplary embodiment of the present disclosure. Detailed Description Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the present disclosure will be more comprehensive and complete, and will fully convey the concept of exemplary embodiments to those skilled in the art. Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the present disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other cases, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this disclosure. The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities.That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices. The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be decomposed, while others can be combined or partially combined, so the actual execution order may change depending on the actual situation. Before describing the embodiments of this disclosure in detail, the following explanations are provided for some of the terms involved in this disclosure. Risk case data refers to structured case scripts written based on real or fictitious cases in business scenarios such as financial risk management, anti-fraud, anti-money laundering, credit investigation, and identification of abnormal over-the-counter transactions. These scripts include elements such as the investigation target, business scenario, initial materials, investigation resource allocation, clues, investigation actions, and interview questions. The customer identity, account, transaction, communication, document, and institution name involved are all anonymized, fictitious, or simulated data. Trainees refer to personnel who receive risk management capability training and evaluation within this system, such as bank tellers, credit investigators, and compliance and anti-fraud personnel. The investigation target refers to the virtual entity being investigated in the risk case data, such as a fictitious business entity or customer. Investigation resources refer to the constrained resources available to trainees during the investigation process, such as time, rounds, or action points. In the training and evaluation process provided by related technologies, on the one hand, online exams or quizzes can only be scored based on the final conclusions given by the trainees. Why trainees choose a certain action or whether they understand the direction of a certain clue cannot be observed and measured during the investigation, resulting in poor accuracy of capability evaluation. On the other hand, the presentation order of materials in linear case learning platforms is fixed. Regardless of the strategy adopted, trainees will obtain the same information, making it impossible to reproduce the dynamic process of evidence gradually emerging with actions and the impact of interview strategies on information quality in real investigations. Therefore, it is also impossible to characterize specific capabilities such as risk identification, investigation progress, and interview strategies. In view of this, this disclosure provides a reverse risk management investigation method. Its core concept lies in decomposing risk cases into a standard clue matrix and an investigation action matrix driven by data tables. A dual matching mechanism—combining clue type with any trigger condition and all trigger conditions—dynamically controls clue release and action unlocking. Furthermore, by analyzing the paths chosen by trained users during the investigation process, the structure of their risk management capabilities is identified in reverse. In other words, this technical solution does not simply judge whether trained users arrive at correct conclusions, but rather achieves a process-oriented, structured, and quantifiable evaluation of risk identification ability, risk investigation ability, and investigation and interview skills through their investigation path.Figure 1 schematically illustrates a flowchart of a reverse risk management investigation method according to an embodiment of the present disclosure. Referring to Figure 1, the embodiment shown includes steps S101 to S107: Step S101. Import risk case data and generate investigation targets, business scenarios, initial materials, and investigation resource configurations; Step S102. Construct a standard clue matrix, registering the clue type, clue importance, key clue identifier, and clue content of each clue; Step S103. Construct an investigation action matrix, configuring any trigger condition and all trigger conditions for each investigation action or interview question; Step S104. Open investigation actions without pre-triggered conditions, receiving selections of investigation actions or interview questions from trained users; Step S105. Release clues corresponding to the selected investigation action or interview question, and write the clue type of the released clues into the acquired clue set; Step S106. The system matches the obtained clue set with any and all trigger conditions of the pending investigation actions or interview questions to dynamically unlock intermediate and in-depth investigation actions or subsequent interview questions; Step S107. Record the investigation path of the trainee user, calculate risk identification ability, risk investigation ability, and investigation and interview skills based on the investigation path, and output the ability evaluation results. The specific implementation methods of each step shown in Figure 1 are explained below. In step S101, the system reads risk case data stored in a structured format (e.g., JSON format or relational database table) and initializes an investigation training task accordingly. Among them, the investigation object is used to characterize the virtual subject being investigated and its attributes; the business scenario is used to limit the business environment in which this investigation takes place, such as counter anti-fraud, post-loan inspection of personal business loans, and identification of suspicious transactions; the initial materials refer to the background information that is opened to the trainee user at the beginning of the investigation, such as task background description, basic account information, etc.; the investigation resource configuration is used to limit the investigation resources that the trainee user can use, such as the number of investigation rounds, the available time for each round, or the number of action points, etc. In step S102, the standard clue matrix is ​​implemented in the form of a data table, with each clue corresponding to a record in the data table.In an exemplary embodiment, the standard clue matrix can be composed of a response generation intermediate table and a survey clue intermediate table: the response generation intermediate table stores interview clues released by interview questions, and its key fields include clue identifier (id), information source (info_source), stage (stage), interview strategy (strategy_name), clue release quality (release_quality), clue content (info_text), clue tags (tags), clue type (clue_type), and clue importance (importance); the survey clue intermediate table stores survey clues released by survey actions, and its key fields include clue identifier (id), associated action identifier (action_id), action name (card_name), any triggering condition (triggering_conditions_any), all triggering conditions (triggering_conditions_all), clue content (clue_text), clue tags (tags), clue type (clue_type), stage (stage), and clue importance (importance). In addition, each clue is also configured with a key clue identifier (is_key_clue) to mark whether the clue is a key clue pointing to the core risk facts of the case. The clue type (clue_type) is the core field of the triggering mechanism in this technical solution. Its value is a standardized description of the risk indicated by the clue, such as "incomplete explanation of the recipient," "discrepancy between operating cash flow and declared income," and "multi-entity fund transfer." The clue importance is a value within a preset range, for example, from zero to one. The larger the value, the greater the contribution of the clue to the risk assessment. For example, if a clue's clue type is "incomplete explanation of the recipient," the clue content is "the customer claims the recipient was introduced by a friend, but cannot explain the specific relationship and transaction background," the clue importance is 0.8, and the key clue identifier is one, indicating that this clue is a key clue. In step S103, the investigation action matrix is ​​also implemented in the form of a data table.In an exemplary embodiment, the survey action matrix can be composed of a question generation intermediate table and a survey card intermediate table: the question generation intermediate table stores interview questions, and its key fields include question identifier (id), information source (info_source), stage (stage), interview strategy (strategy_name), any triggering condition (triggering_conditions_any), all triggering conditions (triggering_conditions_all), question text (question_text), default round (default_round), and include tags (include_tags); the survey card intermediate table stores survey actions, and its key fields include action identifier (id), action name (name), action type (type), any triggering condition (triggering_conditions_any), all triggering conditions (triggering_conditions_all), action description (description), stage (stage), and action tag (tag). The values ​​of any triggering condition (triggering_conditions_any) and all triggering conditions (triggering_conditions_all) are both sets of clue types. The "any trigger condition" means that the corresponding investigation action or interview question can be unlocked when the trainee has obtained clues corresponding to any type of clue in the set. The "all trigger condition" means that the corresponding investigation action or interview question can only be unlocked when the trainee has obtained clues corresponding to all types of clues in the set. By configuring different "any trigger condition" and "all trigger condition" for different investigation actions and interview questions, a clue release network that closely resembles real investigation patterns can be flexibly constructed. In step S104, the system first opens investigation actions without prerequisite trigger conditions, that is, investigation actions where both "any trigger condition" and "all trigger condition" are empty, such as basic investigation actions like document retrieval, off-site analysis, field investigation, and on-site interviews, as well as basic interview questions. Under the constraints of the configured investigation resources, the trainee selects from the opened investigation actions and interview questions through the user terminal, and each selection consumes the corresponding investigation resources. In step S105, the system retrieves the corresponding clues from the standard clue matrix according to the investigation action or interview question selected by the trainee and releases them, while simultaneously writing the clue type of the released clues into the set of obtained clues.In an exemplary embodiment, the acquired clue set (acquired_clue_types) is maintained using a set data structure. Each element in the set represents a clue type, and the same clue type is not recorded repeatedly. The acquired clue set represents the current state of risk information possessed by the trained user and serves as the basis for subsequent trigger judgments. In step S106, the system matches the acquired clue set with any and all trigger conditions for each pending investigation action or interview question, and dynamically unlocks intermediate-in-depth investigation actions, re-trigger investigation actions, or subsequent interview questions based on the matching results. Intermediate-in-depth investigation actions refer to in-depth investigation actions that require a certain number of clues to execute, such as "verifying the historical transaction relationships of the receiving account" or "retrieving details of multi-entity related transactions." Re-trigger investigation actions refer to investigation actions that can be executed again after obtaining new clues to obtain incremental information for investigation directions that have already been executed. The specific implementation method for trigger condition judgment will be explained in conjunction with Figure 2. In step S107, the system continuously records the survey path of the trainee user through the survey path recording module. The survey path is stored in the form of a survey path log (player_action_log), which includes the sequence of survey action selections, the order of clue acquisition, resource consumption, and the trainee user's subsequent response actions and interview strategy selections after obtaining each clue. After the survey training task is completed, the system calculates the trainee user's risk identification ability, risk investigation ability, and survey interview skills based on the survey path, and outputs the ability evaluation results. The specific implementation of the ability calculation will be explained with reference to Figure 3. Figure 2 schematically shows a flowchart of a trigger condition judgment method according to an embodiment of the present disclosure. Referring to Figure 2, the embodiment shown in the figure includes steps S201 to S207: Step S201. Receive the trainee user's selection of survey actions or interview questions; Step S202. Release the corresponding clues and extract the clue type of the released clues; Step S203. Write the clue type into the set of obtained clues; Step S204. Read any and all trigger conditions of the survey action or interview question to be opened; Step S205. Determine whether the obtained set of clues meets the triggering conditions; Step S206. If the triggering conditions are met, then open the intermediate and in-depth investigation action, re-trigger the investigation action, or the follow-up interview questions; Step S207. If the triggering conditions are not met, then keep the investigation action or interview questions locked.In an exemplary embodiment, the judgment logic for the triggering condition in step S205 is as follows: If any and all triggering conditions for the investigation action or interview question to be opened are empty, then the investigation action or interview question is an investigation action or interview question without prerequisite triggering conditions and can be opened directly; if any triggering condition is not empty, then when the obtained clue set contains any clue type indicated by any triggering condition, it is determined that the triggering condition is met; if all triggering conditions are not empty, then when the obtained clue set contains all clue types indicated by all triggering conditions, it is determined that the triggering condition is met; if both triggering conditions are not empty, a judgment can be made according to preset logic, such as requiring both to be met simultaneously, or requiring all triggering conditions to be met and at least one clue type of any triggering condition to be met. Through the above dual triggering mechanism combining any and all, it is possible to depict two typical patterns in real investigations: "a single signal can lead to a certain investigation direction" and "multiple signals pointing together can lead to in-depth verification," making the clue release network closer to reality. For example, when a trainee obtains a lead of type "incomplete explanation of the recipient" through a basic interview, the system reads a mid-to-deep investigation action named "verify historical transaction relationships of the recipient account" from the intermediate table of the investigation cards. Any trigger condition for this action is a set of leads containing the "incomplete explanation of the recipient" lead type. Since this lead type is already included in the obtained lead set, the mid-to-deep investigation action is unlocked, and the trainee can subsequently choose to execute it. In an exemplary embodiment, each lead in the standard lead matrix is ​​also configured with a lead release quality, which includes one of three levels: clear, obscure, and decay. When releasing clues, the system determines the presentation format of the clues based on the quality of the release: for clear clues, the clue content is presented in its entirety; for ambiguous clues, the clue content is presented in a vague, indirect, or distracting manner to simulate situations where customer statements are unstable and the authenticity of external materials is difficult to verify; for decaying clues, their information value decreases with each round of investigation. If the trainee does not obtain the clue within a preset round, the presented content of the clue is reduced or no longer released to simulate situations where evidence is lost over time in real-world investigations. By configuring the quality of clue release, this technical solution can examine the risk judgment ability of trainees under conditions of incomplete information. Figure 3 schematically illustrates the structural diagram of a capability evaluation model according to an embodiment of this disclosure.Referring to Figure 3, the survey path recording data includes the sequence of survey action selections, the order of clue acquisition, resource consumption, and interview strategy selection. These are respectively input into the risk identification capability evaluation unit, the risk investigation capability evaluation unit, the survey interview skills evaluation unit, and the interview risk control evaluation unit. The outputs of each evaluation unit are aggregated to generate a capability profile and output a review report. The calculation logic of each evaluation unit is explained below. The risk identification capability evaluation unit calculates risk identification capability based on the matching degree between clue type and subsequent actions. Specifically, according to the specification document 10 HK 30137921 A, it obtains the subsequent response actions of the trainee after obtaining each clue, matches the preset response action pointed to by the clue type with the subsequent response action, and obtains the response matching degree for that clue. If the trainee selects a survey action or interview question corresponding to the clue type after obtaining a clue, it is considered that the trainee understands the risk indication of the clue, and the response matching degree is one; otherwise, the response matching degree is zero, or a decay value between zero and one is taken based on the delay round of the response. Furthermore, the Risk Identification Capability (RIDC) is calculated according to formula (1): RIDC=Σ(wi×mi) / Σwi (1) where wi is the importance of the i-th released clue, and mi is the response matching degree of the i-th released clue. The summation iterates through all clues released in this survey training task. It can be seen that the higher the importance of the clue, the greater the impact of its response matching on the risk identification capability, thus making the evaluation results focus on the accuracy of the trainee's understanding of key risk information. The risk investigation capability evaluation unit calculates the risk investigation capability based on the reach, focus, and efficiency of key clues. Specifically, the key clue reach rate K is obtained by comparing the number of key clues reached by the trained user with the total number of key clues in the case based on the key clue identification statistics; the investigation focus F is obtained by comparing the number of investigation actions related to the target investigation object in the investigation action selection sequence with the total number of investigation actions executed; and the investigation efficiency E is obtained by comparing the weighted sum of the importance of the clues obtained by the trained user with the actual investigation resources consumed. That is, the investigation efficiency E represents the value of risk clues obtained per unit of investigation resources. The larger the value of E, the higher the value of risk clues obtained by the trained user with fewer investigation resources. Further, the risk investigation capability RIVC is calculated according to formula (2): RIVC=α×K+β×F+γ×E (2) where α, β, and γ are preset weight coefficients, and the sum of the three is one. By comprehensively measuring the above three dimensions of the investigation path structure, the quality of the trained user's investigation progress can be characterized, that is, whether key clues are reached, whether the focus is on the risk direction, and whether limited resources are used efficiently. The survey and interview skills evaluation unit calculates survey and interview skills based on the probability matching between interview strategies and the state of the interviewees.Specifically, each interview question in the question generation intermediate table is configured with an interview strategy (strategy_name), such as "indirect questioning and listening", "direct questioning", "empathic reassurance", etc.; each survey subject in the risk case data is configured with a subject status parameter, which includes one or more of psychological state, conflict level, age group and position. The system has a preset strategy probability table, which records the matching probability of each interview strategy obtaining effective information under different combinations of subject status parameters. Further, the survey interview skill IS is calculated according to formula (3): IS=(1 / Q)×ΣP(sq|θq) (3) 11 HK 30137921 A Instruction Manual Where, Q is the total number of interview rounds in this survey training task, sq is the interview strategy selected by the trainee in the qth interview round, θq is the subject status parameter of the survey subject in the qth interview round, and P(sq|θq) is the matching probability of selecting interview strategy sq under the condition of subject status parameter θq, as recorded in the strategy probability table. The higher the matching probability, the more suitable the interview strategy is to the current state of the interviewee, and the more likely it is to obtain true and sufficient information. The interview risk control evaluation unit deducts points based on the triggering of high-risk interview rules. Specifically, the system has preset high-risk interview rules to define obviously inappropriate interview strategies, such as directly questioning the interviewee in a high-conflict state or directly presenting core evidence in the interview stage before establishing basic trust. If the interview strategy selected by the trainee triggers a high-risk interview rule, the interview skills are deducted according to formula (4) to obtain the correction value IS′ under the HIRI constraint of the interview risk control result: IS′=IS-Σδj (4) where δj is the preset deduction value corresponding to the j-th triggering of the high-risk interview rule. Through the deduction constraint, it is possible to prevent the trainee from using aggressive strategies to obtain information while ignoring the interview risk and guide them to form a robust interview style. In an exemplary embodiment, the debriefing report output module generates a competency profile of the trained user based on the calculation results of each evaluation unit. The competency profile presents risk identification ability, risk investigation ability, investigation and interview skills, and interview risk control results in the form of sub-item scores. Furthermore, it can map each sub-item score to competency levels such as "Excellent," "Good," "Average," and "Poor" according to preset grading rules. The debriefing report also includes a replay of the trained user's investigation path, the reach and omission of key clues, a detailed strategy matching breakdown for each interview round, and improvement suggestions for weak competency sub-items, enabling the trained user to retrospectively observe errors during the investigation process. Figure 5 schematically illustrates a flowchart of a closed-loop calibration method based on investigation path logs according to an embodiment of this disclosure.Referring to Figure 5, the embodiment shown includes steps S501 to S505: Step S501. Aggregate the investigation path logs and corresponding capability evaluation results of multiple trained users to form a capability-annotated investigation behavior dataset; Step S502. Statistically calculate the correct response rate of each clue, the unlocking rate of each investigation action, and the distribution of investigation resource consumption; Step S503. Calibrate the importance of clues, the quality of clue release, and any and all triggering conditions based on the statistical results; Step S504. Update the calibrated parameters to the standard clue matrix and investigation action matrix, and iteratively update the capability evaluation model; Step S505. Output the investigation behavior dataset after anonymization for use in auxiliary training of the risk management model. Specifically, in step S501, the system associates and stores the investigation path logs generated by multiple trained users on the same risk case or the same batch of risk cases with the capability evaluation results corresponding to each trained user, thereby forming a capability-annotated investigation behavior dataset. Each sample in this dataset includes a complete survey path and sub-scores of a trainee user, essentially generating labeled behavioral samples automatically during the training process without additional manual labeling costs. In step S502, the system performs statistical analysis on the survey behavior dataset: for each clue, it calculates the proportion of trainees who obtained the clue and made the correct follow-up response, obtaining the correct response rate for that clue; for each survey action or interview question, it calculates the proportion that was unlocked in all survey training tasks, obtaining the unlocking rate; and it also calculates the distribution of survey resource consumption among the trainee user group. In step S503, the system calibrates the parameters in the standard clue matrix and survey action matrix based on the statistical results. For example, if the correct response rate of a clue is significantly lower than a preset threshold, it indicates that the risk of the clue is too obscure for the trainee user group, and its importance can be appropriately increased, or its release quality can be adjusted from vague to clear; conversely, if the correct response rate of a clue is significantly higher than a preset threshold, its importance can be decreased, or its release quality can be adjusted to vague or even attenuated, to increase the training difficulty. For example, if the unlocking rate of a certain in-depth investigation action is abnormally low, it indicates that any or all of its trigger conditions are set too strictly. The number of clue types indicated by all trigger conditions can be reduced, or a new clue type can be added to any trigger condition. In an exemplary embodiment, the calibration of the clue importance can be performed according to formula (5): wi′=wi+η×(1-ri) (5) where wi is the clue importance of the i-th clue before calibration, ri is the correct response rate of the i-th clue, η is the preset calibration step size, wi′ is the clue importance after calibration, and wi′ is limited to a preset value range.In step S504, the system updates the calibrated parameters to the standard clue matrix and the investigation action matrix, so that subsequent investigation training tasks can run based on the calibrated parameters. At the same time, the system iteratively updates the capability evaluation model based on the investigation behavior dataset. For example, it refits the weight coefficients α, β, and γ in formula (2), or updates the matching probability under the combination of state parameters of each object in the strategy probability table, so that the capability evaluation caliber continuously approaches the true capability distribution of the training user group as the training data accumulates. In step S505, the system desensitizes the investigation behavior dataset, removes information that may be related to the real identity of the training users, and outputs the data. The desensitized investigation behavior dataset depicts the behavioral patterns of people with different capability levels in risk investigations. It can be used as auxiliary training data for financial institutions to build risk management models and anti-fraud models, thereby realizing the closed-loop reuse of training data. As can be seen, through the closed-loop calibration mechanism shown in Figure 5, this technical solution can not only train and evaluate trained users, but also continuously optimize the system's own case parameters and evaluation model using the data automatically accumulated during the training process, and output valuable behavioral data to external risk control modeling scenarios. The following, in conjunction with Figure 6, uses a bank personal business loan serial loan investigation as an example to illustrate a complete embodiment of this technical solution. Referring to Figure 6, this embodiment includes the following process: First, import risk case data for personal business loan serial loan investigations and generate four business entity investigation subjects: supermarket operators, restaurant operators, software development operators, and electronics repair shop operators. Among these, some investigation subjects have hidden relationships, such as mutual fund transfers, shared business premises, or identical supporting documents, constituting serial loan risk; other investigation subjects are normal business customers. Each investigation subject is configured with object status parameters, for example, the restaurant operator's psychological state is tense, conflict level is moderate, age group is middle-aged, and occupation is self-employed. Second, configure three rounds of investigation resources, each round for twenty-four hours, for a total of seventy-two hours. Each time a trainee performs a survey action or interview question, the remaining resources for the current round will be deducted according to the preset time consumption of that action. Furthermore, survey actions without pre-triggered conditions are available, including four basic survey actions: document retrieval, off-site analysis, on-site investigation, and on-site interviews. Trainees can freely choose the direction of their investigation, such as first reviewing loan application materials and business transaction records of various business entities, or first conducting on-site interviews with a specific business entity. Then, based on the type of leads the trainee has obtained, intermediate and advanced investigation actions and re-triggered investigation actions will be triggered.For example, when a trainee obtains a lead of type "multi-entity fund transfer" through off-site analysis, any in-depth investigation action "retrieve details of related-party transactions of multiple entities" triggered by this lead type is unlocked. When a trainee obtains two leads simultaneously, one of type "discrepancy between operating cash flow and declared revenue" and the other of "identical supporting documents," all in-depth investigation actions "conduct cross-entity on-site verification" triggered by both lead types are unlocked. During the investigation, the system releases four types of leads according to their configuration: risk-deepening leads, normal disproving leads, interfering explanation leads, and low-value extension leads. Risk-deepening leads point to the core risk facts of related cases and are often key leads; normal disproving leads are used to prove that an investigated entity is a normal business customer; interfering explanation leads are reasonable explanations given by the investigated entity that are somewhat misleading; and low-value extension leads have limited information value and are mainly used to assess the trainee's focus in the investigation. Finally, after the training task is completed, the system evaluates the trainees' capabilities in four areas based on their investigation paths: high-risk identification, normal customer protection, case linkage identification, and single-signal misjudgment prevention. High-risk identification assesses whether trainees accurately identify business entities with potential case linkage risks; normal customer protection assesses whether trainees avoid over-investigating normal business customers; case linkage identification assesses whether trainees discover the correlation between investigation subjects through cross-validation of multiple clues; and single-signal misjudgment prevention assesses whether trainees remain cautious when only a single risk signal is obtained, rather than hastily drawing risk conclusions. These capability evaluation results, along with risk identification capability, risk investigation capability, investigation and interview skills, and interview risk control results, are included in the debriefing report. It is evident that the capability evaluation of trainees in this technical solution runs throughout the entire investigation process. The evaluation is based on the trainees' actual chosen paths rather than single-answer results, thus enabling reverse identification of the trainees' risk management capability structure. Furthermore, the clue release network constructed through any and all trigger conditions allows the same risk case to unfold differently under different investigation strategies of different trainees, enhancing the authenticity and reusability of the training. Furthermore, the risk case data, clue matrix, and action matrix in this technical solution exist independently of the system engine in the form of data tables, allowing for timely updates or expansion of the case library. This makes it suitable for training needs in various business scenarios such as financial risk management, anti-fraud, anti-money laundering, credit investigation, and identification of abnormal over-the-counter transactions. The following describes an embodiment of the reverse risk management investigation system of this disclosure, which can be used to execute the reverse risk management investigation method described above. Figure 4 schematically illustrates a structural block diagram of a reverse risk management investigation system according to an embodiment of this disclosure.As shown in Figure 4, the trainee user 100 accesses the system through the user terminal 200. The system includes: a survey interaction module 410, a data processing module 420, a survey path recording module 430, a capability evaluation module 440, and a debriefing report output module 450. Specifically: The survey interaction module 410 presents the open survey actions and interview questions, the remaining survey resources for the current round, and the released clues to the trainee user 100's user terminal 200, and receives the trainee user 100's selection of survey actions or interview questions. The user terminal 200 can be various electronic devices with a display screen, including but not limited to desktop computers, laptops, smartphones, and tablets. The data processing module 420 includes a case import unit 421, a standard clue matrix unit 422, a survey action matrix unit 423, a trigger condition judgment unit 424, and a clue release unit 425. Specifically: Case import unit 421 is configured to execute step S101 above, importing risk case data and generating investigation subjects, business scenarios, initial materials, and investigation resource configurations; Standard clue matrix unit 422 is configured to execute step S102 above, constructing and storing a standard clue matrix; Investigation action matrix unit 423 is configured to execute step S103 above, constructing and storing an investigation action matrix; Trigger condition judgment unit 424 is configured to execute step S106 above and steps S204 to S207 shown in Figure 2, completing the matching judgment of any trigger condition and all trigger conditions; Clue release unit 425 is configured to execute step S105 above, releasing clues and maintaining the set of obtained clues. The investigation path recording module 430 above is configured to execute the path recording operation in step S107 above, recording the investigation action selection sequence, clue acquisition order, resource consumption, subsequent response actions, and interview strategy selection of the trained user 100 in the form of an investigation path log. The aforementioned capability evaluation module 440 includes a risk identification capability evaluation unit 441, a risk investigation capability evaluation unit 442, an investigation and interview skills evaluation unit 443, and an interview risk control evaluation unit 444. Specifically: the risk identification capability evaluation unit 441 is configured to calculate risk identification capability according to formula (1); the risk investigation capability evaluation unit 442 is configured to calculate risk investigation capability according to formula (2); the investigation and interview skills evaluation unit 443 is configured to calculate investigation and interview skills according to formula (3); and the interview risk control evaluation unit 444 is configured to deduct points from investigation and interview skills according to formula (4) to obtain the interview risk control result.The aforementioned debriefing report output module 450 is configured to generate a capability profile based on the calculation results of each unit of the capability evaluation module 440, and output a debriefing report including investigation path replay, key clue reach and omission status, strategy matching details, and improvement suggestions. In some embodiments of this disclosure, the aforementioned reverse risk management investigation system also supports user permission management, operation log recording, data access control, case data version management, and output report anonymization. Furthermore, the customer identity, account, transaction, communication, document, and institution name information used by the system are all anonymized, fictitious, or simulated data, and do not directly expose real personal information, real account information, or real customer privacy. It should be noted that this system is only used for risk management training, capability evaluation, and simulation exercises, and does not directly replace real business approvals, judicial judgments, or regulatory conclusions. The specific details of each module and unit in the aforementioned reverse risk management investigation system have been described in detail in the aforementioned reverse risk management investigation method embodiments, and therefore will not be repeated here. It should be noted that although several modules or units of the equipment used for action execution have been mentioned in the above detailed description, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units. 16 HK 30137921 A Specification Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that these steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps, etc. Through the above description of embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of this disclosure can be embodied in the form of a software product. This software product can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, portable hard drive, etc.) or on a network, and includes several instructions to cause a computing device (such as a personal computer, server, mobile terminal, or network device, etc.) to execute the method according to the embodiments of this disclosure. In an exemplary embodiment of this disclosure, a computer storage medium capable of implementing the above-described method is also provided, on which a program product capable of implementing the methods described in this specification is stored.In some possible embodiments, various aspects of this disclosure can also be implemented as a program product, including program code, which, when run on a device, causes the device to perform the steps described in the above embodiments of this specification according to various exemplary implementations of this disclosure. Figure 7 shows a schematic diagram of an electronic device suitable for implementing embodiments of this disclosure. It should be noted that the electronic device shown in Figure 7 is only an example and should not impose any limitations on the functionality and scope of use of the embodiments of this disclosure. As shown in Figure 7, the electronic device includes a processor 710, a memory 720, a bus 730, an input / output interface 740, and a communication section 750. The processor 710 may include a central processing unit and a graphics processing unit. The processor 710 performs various appropriate actions and processes according to the program stored in the memory 720. The memory 720 may include a read-only memory and a random access memory. The memory 720 also stores various programs and data required for system operation. The processor 710 and the memory 720 are connected to each other via the bus 730, and the input / output interface 740 and the communication section 750 are also connected to the bus 730. Input / output interface 740 is used to connect input components including a keyboard, mouse, etc., and output components including a display, speaker, etc.; communication section 750 includes a network interface card such as a local area network card, modem, etc., and performs communication processing via a network such as the Internet. Specifically, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 750. When the computer program is executed by processor 710, it performs the methods and various functions defined in the system of this application. It should be noted that the computer-readable medium shown in embodiments of this disclosure can be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. Computer-readable storage media may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof.More specific examples of computer-readable storage media may include, but are not limited to, electrical connections having one or more wires, portable computer disks, hard disks, random access memory, read-only memory, erasable programmable read-only memory, flash memory, optical fiber, portable compact disk read-only memory, optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, program segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the boxes may occur in a different order than those shown in the figures. For example, two consecutively indicated boxes may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functions involved. Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims. It should be understood that this disclosure is not limited to the precise structures described above and shown in the figures, and various modifications and changes may be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.18 HK 30137921 A Claims 1. A reverse risk management investigation method, executed by an electronic device, is characterized by comprising: importing risk case data, constructing a standard clue matrix and an investigation action matrix, wherein each clue in the standard clue matrix is ​​configured with a clue type, clue importance, and key clue identifier, and each investigation action or interview question in the investigation action matrix is ​​configured with any trigger condition and all trigger conditions; wherein the values ​​of any trigger condition and all trigger conditions are clue types pre-registered in the standard clue matrix; the acquired clue set is used to store the clue types already acquired by the trainee during the investigation process and serves as the basis for dynamically unlocking investigation actions or interview questions; receiving the trainee's selection of an open investigation action or interview question, releasing the clue corresponding to the selected investigation action or interview question, and writing the clue type of the released clue into the acquired clue set; matching the acquired clue set with the any trigger condition and all trigger conditions of the investigation action or interview question to be opened, and dynamically unlocking the investigation action or interview question to be opened based on the matching results; and recording the trainee's investigation path, the investigation path including the investigation action selection sequence, clue acquisition order, and resource consumption. 1. Calculate the risk identification ability, risk investigation ability, and interview skills of the trained user according to the investigation path, and output the ability evaluation results. 2. The reverse risk management investigation method according to claim 1, characterized in that the step of matching the obtained clue set with any and all trigger conditions of the investigation action or interview question to be opened, and dynamically unlocking the investigation action or interview question to be opened according to the matching result, includes: If any and all trigger conditions are empty, then the investigation action or interview question to be opened is determined as an investigation action or interview question without a prerequisite trigger condition and is directly opened; If any trigger condition is not empty, then if the obtained clue set contains any clue type indicated by any trigger condition, the investigation action or interview question to be opened is unlocked; If all trigger conditions are not empty, then if the obtained clue set contains all clue types indicated by all trigger conditions, the investigation action or interview question to be opened is unlocked; Wherein, the unlocked investigation actions include medium-deep investigation actions and re-triggered investigation actions.3. The reverse risk management survey method according to claim 1, characterized in that each clue in the standard clue matrix is ​​further configured with a clue release quality, the clue release quality including one of clarity, ambiguity, and attenuation; the release of clues corresponding to selected survey actions or interview questions includes: determining the presentation form of the clue according to the clue release quality of the clue, and releasing the clue according to the presentation form. 4. The reverse risk management survey method according to claim 1, characterized in that the calculation of the risk identification ability of the trained user according to the survey path includes: obtaining the subsequent response action of the trained user after obtaining the clue; matching the preset response action pointed to by the clue type of the clue with the subsequent response action to obtain the response matching degree of the clue; and performing a weighted calculation based on the response matching degree of each clue and the corresponding clue importance to obtain the risk identification ability of the trained user. 5. The reverse risk management investigation method according to claim 1, characterized in that, the step of calculating the risk investigation capability of the trained user based on the investigation path includes: calculating the proportion of key clues reached by the trained user based on the key clue identifiers to obtain the key clue reach rate; obtaining the investigation focus degree based on the proportion of investigation actions related to the target investigation object in the investigation action selection sequence; obtaining the investigation efficiency based on the weighted sum of the importance of the clues already obtained by the trained user and the ratio of the resource consumption, wherein the investigation efficiency characterizes the value of risk clues obtained per unit of investigation resources; and performing a weighted calculation on the key clue reach rate, the investigation focus degree, and the investigation efficiency to obtain the risk investigation capability of the trained user. 6. The reverse risk management survey method according to claim 1 is characterized in that the interview questions are configured with interview strategies, and the survey subjects in the risk case data are configured with object status parameters, the object status parameters including one or more of psychological state, conflict level, age group and position; the step of calculating the survey interview skills of the trained user according to the survey path includes: 2 HK 30137921 A claim determines the matching probability between the interview strategy selected by the trained user and the object status parameters of the survey subject according to a preset strategy probability table, and calculates the survey interview skills of the trained user according to the matching probability of each interview round; and, if the interview strategy selected by the trained user triggers a high-risk interview rule, the survey interview skills are deducted according to a preset deduction constraint to obtain the interview risk control result.7. The reverse risk management investigation method according to any one of claims 1 to 6, characterized in that the method further comprises: aggregating the investigation path logs and corresponding capability evaluation results of multiple trained users to form a capability-labeled investigation behavior dataset; statistically analyzing the correct response rate of each clue, the unlocking rate of each investigation action, and the distribution of investigation resource consumption based on the investigation behavior dataset; calibrating the importance of clues and the quality of clue release in the standard clue matrix, as well as any triggering condition and all triggering conditions in the investigation action matrix, based on the statistical results; updating the calibrated parameters to the standard clue matrix and the investigation action matrix, and iteratively updating the capability evaluation model used to calculate the risk identification capability, the risk investigation capability, and the investigation and interview skills. 8. A reverse risk management investigation system, characterized in that the system comprises: an investigation interaction module, used to present open investigation actions and interview questions to the user terminal of the trainee, and receive the trainee's selection; a data processing module, including a case import unit, a standard clue matrix unit, an investigation action matrix unit, a trigger condition judgment unit, and a clue release unit; wherein, the case import unit is used to import risk case data, the standard clue matrix unit is used to store a standard clue matrix configured with clue type, clue importance, and key clue identifier, the investigation action matrix unit is used to store an investigation action matrix configured with any trigger condition and all trigger conditions, the clue release unit is used to release clues corresponding to the selected investigation action or interview question and write the clue type into the acquired clue set, and the trigger condition judgment unit is used to match the acquired clue set with any trigger condition and all trigger conditions of the investigation action or interview question to be opened to dynamically unlock; and an investigation path recording module, used to record the trainee's investigation path, the investigation path including the investigation action selection sequence, clue acquisition order, and resource consumption; 3 HK 30137921 A The claim capability evaluation module includes a risk identification capability evaluation unit, a risk investigation capability evaluation unit, an investigation and interview skills evaluation unit, and an interview risk control evaluation unit, used to calculate the risk identification capability, risk investigation capability, and investigation and interview skills of the trained user according to the investigation path; the debriefing report output module is used to generate a capability profile and output a debriefing report based on the calculation results of the capability evaluation module. 9. The reverse risk management investigation system according to claim 8, characterized in that each clue in the standard clue matrix is ​​further configured with a clue release quality, the clue release quality including one of clarity, ambiguity, and attenuation; the clue release unit is further configured to: determine the presentation form of the clue according to the clue release quality of the clue, and release the clue according to the presentation form.10. The reverse risk management investigation system according to claim 8, characterized in that the system is further configured to: aggregate the investigation path logs and corresponding capability evaluation results of multiple trained users to form a capability-labeled investigation behavior dataset; statistically analyze the correct response rate of each clue, the unlocking rate of each investigation action, and the distribution of investigation resource consumption based on the investigation behavior dataset; calibrate the importance of clues and the quality of clue release in the standard clue matrix, as well as any triggering condition and all triggering conditions in the investigation action matrix, based on the statistical results; update the calibrated parameters to the standard clue matrix and the investigation action matrix, and iteratively update the capability evaluation model used by the capability evaluation module. 4 HK 30137921 A Instruction Manual Appendix Figure 1 1 HK 30137921 A Instruction Manual Appendix Figure 2 2 HK 30137921 A Instruction Manual Appendix Figure 3 3 HK 30137921 A Instruction Manual Appendix Figure 4 4 ​​HK 30137921 A Instruction Manual Appendix Figure 5 5 HK 30137921 A Instruction Manual Appendix Figure 6 6 HK 30137921 A Instruction Manual Appendix Figure 7 7 HK 30137921 A.