Financial lease risk assessment method and system

Abstract

The invention discloses a financing lease risk assessment method and system. The method comprises the steps of obtaining financing lease information of a customer; inputting the financing lease information into a risk rating model, and determining risk rating data; mapping the risk rating data to a predefined rule model, and adding the mapped rule model to a logic method set to obtain a target logic method set; verifying the target logic method set; if the target logic method set passes the verification, performing risk rule assessment on the risk rating data according to a rule model in the target logic method set to obtain a risk result; and determining a financing lease evaluation result of the customer according to the risk result. Therefore, risk identification is performed on the financing and leasing information of the customer through the plurality of risk rules in the rule model, the potential risk of the financing and leasing information can be effectively and accurately identified, and meanwhile, a decision scheme can be made for the customer according to the risk of the customer, so that the decision accuracy is effectively improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method and system for evaluating financial leasing risks. Background Art

[0002] In order to improve the decision-making efficiency and accuracy of financial leasing companies and enhance the company's market competitiveness, it is necessary to assess the risks in customer leasing business, which will help reduce the risk level of financial leasing companies and ensure the safety and stability of the company's assets.

[0003] In existing technologies, the risk of customer leasing business is mainly assessed through logistic regression algorithms or scoring card rules. However, the existing assessment methods cannot assess more complex business scenarios, resulting in inaccurate risk assessment, which in turn affects the accuracy of corporate decision-making. Summary of the invention

[0004] Based on the above-mentioned deficiencies of the prior art, the present application provides a method and system for assessing financial leasing risks to solve the problem that the prior art is not accurate enough in assessing leasing risks.

[0005] In order to achieve the above objectives, this application provides the following technical solutions:

[0006] The first aspect of the present application provides a method for assessing financial leasing risks, comprising:

[0007] Obtain the client's financial leasing information;

[0008] Inputting the financial lease information into the risk rating model to determine the risk rating data;

[0009] Mapping the risk rating data to a predefined rule model, and adding the mapped rule model to a logic method set to obtain a target logic method set; wherein the rule model is composed of a plurality of preset risk rules; and the logic method set refers to a method set for rating processing of the risk rating model;

[0010] Verifying the target logic method set;

[0011] If the target logic method set passes the verification, then the risk rating data is evaluated according to the rule model in the target logic method set to obtain a risk result;

[0012] Based on the risk results, the financial lease assessment results of the customer are determined.

[0013] Optionally, in the above-mentioned method for assessing financial leasing risk, before mapping the risk rating data to a predefined rule model, the method further includes:

[0014] Adding the risk rating data to a pre-built data intermediate result set;

[0015] The step of mapping the risk rating data to a predefined rule model includes:

[0016] The risk rating data is extracted from the data intermediate result set and mapped into a predefined rule model.

[0017] Optionally, in the above-mentioned method for assessing the risk of financial leasing, the risk rating model includes a preliminary review model, a review model, and a loan model, and the step of inputting the financial leasing information into the risk rating model to determine the risk rating data includes:

[0018] Extracting the basic information and asset information of the customer from the financial leasing information;

[0019] Inputting the basic information into the preliminary review model to obtain a preliminary review result;

[0020] Inputting the asset information into the review model to obtain a review result;

[0021] The preliminary review result and the review result are combined to obtain loan conditions;

[0022] Input the loan conditions into the loan model to obtain a loan result, and collate the preliminary review result, the review result, and the loan result to obtain a final result;

[0023] The final result is determined as risk rating data.

[0024] Optionally, in the above-mentioned method for assessing financial leasing risk, the verifying the target logic method set includes:

[0025] Verify whether the risk rating data in the target logical method set is empty, or detect whether the risk rating data exists in the target logical method set;

[0026] If the risk rating data in the target logical method set is not empty, or the risk rating data exists in the target logical method set, determining that the target logical method set passes the verification;

[0027] If the risk rating data in the target logical method set is empty, or the risk rating data does not exist in the target logical method set, it is determined that the target logical method set has failed the verification.

[0028] Optionally, in the above-mentioned method for assessing the risk of financial leasing, the rule model includes customer group determination, geographic location calculation, income calculation, liability calculation, basic blacklisting admission rules, customer group admission, and quota calculation, and the risk rating data is evaluated according to the rule model in the target logic method set to obtain a risk result, including:

[0029] extracting the risk rating data from the target logical method set;

[0030] According to the customer group determination, geographic location calculation, income calculation, debt calculation, basic blacklisting admission rules, customer group admission, and quota calculation in the target logic method set, the risk rating data is evaluated in turn for risk rules to obtain multiple indicator results;

[0031] The results of each indicator are integrated to obtain the risk result.

[0032] Optionally, in the above-mentioned method for assessing financial leasing risk, determining the financial leasing assessment result of the customer according to the risk result includes:

[0033] Record the risk result in a log to obtain a target log;

[0034] Traversing the target log;

[0035] When each risk indicator in the risk result recorded in the target log is found to meet the preset risk indicator, the financial lease assessment result of the customer is determined to be passed;

[0036] When each risk indicator in the risk result recorded in the target log does not meet the preset risk indicator, it is determined that the financial lease assessment result of the customer is failed;

[0037] When there are multiple risk indicators in the risk results of the target log records that do not meet the preset risk indicators, the number of risk indicators that do not meet the preset risk indicators is obtained;

[0038] Determine whether the number of risk indicators is greater than a preset number threshold;

[0039] If the number of risk indicators is greater than the preset number threshold, the financial lease assessment result of the customer is determined to be unsuccessful;

[0040] If the number of risk indicators is not greater than the preset threshold, the financial leasing assessment result of the customer is determined to be passed.

[0041] The second aspect of the present application provides a financial leasing risk assessment system, including:

[0042] An information acquisition unit, used to acquire the customer's financial leasing information;

[0043] An information input unit, used to input the financial leasing information into a risk rating model to determine risk rating data;

[0044] A mapping unit, used to map the risk rating data to a predefined rule model, and add the mapped rule model to a logic method set to obtain a target logic method set; wherein the rule model is composed of a plurality of preset risk rules; and the logic method set refers to a method set for rating processing of the risk rating model;

[0045] A verification unit, used to verify the target logic method set;

[0046] An evaluation unit, configured to, if the target logic method set passes the verification, perform risk rule evaluation on the risk rating data according to the rule model in the target logic method set to obtain a risk result;

[0047] A result determination unit is used to determine the financial lease assessment result of the customer according to the risk result.

[0048] Optionally, the above-mentioned financial leasing risk assessment system further includes:

[0049] An adding unit, used for adding the risk rating data to a pre-built data intermediate result set;

[0050] Wherein, the mapping unit includes:

[0051] The extraction unit is used to extract the risk rating data from the data intermediate result set and map it into a predefined rule model.

[0052] Optionally, in the above-mentioned method for assessing the risk of financial leasing, the risk rating model includes a preliminary review model, a review model, and a loan model, and the information input unit includes:

[0053] An information extraction unit, used to extract the basic information and asset information of the customer from the financial lease information;

[0054] A first input unit, used to input the basic information into the preliminary review model to obtain a preliminary review result;

[0055] A second input unit, used to input the asset information into the review model to obtain a review result;

[0056] A merging unit, used to merge the preliminary review result and the review result to obtain loan conditions;

[0057] A third input unit is used to input the loan conditions into the loan model to obtain a loan result, and to sort out the preliminary review result, the review result, and the loan result to obtain a final result;

[0058] The first determining unit is used to determine the final result as risk rating data.

[0059] Optionally, in the above-mentioned method for assessing financial leasing risk, the verification unit includes:

[0060] A verification subunit, used to verify whether the risk rating data in the target logical method set is empty, or to detect whether the risk rating data exists in the target logical method set;

[0061] A second determination unit, configured to determine that the target logical method set passes verification if the risk rating data in the target logical method set is not empty or the risk rating data exists in the target logical method set;

[0062] The third determination unit is configured to determine that the target logical method set fails the verification if the risk rating data in the target logical method set is empty or the risk rating data does not exist in the target logical method set.

[0063] Optionally, in the above-mentioned method for assessing the risk of financial leasing, the rule model includes customer group determination, geographic location calculation, income calculation, liability calculation, basic blacklisting admission rules, customer group admission, and quota calculation, and the assessment unit includes:

[0064] A data extraction unit, used to extract the risk rating data from the target logical method set;

[0065] An evaluation subunit is used to perform risk rule evaluation on the risk rating data in sequence according to customer group determination, geographic location calculation, income calculation, debt calculation, basic blacklisting admission rules, customer group admission, and quota calculation in the target logic method set to obtain multiple indicator results;

[0066] The integration unit is used to integrate each of the indicator results to obtain a risk result.

[0067] Optionally, in the above-mentioned method for assessing financial leasing risk, the result determination unit includes:

[0068] A recording unit, used for recording the risk result into a log to obtain a target log;

[0069] A traversal unit, used for traversing the target log;

[0070] A fourth determination unit, configured to determine that the financial lease assessment result of the customer is passed when each risk indicator in the risk result recorded in the target log is found to meet a preset risk indicator;

[0071] A fifth determination unit, configured to determine that the financial lease assessment result of the customer is not passed when each risk indicator in the risk result recorded in the target log does not meet the preset risk indicator;

[0072] A quantity acquisition unit, configured to acquire the number of risk indicators that do not meet the preset risk indicators when there are multiple risk indicators in the risk results of the target log records that do not meet the preset risk indicators;

[0073] A threshold determination unit, used to determine whether the number of risk indicators is greater than a preset number threshold;

[0074] A sixth determination unit, configured to determine that the financial lease assessment result of the customer is not passed if the number of risk indicators is greater than a preset number threshold;

[0075] The seventh determination unit is used to determine that the financial leasing evaluation result of the customer is passed if the number of risk indicators is not greater than a preset number threshold.

[0076] The present application provides a method for assessing the risk of a financial lease, which obtains the financial lease information of the customer, then inputs the financial lease information into a risk rating model, determines the risk rating data, maps the risk rating data to a predefined rule model, and adds the mapped rule model to a logic method set to obtain a target logic method set, wherein the rule model is composed of a plurality of preset risk rules, and the logic method set refers to a method set for rating processing of the risk rating model, and then verifies the target logic method set, and if the target logic method set passes the verification, the risk rating data is evaluated according to the rule model in the target logic method set, and a risk result is obtained, and finally the financial lease assessment result of the customer is determined according to the risk result. Thus, through the plurality of risk rules in the rule model, the risk identification of the financial lease information of the customer can be carried out, and the potential risks of the financial lease information can be effectively and accurately identified, and at the same time, a decision plan can be formulated for the customer according to the risk of the customer, thereby effectively improving the accuracy of the decision. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0078] Figure 1 A flowchart of a method for assessing financial leasing risk provided in an embodiment of the present application;

[0079] Figure 2 A flowchart of a method for determining risk rating data provided in another embodiment of the present application;

[0080] Figure 3 A flowchart of a risk rating data verification method provided by another embodiment of the present application;

[0081] Figure 4 A flowchart of a method for obtaining risk results provided in an embodiment of the present application;

[0082] Figure 5 A flow chart of another method for assessing financial leasing risk provided in another embodiment of the present application;

[0083] Figure 6 A schematic diagram of the structure of a financial leasing risk assessment system provided in another embodiment of the present application. DETAILED DESCRIPTION

[0084] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0085] In this application, relational terms such as first and second, etc. are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the statement "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device including the element.

[0086] The present application embodiment provides a method for assessing the risk of a financial lease, such as Figure 1 As shown, the specific steps include:

[0087] S101. Obtain the customer's financial leasing information.

[0088] It is understandable that in order to improve the decision-making efficiency and accuracy of financial leasing companies, it is necessary to assess the potential risks of customers to avoid risks, so as to formulate decision-making plans that meet the needs of customers and effectively enhance the company's market competitiveness. Therefore, it is necessary to obtain the financial leasing information of customers. Among them, financial leasing information can include personal credit reports of the People's Bank of China, provident funds of the People's Bank of China, judicial big data, Pudao, China Unicom, China Telecom, China Mobile, Baihang Baiyun, Rural Credit Bank, Western Data Trading Center, Shanghai Digital Certificate Certification Center and other external information. At the same time, after obtaining the financial leasing information of customers, the financial leasing information can also be stored in the database for subsequent information maintenance.

[0089] Optionally, customers can send financial leasing information through the financial leasing business interface provided by the financial leasing intelligent decision-making system, so that the financial leasing intelligent decision-making system can obtain the financial leasing information for a series of subsequent related risk assessment operations. It should be emphasized that the financial leasing intelligent decision-making system can identify and assess the potential risks in the financial leasing business, such as credit risk, market risk, operational risk, etc. Optionally, by formulating corresponding risk control strategies and emergency plans, it will help reduce the company's risk level, and the financial leasing intelligent decision-making system can monitor and evaluate the value and risk status of assets in real time, discover potential problems in a timely manner and take corresponding measures to ensure the safety and stability of the company's assets.

[0090] It should be noted that the information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.

[0091] S102. Input the financial leasing information into the risk rating model to determine the risk rating data.

[0092] Specifically, before conducting a risk assessment on a customer, the financial leasing intelligent decision-making system will first conduct an initial review, evaluation, and loan risk assessment on the financial leasing information, thereby initially obtaining preliminary risk rating data for the financial leasing information. Therefore, the risk rating model in step S102 includes initial review, evaluation, and loan risk assessment.

[0093] Optionally, in another embodiment of the present application, the risk rating model includes a preliminary review model, a review model, and a loan model, so a specific implementation of step S102 is as follows: Figure 2 As shown, the following steps are included:

[0094] S201. Extract the basic information and asset information of the customer from the financial leasing information.

[0095] It should be noted that the initial review, evaluation, and loan risk assessment only require the customer's basic information and asset information. Therefore, in order to be able to flexibly and conveniently process the basic information and asset information accordingly in the future, it is necessary to extract the customer's basic information and asset information from the financial leasing information in advance.

[0096] It is understandable that basic information refers to the customer's personal information, such as name, job, and home address, etc. Asset information can include the customer's deposits, liabilities, etc. Among them, asset information also includes external information, such as judicial big data, Pudao, China Unicom, China Telecom, China Mobile, Baihang Baiyun, Rural Credit Bank, Western Data Trading Center, Shanghai Digital Certificate Certification Center, etc., so as to carry out blacklisting according to subsequent reviews.

[0097] S202: Input the basic information into the preliminary review model to obtain the preliminary review result.

[0098] It is understandable that the preliminary review stage usually calls the preliminary review model, which may include customer group determination, customer group admission and other rules. These rules are used by the preliminary review model to preliminarily screen and determine whether the applicant meets the most basic loan application conditions. Therefore, the customer's basic information is input into the preliminary review model to obtain the preliminary review result. Among them, the preliminary review result indicates the result of reviewing whether the customer meets the most basic loan application conditions.

[0099] S203: Input the asset information into the review model to obtain the review result.

[0100] Specifically, the review stage will call more complex review models, such as geographic location calculation, income calculation model, debt calculation model, etc. These models are used to more accurately assess the applicant's repayment ability and risk level. Therefore, the review model will decide whether to allow the applicant to apply based on the applicant's asset information and calculate the appropriate loan amount. Therefore, the review result will indicate whether the applicant is allowed to apply for a loan and the amount of the loan allowed for the applicant.

[0101] S204. Combine the preliminary review results and the review results to obtain loan conditions.

[0102] It should be noted that the data needs to be further processed during the loan stage, so the preliminary review results and review results of each stage will be combined as the evaluation conditions for the loan stage.

[0103] S205. Input the loan conditions into the loan model to obtain the loan result, and organize the preliminary review results, review results, and loan results to obtain the final result.

[0104] Specifically, during the loan disbursement stage, the applicant's qualifications will be confirmed again, and the loan disbursement model will be called for final loan approval and decision-making. Therefore, the loan disbursement result indicates the customer's final loan approval and decision. In order to optimize the process, identify and improve potential bottlenecks or problems, and assess loan risks, ensure that the customer's credit status matches the loan conditions, and provide clear basis to help make reasonable loan approval or rejection decisions, it is necessary to organize the preliminary review results, review results, and loan disbursement results to obtain the final result.

[0105] S206. Determine the final result as risk rating data.

[0106] Specifically, each piece of data in the final result is used as risk rating data.

[0107] S103: Map the risk rating data to a predefined rule model, and add the mapped rule model to a logic method set to obtain a target logic method set.

[0108] The rule model is composed of a plurality of preset risk rules. Optionally, the rule model can be divided into a rule set and a score card, wherein the rule set is a plurality of risk rules and the score card is used to judge the risk level.

[0109] In addition, the logical method set refers to the method set of risk rating model rating processing. Specifically, the method set includes data processing path, data processing method, rule name, rule code, rule result, rule failure reason, and reserved information.

[0110] It should be noted that before mapping the risk rating data to the pre-defined rule model, it is necessary to first obtain the configuration rules of the rule model, and then freely add, modify or delete the rule model according to the rule series of risk information descriptions of customers compiled by business personnel based on previous actual financial leasing business processing. For example, if one day the rule is about the number of days overdue for the customer, then the rule model will set the indicator threshold to judge the size of the number of days overdue for the customer and the indicator threshold. The if...else rule can be directly configured on the page according to the risk rules defined by the business for judgment. Then, after the rule model is configured, it is necessary to map the obtained risk rating data to the rule model, and then match the risk rating data to the corresponding rules of the rule model according to the definition of the rule model. Then, the matched rule model is added to the logic method set, so that the logic method set can perform operations such as data processing path, data processing method, rule name, rule code, rule result, reason for rule failure, reserved information, etc. on the matched rule model, so as to obtain the target logic method set, and then the target logic method set can be used to ensure that the mapping result of the risk rating data can effectively support the decision logic.

[0111] S104: Verify the target logic method set.

[0112] It should be noted that in order to ensure the accuracy and reliability of the target logical method set, the target logical method set needs to be verified so as to verify the correctness of the rules, ensure consistency: ensure the consistency and reliability of the logical method set in different situations, and detect anomalies: discover and correct logical loopholes or abnormal situations that may lead to wrong decisions, thereby ensuring the comprehensiveness of the risk assessment process and the quality of decision-making.

[0113] Optionally, in another embodiment of the present application, a specific implementation of step S104 is as follows: Figure 3 As shown, the following steps are included:

[0114] S301. Check whether the risk rating data in the target logical method set is empty, or check whether there is risk rating data in the target logical method set.

[0115] It is understandable that in order for the mapped rule model to accurately reflect the risk rating data and predefined standards, it is necessary to verify whether the risk rating data in the target logical method set is empty, or to detect whether there is risk rating data in the target logical method set, so as to avoid incomplete mapped risk rating data, or to avoid loss of risk rating data during processing, thereby failing to conduct risk assessment on the customer's financial leasing information. Therefore, after obtaining the target logical method set, it is necessary to verify the target logical method set. Therefore, if the risk rating data in the target logical method set is not empty, or there is risk rating data in the target logical method set, it means that the data in the target logical method set is complete, so the next step can be continued, so step S302 is executed.

[0116] Furthermore, if the risk rating data in the target logical method set is empty, or the risk rating data does not exist in the target logical method set, it means that the data in the target logical method set is incomplete, and therefore step S303 is executed.

[0117] S302: Determine whether the target logic method set passes verification.

[0118] Specifically, when the risk rating data in the target logical method set is not empty, or when the risk rating data exists in the target logical method set, step S106 may be executed next.

[0119] S303: Determine whether the target logic method set has failed verification.

[0120] S105: Determine whether the target logic method set passes verification.

[0121] Specifically, if the target logic method set passes the verification, step S106 is executed.

[0122] Optionally, if the target logical method set fails to pass the verification, a verification report can be generated first to explain the reason why the target logical method set failed to be verified, and feedback can be given to the front end so that the front end can arrange for operation and maintenance personnel to correct the target logical method set according to the verification report so that the target logical method set can pass the verification and then continue with the next risk assessment operation.

[0123] S106. According to the rule model in the target logic method set, risk rule evaluation is performed on the risk rating data to obtain risk results.

[0124] Specifically, when the target logical method set passes the verification, the target logical method set can be executed, that is, according to each rule in the rule model contained in the target logical method set, the mapped risk rating data is evaluated in turn, and then according to the risk evaluated by each rule, the risk evaluated by each rule is scored using a scorecard to obtain the risk result.

[0125] Optionally, in another embodiment of the present application, the rule model includes customer group determination, geographic location calculation, income calculation, debt calculation, basic black exclusion admission rules, customer group admission, and quota calculation, so a specific implementation of step S106 is as follows: Figure 4 As shown, the following steps are included:

[0126] S401. Extract risk rating data from the target logic method set.

[0127] Specifically, determine the source and format of the risk rating data contained in the target logical method set, and then use appropriate tools or methods to parse the risk rating data contained in the target logical method set according to the source and format of the risk rating data, so that the risk rating data can be extracted from the target logical method set.

[0128] S402, according to the target logic method set of customer group determination, geographic location calculation, income calculation, liability calculation, basic blacklisting admission rules, customer group admission, and quota calculation, the risk rating data is evaluated in turn for risk rules to obtain multiple indicator results.

[0129] Specifically, customer group determination is based on risk rating data to assess the customer's financing lease type, thereby obtaining the indicator results of the financing lease type.

[0130] Geolocation calculation is a location calculation that checks whether the user's current location is consistent with the device's historical location based on the risk rating data. For example, if a device is usually used in New York, but suddenly appears in Beijing, the system may mark it as abnormal, so whether the location is abnormal is an indicator result.

[0131] Income calculation is to calculate the customer's income based on risk rating data to obtain income indicator results.

[0132] Liability calculation is to know whether the customer owes debts based on risk rating data. If so, the amount of debt owed by the customer is obtained. Therefore, the amount of debt owed is the indicator result of liability calculation. If not, the indicator result of liability calculation is 0.

[0133] The basic blacklisting access rule uses external information such as credit data and communication data in the risk rating data to determine whether the customer is on the blacklist of each company. If so, the name of the company that the customer belongs to in the blacklist is obtained, so the company name is the indicator result of the basic blacklisting access rule. If not, the indicator result of the basic blacklisting access rule is none.

[0134] Customer access is to assess which type of financial leasing business the customer meets the entry requirements based on risk rating data. The financial leasing business corresponding to the entry requirements that the risk rating data meets is the indicator result of customer access.

[0135] The credit limit calculation is to calculate the customer's financial leasing credit limit based on the risk rating data, so the financial leasing credit limit is the indicator result of the credit limit calculation.

[0136] S403. Integrate the results of each indicator to obtain risk results.

[0137] Specifically, after obtaining the results of all indicators, in order to obtain a more comprehensive and accurate risk assessment and thus formulate more effective risk management strategies and decisions, it is necessary to integrate the results of each indicator to obtain the risk results.

[0138] Optionally, risk results are obtained and the indicator results are updated into the target logic method set.

[0139] S107. Determine the customer's financial leasing assessment results based on the risk results.

[0140] Specifically, you can first analyze the risk assessment results, that is, check the customer's overall risk score, such as the indicator results of the basic blacklist entry rules (that is, credit score), income situation, etc., and then determine the customer's risk category, such as low risk, medium risk or high risk. Then set the minimum credit requirements based on the customer's credit score, and consider the customer's income, debt ratio and asset status. A stable financial situation usually increases the possibility of financial leasing approval. Then, combine the results of various indicators to comprehensively evaluate the customer's financial leasing capabilities and risks. For example, although a customer may have a low credit score, if they have a stable source of income, this may have a positive impact on the financial leasing assessment and finally the financial leasing approval.

[0141] Optionally, for approved finance lease contracts, continuously monitor the customer's risk profile and adjust the finance lease terms to reduce risks when necessary, as well as regularly review the customer's risk assessment results to promptly identify and respond to potential problems.

[0142] For example, suppose a customer's risk assessment results show that his credit score is low, but his income is stable and he has no history of default. Based on the financial lease assessment results: the customer may be required to pay a higher deposit to reduce the risk, and may be required to provide a guarantor, and may choose to approve the financial lease, but add clauses to the financial lease contract to protect the interests of the lessee. Therefore, the financial lease assessment results help ensure that the financial lease decision is both reasonable and meets the company's risk management requirements.

[0143] Optionally, in another embodiment of the present application, a specific implementation of step S107 includes the following steps:

[0144] Record the risk results into the log and obtain the target log.

[0145] It is understandable that recording risk results in a log can facilitate maintenance and subsequent inspections.

[0146] The target log includes risk results, execution time, interface call information, and customer information. The execution time refers to the time when the risk assessment of the customer's financial leasing information is performed. The interface call information refers to the information about which interfaces the financial leasing intelligent decision-making system calls to perform risk assessment on the financial leasing information.

[0147] Optionally, after obtaining the target log, it can be displayed to the front end so that the feedforward can view the records of the target log and customer information, etc.

[0148] Traverse the target log.

[0149] It should be noted that the target log records will be traversed one by one first, and the indicators involved in each log record will be traversed and checked. That is, each risk indicator in the risk result of the target log record will be traversed to see whether it meets the preset risk indicator, and finally each risk indicator in the risk result of the target log record will be analyzed and evaluated to finally generate a decision or action suggestion that is beneficial to the financial leasing business. Among them, each risk indicator refers to the indicator result in step S402.

[0150] Therefore, when every risk indicator in the risk results recorded in the target log meets the preset risk indicators, it means that the customer's risk results meet the preset risk indicators set by the system and are not enough to pose a threat to the company's asset security. Therefore, the customer's financial leasing assessment result is determined to be passed.

[0151] Or when each risk indicator in the risk results recorded in the target log does not meet the preset risk indicators, it means that the customer's risk results do not meet the preset risk indicators set by the system, which may pose a threat to the company's asset security. Therefore, at this time, the customer's financial leasing assessment result is determined to be failed.

[0152] In other words, when there are multiple risk indicators in the risk results of traversing the target log records that do not meet the preset risk indicators, it means that there are only partial risks in the customer's risk results. It cannot be generalized that there is a huge risk in the customer's financial leasing information, and the customer's financial leasing application will be rejected. Therefore, in the embodiment of the present application, when there are multiple risk indicators in the risk results that do not meet the preset risk indicators, the system will determine whether the customer's financial leasing information has a huge risk based on a pre-set quantity threshold. Therefore, when there are multiple risk indicators in the risk results of traversing the target log records that do not meet the preset risk indicators, the number of risk indicators that do not meet the preset risk indicators will be obtained, and then it will be determined whether the number of risk indicators is greater than the preset quantity threshold.

[0153] If the number of risk indicators is greater than the preset threshold, it can be determined that the customer's financial leasing information has a huge risk, and therefore the customer's financial leasing assessment result is determined to be failed.

[0154] If the number of risk indicators is not greater than the preset threshold, it can be determined that the customer's financial leasing information only has a small risk that is not enough to pose a threat to the company's asset security. Therefore, the customer's financial leasing assessment result can be determined to be passed.

[0155] Optionally, when the customer's financial lease assessment result is passed, the financial lease application can be approved, and financial lease terms such as deposit, rent prepayment, etc. may be set according to the risk level, and the financial lease assessment result will be fed back to the customer.

[0156] Optionally, when the customer's financial lease assessment result is not passed, it means that the customer may be a medium-risk or high-risk customer, and the customer's financial lease assessment result is not passed. Then notify the customer service staff to intervene and let the customer service staff set additional conditions, such as a higher deposit, prepayment of initial rent, and provision of a guarantor.

[0157] Alternatively, if the client’s finance lease assessment results ultimately indicate a high risk and that the necessary conditions cannot be met, the finance lease application may be rejected.

[0158] The present application provides a method for assessing the risk of a financial lease, which obtains the financial lease information of the customer, then inputs the financial lease information into a risk rating model, determines the risk rating data, maps the risk rating data to a predefined rule model, and adds the mapped rule model to a logic method set to obtain a target logic method set, wherein the rule model is composed of a plurality of preset risk rules, and the logic method set refers to a method set for rating processing of the risk rating model, and then verifies the target logic method set, and if the target logic method set passes the verification, the risk rating data is evaluated according to the rule model in the target logic method set, and a risk result is obtained, and finally the financial lease assessment result of the customer is determined according to the risk result. Thus, through the plurality of risk rules in the rule model, the risk identification of the financial lease information of the customer can be carried out, and the potential risks of the financial lease information can be effectively and accurately identified, and at the same time, a decision plan can be formulated for the customer according to the risk of the customer, thereby effectively improving the accuracy of the decision.

[0159] Another embodiment of the present application provides another method for assessing financial leasing risk, such as Figure 5 As shown, the following steps are included:

[0160] S501. Obtain the customer's financial leasing information.

[0161] It should be noted that the specific implementation of step S501 may refer to step S101 in the above method embodiment, and will not be described in detail here.

[0162] S502: Input the financial leasing information into the risk rating model to determine the risk rating data.

[0163] It should be noted that the specific implementation of step S502 may refer to step S102 in the above method embodiment, which will not be described in detail here.

[0164] S503: Add the risk rating data to the pre-built data intermediate result set.

[0165] It should be noted that adding risk rating data to the pre-built data intermediate result set can enable the financing and leasing intelligent decision-making system to use data more effectively to improve the quality and reliability of decisions.

[0166] S504: extract risk rating data from the data intermediate result set and map it to a predefined rule model, and add the mapped rule model to the logic method set to obtain a target logic method set.

[0167] The rule model is composed of multiple preset risk rules. The rule logic method set refers to the method set of risk rating model rating processing.

[0168] It should be noted that extracting risk rating data from the data intermediate result set and mapping it to a pre-defined rule model can identify and analyze risk trends or patterns, which helps the rule model understand the risk rating data.

[0169] It should also be noted that the specific implementation of step S504 may refer to step S103 in the above method embodiment, and will not be described in detail here.

[0170] S505: Verify the target logic method set.

[0171] It should be noted that the specific implementation of step S505 may refer to step S104 in the above method embodiment, and will not be described in detail here.

[0172] S506: Determine whether the target logic method set passes verification.

[0173] Specifically, if the target logic method set passes the verification, step S507 is executed.

[0174] If the target logic method set fails the verification, a verification report is fed back to the feedforward.

[0175] S507. According to the rule model in the target logic method set, risk rule evaluation is performed on the risk rating data to obtain risk results.

[0176] It should be noted that the specific implementation of step S507 may refer to step S106 in the above method embodiment, which will not be described in detail here.

[0177] S508. Determine the customer's financial leasing assessment results based on the risk results.

[0178] It should be noted that the specific implementation of step S508 may refer to step S107 in the above method embodiment, which will not be described in detail here.

[0179] Another embodiment of the present application provides a financial leasing risk assessment system, wherein the assessment system refers to the financial leasing intelligent decision-making system in the above embodiment, such as Figure 6 As shown, it includes the following units:

[0180] The information acquisition unit 601 is used to acquire the financial leasing information of the customer.

[0181] The information input unit 602 is used to input the financial leasing information into the risk rating model to determine the risk rating data.

[0182] The mapping unit 603 is used to map the risk rating data to a predefined rule model, and add the mapped rule model to the logic method set to obtain a target logic method set. The rule model is composed of a plurality of preset risk rules. The logic method set refers to the method set of the risk rating model rating processing.

[0183] The verification unit 604 is used to verify the target logic method set.

[0184] The evaluation unit 605 is used to perform risk rule evaluation on the risk rating data according to the rule model in the target logical method set to obtain a risk result if the target logical method set passes the verification.

[0185] The result determination unit 606 is used to determine the financial lease assessment result of the customer according to the risk result.

[0186] It should be noted that the specific working process of the above-mentioned units in the embodiment of the present application can refer to steps S101 to S106 in the above-mentioned method embodiment, and will not be repeated here.

[0187] Optionally, a financial leasing risk assessment system provided in another embodiment of the present application further includes:

[0188] An adding unit is used to add risk rating data to a pre-built data intermediate result set.

[0189] The mapping unit includes:

[0190] The extraction unit is used to extract risk rating data from the data intermediate result set and map it to a pre-defined rule model.

[0191] Optionally, in a financial leasing risk assessment system provided by another embodiment of the present application, the risk rating model includes a preliminary review model, a review model, and a loan model, and the information input unit 602 includes:

[0192] The information extraction unit is used to extract the basic information and asset information of the customer from the financial leasing information.

[0193] The first input unit is used to input basic information into the preliminary review model to obtain a preliminary review result.

[0194] The second input unit is used to input the asset information into the review model to obtain the review result.

[0195] The merging unit is used to merge the preliminary review results and the review results to obtain the loan conditions.

[0196] The third input unit is used to input the loan conditions into the loan model to obtain the loan result, and to organize the preliminary review results, review results, and loan results to obtain the final result.

[0197] The first determining unit is used to determine the final result as risk rating data.

[0198] Optionally, in a financial leasing risk assessment system provided by another embodiment of the present application, the verification unit 604 includes:

[0199] The verification subunit is used to verify whether the risk rating data in the target logical method set is empty, or to detect whether the risk rating data exists in the target logical method set.

[0200] The second determination unit is configured to determine that the target logical method set passes the verification if the risk rating data in the target logical method set is not empty or the risk rating data exists in the target logical method set.

[0201] The third determination unit is configured to determine that the target logical method set has failed verification if the risk rating data in the target logical method set is empty or the risk rating data does not exist in the target logical method set.

[0202] Optionally, in a financial leasing risk assessment system provided by another embodiment of the present application, the rule model includes customer group determination, geographic location calculation, income calculation, liability calculation, basic blacklisting admission rules, customer group admission, and quota calculation, and the assessment unit 605 includes:

[0203] A data extraction unit is used to extract risk rating data from the target logic method set.

[0204] The evaluation sub-unit is used to evaluate the risk rules of the risk rating data in sequence according to the customer group determination, geographic location calculation, income calculation, liability calculation, basic blacklisting admission rules, customer group admission, and quota calculation in the target logic method set to obtain multiple indicator results.

[0205] The integration unit is used to integrate the results of each indicator to obtain the risk results.

[0206] Optionally, in a financial leasing risk assessment system provided by another embodiment of the present application, the result determination unit 606 includes:

[0207] The recording unit is used to record the risk results into the log to obtain the target log.

[0208] The traversal unit is used to traverse the target log.

[0209] The fourth determination unit is used to determine that the financial leasing assessment result of the customer is passed when each risk indicator in the risk result recorded in the target log traversal meets the preset risk indicator.

[0210] The fifth determination unit is used to determine that the financial leasing evaluation result of the customer is failed when each risk indicator in the risk result recorded in the target log does not meet the preset risk indicator.

[0211] The quantity acquisition unit is used to acquire the quantity of risk indicators that do not meet the preset risk indicators when there are multiple risk indicators in the risk results of the traversed target log records that do not meet the preset risk indicators.

[0212] The threshold judgment unit is used to judge whether the number of risk indicators is greater than a preset number threshold.

[0213] The sixth determination unit is used to determine that the financial lease assessment result of the customer is not passed if the number of risk indicators is greater than a preset number threshold.

[0214] The seventh determination unit is used to determine that the financial leasing evaluation result of the customer is passed if the number of risk indicators is not greater than a preset number threshold.

[0215] It should be noted that the specific working process of each unit provided in the above embodiments of the present application can refer to the corresponding steps in the above method embodiments, and will not be repeated here.

[0216] It should be noted that the financial leasing risk assessment system provided in the embodiment of the present application has the technical effects of any of the above embodiments, and the embodiment of the present application will not be described in detail here.

[0217] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0218] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for assessing financial leasing risk, characterized in that: include: Obtain the client's financial leasing information; Inputting the financial lease information into the risk rating model to determine the risk rating data; Mapping the risk rating data to a predefined rule model, and adding the mapped rule model to a logic method set to obtain a target logic method set; wherein the rule model is composed of a plurality of preset risk rules; and the logic method set refers to a method set for rating processing of the risk rating model; Verifying the target logic method set; If the target logic method set passes the verification, then the risk rating data is evaluated according to the rule model in the target logic method set to obtain a risk result; Based on the risk results, the financial lease assessment results of the customer are determined.

2. The method according to claim 1, characterized in that Before mapping the risk rating data to a predefined rule model, the method further includes: Adding the risk rating data to a pre-built data intermediate result set; The step of mapping the risk rating data to a predefined rule model includes: The risk rating data is extracted from the data intermediate result set and mapped into a predefined rule model.

3. The method according to claim 1, characterized in that The risk rating model includes a preliminary review model, a review model, and a loan model. The inputting of the financial leasing information into the risk rating model to determine the risk rating data includes: Extracting the basic information and asset information of the customer from the financial leasing information; Inputting the basic information into the preliminary review model to obtain a preliminary review result; Inputting the asset information into the review model to obtain a review result; The preliminary review result and the review result are combined to obtain loan conditions; Input the loan conditions into the loan model to obtain a loan result, and collate the preliminary review result, the review result, and the loan result to obtain a final result; The final result is determined as risk rating data.

4. The method according to claim 1, characterized in that: The verifying the target logic method set includes: Verify whether the risk rating data in the target logical method set is empty, or detect whether the risk rating data exists in the target logical method set; If the risk rating data in the target logical method set is not empty, or the risk rating data exists in the target logical method set, determining that the target logical method set passes the verification; If the risk rating data in the target logical method set is empty, or the risk rating data does not exist in the target logical method set, it is determined that the target logical method set has failed the verification.

5. The method according to claim 1, characterized in that The rule model includes customer group determination, geographic location calculation, income calculation, debt calculation, basic blacklisting admission rules, customer group admission, and quota calculation. The risk rating data is evaluated according to the rule model in the target logic method set to obtain risk results, including: extracting the risk rating data from the target logical method set; According to the customer group determination, geographic location calculation, income calculation, debt calculation, basic blacklisting admission rules, customer group admission, and quota calculation in the target logic method set, the risk rating data is evaluated in turn for risk rules to obtain multiple indicator results; The results of each indicator are integrated to obtain the risk result.

6. The method according to claim 1, characterized in that Determining the financial lease assessment result of the customer based on the risk result includes: Record the risk result in a log to obtain a target log; Traversing the target log; When each risk indicator in the risk result recorded in the target log is found to meet the preset risk indicator, the financial lease assessment result of the customer is determined to be passed; When each risk indicator in the risk result recorded in the target log does not meet the preset risk indicator, it is determined that the financial lease assessment result of the customer is failed; When there are multiple risk indicators in the risk results of the target log records that do not meet the preset risk indicators, the number of risk indicators that do not meet the preset risk indicators is obtained; Determine whether the number of risk indicators is greater than a preset number threshold; If the number of risk indicators is greater than the preset number threshold, the financial lease assessment result of the customer is determined to be unsuccessful; If the number of risk indicators is not greater than the preset threshold, the financial leasing assessment result of the customer is determined to be passed.

7. A financial leasing risk assessment system, characterized in that: include: An information acquisition unit, used to acquire the customer's financial leasing information; An information input unit, used to input the financial leasing information into a risk rating model to determine risk rating data; A mapping unit, used to map the risk rating data to a predefined rule model, and add the mapped rule model to a logic method set to obtain a target logic method set; wherein the rule model is composed of a plurality of preset risk rules; and the logic method set refers to a method set for rating processing of the risk rating model; A verification unit, used to verify the target logic method set; An evaluation unit, configured to, if the target logic method set passes the verification, perform risk rule evaluation on the risk rating data according to the rule model in the target logic method set to obtain a risk result; A result determination unit is used to determine the financial lease assessment result of the customer according to the risk result.

8. The system according to claim 7, characterized in that Also includes: An adding unit, used for adding the risk rating data to a pre-built data intermediate result set; Wherein, the mapping unit includes: The extraction unit is used to extract the risk rating data from the data intermediate result set and map it into a predefined rule model.

9. The system according to claim 7, characterized in that The risk rating model includes a preliminary review model, a review model, and a loan model, and the information input unit includes: An information extraction unit, used to extract the basic information and asset information of the customer from the financial lease information; A first input unit, used to input the basic information into the preliminary review model to obtain a preliminary review result; A second input unit, used to input the asset information into the review model to obtain a review result; A merging unit, used to merge the preliminary review result and the review result to obtain loan conditions; A third input unit is used to input the loan conditions into the loan model to obtain a loan result, and to sort out the preliminary review result, the review result, and the loan result to obtain a final result; The first determining unit is used to determine the final result as risk rating data.

10. The system according to claim 7, characterized in that The verification unit comprises: A verification subunit, used to verify whether the risk rating data in the target logical method set is empty, or to detect whether the risk rating data exists in the target logical method set; A second determination unit, configured to determine that the target logical method set passes verification if the risk rating data in the target logical method set is not empty or the risk rating data exists in the target logical method set; The third determination unit is configured to determine that the target logical method set fails the verification if the risk rating data in the target logical method set is empty or the risk rating data does not exist in the target logical method set.

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