CN116452206A - Method, device, computer equipment and storage medium for determining resource processing strategy - Google Patents

Method, device, computer equipment and storage medium for determining resource processing strategy Download PDF

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Publication number
CN116452206A
CN116452206A CN202310379874.1A CN202310379874A CN116452206A CN 116452206 A CN116452206 A CN 116452206A CN 202310379874 A CN202310379874 A CN 202310379874A CN 116452206 A CN116452206 A CN 116452206A
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China
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target
transaction
characteristic information
determining
transfer
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Chinese (zh)
Inventor
郑广昱
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Industrial and Commercial Bank of China Ltd ICBC
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Industrial and Commercial Bank of China Ltd ICBC
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Priority to CN202310379874.1A priority Critical patent/CN116452206A/en
Publication of CN116452206A publication Critical patent/CN116452206A/en
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/38—Payment protocols; Details thereof
    • G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
    • G06Q20/401—Transaction verification
    • G06Q20/4016—Transaction verification involving fraud or risk level assessment in transaction processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/08—Payment architectures
    • G06Q20/10—Payment architectures specially adapted for electronic funds transfer [EFT] systems; specially adapted for home banking systems
    • G06Q20/108—Remote banking, e.g. home banking
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange

Abstract

The present application relates to a method, an apparatus, a computer device, a storage medium and a computer program product for determining a resource processing policy, and relates to the technical field of information security, and may be used in the financial and technological field or other fields. The method comprises the following steps: responding to the resource processing request, determining a target payee and acquiring historical transaction data of the target payee; based on historical transaction data, extracting a plurality of transaction characteristic information, and judging whether each transaction characteristic information meets characteristic judgment conditions or not; under the condition that the characteristic discrimination conditions are not met, determining target transactions of which the resource transfer-in-transfer-out proportion meets preset conditions, and determining target risk levels of target payee according to the times of the target transactions, the resource transfer-in-transfer-out proportion and a risk level matching strategy; and determining a resource processing strategy matched with the target risk level based on the target risk level, and processing the resource processing request based on the resource processing strategy. By adopting the method, the transfer safety can be improved.

Description

Method, device, computer equipment and storage medium for determining resource processing strategy
Technical Field
The present invention relates to the field of information security technologies, and in particular, to a method, an apparatus, a computer device, a storage medium, and a computer program product for determining a resource processing policy.
Background
With the proliferation of means for fraudulently criminals, it is difficult to avoid the transfer behavior of customers of financial enterprises due to fraud. At present, a countermeasure against fraud is mainly to carry out universal transaction risk reminding on clients, but unsafe transfer of the clients cannot be prevented, so that the transfer security is poor.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a method, apparatus, computer device, computer-readable storage medium, and computer program product for determining a resource handling policy that can improve transfer security.
In a first aspect, the present application provides a method for determining a resource processing policy. The method comprises the following steps:
responding to a resource processing request, determining a target payee contained in the resource processing request, and acquiring historical transaction data of the target payee;
extracting a plurality of transaction characteristic information based on the historical transaction data, and judging whether each transaction characteristic information meets a characteristic judgment condition or not;
Under the condition that the transaction characteristic information does not meet the characteristic discrimination conditions, determining a target transaction of which the resource in-out proportion meets the preset conditions based on the historical transaction data, and determining a target risk level of the target payee according to the number of times of the target transaction, the resource in-out proportion corresponding to the target transaction and a preset risk level matching strategy;
and determining a resource processing strategy matched with the target risk level based on the target risk level, and processing the resource processing request based on the resource processing strategy.
In one embodiment, the determining, based on the historical transaction data, the target transaction in which the resource in-out ratio meets the preset condition includes:
determining each transfer transaction with the transfer resource number being greater than or equal to the target resource number from the historical transaction data, and determining transfer time of each transfer transaction;
for each transfer-in transaction, determining a preset period after transfer-in time of the transfer-in transaction as a target period, and determining the number of transfer-out resources in the target period;
obtaining a resource transfer-in-transfer-out ratio corresponding to the transfer-in transaction according to the ratio of the transfer-out resource number to the transfer-in resource number;
And determining the target transaction based on the in-transaction that the resource in-out proportion is greater than or equal to a preset threshold.
In one embodiment, the determining the target risk level of the target payee according to the number of times of the target transaction, the resource transfer-in-transfer-out ratio corresponding to the target transaction, and a preset risk level matching policy includes:
in a plurality of preset proportion intervals, determining a target proportion interval in which the resource transfer-in and transfer-out proportion of the target transaction is positioned and the times of the target transaction corresponding to each target proportion interval;
determining at least one candidate risk level corresponding to the target transaction according to the corresponding relation among the transaction times, the proportion interval and the risk level;
and determining the target risk level of the target payee based on at least one candidate risk level corresponding to the target transaction.
In one embodiment, the determining whether each transaction characteristic information meets a characteristic determining condition includes:
under the condition that target transaction characteristic information in the transaction characteristic information does not meet preset conditions, judging that the transaction characteristic information does not meet characteristic judging conditions; the target transaction characteristic information is transaction characteristic information of a target characteristic type;
And under the condition that any one target transaction characteristic information in the transaction characteristic information meets the preset condition, judging that the transaction characteristic information meets the characteristic judgment condition.
In one embodiment, the method further comprises:
and under the condition that any one target transaction characteristic information in the transaction characteristic information meets a preset condition, determining the target risk level of the target payee based on the risk level matched with the target transaction characteristic information meeting the preset condition.
In one embodiment, the method further comprises:
acquiring historical transaction data of a plurality of sample accounts, wherein the sample accounts comprise positive sample accounts and negative sample accounts, the positive sample accounts refer to accounts without abnormal transaction records, and the negative sample accounts refer to accounts with abnormal transaction records;
based on the historical transaction data of each sample account, extracting positive sample transaction characteristic information and negative sample transaction characteristic information corresponding to a plurality of characteristic types;
determining the difference degree of positive sample transaction characteristic information and negative sample transaction characteristic information corresponding to each characteristic type;
And determining target feature types with the difference degree meeting preset conditions according to the difference degree corresponding to each feature type.
In one embodiment, the determining whether each transaction characteristic information meets a characteristic determining condition includes:
under the condition that target transaction characteristic information in each transaction characteristic information does not meet the preset condition, determining a strategy according to each transaction characteristic information and the preset score to obtain a target score corresponding to each transaction characteristic information;
under the condition that the target score meets the preset condition, judging that each transaction characteristic information meets the characteristic judgment condition;
and under the condition that the target score does not meet the preset condition, judging that the transaction characteristic information does not meet the characteristic judging condition.
In one embodiment, the obtaining the target score corresponding to each transaction characteristic information according to each transaction characteristic information and a preset score determining policy includes:
determining the score corresponding to each transaction characteristic information according to the corresponding relation between the pre-established transaction characteristic information and the score, and obtaining the target score corresponding to each transaction characteristic information according to the score corresponding to each target transaction characteristic information.
In one embodiment, the method further comprises:
and under the condition that the target scores meet preset conditions, determining the risk level matched with the target scores as the target risk level of the target payee.
In a second aspect, the present application further provides a device for determining a resource processing policy. The device comprises:
the first acquisition module is used for responding to the resource processing request, determining a target payee contained in the resource processing request and acquiring historical transaction data of the target payee;
the judging module is used for extracting a plurality of transaction characteristic information based on the historical transaction data and judging whether each transaction characteristic information meets the characteristic judging condition or not;
the first determining module is used for determining a target transaction of which the resource transfer-in-transfer-out proportion meets a preset condition based on the historical transaction data under the condition that the transaction characteristic information does not meet the characteristic discrimination condition, and determining a target risk level of the target payee according to the times of the target transaction, the resource transfer-in-transfer-out proportion corresponding to the target transaction and a preset risk level matching strategy;
and the second determining module is used for determining a resource processing strategy matched with the target risk level based on the target risk level and processing the resource processing request based on the resource processing strategy.
In one embodiment, the first determining module is specifically configured to:
determining each transfer transaction with the transfer resource number being greater than or equal to the target resource number from the historical transaction data, and determining transfer time of each transfer transaction; for each transfer-in transaction, determining a preset period after transfer-in time of the transfer-in transaction as a target period, and determining the number of transfer-out resources in the target period; obtaining a resource transfer-in-transfer-out ratio corresponding to the transfer-in transaction according to the ratio of the transfer-out resource number to the transfer-in resource number; and determining the target transaction based on the in-transaction that the resource in-out proportion is greater than or equal to a preset threshold.
In one embodiment, the first determining module is specifically configured to:
in a plurality of preset proportion intervals, determining a target proportion interval in which the resource transfer-in and transfer-out proportion of the target transaction is positioned and the times of the target transaction corresponding to each target proportion interval; determining at least one candidate risk level corresponding to the target transaction according to the corresponding relation among the transaction times, the proportion interval and the risk level; and determining the target risk level of the target payee based on at least one candidate risk level corresponding to the target transaction.
In one embodiment, the judging module is specifically configured to:
under the condition that target transaction characteristic information in the transaction characteristic information does not meet preset conditions, judging that the transaction characteristic information does not meet characteristic judging conditions; the target transaction characteristic information is transaction characteristic information of a target characteristic type; and under the condition that any one target transaction characteristic information in the transaction characteristic information meets the preset condition, judging that the transaction characteristic information meets the characteristic judgment condition.
In one embodiment, the apparatus further comprises:
and a third determining module, configured to determine, when any one of the transaction characteristic information satisfies a preset condition, a target risk level of the target payee based on a risk level matched with the target transaction characteristic information that satisfies the preset condition.
In one embodiment, the apparatus further comprises:
the second acquisition module is used for acquiring historical transaction data of a plurality of sample accounts, wherein the sample accounts comprise positive sample accounts and negative sample accounts, the positive sample accounts refer to accounts without abnormal transaction records, and the negative sample accounts refer to accounts with abnormal transaction records;
The extraction module is used for extracting positive sample transaction characteristic information and negative sample transaction characteristic information corresponding to a plurality of characteristic types based on historical transaction data of each sample account;
a fourth determining module, configured to determine, for each feature type, a degree of difference between positive sample transaction feature information and negative sample transaction feature information corresponding to the feature type;
and a fifth determining module, configured to determine, according to the difference degrees corresponding to the feature types, a target feature type whose difference degree meets a preset condition.
In one embodiment, the judging module is specifically configured to:
under the condition that target transaction characteristic information in each transaction characteristic information does not meet the preset condition, determining a strategy according to each transaction characteristic information and the preset score to obtain a target score corresponding to each transaction characteristic information; under the condition that the target score meets the preset condition, judging that each transaction characteristic information meets the characteristic judgment condition; and under the condition that the target score does not meet the preset condition, judging that the transaction characteristic information does not meet the characteristic judging condition.
In one embodiment, the judging module is specifically configured to:
Determining the score corresponding to each transaction characteristic information according to the corresponding relation between the pre-established transaction characteristic information and the score, and obtaining the target score corresponding to each transaction characteristic information according to the score corresponding to each target transaction characteristic information.
In one embodiment, the apparatus further comprises:
and a sixth determining module, configured to determine, as a target risk level of the target payee, a risk level that matches the target score if the target score meets a preset condition.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor implementing the steps of the method of the first aspect when the processor executes the computer program.
In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method of the first aspect.
In a fifth aspect, the present application also provides a computer program product. The computer program product comprising a computer program which, when executed by a processor, implements the steps of the method of the first aspect.
The method, the device, the computer equipment, the storage medium and the computer program product for determining the resource processing strategy are characterized in that when a client triggers a resource processing request (such as a transfer request), transaction characteristic information is extracted according to historical transaction data of a payee, if the transaction characteristic information does not meet a characteristic judgment condition, a target risk level of the target payee is determined according to the number of times of target transactions that the resource transfer-in-out proportion meets the condition in the historical transaction data and the resource transfer-in-out proportion, and then the resource processing strategy is determined according to the target risk level and is used for processing the resource processing request. Therefore, the risk level of the payee can be accurately identified according to the historical transaction data of the payee, and if the risk level is higher, the transfer can be stopped or delayed, so that the transfer safety can be improved.
Drawings
FIG. 1 is a flow diagram of a method for determining a resource processing policy in one embodiment;
FIG. 2 is a flow diagram of determining a target transaction in one embodiment;
FIG. 3 is a flow chart illustrating a method for determining a target risk level in one embodiment;
FIG. 4 is a flow chart of determining whether a criterion is met in one embodiment;
FIG. 5 is a flowchart illustrating a method for determining whether a determination condition is satisfied according to another embodiment;
FIG. 6 is a block diagram of the architecture of a resource processing policy determination device in one embodiment;
fig. 7 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
First, before the technical solution of the embodiments of the present application is specifically described, a description is first given of a technical background or a technical evolution context on which the embodiments of the present application are based. With the proliferation of means for fraudulently criminals, it is difficult to avoid the transfer behavior of customers of financial enterprises due to fraud. At present, a countermeasure against fraud is mainly to carry out universal transaction risk reminding on clients, but unsafe transfer of the clients cannot be prevented, so that the transfer security is poor. Based on the background, the applicant puts forward a resource processing strategy determination method through long-term research and development and experimental verification, and the risk level of the resource processing strategy determination method is identified according to historical transaction data of a payee, so that a resource processing strategy matched with the risk level of the payee is adopted to process a resource processing request (including a transfer request and the like), such as stopping payment, delaying payment and the like, and the aim of improving transfer safety is achieved. In addition, the applicant has made a great deal of creative effort to find out the technical problems of the present application and to introduce the technical solutions of the following embodiments.
In one embodiment, as shown in fig. 1, a method for determining a resource processing policy is provided, where the method may be applied to a computer device, and may be, but not limited to, a computer device of a business system of a financial institution such as a bank, such as various personal computers, notebook computers, servers, server clusters, and the like. In this embodiment, the method includes the steps of:
in step 101, in response to the resource processing request, a target payee included in the resource processing request is determined, and historical transaction data of the target payee is obtained.
In practice, the resource handling request may specifically be a transfer request. The user can initiate the resource processing request through a mobile banking application, an automatic teller machine (Automated Teller Machine, ATM), a counter terminal and the like. The computer device may extract payee information (e.g., payee name, payee account number, etc.) contained by the resource processing request as information for the target payee in response to the resource processing request. The computer device may then query the database for historical transaction data for the target payee based on the information for the target payee.
The historical transaction data may include transaction time, transaction amount, payer information for the in-transaction, payee information for the out-transaction, etc., for each transaction (including in-transaction and out-transaction) associated with the target payee.
Step 102, extracting a plurality of transaction characteristic information based on the historical transaction data, and judging whether each transaction characteristic information meets the characteristic judgment condition.
In implementations, after the computer device obtains historical transaction data for the target payee, transaction characteristic information may be extracted from the historical transaction data. The transaction characteristic information may be plural, and each transaction characteristic information may reflect a degree of trust or a degree of risk of the target payee. Specifically, the transaction characteristic information may include characteristics reflecting whether transaction records exist, characteristics reflecting whether trusted transfer transaction records exist, characteristics reflecting whether wages are running, characteristics reflecting whether trusted internet platform shopping records exist, characteristics reflecting the occupation characteristics of the payee, and characteristics such as the number of overseas transactions, the time difference of resource transfer-in and transfer-out, and the like.
If the historical transaction number of the target payee is 0, the corresponding transaction characteristic information is no transaction record, which can reflect that the target payee has a certain risk and needs careful transfer (such as delayed payment).
If there are historical transaction records in the historical transaction data that the target payee makes two or more transactions with the same account and the time interval between the target payee and at least two transactions with the same account is greater than a preset threshold (for example, 28 days), the corresponding transaction characteristic information is a trusted transfer record, which can reflect that the risk of the target payee is low and normal transfer (for example, instant payment) can be performed.
If the historical transaction data contains a wage flow record for the wage of the public enterprise account, the corresponding transaction characteristic information is wage flow, and the risk of the target payee can be reflected to be lower.
If the historical transaction data has payment records of the target payee and the internet shopping platform and the internet platform is a trusted platform (a platform black-and-white list can be set for matching determination), the corresponding transaction characteristic information is the trusted internet platform shopping record, and the risk of the target payee can be reflected to be lower.
In addition, the payees with different professional characteristics may have different payroll amounts, payroll distribution times, payroll distribution modes and other characteristics, so that the payees can be classified according to payroll running water records to obtain characteristic information reflecting the professional characteristics of the payees, and the risks of the different professional characteristics are different. And identifying the overseas transaction according to whether the account of the transaction counterpart (such as a payment account of a transfer transaction and a collection account of a transfer transaction) is an overseas account, wherein the overseas transaction number (total number or recent average monthly overseas transaction number) can be used as transaction characteristic information, and the more the transaction number is, the greater the risk is. The time difference between the transfer-in and transfer-out of a resource (the transfer-out time of the transfer-out resource number in the preset proportion of the transfer-in resource number can be considered as the transfer-out time corresponding to the transfer-in resource) can be calculated, and the time difference between the transfer-in and transfer-out of the resource is used as the transaction characteristic information, and the shorter the time difference is, the greater the risk is.
The computer device may determine whether each transaction characteristic information satisfies a characteristic discrimination condition. Each transaction characteristic information meets characteristic discrimination conditions, namely the risk level or the trust level of the target payee can be directly identified based on the transaction characteristic information. The transaction characteristic information does not meet the characteristic discrimination condition, namely the risk level of the target payee cannot be directly identified according to the transaction characteristic information. For example, the feature discrimination criteria may reflect a low risk for a target number of transaction feature information. If the risk is lower as the target number of transaction characteristic information is reflected, and the transaction characteristic information meets the characteristic discrimination conditions, the target risk level of the target payee can be identified as a low risk level.
And step 103, determining a target transaction of which the resource in-out ratio meets the preset condition based on the historical transaction data under the condition that the characteristic discrimination condition is not met by the transaction characteristic information, and determining a target risk level of a target payee according to the number of times of the target transaction, the resource in-out ratio corresponding to the target transaction and a preset risk level matching strategy.
In implementation, if the feature discrimination condition is not satisfied by each transaction feature information extracted from the historical transaction data of the target payee, the computer device may calculate, according to the historical transaction data, a resource in-out ratio of each in-out transaction and a corresponding out-out transaction, so as to identify a target transaction in which the resource in-out ratio satisfies a preset condition. The resource transfer-in-transfer-out proportion pointer is used for transferring in a transaction to a target payee, and the number of transferred resources accounts for the proportion of the number of transferred resources of the transfer-in transaction within a preset duration. If the resource in-out proportion meets a preset condition, such as greater than or equal to a preset threshold, the in-out transaction (and/or out-out transaction) may be identified as a target transaction. Then, the computer device can determine the target risk level of the target payee according to the number of target transactions and the resource transfer-in-out proportion of each target transaction and a preset risk level matching strategy.
The risk level matching policy may be a pre-established correspondence of transaction times, proportions and risk levels. Wherein the number of transactions can be inversely related to the risk level, and the resource in-out ratio can be inversely related to the risk level. For example, the correspondence between the transaction number, the resource transfer-in-transfer-out ratio and the risk level may be as shown in table 1, in this example, the threshold value of the resource transfer-in-transfer-out ratio is 95%, and if the resource transfer-in-transfer-out ratio of 1 historical transaction is greater than or equal to 95%, the corresponding high risk level is obtained; if the resource transfer-in-transfer-out ratio of more than 1 historical transaction is more than or equal to 95%, the risk grade is corresponding; if there is no historical transaction with a resource in-out ratio greater than or equal to 95%, the target risk level of the target payee may be determined to be a low risk level. Table 1 is only an example, and other proportion thresholds (or multiple proportion thresholds) may be actually set as required, the corresponding transaction times may be set under each proportion threshold, and the risk level may also be set at more levels.
TABLE 1 risk level correspondence table
Resource transfer-in-out ratio Number of transactions Risk level
95％ 1 time High risk level
95％ More than 1 time Risk of stroke grade
Step 104, determining a resource processing strategy matched with the target risk level based on the target risk level, and processing the resource processing request based on the resource processing strategy.
In an implementation, after determining the target risk level of the target payee, the computer device may determine a resource processing policy that matches the target risk level and process the user's resource processing request based on the resource processing policy. For example, a corresponding relationship between each risk level and the resource processing policy may be pre-established, such as a resource processing policy lookup table shown in table 2, and the computer device may query the table for the resource processing policy corresponding to the target risk level.
Table 2 resource handling policy lookup table
Risk level Resource handling policies
High height All freeze, stop payment
In (a) Delay 3 hours payment
Low and low Instant payment
In the method for determining the resource processing policy, when a client triggers a resource processing request (such as a transfer request), transaction characteristic information is extracted according to historical transaction data of a payee in the request, if the transaction characteristic information does not meet a characteristic discrimination condition, a target risk level of the target payee is determined according to the number of times of target transactions in which a resource transfer-in-and-transfer-out proportion meets a condition in the historical transaction data and the resource transfer-in-and-transfer-out proportion, and then the resource processing policy is determined according to the target risk level. Therefore, the risk level of the payee can be accurately identified according to the historical transaction data of the payee, and if the risk level is higher, the transfer can be stopped or delayed, so that the transfer safety can be improved.
In one embodiment, as shown in fig. 2, the process of determining in step 103 that the resource in-out ratio satisfies the target transaction of the preset condition specifically includes the following steps:
step 201, determining each transfer transaction with the number of transfer resources being greater than or equal to the number of target resources from the historical transaction data, and determining transfer time of each transfer transaction.
In implementation, the computer device may identify, from the historical transaction data of the target payee, transaction data with a transaction type of a transfer transaction (i.e., a transaction in which other accounts transfer resources to the target payee) according to the transaction type identifier, compare the transfer resource number (transfer amount) in the transaction data of each transfer transaction with the target resource number (which may be set as required, for example, set as 3000 yuan), so as to screen out a target transfer transaction with a transfer resource number greater than or equal to the target resource number, and determine a transfer time (i.e., a transaction time) of each target transfer transaction from the transaction data.
Step 202, for each transfer-in transaction, determining a preset period after the transfer-in time of the transfer-in transaction as a target period, and determining the number of transfer-out resources in the target period.
In implementation, the computer device may determine a preset period (e.g., 30 minutes or 1 hour) after the time of each transfer-in transaction is a target period, and then query the historical transaction data for the transaction data of each transfer-out transaction in the target period, and calculate the number of transfer-out resources according to the transaction data of each transfer-out transaction (if a plurality of transfer-out transactions are included in the target period, calculate the sum of resources of each transfer-out transaction as the number of transfer-out resources).
And 203, obtaining a resource transfer-in-transfer-out ratio corresponding to the transfer-in transaction according to the ratio of the transfer-out resource number to the transfer-in resource number.
In implementation, for each transfer-in transaction, the computer device may calculate a ratio of the number of transfer-out resources corresponding to the transfer-in transaction to the number of transfer-in resources corresponding to the transfer-in transaction, to obtain a resource transfer-in-transfer-out ratio corresponding to the transfer-in transaction.
Step 204, determining a target transaction based on the in-transaction that the resource in-out ratio is greater than or equal to a preset threshold.
In implementation, after the computer device calculates the resource in-out proportion corresponding to each in-out transaction, the resource in-out proportion may be compared with a preset threshold (if multiple thresholds are included or multiple proportion intervals are included, the preset threshold is determined according to the minimum proportion value of the minimum threshold or the proportion interval), so as to identify the in-out transaction in which the resource in-out proportion is greater than or equal to the preset threshold as the target transaction.
The embodiment provides an implementation manner of determining the target transaction that the resource in-out ratio meets the preset condition, and can quickly and accurately identify the target transaction.
In one embodiment, as shown in fig. 3, the process of determining the target risk level of the target payee in step 103 specifically includes the steps of:
Step 301, determining a target proportion interval in which a resource transfer-in-transfer-out proportion of a target transaction is located and the number of times of target transactions corresponding to each target proportion interval in a plurality of preset proportion intervals.
In implementation, a plurality of proportion intervals of the resource in-out proportion can be preset, and the proportion size corresponding to each proportion interval can be positively correlated with the risk level. In one example, the risk level and policy lookup table as shown in table 3, the proportion interval may include three intervals of 80% or more and 95% or less, 95% or more and 100% or less, respectively. The computer device may determine a target proportion interval in which the resource transfer-in-out proportion of each target transaction is matched, and calculate the number of times of target transactions included in each target proportion interval.
TABLE 3 Risk level and policy lookup tables
Step 302, determining at least one candidate risk level corresponding to the target transaction according to the corresponding relation among the transaction times, the proportion interval and the risk level.
In implementation, the correspondence between the transaction number, the proportion interval and the risk level may be pre-established and stored, for example, as a risk level and policy lookup table shown in table 3. The computer device may query, from the stored correspondence, a risk level corresponding to the target proportion interval and the number of target transactions contained in each target proportion interval as a candidate risk level. If the target transaction has a plurality of target proportion intervals, a plurality of candidate risk levels can be determined.
Step 303, determining a target risk level of the target payee based on the at least one candidate risk level corresponding to the target transaction.
In practice, if the candidate risk level is one, the computer device may determine the candidate risk level as the target risk level; if the candidate risk level is a plurality of, the computer device may determine the highest risk level or the lowest risk level of the plurality of candidate risk levels as the target risk level of the target payee.
In this embodiment, by setting a proportion interval of a plurality of resource transfer-in-transfer-out proportions, the proportion size corresponding to the proportion interval is positively correlated with the risk level, and 1 or more transaction frequency thresholds may be set in each proportion interval, and the transaction frequency may be negatively correlated with the risk level, thereby, the target risk level of the target payee may be accurately identified according to the resource transfer-in-transfer-out proportion of the historical transaction of the target payee and the transaction frequency satisfying the proportion condition, and further the accuracy of the determined resource processing policy may be improved.
In one embodiment, as shown in fig. 4, the process of determining whether each transaction characteristic information meets the characteristic determining condition in step 102 specifically includes the following steps:
Step 401, judging that each transaction characteristic information does not meet the characteristic judgment condition under the condition that the target transaction characteristic information in each transaction characteristic information does not meet the preset condition.
The target transaction characteristic information is transaction characteristic information of a target characteristic type.
In implementations, the transaction characteristic information includes information of a plurality of characteristic types, and the target transaction characteristic information may be transaction characteristic information of part or all of the characteristic types in the transaction characteristic information. For example, the target feature type may be a feature reflecting information such as whether there is a transaction record, whether there is a payroll, whether there is a trusted transfer transaction record, whether there is a trusted internet platform shopping record, and the like. The preset condition may be no transaction record or a transaction record with at least one of payroll, trusted transfer transaction record, trusted internet platform shopping record. If all the target transaction characteristic information does not meet the preset conditions, such as transaction records, but none of the wage flow, the trusted transfer transaction records and the trusted internet platform shopping records, the fact that all the transaction characteristic information does not meet the characteristic judgment conditions can be judged, and the fact that the computer equipment cannot directly identify risk grades according to all the transaction characteristic information is indicated.
Step 402, when any one of the target transaction characteristic information in the transaction characteristic information satisfies a preset condition, determining that the transaction characteristic information satisfies a characteristic discrimination condition.
In the implementation, if any one of the target transaction characteristic information meets the preset condition, the computer equipment can judge that each transaction characteristic information meets the characteristic judgment condition, which indicates that the risk level can be directly identified according to each transaction characteristic information. Without any transaction records, the computer device may identify the target payee as a risk level. If there is a transaction record and at least one of a payroll, a trusted transfer transaction record, a trusted internet platform shopping record, the computer device may identify the target payee as a low risk level.
In this embodiment, whether the transaction characteristic information meets the characteristic discrimination condition can be determined according to whether the transaction characteristic information meets the preset condition, so that the next step of identification of the target transaction and the target risk level can be performed under the condition that the transaction characteristic information does not meet the discrimination condition, and the risk level can be identified from multiple dimensions, thereby improving the accuracy of risk level identification and the accuracy of resource processing.
In one embodiment, the method further comprises the steps of: and under the condition that any one target transaction characteristic information in the transaction characteristic information meets the preset condition, determining the target risk level of the target payee based on the risk level matched with the target transaction characteristic information meeting the preset condition.
In the implementation, if the computer device determines that any one of the target transaction characteristic information in the transaction characteristic information meets a preset condition, for example, meets a condition of no transaction record, or meets any one of a payroll transaction record, a trusted transfer transaction record or a trusted internet platform shopping record, the computer device may determine a target risk level of the target payee based on a risk level matched with the target transaction characteristic information meeting the preset condition. For example, if the condition of no transaction records is satisfied, the computer device may determine a risk level (e.g., risk level) corresponding to the no transaction record as a target risk level for the target payee. If there is payroll, the computer device may determine a risk level (e.g., a low risk level) corresponding to the payroll water as the target risk level.
Optionally, whether the discrimination condition is satisfied may also be determined based on other attribute information of the payee. For example, the payee may be matched with the case-related blacklist account, and if the payee belongs to the case-related blacklist account, the payee may be directly identified as a high risk level.
In this embodiment, corresponding preset conditions may be set according to each target transaction characteristic information, and a corresponding relationship between each target transaction characteristic information satisfying the conditions and risk levels may be established, so that risk levels matched with the target transaction characteristic information satisfying the preset conditions may be determined as target risk levels, and risk levels of the payee may be determined timely, quickly and accurately.
In one embodiment, the method further comprises the step of determining the target feature type: acquiring historical transaction data of a plurality of sample accounts, wherein the sample accounts comprise positive sample accounts and negative sample accounts, the positive sample accounts refer to accounts without abnormal transaction records, and the negative sample accounts refer to accounts with abnormal transaction records; based on historical transaction data of each sample account, positive sample transaction characteristic information and negative sample transaction characteristic information corresponding to a plurality of characteristic types are extracted; for each feature type, determining the difference degree of positive sample transaction feature information and negative sample transaction feature information corresponding to the feature type; and determining the target feature type of which the difference degree meets the preset condition according to the difference degree corresponding to each feature type.
In implementations, the computer device may obtain historical transaction data for a plurality of sample accounts from a database, the sample accounts may include a positive sample account for which no abnormal transaction records exist and a negative sample account for which abnormal transaction records exist. The computer device may then extract positive sample transaction characteristic information and negative sample transaction characteristic information corresponding to the plurality of characteristic types based on historical transaction data for each of the positive sample account and the negative sample account. The features of each feature type may include features reflecting information such as whether transaction records exist, whether wages flow, whether trusted transfer transaction records exist, whether trusted internet platform shopping records exist, features reflecting professional characteristic information of a payee, and features such as the number of overseas transactions, resource transfer-in and transfer-out time differences. For each feature type, the computer device may calculate a difference between the positive sample transaction feature information and the negative sample transaction feature information corresponding to the feature type, e.g., map the positive sample transaction feature information and the negative sample transaction feature information into vectors, and calculate a euclidean distance between the two vectors to obtain the difference. The computer device may then take as the target feature type the feature type for which the degree of difference satisfies a preset condition (e.g., is greater than a preset threshold). In one example, the target feature type may include features reflecting information such as whether there is a transaction record, whether there is payroll, whether there is a trusted transfer transaction record, whether there is a trusted internet platform shopping record, and the like.
In this embodiment, the transaction characteristic information of the target characteristic type with a larger difference degree can be determined based on the difference degree of the transaction characteristic information of the positive sample account and the transaction characteristic information of the negative sample account, so that the risk level of the payee can be quickly and accurately identified from multiple dimensions by combining the transaction characteristic information of the target characteristic type and the resource transfer-in-transfer-out proportion of each historical transaction, and the accuracy of resource processing is improved.
In one embodiment, as shown in fig. 5, the process of determining whether each transaction characteristic information meets the characteristic determining condition in step 102 specifically includes the following steps:
step 501, under the condition that the target transaction characteristic information in each transaction characteristic information does not meet the preset condition, determining a strategy according to each transaction characteristic information and the preset score, and obtaining the target score corresponding to each transaction characteristic information.
In implementation, if the target transaction characteristic information does not meet the preset condition, the computer device may determine a policy according to the transaction characteristic information and the preset score, and obtain a target score corresponding to the transaction characteristic information. The corresponding score of each transaction characteristic information can reflect the risk of the payee. The score may be a score corresponding to each transaction characteristic information, or may be a score obtained by integrating the scores of the transaction characteristic information into one total score.
Step 502, under the condition that the target score meets the preset condition, judging that each transaction characteristic information meets the characteristic judgment condition.
In implementation, the computer device may determine whether the target score corresponding to each transaction characteristic information meets a preset condition. For example, the target score may be compared with a preset threshold, and if the target score is equal to or greater than a first preset threshold (a higher score, for example, 90 score), the risk may be determined to be low; if the score is equal to or less than the second preset threshold (a lower score, for example, 10 score), the risk is determined to be high. That is, when the score satisfies the preset condition, the risk level of the payee can be directly identified according to the transaction characteristic information, so that each transaction characteristic information can be judged to satisfy the characteristic judgment condition.
In step 503, if the target score does not meet the preset condition, it is determined that each transaction feature information does not meet the feature discrimination condition.
In implementation, if the target score does not meet the preset condition, for example, the score is greater than the second preset threshold (10 minutes) and less than the first preset threshold (90 minutes), the risk level of the payee cannot be accurately identified at this time, so that it is determined that the feature information of each transaction does not meet the feature discrimination condition.
In this embodiment, the risk level of the payee may be directly identified by discriminating based on the scores of the transaction feature information, if the scores meet the conditions, and if the scores do not meet the conditions, the risk identification may be further performed by combining the information such as the subsequent resource transfer-in-transfer-out ratio, so as to realize multidimensional risk identification and improve the accuracy of risk identification and the accuracy of resource processing.
In one embodiment, the process of obtaining the target score in step 501 specifically includes the following steps: determining the score corresponding to each transaction characteristic information according to the corresponding relation between the pre-established transaction characteristic information and the score, and obtaining the target score corresponding to each transaction characteristic information according to the score corresponding to each target transaction characteristic information.
In practice, the correspondence of transaction characteristic information and scores may be pre-established, as stored as a score lookup table as shown in Table 4, where (-10) represents minus 10 scores. The computer device may calculate a target score based on the initial default score (e.g., 100 points) and the score corresponding to each transaction characteristic information.
Table 4 score lookup table
In this embodiment, a calculation example of a target score is provided, and the target score may be obtained according to score synthesis of a plurality of transaction feature information, so that risk of a payee may be accurately determined.
In one embodiment, the method further comprises the steps of: and under the condition that the target scores meet the preset conditions, determining the risk level matched with the target scores as the target risk level of the target payee.
In implementation, a correspondence between the target score and the risk level may be established, for example, if the target score is greater than or equal to 90 score, a low risk level may be associated; if the target score is less than or equal to 10 points, a high risk level may be associated. So that the computer device may determine the risk level at which the target score matches as the target risk level for the payee.
In this embodiment, the risk level of the payee is directly identified according to the target score satisfying the condition, so that the identification efficiency can be improved, and if the condition is not satisfied, the risk level can be further identified by combining the information such as the resource transfer-in-out ratio and the like, so that the identification accuracy is improved.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a device for determining the resource processing strategy for implementing the method for determining the resource processing strategy. The implementation of the solution provided by the apparatus is similar to the implementation described in the above method, so the specific limitation in the embodiments of the determining apparatus for one or more resource processing policies provided below may refer to the limitation of the determining method for a resource processing policy hereinabove, and will not be repeated herein.
In one embodiment, as shown in fig. 6, there is provided a determining apparatus 600 of a resource processing policy, including: a first acquisition module 601, a judgment module 602, a first determination module 603, and a second determination module 604, wherein:
the first obtaining module 601 is configured to determine a target payee included in the resource processing request in response to the resource processing request, and obtain historical transaction data of the target payee.
The judging module 602 is configured to extract a plurality of transaction feature information based on the historical transaction data, and judge whether each transaction feature information meets the feature discrimination condition.
The first determining module 603 is configured to determine, based on the historical transaction data, a target transaction in which the resource in-out proportion meets a preset condition when the feature discrimination condition is not met by each transaction feature information, and determine a target risk level of the target payee according to the number of times of the target transaction, the resource in-out proportion corresponding to the target transaction, and a preset risk level matching policy.
A second determining module 604 is configured to determine a resource processing policy matching the target risk level based on the target risk level, and process the resource processing request based on the resource processing policy.
In one embodiment, the first determining module 603 is specifically configured to: determining each transfer transaction with the transfer resource number being greater than or equal to the target resource number from the historical transaction data, and determining transfer time of each transfer transaction; for each transfer-in transaction, determining a preset period after transfer-in time of the transfer-in transaction as a target period, and determining the transfer-out resource number in the target period; obtaining a resource transfer-in-transfer-out ratio corresponding to transfer-in transaction according to the ratio of the transfer-out resource number to the transfer-in resource number; and determining the target transaction based on the in-transaction that the resource in-out proportion is greater than or equal to a preset threshold.
In one embodiment, the first determining module is specifically 603 configured to: in a plurality of preset proportion intervals, determining a target proportion interval in which the resource transfer-in and transfer-out proportion of target transaction is positioned and the times of target transactions corresponding to the target proportion intervals; determining at least one candidate risk level corresponding to the target transaction according to the corresponding relation among the transaction times, the proportion interval and the risk level; a target risk level of the target payee is determined based on the at least one candidate risk level corresponding to the target transaction.
In one embodiment, the determining module 602 is specifically configured to: under the condition that target transaction characteristic information in the transaction characteristic information does not meet the preset condition, judging that the transaction characteristic information does not meet the characteristic judgment condition; the target transaction characteristic information is transaction characteristic information of a target characteristic type; and under the condition that any one target transaction characteristic information in the transaction characteristic information meets the preset condition, judging that the transaction characteristic information meets the characteristic judgment condition.
In one embodiment, the apparatus further includes a third determining module, configured to determine, when any one of the target transaction characteristic information in the transaction characteristic information satisfies a preset condition, a target risk level of the target payee based on the risk level matched by the target transaction characteristic information satisfying the preset condition.
In one embodiment, the apparatus further includes a second acquisition module, an extraction module, a fourth determination module, and a fifth determination module, where:
and the second acquisition module is used for acquiring historical transaction data of a plurality of sample accounts, wherein the sample accounts comprise positive sample accounts and negative sample accounts, the positive sample accounts refer to accounts without abnormal transaction records, and the negative sample accounts refer to accounts with abnormal transaction records.
And the extraction module is used for extracting positive sample transaction characteristic information and negative sample transaction characteristic information corresponding to the plurality of characteristic types based on the historical transaction data of each sample account.
And the fourth determining module is used for determining the difference degree of the positive sample transaction characteristic information and the negative sample transaction characteristic information corresponding to the characteristic type aiming at each characteristic type.
And a fifth determining module, configured to determine, according to the difference degrees corresponding to the feature types, a target feature type for which the difference degrees satisfy a preset condition.
In one embodiment, the determining module 602 is specifically configured to: under the condition that target transaction characteristic information in the transaction characteristic information does not meet the preset condition, determining a strategy according to the transaction characteristic information and the preset score to obtain a target score corresponding to the transaction characteristic information; under the condition that the target score meets the preset condition, judging that the transaction characteristic information meets the characteristic judging condition; and under the condition that the target score does not meet the preset condition, judging that the characteristic information of each transaction does not meet the characteristic judging condition.
In one embodiment, the determining module 602 is specifically configured to: determining the score corresponding to each transaction characteristic information according to the corresponding relation between the pre-established transaction characteristic information and the score, and obtaining the target score corresponding to each transaction characteristic information according to the score corresponding to each target transaction characteristic information.
In one embodiment, the apparatus further includes a sixth determining module, configured to determine, as the target risk level of the target payee, a risk level of the target score matching if the target score meets a preset condition.
The respective modules in the above-described determination means of the resource processing policy may be implemented in whole or in part by software, hardware, or a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 7. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used to store data required or generated to perform the above-described method of determining the resource processing policy. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements a method of determining a resource processing policy.
It will be appreciated by those skilled in the art that the structure shown in fig. 7 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the method embodiments described above when the computer program is executed.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, implements the steps of the method embodiments described above.
In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the method embodiments described above.
The method, the device, the computer equipment, the storage medium and the computer program product for determining the resource processing strategy provided by the application relate to the technical field of information security, can be used in the technical field of finance and technology or other fields, and are not limited in application field.
It should be noted that, user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.

Claims (13)

1. A method for determining a resource processing policy, the method comprising:
responding to a resource processing request, determining a target payee contained in the resource processing request, and acquiring historical transaction data of the target payee;
extracting a plurality of transaction characteristic information based on the historical transaction data, and judging whether each transaction characteristic information meets a characteristic judgment condition or not;
Under the condition that the transaction characteristic information does not meet the characteristic discrimination conditions, determining a target transaction of which the resource in-out proportion meets the preset conditions based on the historical transaction data, and determining a target risk level of the target payee according to the number of times of the target transaction, the resource in-out proportion corresponding to the target transaction and a preset risk level matching strategy;
and determining a resource processing strategy matched with the target risk level based on the target risk level, and processing the resource processing request based on the resource processing strategy.
2. The method of claim 1, wherein determining, based on the historical transaction data, a target transaction in which a resource in-out-of-turn ratio meets a preset condition comprises:
determining each transfer transaction with the transfer resource number being greater than or equal to the target resource number from the historical transaction data, and determining transfer time of each transfer transaction;
for each transfer-in transaction, determining a preset period after transfer-in time of the transfer-in transaction as a target period, and determining the number of transfer-out resources in the target period;
obtaining a resource transfer-in-transfer-out ratio corresponding to the transfer-in transaction according to the ratio of the transfer-out resource number to the transfer-in resource number;
And determining the target transaction based on the in-transaction that the resource in-out proportion is greater than or equal to a preset threshold.
3. The method of claim 1, wherein the determining the target risk level of the target payee according to the number of target transactions, the resource transfer-in-transfer-out ratio corresponding to the target transactions, and a preset risk level matching policy comprises:
in a plurality of preset proportion intervals, determining a target proportion interval in which the resource transfer-in and transfer-out proportion of the target transaction is positioned and the times of the target transaction corresponding to each target proportion interval;
determining at least one candidate risk level corresponding to the target transaction according to the corresponding relation among the transaction times, the proportion interval and the risk level;
and determining the target risk level of the target payee based on at least one candidate risk level corresponding to the target transaction.
4. The method of claim 1, wherein said determining whether each of said transaction characteristic information satisfies a characteristic discrimination condition comprises:
under the condition that target transaction characteristic information in the transaction characteristic information does not meet preset conditions, judging that the transaction characteristic information does not meet characteristic judging conditions; the target transaction characteristic information is transaction characteristic information of a target characteristic type;
And under the condition that any one target transaction characteristic information in the transaction characteristic information meets the preset condition, judging that the transaction characteristic information meets the characteristic judgment condition.
5. The method according to claim 4, wherein the method further comprises:
and under the condition that any one target transaction characteristic information in the transaction characteristic information meets a preset condition, determining the target risk level of the target payee based on the risk level matched with the target transaction characteristic information meeting the preset condition.
6. The method according to claim 4, wherein the method further comprises:
acquiring historical transaction data of a plurality of sample accounts, wherein the sample accounts comprise positive sample accounts and negative sample accounts, the positive sample accounts refer to accounts without abnormal transaction records, and the negative sample accounts refer to accounts with abnormal transaction records;
based on the historical transaction data of each sample account, extracting positive sample transaction characteristic information and negative sample transaction characteristic information corresponding to a plurality of characteristic types;
determining the difference degree of positive sample transaction characteristic information and negative sample transaction characteristic information corresponding to each characteristic type;
And determining target feature types with the difference degree meeting preset conditions according to the difference degree corresponding to each feature type.
7. The method of claim 1, wherein said determining whether each of said transaction characteristic information satisfies a characteristic discrimination condition comprises:
under the condition that target transaction characteristic information in each transaction characteristic information does not meet the preset condition, determining a strategy according to each transaction characteristic information and the preset score to obtain a target score corresponding to each transaction characteristic information;
under the condition that the target score meets the preset condition, judging that each transaction characteristic information meets the characteristic judgment condition;
and under the condition that the target score does not meet the preset condition, judging that the transaction characteristic information does not meet the characteristic judging condition.
8. The method of claim 7, wherein the determining a policy according to each transaction characteristic information and a preset score to obtain a target score corresponding to each transaction characteristic information includes:
determining the score corresponding to each transaction characteristic information according to the corresponding relation between the pre-established transaction characteristic information and the score, and obtaining the target score corresponding to each transaction characteristic information according to the score corresponding to each target transaction characteristic information.
9. The method of claim 8, wherein the method further comprises:
and under the condition that the target scores meet preset conditions, determining the risk level matched with the target scores as the target risk level of the target payee.
10. A device for determining a resource processing policy, the device comprising:
the first acquisition module is used for responding to the resource processing request, determining a target payee contained in the resource processing request and acquiring historical transaction data of the target payee;
the judging module is used for extracting a plurality of transaction characteristic information based on the historical transaction data and judging whether each transaction characteristic information meets the characteristic judging condition or not;
the first determining module is used for determining a target transaction of which the resource transfer-in-transfer-out proportion meets a preset condition based on the historical transaction data under the condition that the transaction characteristic information does not meet the characteristic discrimination condition, and determining a target risk level of the target payee according to the times of the target transaction, the resource transfer-in-transfer-out proportion corresponding to the target transaction and a preset risk level matching strategy;
And the second determining module is used for determining a resource processing strategy matched with the target risk level based on the target risk level and processing the resource processing request based on the resource processing strategy.
11. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any one of claims 1 to 9 when the computer program is executed.
12. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 9.
13. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any one of claims 1 to 9.
CN202310379874.1A 2023-04-11 2023-04-11 Method, device, computer equipment and storage medium for determining resource processing strategy Pending CN116452206A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116823273A (en) * 2023-08-30 2023-09-29 环球数科集团有限公司 Distributed network payment system based on Web3 technology
CN117273749A (en) * 2023-11-21 2023-12-22 青岛巨商汇网络科技有限公司 Transaction management method and system based on intelligent interaction

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116823273A (en) * 2023-08-30 2023-09-29 环球数科集团有限公司 Distributed network payment system based on Web3 technology
CN116823273B (en) * 2023-08-30 2023-11-17 环球数科集团有限公司 Distributed network payment system based on Web3 technology
CN117273749A (en) * 2023-11-21 2023-12-22 青岛巨商汇网络科技有限公司 Transaction management method and system based on intelligent interaction

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