Risk control method and device

Abstract

The invention provides a risk control method and device, and the method comprises the steps: obtaining at least one target risk control model matched with a business request in response to the received business request sent by a target object; for any target risk control model, determining at least one target risk characteristic factor associated with the target object from a plurality of candidate risk characteristic factors of any target risk control model; determining a risk score of a target business corresponding to the business handling request of the target object according to the at least one target risk feature factor; and performing risk control on the business request in response to the fact that the risk score is greater than the risk score threshold, thereby realizing dynamic adjustment of the target risk control model and the risk characteristic factor of the target risk control model according to different target objects and business requests, improving the flexibility and accuracy of risk control, and improving the risk control efficiency. A new risk control model is prevented from being independently developed or adapted for each new service request, and the processing speed and efficiency are improved.

Description

Technical Field

[0001] The present disclosure relates to the field of risk control technologies, and in particular, to a risk control method and apparatus. Background Art

[0002] With the rapid development of the global economy and the continuous deepening of international trade, financial transaction services (such as foreign exchange transactions, loan services, etc.) have become an indispensable part of enterprise operations. However, due to changes in the economic environment and the complexity of the financial market, financial transaction services face many risks; these risks not only affect the financial stability of enterprises or individuals, but may also have a profound impact on the stability and sustainable development of the financial market. Therefore, in order to ensure the compliance, transparency, and robustness of financial transaction services, it is very important to perform risk control when an enterprise or an individual applies for financial transaction services. Summary of the Invention

[0003] The present disclosure provides a risk control method and apparatus to at least partly solve one of the technical problems in the related art. The technical solutions of the present disclosure are as follows:

[0004] According to a first aspect of an embodiment of the present disclosure, a risk control method is provided, including: in response to receiving a service request sent by a target object, obtaining at least one target risk control model adapted to the service request; for any one of the target risk control models, determining at least one target risk characteristic factor associated with the target object from a plurality of candidate risk characteristic factors of the any one of the target risk control models; determining a risk score of the target object for handling the target service corresponding to the service request according to the at least one target risk characteristic factor; and in response to the risk score being greater than a risk score threshold, performing risk control on the service request.

[0005] According to a second aspect of an embodiment of the present disclosure, a risk control apparatus is provided, including: an obtaining module, configured to obtain at least one target risk control model adapted to the service request in response to receiving a service request sent by a target object; a first determination module, configured to determine at least one target risk characteristic factor associated with the target object from a plurality of candidate risk characteristic factors of any one of the target risk control models; a second determination module, configured to determine a risk score of the target object for handling the target service corresponding to the service request according to the at least one target risk characteristic factor; and a control module, configured to perform risk control on the service request in response to the risk score being greater than a risk score threshold.

[0006] According to a third aspect of the embodiments of the present disclosure, there is provided an electronic device, including: a processor; a memory for storing executable instructions of the processor; wherein, the processor is configured to execute the instructions to implement the risk control method as described in the embodiments of the first aspect of the present disclosure.

[0007] According to a fourth aspect of the embodiments of the present disclosure, there is provided a computer-readable storage medium, when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, enabling the electronic device to execute the risk control method as described in the embodiments of the first aspect of the present disclosure.

[0008] According to a fifth aspect of the embodiments of the present disclosure, there is provided a computer program product, including: a computer program, which implements the risk control method as described in the embodiments of the first aspect of the present disclosure when executed by a processor.

[0009] The technical solutions provided by the embodiments of the present disclosure at least bring the following beneficial effects:

[0010] In this technical solution, different target risk control models are adapted to different service requests, and from multiple candidate risk feature factors of any target risk control model, the target risk feature factors associated with the target object are selected. According to the target risk feature factors, the risk score of the target service corresponding to the service request of the target object is determined. When the risk score is greater than the risk score threshold, risk control is performed on the service request, realizing dynamic adjustment of the target risk control model and the risk feature factors of the target risk control model according to different target objects and service requests, improving the flexibility and accuracy of risk control, and avoiding developing or adapting a new risk control model for each new service request separately. Instead, a suitable model is selected from the existing model library for adjustment and use, thus significantly improving the processing speed and efficiency. Among them, when determining the risk score of the target service corresponding to the service request of the target object, a corresponding transaction rule list is obtained for each target risk feature factor, and the transaction data of the target object is matched with these transaction rules one by one. Finally, the risk score of the service request is determined according to the rule matching result, realizing automatic, accurate and flexible risk assessment; in addition, when determining the rule matching result of the currently traversed transaction rule, by traversing multiple sub-rules corresponding to each transaction rule, the target transaction data associated with the target object is refined to be matched with the currently traversed transaction rule, further improving the accuracy of risk assessment.

[0011] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. Description of the Drawings

[0012] The accompanying drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with the present disclosure, and are used together with the specification to explain the principles of the present disclosure, and do not constitute an undue limitation on the present disclosure.

[0013] Figure 1 is a schematic flowchart of the risk control method shown in the first embodiment of the present disclosure;

[0014] Figure 2 is a schematic flowchart of the risk control method shown in the second embodiment of the present disclosure;

[0015] Figure 3 is a schematic flowchart of the risk control method shown in the third embodiment of the present disclosure;

[0016] Figure 4 is a schematic flowchart of the risk control method shown in the fourth embodiment of the present disclosure;

[0017] Figure 5 is a schematic diagram of the principle of the risk control method shown in the embodiments of the present disclosure;

[0018] Figure 6 is a schematic structural diagram of the risk control device shown in the fifth embodiment of the present disclosure;

[0019] Figure 7 is a schematic structural diagram of an electronic device shown in an exemplary embodiment of the present disclosure. Detailed Embodiments

[0020] In order to enable those of ordinary skill in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings.

[0021] It should be noted that in the specification and claims of the present disclosure and the above-mentioned accompanying drawings, the terms "first", "second", etc. are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that such used data can be interchanged under appropriate circumstances so that the embodiments of the present disclosure described here can be implemented in an order other than those illustrated or described here. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0022] It should be noted that in the technical solutions of the present disclosure, the collection, storage, use, processing, transmission, provision, and disclosure of the user's personal information and other processes are all carried out on the premise of obtaining the user's consent, and all comply with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0023] In the related art, a specified risk control model is used to control the risk of specified foreign exchange operations. Since the risk control model is customized to develop corresponding functional codes according to business requirements, when the logic of the specified foreign exchange business is updated, a large amount of code needs to be rewritten to update the risk control model to meet the new business requirements. The above method of rewriting code is not only time-consuming and laborious, but also may introduce errors, thus increasing the complexity of maintenance.

[0024] In view of the above problems, the present disclosure proposes a risk control method and apparatus.

[0025] The risk control method and apparatus according to the embodiments of the present disclosure will be described below with reference to the accompanying drawings.

[0026] Figure 1 FIG. is a schematic flowchart of the risk control method shown in the first embodiment of the present disclosure. It should be noted that the execution subject of the embodiments of the present disclosure may be a risk control apparatus, and the risk control apparatus may be applied to any electronic device with computing capabilities, so that the electronic device can perform risk control functions.

[0027] As shown in Figure 1 , the risk control method includes the following steps:

[0028] Step 101, in response to receiving a service request sent by a target object, obtain at least one target risk control model adapted to the service request.

[0029] To improve the accuracy and applicability of risk control, as a possible implementation, when receiving a service request sent by a target object (such as a customer or an enterprise), it is necessary to obtain the specific nature and requirements of the request, and based on the specific nature and requirements of the request, select one or more target risk control models that are most suitable for the current service request from multiple set candidate risk control models.

[0030] In the embodiments of the present disclosure, the service request is parsed to obtain the content of the service request. The content of the service request includes, but is not limited to, key information such as transaction type, amount, and currency type involved. Furthermore, according to the content of the service request, the most matching target risk control model is searched in the pre-established risk control model library. The service request may be a foreign exchange transaction request, a loan service request, etc.

[0031] Step 102, for any target risk control model, determine at least one target risk characteristic factor associated with the target object from multiple candidate risk characteristic factors of any target risk control model.

[0032] To improve the flexibility of risk control, in the embodiments of the present disclosure, at least one target risk characteristic factor most relevant to the target object is selected from multiple candidate risk characteristic factors of each target risk control model, where the candidate risk characteristic factor refers to various attributes or indicators used to measure and predict the potential risks of specific events or behaviors during risk assessment. For example, the candidate risk characteristic factors include: customer-related information (credit score, transaction history, authentication status), transaction-related information (transaction amount, transaction frequency, transaction time), market factors (exchange rate fluctuations, interest rate changes), etc.

[0033] Step 103: Determine the risk score of the target business corresponding to the business request of the target object according to at least one target risk characteristic factor.

[0034] To improve the accuracy of risk assessment, in the embodiments of the present disclosure, based on at least one target risk characteristic factor, risk assessment is performed on the target business corresponding to the business request of the target object to obtain a risk score. It should be noted that when the business request is a foreign exchange transaction request, the target business corresponding to the business request can be a foreign exchange transaction business; when the business request is a loan business request, the target business can be a loan business.

[0035] Step 104: Respond to the risk score being greater than the risk score threshold, and perform risk control on the business request.

[0036] To improve the accuracy of risk control, in the embodiments of the present disclosure, when the risk score is greater than the risk score threshold, it indicates that the business request has a relatively high potential risk, and risk control needs to be performed on the business request.

[0037] In summary, by adapting different target risk control models to different business requests, selecting the target risk characteristic factors associated with the target object from multiple candidate risk characteristic factors of any target risk control model, determining the risk score of the target business corresponding to the business request of the target object according to the target risk characteristic factors, and performing risk control on the business request when the risk score is greater than the risk score threshold, it realizes dynamic adjustment of the target risk control model and the risk characteristic factors of the target risk control model according to different target objects and business requests, improves the flexibility and accuracy of risk control, and avoids separately developing or adapting a new risk control model for each new business request. Instead, a suitable model is selected from the existing model library for adjustment and use, thereby significantly improving the processing speed and efficiency.

[0038] To clearly illustrate how the above embodiments determine the risk score of the target business corresponding to the business request of the target object according to at least one target risk characteristic factor, the present disclosure proposes another risk control method.

[0039] Figure 2 It is a schematic flowchart of the risk control method shown in the second embodiment of the present disclosure.

[0040] As Figure 2 shown, the risk control method includes the following steps:

[0041] Step 201, in response to receiving a service request sent by a target object, obtain at least one target risk control model adapted to the service request.

[0042] Step 202, for any target risk control model, determine at least one target risk characteristic factor associated with the target object from multiple candidate risk characteristic factors of any target risk control model.

[0043] Step 203, for any target risk characteristic factor, obtain a list of transaction rules adapted to any target risk characteristic factor.

[0044] In order to improve the comprehensiveness of risk assessment, in the embodiments of the present disclosure, for each target risk characteristic factor, obtain a list of transaction rules adapted to it to achieve refined management of each risk dimension. For example, taking the target risk characteristic factor as "transaction amount" as an example, the list of transaction rules adapted to this target risk characteristic factor includes, but is not limited to, verification requirements for large transactions, daily transaction limits, etc.

[0045] Step 204, sequentially traverse each transaction rule in the list of transaction rules, and match the target transaction data associated with the target object with the currently traversed transaction rule to obtain a rule matching result.

[0046] In order to improve the accuracy and comprehensiveness of risk assessment, in the embodiments of the present disclosure, compare each transaction rule with the actual transaction data (such as historical transaction records, account information, etc.) related to the target object to obtain the rule matching result of this transaction rule. For example, if a certain transaction rule stipulates that the single transaction amount shall not exceed a certain amount, then check whether the actual amount of the target transaction meets this requirement; through the above comparison, generate a rule matching result for each transaction rule, where the rule matching result can be a Boolean value (such as, True or False), indicating whether this transaction meets the requirements of this transaction rule.

[0047] Step 205, according to the rule matching results corresponding to each transaction rule, determine the risk score of the target business corresponding to the service request of the target object.

[0048] In order to improve the objectivity of risk assessment, in the embodiments of the present disclosure, based on the rule matching results of all transaction rules, perform a summary analysis on these rule matching results to calculate a comprehensive risk score.

[0049] Step 206: In response to the risk score being greater than the risk score threshold, perform risk control on the service request.

[0050] It should be noted that the execution processes of steps 201 to 202 and step 206 can be implemented in any of the ways in the various embodiments of the present disclosure. The embodiments of the present disclosure do not make any limitations in this regard and will not be elaborated further.

[0051] In summary, by obtaining the corresponding transaction rule list for each target risk characteristic factor, and matching the transaction data of the target object with these transaction rules one by one, and finally determining the risk score of the service request according to the rule matching result, automatic, accurate and flexible risk assessment is achieved, manual intervention is reduced, and the processing efficiency and response speed are improved; moreover, determining the risk score of the service request based on the rule matching results of each transaction rule improves the transparency and compliance of the entire risk assessment process, and ensures the consistency and objectivity of the risk assessment.

[0052] To clearly illustrate how to determine the risk score of the target service corresponding to the service request of the target object according to the rule matching results corresponding to each transaction rule in the above embodiments, the present disclosure proposes another risk control method.

[0053] Figure 3 It is a schematic flowchart of the risk control method shown in the third embodiment of the present disclosure.

[0054] As Figure 3 shown, the risk control method includes the following steps:

[0055] Step 301: In response to receiving a service request sent by a target object, obtain at least one target risk control model adapted to the service request.

[0056] Step 302: For any target risk control model, determine at least one target risk characteristic factor associated with the target object from multiple candidate risk characteristic factors of any target risk control model.

[0057] Step 303: For any target risk characteristic factor, obtain a transaction rule list adapted to any target risk characteristic factor.

[0058] Step 304: Traverse each transaction rule in the transaction rule list in sequence, and match the target transaction data associated with the target object with the currently traversed transaction rule to obtain a rule matching result.

[0059] To improve the accuracy of risk assessment, as a possible implementation manner, by traversing multiple transaction sub-rules corresponding to each transaction rule, the target transaction data associated with the target object is refinedly matched with the currently traversed transaction rule.

[0060] In an embodiment of the present disclosure, multiple trading sub-rules under the currently traversed trading rule are obtained; based on the multiple trading sub-rules and target trading data, a rule matching result corresponding to the currently traversed trading rule is determined; that is, when processing a trading rule, the trading rule is broken down into multiple trading sub-rules, and based on these trading sub-rules and the target trading data, the rule matching result is determined.

[0061] It should be noted that each trading sub-rule represents a specific aspect or condition in the corresponding trading rule. For example, a trading rule is "the transaction must meet certain standards", which can be broken down into multiple sub-rules such as "the amount of a single transaction shall not exceed X yuan" and "the two parties to the transaction are not on a certain list"; another example is that a trading rule is "the time difference between the account opening date and the first transaction date of the target object's account is no more than 180 days", then the sub-rules corresponding to this trading rule can be broken down into "query whether the target object has an account", "if there is an account, query the account opening time and the first transaction date of the account", "calculate the time difference between the account opening date and the first transaction date of the account", and "whether the time difference is greater than 180 days", etc.

[0062] It should be noted that, as an example, the process of determining a rule matching result corresponding to the currently traversed trading rule based on multiple trading sub-rules and target trading data is as follows:

[0063] (1) In response to the existence of a first trading sub-rule of the conditional type among the multiple trading sub-rules, determine whether there is first trading data in the target trading data that satisfies the first trading sub-rule;

[0064] In an embodiment of the present disclosure, the first trading sub-rule of the conditional type refers to a trading sub-rule among the multiple trading sub-rules that has a set condition or standard. For example, "the amount of a single foreign exchange transaction must exceed 5000 US dollars" is a first trading sub-rule of the conditional type. Furthermore, traverse each transaction in the target trading data, calculate its transaction amount, and compare it with the threshold of 5000 US dollars. If at least one piece of transaction data that satisfies the first trading sub-rule (i.e., the transaction amount exceeds 5000 US dollars) is found, it is considered that there is first trading data that satisfies the first trading sub-rule; if no qualified transaction data is found, it indicates that there is no first trading data that satisfies the first trading sub-rule.

[0065] (2) If so, obtain second trading data in the target trading data that is associated with other trading sub-rules; where the other trading sub-rules are trading sub-rules other than the first trading sub-rule among the multiple trading sub-rules;

[0066] Furthermore, when there is first transaction data that satisfies the first transaction sub-rule of the conditional type in the target transaction data associated with the target object, obtain the second transaction data associated with other transaction sub-rules in the target transaction data. For example, taking the transaction rule as "the time difference between the account opening date of the target object's account and the first transaction date of the account is no more than 180 days" as an example, the first transaction sub-rule is "query whether there is an account for the target object", and other transaction sub-rules are "when there is an account, query the account opening time and the first transaction date of the account", "calculate the time difference between the account opening date and the first transaction date of the account", and "whether the time difference is greater than 180 days", etc. The data in the target transaction data associated with the target object that satisfies the first transaction sub-rule is the first transaction data, and the data in the target transaction data associated with other transaction sub-rules is the second transaction data.

[0067] (3) Determine the rule matching result corresponding to the currently traversed transaction rule according to the first transaction data and the second transaction data.

[0068] For example, taking the first transaction data as the account associated with the target object, and the second transaction data as the account opening time of the account and the first transaction time under the account. If the time difference between the account opening time and the first transaction time of the account is greater than 180 days, determine that the rule matching result corresponding to the currently traversed transaction rule is TRUE. If the time difference between the account opening time and the first transaction time of the account is less than or equal to 180 days, determine that the rule matching result corresponding to the currently traversed transaction rule is FALSE.

[0069] In addition, it should also be noted that in order to improve the accuracy and traceability of the rule matching results corresponding to each transaction rule, in the embodiments of the present disclosure, before matching the target transaction data associated with the target object with the currently traversed transaction rule, a context object is created, where the context object is used to record data during the process of matching the target transaction data associated with the target object with the currently traversed transaction rule.

[0070] Step 305, determine the risk score corresponding to any target risk characteristic factor according to the rule matching results corresponding to each transaction rule.

[0071] In order to improve the comprehensiveness of risk assessment, as a possible implementation manner, for each target risk characteristic factor, it is comprehensively determined based on the rule matching results corresponding to multiple transaction rules corresponding to the target risk characteristic factor.

[0072] In an embodiment of the present disclosure, a Boolean expression matching multiple trading rules is obtained; the Boolean expression is used to perform a Boolean operation on the rule matching results corresponding to each trading rule to obtain an overall matching result corresponding to the multiple trading rules; according to the overall matching result, a risk score corresponding to any target risk characteristic factor is queried.

[0073] That is to say, for each target risk characteristic factor, the multiple trading rules correspond to a preset Boolean expression. Using this Boolean expression, the rule matching results corresponding to each trading rule are combined and calculated to obtain a comprehensive overall matching result. This overall matching result can reflect the overall compliance situation of all relevant trading rules. Based on this overall matching result, the risk score corresponding to any target risk characteristic factor can be queried. For example, if the overall matching result is TRUE, the risk score of the corresponding target risk characteristic factor is 5 points; for another example, if the overall matching result is FALSE, the risk score of the corresponding target risk characteristic factor is 2 points.

[0074] Step 306: Determine the risk score of the target service corresponding to the service request of the target object according to the risk scores corresponding to each risk characteristic factor.

[0075] As an example, the risk scores corresponding to each risk characteristic factor are accumulated to obtain the risk score of the target service corresponding to the service request of the target object.

[0076] As another example, obtain the risk weights of each risk characteristic factor, and based on each risk weight, perform a weighted sum of the risk scores corresponding to each target risk characteristic factor to obtain the risk score of the target service corresponding to the service request of the target object.

[0077] Step 307: In response to the risk score being greater than the risk score threshold, perform risk control on the service request.

[0078] It should be noted that the execution processes of steps 301 to 303 and step 307 can be implemented in any one of the embodiments of the present disclosure respectively. The embodiments of the present disclosure do not make any limitations in this regard and will not be elaborated further.

[0079] In summary, by sequentially traversing each trading rule in the trading rule list and matching the target trading data associated with the target object with the currently traversed trading rule, a rule matching result is obtained; according to the rule matching results corresponding to each trading rule, the risk score corresponding to any target risk characteristic factor is determined, and according to the risk scores corresponding to each risk characteristic factor, the risk score of the target business corresponding to the business request of the target object is determined. Thus, through the fine matching of each trading rule and its trading sub-rules, accurate risk assessment of business requests can be achieved. At the same time, when the market environment or business requirements change, only the relevant rules in the trading rule list need to be updated, without changing the entire risk control model, improving the flexibility of risk control.

[0080] To clearly illustrate how, in the above embodiments, at least one target risk characteristic factor associated with the target object is determined from multiple candidate risk characteristic factors of any target risk control model, the present disclosure proposes another risk control method.

[0081] Figure 4 It is a schematic flowchart of the risk control method shown in the fourth embodiment of the present disclosure.

[0082] As Figure 4 shown, the risk control method includes the following steps:

[0083] Step 401, in response to receiving a business request sent by a target object, obtain at least one target risk control model adapted to the business request.

[0084] Step 402, parse the business request to obtain attribute information associated with the target object.

[0085] To improve the effectiveness and pertinence of risk control, as a possible implementation, according to the characteristics of each business request, a matching risk assessment criterion is dynamically selected.

[0086] In the embodiments of the present disclosure, by parsing the business request, attribute information associated with the target object is obtained, and based on the attribute information, an adapted target risk characteristic factor is selected. Among them, the attribute information associated with the target object includes the identity information, financial information, credit history of the target object, and other factors that may affect risk assessment.

[0087] Step 403, according to the attribute information, obtain at least one target risk characteristic factor from multiple candidate risk control factors of any target risk control model.

[0088] In order to effectively reflect the true risk status of a business request, in the embodiments of the present disclosure, according to the attribute information parsed from the business request, target risk characteristic factors with a relatively high degree of relevance to the risk assessment of the target object are selected from multiple candidate risk control factors of the target risk control model.

[0089] Step 404: Determine the risk score of the target object for handling the target business corresponding to the business request according to at least one target risk characteristic factor.

[0090] Step 405: In response to the risk score being greater than the risk score threshold, perform risk control on the business request.

[0091] It should be noted that the execution processes of Step 401, Step 404 to 405 can be implemented in any one of the embodiments of the present disclosure respectively. The embodiments of the present disclosure do not make any limitations in this regard and will not be elaborated further.

[0092] In summary, by parsing the business request to obtain the attribute information associated with the target object; according to the attribute information, obtaining at least one target risk characteristic factor from multiple candidate risk control factors of any target risk control model, thereby, the target risk characteristic factors applicable to the target object can be accurately identified, so that the risk assessment is more in line with the actual situation and the accuracy of risk control is improved.

[0093] Based on any embodiment of the present disclosure, as Figure 5 shown, the risk control method of the embodiments of the present disclosure can also be implemented based on the following steps:

[0094] Step 1: Obtain a model (target risk control model) according to the customer's public / private attribute.

[0095] Step 2: Traverse the model list obtained in Step 1.

[0096] Step 3: Create a context object before execution to save the process data of model calculation.

[0097] Step 4: Obtain the list of characteristic factors of the current model (i.e., at least one target risk characteristic factor).

[0098] Step 5: Traverse the list of characteristic factors obtained in Step 4.

[0099] Step 6: Obtain the list of rules of the current characteristic factor (transaction rule list).

[0100] Step 7: Traverse the list of rules obtained in Step 6.

[0101] Step 8: Obtain the list of calculation sub-rules (transaction sub-rules) of the current rule.

[0102] Step 9. Traverse the list of calculation sub-rules obtained in Step 8;

[0103] Step 10. According to the type of the calculation sub-rule (transaction sub-rule), call different implementation logics and store the results in the context object, specifically including:

[0104] (1) Sub-rule of condition type: used to retrieve values from the context or obtain configured data in the external database. If the execution result is empty, directly exit the operation logic of the current rule and mark the calculation result of the current rule as FALSE; for example, the sub-rule of condition type is "query whether the target object has an account".

[0105] (2) Sub-rule of value-taking type: used to retrieve values from the context or obtain configured data in the external database; for example, the sub-rule of value-taking type is "when there is an account, query the account opening time and the first transaction date of the account".

[0106] (3) Sub-rule of operation type: used to convert or calculate specified data; for example, "calculate the time difference between the account opening date and the first transaction date of the account".

[0107] (4) Sub-rule of hit type: judge whether the current rule is hit according to the configured conditions; for example, the sub-rule of hit type is "whether the time difference is greater than 180 days".

[0108] (5) Sub-rule of output type: used to mark the output data of the current rule, and the output data will be used by other feature factors or other rules, that is, obtain and output the data used by the next rule or feature factor;

[0109] (6) Loop sub-rule: used to mark the list of calculation sub-rules that need to be looped for operation, that is, used for the loop of calculation sub-rules.

[0110] Step 11. After all calculation sub-rules of the current rule are processed, record the calculation result and output content of the current rule, and clear the processed result data of all sub-rules under the current rule cached in the context object;

[0111] Step 12. After all rules under the current feature factor are calculated, judge whether the current feature factor is hit according to the configured hit conditions;

[0112] Step 13. After all feature factors of the current model are calculated, the system will summarize the scores of the feature factors. If the score is greater than the warning score of the model, then judge whether the set warning conditions are met. If the set warning conditions are met, automatically initiate a supervision and rectification transaction.

[0113] Corresponding to the risk control method provided in the above embodiments, the present disclosure also provides a risk control device. Since the risk control device provided in the embodiments of the present disclosure corresponds to the risk control method provided in the above embodiments, the implementation manners of the risk control method are also applicable to the risk control device provided in the embodiments of the present disclosure, and will not be described in detail in the embodiments of the present disclosure.

[0114] Figure 6 It is a schematic structural diagram of the risk control device shown in the fifth embodiment of the present disclosure.

[0115] As Figure 6 shown, the risk control device 600 includes: an acquisition module 610, a first determination module 620, a second determination module 630, and a control module 640.

[0116] Among them, the acquisition module 610 is configured to, in response to receiving a service request sent by a target object, acquire at least one target risk control model adapted to the service request; the first determination module 620 is configured to, for any target risk control model, determine at least one target risk characteristic factor associated with the target object from a plurality of candidate risk characteristic factors of any target risk control model; the second determination module 630 is configured to determine a risk score of the target object for handling the target service corresponding to the service request according to at least one target risk characteristic factor; the control module is configured to, in response to the risk score being greater than a risk score threshold, perform risk control on the service request.

[0117] As a possible implementation manner of the embodiment of the present disclosure, the second determination module 630 is configured to, for any target risk characteristic factor, acquire a transaction rule list adapted to any target risk characteristic factor; sequentially traverse each transaction rule in the transaction rule list, and match the target transaction data associated with the target object with the currently traversed transaction rule to obtain a rule matching result; determine a risk score of the target object for handling the target service corresponding to the service request according to the rule matching results corresponding to each transaction rule.

[0118] As a possible implementation manner of the embodiment of the present disclosure, the second determination module 630 is configured to determine a risk score corresponding to any target risk characteristic factor according to the rule matching results corresponding to each transaction rule; determine a risk score of the target object for handling the target service corresponding to the service request according to the risk scores corresponding to each risk characteristic factor.

[0119] As a possible implementation manner of the embodiment of the present disclosure, the second determination module 630 is configured to acquire a Boolean expression matching a plurality of transaction rules; use the Boolean expression to perform a Boolean operation on the rule matching results corresponding to each transaction rule to obtain an overall matching result corresponding to the plurality of transaction rules; query a risk score corresponding to any target risk characteristic factor according to the overall matching result.

[0120] As a possible implementation manner of an embodiment of the present disclosure, the second determination module 630 is configured to accumulate the risk scores corresponding to each risk characteristic factor to obtain the risk score of the target service corresponding to the service request of the target object; or, obtain the risk weights of each risk characteristic factor, and based on each risk weight, perform a weighted sum of the risk scores corresponding to each target risk characteristic factor to obtain the risk score of the target service corresponding to the service request of the target object.

[0121] As a possible implementation manner of an embodiment of the present disclosure, the second determination module 630 is configured to obtain a plurality of transaction sub-rules under the currently traversed transaction rule; and determine a rule matching result corresponding to the currently traversed transaction rule based on the plurality of transaction sub-rules and the target transaction data.

[0122] As a possible implementation manner of an embodiment of the present disclosure, the second determination module 630 is configured to, in response to the existence of a first transaction sub-rule of a conditional type among the plurality of transaction sub-rules, determine whether there is first transaction data in the target transaction data that satisfies the first transaction sub-rule; if so, obtain second transaction data associated with other transaction sub-rules in the target transaction data; where the other transaction sub-rules are transaction sub-rules other than the first transaction sub-rule among the plurality of transaction sub-rules; and determine a rule matching result corresponding to the currently traversed transaction rule according to the first transaction data and the second transaction data.

[0123] As a possible implementation manner of an embodiment of the present disclosure, the risk control device 600 further includes: a creation module.

[0124] Wherein, the creation module is configured to create a context object; wherein, the context object is used to record the rule matching results corresponding to each transaction rule.

[0125] As a possible implementation manner of an embodiment of the present disclosure, the first determination module 620 is configured to parse the service request to obtain attribute information associated with the target object; and obtain at least one target risk characteristic factor from a plurality of candidate risk control factors of any target risk control model according to the attribute information.

[0126] The risk control device according to the embodiments of the present disclosure adapts different target risk control models for different service requests, selects target risk characteristic factors associated with the target object from multiple candidate risk characteristic factors of any target risk control model, determines the risk score of the target service corresponding to the service request of the target object according to the target risk characteristic factors, and performs risk control on the service request when the risk score is greater than the risk score threshold. It realizes the dynamic adjustment of the target risk control model and the risk characteristic factors of the target risk control model according to different target objects and service requests, improves the flexibility and accuracy of risk control, and avoids developing or adapting a new risk control model for each new service request. Instead, it selects a suitable model from the existing model library for adjustment and use, thereby significantly improving the processing speed and efficiency.

[0127] In an exemplary embodiment, an electronic device is also proposed.

[0128] Wherein, the electronic device includes:

[0129] A processor;

[0130] A memory for storing instructions executable by the processor;

[0131] Wherein, the processor is configured to execute instructions to implement the risk control method proposed in any of the foregoing embodiments.

[0132] As an example, Figure 7 is a schematic structural diagram of the electronic device 700 shown in an exemplary embodiment of the present disclosure. As Figure 7 shown, the above-mentioned electronic device 700 may further include:

[0133] A memory 710 and a processor 720, a bus 730 connecting different components (including the memory 710 and the processor 720). The memory 710 stores a computer program, and when the processor 720 executes the program, the risk control method described in the embodiments of the present disclosure is implemented.

[0134] The bus 730 represents one or more of several types of bus structures, including a memory bus or a memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any bus structure in multiple bus structures. For example, these architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

[0135] The electronic device 700 typically includes a variety of electronically readable media. These media can be any available media accessible to the electronic device 700, including volatile and non-volatile media, removable and non-removable media.

[0136] The memory 710 may also include computer system readable media in the form of volatile memory, such as random access memory (RAM) 740 and / or cache memory 750. The server 700 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 760 can be used for reading and writing on non-removable, non-volatile magnetic media ( Figure 7 not shown, typically referred to as a "hard disk drive"). Although Figure 7 not shown in the figure, a disk drive for reading and writing on a removable non-volatile disk (such as a "floppy disk"), and an optical disk drive for reading and writing on a removable non-volatile optical disk (such as a CD-ROM, DVD-ROM or other optical media) can be provided. In these cases, each drive can be connected to the bus 730 through one or more data media interfaces. The memory 710 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the embodiments of the present disclosure.

[0137] A program / utility 780 having a set (at least one) of program modules 770 can be stored, for example, in the memory 710. Such program modules 770 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include the implementation of a network environment. The program modules 770 generally execute the functions and / or methods in the embodiments described in the present disclosure.

[0138] The electronic device 700 can also communicate with one or more external devices 790 (such as a keyboard, a pointing device, a display 791, etc.), and can also communicate with one or more devices that enable a user to interact with the electronic device 700, and / or communicate with any device that enables the electronic device 700 to communicate with one or more other computing devices (such as a network card, a modem, etc.). Such communication can be carried out through the input / output (I / O) interface 792. Moreover, the electronic device 700 can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through the network adapter 793. As shown in the figure, the network adapter 793 communicates with other modules of the electronic device 700 through the bus 730. It should be understood that although not shown in the figure, other hardware and / or software modules can be used in combination with the electronic device 700, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0139] The processor 720 executes various functional applications and data processing by running the programs stored in the memory 710.

[0140] It should be noted that for the implementation process and technical principle of the electronic device in this embodiment, refer to the foregoing explanation of the risk control method in the embodiments of the present disclosure, and details will not be elaborated here.

[0141] In an exemplary embodiment, there is also provided a computer-readable storage medium including instructions, such as a memory including instructions. The above instructions can be executed by the processor of the electronic device to complete the risk control method proposed in any of the foregoing embodiments. Optionally, the computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0142] In an exemplary embodiment, there is also provided a computer program product including a computer program / instructions, characterized in that when the computer program / instructions are executed by a processor, the risk control method proposed in any of the foregoing embodiments is implemented.

[0143] Those skilled in the art will readily think of other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variations, uses, or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common general knowledge or conventional technical means in the technical field not disclosed in the present disclosure. The specification and embodiments are only regarded as exemplary, and the true scope and spirit of the present disclosure are pointed out by the following claims.

[0144] It should be understood that the present disclosure is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present disclosure is only limited by the appended claims.

Claims

1. A risk control method, characterized in that: include: In response to receiving a business request sent by a target object, obtaining at least one target risk control model adapted to the business request; For any target risk control model, determining at least one target risk characteristic factor associated with the target object from a plurality of candidate risk characteristic factors of the any target risk control model; Determining, according to the at least one target risk characteristic factor, a risk score of a target business corresponding to the business request handled by the target object; In response to the risk score being greater than a risk score threshold, risk control is performed on the business request.

2. The method according to claim 1, characterized in that Determining, based on the at least one target risk characteristic factor, a risk score of a target business corresponding to the business request handled by the target object includes: For any target risk characteristic factor, obtaining a list of trading rules adapted to the any target risk characteristic factor; Traversing each transaction rule in the transaction rule list in sequence, and matching the target transaction data associated with the target object with the currently traversed transaction rule to obtain a rule matching result; According to the rule matching results corresponding to the transaction rules, a risk score of the target business corresponding to the business request handled by the target object is determined.

3. The method according to claim 2, characterized in that Determining the risk score of the target business corresponding to the business request handled by the target object according to the rule matching results corresponding to each of the transaction rules includes: Determine the risk score corresponding to any target risk characteristic factor according to the rule matching results corresponding to each of the transaction rules; The risk score of the target business corresponding to the business request handled by the target object is determined according to the risk score corresponding to each of the risk characteristic factors.

4. The method according to claim 3, characterized in that Determining the risk score corresponding to any target risk characteristic factor according to the rule matching results corresponding to each of the transaction rules includes: Obtaining a Boolean expression matching the plurality of transaction rules; Using the Boolean expression, a Boolean operation is performed on the rule matching results corresponding to each of the transaction rules to obtain an overall matching result corresponding to the multiple transaction rules; According to the overall matching result, the risk score corresponding to any target risk characteristic factor is queried.

5. The method according to claim 3, characterized in that: Determining the risk score of the target business corresponding to the business request handled by the target object according to the risk score corresponding to each of the risk characteristic factors includes: Accumulating the risk scores corresponding to the risk characteristic factors to obtain the risk score of the target business corresponding to the business request handled by the target object; or, The risk weight of each of the risk characteristic factors is obtained, and based on each of the risk weights, the risk scores corresponding to each of the target risk characteristic factors are weighted and summed to obtain the risk score of the target business corresponding to the business request handled by the target object.

6. The method according to claim 2, characterized in that The step of matching the target transaction data associated with the target object with the currently traversed transaction rules to obtain a rule matching result includes: Get multiple transaction sub-rules under the currently traversed transaction rule; Based on the multiple transaction sub-rules and the target transaction data, a rule matching result corresponding to the currently traversed transaction rule is determined.

7. The method according to claim 6, characterized in that The determining, based on the multiple transaction sub-rules and the target transaction data, a rule matching result corresponding to the currently traversed transaction rule includes: In response to a first transaction sub-rule of a conditional type existing in the plurality of transaction sub-rules, determining whether there is first transaction data satisfying the first transaction sub-rule in the target transaction data; If yes, obtain second transaction data associated with other transaction sub-rules in the target transaction data; wherein the other transaction sub-rules are transaction sub-rules other than the first transaction sub-rule in the multiple transaction sub-rules; A rule matching result corresponding to the currently traversed transaction rule is determined according to the first transaction data and the second transaction data.

8. The method according to claim 2, characterized in that: Before sequentially traversing each transaction rule in the transaction rule list, the method further includes: Create a context object; The context object is used to record the rule matching results corresponding to each of the transaction rules.

9. The method according to claim 1, characterized in that: The step of determining, for any target risk control model, at least one target risk characteristic factor associated with the target object from a plurality of candidate risk characteristic factors of the target risk control model comprises: Parsing the service request to obtain attribute information associated with the target object; According to the attribute information, the at least one target risk characteristic factor is obtained from a plurality of candidate risk control factors of any target risk control model.

10. A risk control device, characterized in that: include: An acquisition module, configured to acquire, in response to receiving a business request sent by a target object, at least one target risk control model adapted to the business request; A first determination module is used to determine, for any target risk control model, at least one target risk characteristic factor associated with the target object from a plurality of candidate risk characteristic factors of the any target risk control model; A second determination module is used to determine the risk score of the target business corresponding to the business request handled by the target object according to the at least one target risk characteristic factor; A control module is used to perform risk control on the business request in response to the risk score being greater than a risk score threshold.

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