WO2020107756A1 - Procédé anti-fraude sur crédit, système, dispositif et support d'informations lisible par ordinateur - Google Patents

Procédé anti-fraude sur crédit, système, dispositif et support d'informations lisible par ordinateur Download PDF

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Publication number
WO2020107756A1
WO2020107756A1 PCT/CN2019/079495 CN2019079495W WO2020107756A1 WO 2020107756 A1 WO2020107756 A1 WO 2020107756A1 CN 2019079495 W CN2019079495 W CN 2019079495W WO 2020107756 A1 WO2020107756 A1 WO 2020107756A1
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Prior art keywords
fraud
strategy
credit
customer
core
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PCT/CN2019/079495
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English (en)
Chinese (zh)
Inventor
徐倩
杨海军
杨强
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深圳前海微众银行股份有限公司
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Publication of WO2020107756A1 publication Critical patent/WO2020107756A1/fr

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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
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/03—Credit; Loans; Processing thereof

Definitions

  • the invention relates to the field of anti-fraud, in particular to a credit anti-fraud method, system, equipment and computer-readable storage medium.
  • the identification of fraudulent customers and events is mainly based on face recognition technology and voiceprint recognition technology, which has improved the recognition rate of fraud to a certain extent.
  • fraudulent customers are driven by interests and change various fraud methods to search for anti-fraud
  • fraud is used to attempt to defraud financial institutions.
  • the existing anti-fraud methods cannot identify all fraudulent customers and events, and the coverage rate of fraud identification is low. Therefore, how to improve fraud recognition rate and coverage rate and reduce credit risk is an urgent problem to be solved.
  • the main purpose of the present invention is to provide a credit anti-fraud method, system, device and computer-readable storage medium, aiming to increase fraud recognition rate and coverage rate and reduce credit risk.
  • the present invention provides a credit anti-fraud method, which includes the following steps:
  • the target anti-fraud strategy is voiceprint anti-fraud strategy, voice anti-fraud strategy, light voice anti-fraud strategy, background sound anti-fraud strategy and voice emotion anti-fraud strategy.
  • an anti-fraud identification operation is performed to obtain the anti-fraud identification result of the current nuclear customer.
  • the step of determining the target anti-fraud strategy includes:
  • mapping relationship table obtains a policy identification code corresponding to the risk level, and determine an anti-fraud strategy corresponding to the policy identification code as a target anti-fraud strategy.
  • the current nuclear customer's risk level is adjusted according to the anti-fraud identification result, and a fraud risk reminder operation is performed.
  • the step of adjusting the current risk level of the nuclear customer includes:
  • the anti-fraud identification result determine the fraud index of the current nuclear customer, and determine whether the fraud index is greater than or equal to a preset threshold;
  • the fraud index is greater than or equal to a preset threshold, the risk level of the current nuclear customer is increased by one level.
  • the method further includes:
  • the core adding issue broadcast operation is performed in the credit core body call according to the preset core adding question tree.
  • step of performing the auditing issue broadcast operation in the credit core call according to the preset auditing issue tree includes:
  • the corresponding next added core question in the added core question tree is broadcasted in the credit core body call according to the current answer option;
  • the present invention also provides a credit anti-fraud system.
  • the credit anti-fraud system includes:
  • An obtaining module used to obtain the current risk level of the customer of the nuclear nucleus when the credit nucleus call is monitored, and obtain the customer voice data collected during the credit nucleus call;
  • a strategy determination module for determining a target anti-fraud strategy based on the risk level, wherein the target anti-fraud strategy is voiceprint anti-fraud strategy, voice anti-fraud strategy, light voice anti-fraud strategy, background voice anti-fraud strategy and One or more of voice emotion anti-fraud strategies;
  • the anti-fraud module is used to perform an anti-fraud identification operation based on the customer voice data and the target anti-fraud strategy to obtain the anti-fraud identification result of the current nuclear customer.
  • the present invention also provides a credit anti-fraud device
  • the credit anti-fraud device includes: a memory, a processor, and a credit anti-fraud device stored on the memory and operable on the processor Program, when the credit anti-fraud program is executed by the processor, the following steps are realized:
  • the target anti-fraud strategy is voiceprint anti-fraud strategy, voice anti-fraud strategy, light voice anti-fraud strategy, background sound anti-fraud strategy and voice emotion anti-fraud strategy.
  • an anti-fraud identification operation is performed to obtain the anti-fraud identification result of the current nuclear customer.
  • the present invention also provides a computer-readable storage medium on which a credit anti-fraud program is stored.
  • a credit anti-fraud program is stored on a computer-readable storage medium on which a credit anti-fraud program is stored.
  • the target anti-fraud strategy is voiceprint anti-fraud strategy, voice anti-fraud strategy, light voice anti-fraud strategy, background sound anti-fraud strategy and voice emotion anti-fraud strategy.
  • an anti-fraud identification operation is performed to obtain the anti-fraud identification result of the current nuclear customer.
  • the invention provides a credit anti-fraud method, system, equipment and computer-readable storage medium.
  • the invention initiates an outgoing call to the corresponding terminal device, and when the outgoing call is connected, Receive the customer voice data sent by the terminal device during the call, and then determine the target anti-fraud strategy based on the risk level in the credit verification instruction, and execute the corresponding anti-fraud based on the customer voice data and the target anti-fraud strategy Recognition operation to obtain the anti-fraud identification result of the current nuclear customer, through the above-mentioned methods, in the nuclear verification process, based on the current nuclear customer's risk level, select the corresponding anti-fraud strategy, and based on the customer's voice data and anti-fraud strategy , Perform the corresponding anti-fraud identification operation, which can accurately and comprehensively identify anti-fraud, effectively improve the identification rate and coverage of fraud, and reduce credit risk.
  • FIG. 1 is a schematic diagram of a device structure of a hardware operating environment according to an embodiment of the present invention
  • FIG. 2 is a schematic flowchart of a first embodiment of a credit anti-fraud method of the present invention
  • FIG. 3 is a schematic flowchart of a second embodiment of the credit anti-fraud method of the present invention.
  • FIG. 4 is a schematic diagram of functional modules of the first embodiment of the credit anti-fraud system of the present invention.
  • FIG. 1 is a schematic diagram of a device structure of a hardware operating environment according to a solution of an embodiment of the present invention.
  • the credit anti-fraud device may include: a processor 1001, such as a CPU, a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005.
  • the communication bus 1002 is used to implement connection communication between these components.
  • the user interface 1003 may include a display (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface.
  • the network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).
  • the memory 1005 may be a high-speed RAM memory or a stable memory (non-volatile memory), such as disk storage.
  • the memory 1005 may optionally be a storage device independent of the foregoing processor 1001.
  • FIG. 1 does not constitute a limitation on the credit anti-fraud device, and may include more or fewer components than the illustration, or a combination of certain components, or different Parts arrangement.
  • the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a credit anti-fraud program.
  • the network interface 1004 is mainly used to connect to the back-end server and perform data communication with the back-end server;
  • the user interface 1003 is mainly used to connect to the client (user end) and perform data communication with the client;
  • the processor 1001 can be used to call the credit anti-fraud program stored in the memory 1005 and perform the following steps:
  • the target anti-fraud strategy is voiceprint anti-fraud strategy, voice anti-fraud strategy, light voice anti-fraud strategy, background sound anti-fraud strategy and voice emotion anti-fraud strategy.
  • an anti-fraud identification operation is performed to obtain the anti-fraud identification result of the current nuclear customer.
  • processor 1001 may be used to call the credit anti-fraud program stored in the memory 1005, and further perform the following steps:
  • mapping relationship table obtains a policy identification code corresponding to the risk level, and determine an anti-fraud strategy corresponding to the policy identification code as a target anti-fraud strategy.
  • processor 1001 may be used to call the credit anti-fraud program stored in the memory 1005, and further perform the following steps:
  • the current nuclear customer's risk level is adjusted according to the anti-fraud identification result, and a fraud risk reminder operation is performed.
  • processor 1001 may be used to call the credit anti-fraud program stored in the memory 1005, and further perform the following steps:
  • the anti-fraud identification result determine the fraud index of the current nuclear customer, and determine whether the fraud index is greater than or equal to a preset threshold;
  • the fraud index is greater than or equal to a preset threshold, the risk level of the current nuclear customer is increased by one level.
  • processor 1001 may be used to call the credit anti-fraud program stored in the memory 1005, and further perform the following steps:
  • the core adding issue broadcast operation is performed in the credit core body call according to the preset core adding question tree.
  • processor 1001 may be used to call the credit anti-fraud program stored in the memory 1005, and further perform the following steps:
  • the corresponding next added core question in the added core question tree is broadcasted in the credit core body call according to the current answer option;
  • the specific embodiments of the credit anti-fraud device of the present invention are basically the same as the specific embodiments of the credit anti-fraud method described below, and are not repeated here.
  • the invention provides a credit anti-fraud method.
  • FIG. 2 is a schematic flowchart of a first embodiment of a credit anti-fraud method of the present invention.
  • the credit anti-fraud method includes:
  • Step S101 when a credit verification call is monitored, the current risk level of the verification client is obtained, and the customer voice data collected during the credit verification call is acquired;
  • the credit anti-fraud method is applied to a credit anti-fraud system, which can identify anti-fraud customers based on the credit anti-fraud system during the process of verifying customers, and determine whether the nuclear customers There is suspicion of fraud.
  • the electrical auditor pulls the nuclear body work order through the nuclear body front, that is, requests the nuclear body work order from the nuclear body server through the nuclear body front, and the nuclear body server issues the corresponding nuclear based on the request of the nuclear body front
  • the body work order and after receiving the body work order, the body front page displays the body work page.
  • the body work page displays the body work order, outgoing control, body work process, takeover control, and check. Controls, etc.
  • the verification process includes self-reported identity, confirmation of the convenience of customer calls, phone recording prompts, confirmation of customer information, broadcast and verification issues, and loan use reminders, etc.
  • the verification work order contains the work order type and work order identification code , Business basic information, customer basic information and historical verification records, etc., and the basic business information includes but is not limited to review type, loan application channel, work order level and nuclear verification information, the basic customer information includes but not limited to name, Gender, date of birth, ID card number, mobile phone number and marital status.
  • the electric nuclear operator triggers a credit verification request containing the risk level and mobile phone number, and sends the credit verification request to the nuclear verification server.
  • the credit verification request is forwarded to the soft phone platform, and the soft verification platform initiates a credit verification call based on the mobile phone number in the credit verification request.
  • the electric auditor or intelligent robot can communicate with The current nuclear customer communication is to make a credit nuclear phone call.
  • the customer voice data in the nuclear phone call is collected, and the customer voice data is transmitted to the credit anti-fraud system.
  • the credit anti-fraud system When the credit anti-fraud system detects a credit verification call, it obtains the current risk level of the verification client and obtains the customer voice data collected during the credit verification call. It should be noted that the higher the risk level, the higher the customer's suspected fraud, and the lower the risk level, the lower the customer's fraud suspected.
  • the risk level includes S level, A level, B level, C level, D level, and E level, a total of six levels, among which, credit customers with risk level S are not suspected of fraud, and credit customers with risk level A Has a very low suspicion of fraud, credit customers with a risk rating of B have a low suspicion of fraud, credit customers with a risk rating of C have a high suspicion of fraud, and credit customers with a risk rating of D have a high suspicion of fraud with a risk rating of E-grade credit customers are extremely suspected of fraud.
  • Step S102 Determine the target anti-fraud strategy based on the risk level, where the target anti-fraud strategy is voiceprint anti-fraud strategy, voice anti-fraud strategy, light voice anti-fraud strategy, background voice anti-fraud strategy and voice emotion anti-fraud strategy.
  • the target anti-fraud strategy is voiceprint anti-fraud strategy, voice anti-fraud strategy, light voice anti-fraud strategy, background voice anti-fraud strategy and voice emotion anti-fraud strategy
  • the credit anti-fraud system determines the target anti-fraud strategy of the current nuclear client based on the risk level, that is, between the pre-stored risk level and the policy identification code And then query the mapping relationship table to obtain the policy identification code corresponding to the risk level, and determine the anti-fraud strategy corresponding to the policy identification code as the target anti-fraud strategy.
  • the target anti-fraud strategy is one or more of voiceprint anti-fraud strategy, voice anti-fraud strategy, light voice anti-fraud strategy, background sound anti-fraud strategy and voice emotional anti-fraud strategy.
  • the mapping relationship table between the risk level and the policy identification code can be set by a person skilled in the art based on actual conditions, which is not specifically limited in this embodiment.
  • the risk level includes six levels: S level, A level, B level, C level, D level, and E level.
  • the corresponding policy identification codes of the voice emotion anti-fraud strategy are Pxxxx, Oxxxx, Ixxxx, Uxxxx, and Yxxxx respectively.
  • Risk level Strategy ID S grade Pxxxx Class A Pxxxx Oxxxx Class B Pxxxx Oxxxx Ixxxx Class C Pxxxx Oxxxx Ixxxx Uxxxx Class D Pxxxx Oxxxx Ixxxx Uxxxx Yxxxx Class E Pxxxx Oxxxx Ixxxx Uxxxx Yxxxx
  • the target anti-fraud strategy is the voiceprint anti-fraud strategy corresponding to Pxxxx; if the risk level is A level, then the target anti-fraud strategy includes the voiceprint anti-fraud strategy corresponding to Pxxxx and Oxxxx Corresponding voice anti-fraud strategy; if the risk level is B, the target anti-fraud strategy includes the voiceprint anti-fraud strategy corresponding to Pxxxx, the voice anti-fraud strategy corresponding to Oxxxx, and the light voice anti-fraud strategy corresponding to Ixxxx; the risk level is C , The target anti-fraud strategy includes the voiceprint anti-fraud strategy corresponding to Pxxxx, the voice anti-fraud strategy corresponding to Oxxxx, the light voice anti-fraud strategy corresponding to Ixxxx, and the background sound anti-fraud strategy corresponding to Uxxxx; the risk levels are D and E , The target anti-fraud strategies include the voiceprint anti-fraud strategy corresponding to Pxxxx,
  • the voiceprint anti-fraud strategy is to perform voiceprint recognition on customer voice data to obtain the voiceprint of the current core customer, and compare the obtained voiceprint with the pre-recorded voiceprint of the current core customer in the voiceprint library. To determine whether the voiceprint is the same as the pre-recorded voiceprint of the current core customer in the voiceprint library. If the voiceprint is the same, it can be determined that the person answering the call is the person. Determine that the current nuclear customer is suspected of fraud; or, compare the recognized voiceprint with the voiceprint in the voiceprint blacklist library. If it hits, it is determined that the current nuclear customer is suspected of fraud. If it does not hit, it will not be processed. .
  • the voice anti-fraud strategy is to perform voice recognition on the customer's voice data, convert the customer's voice into text information, and match the text information with the currently pre-recorded related information. If it does not match, then it can be determined that the current nuclear customer exists Suspect fraud, if it does not match, it will not be processed; light voice anti-fraud strategy is to perform light voice recognition on customer voice data to determine whether there is light voice in customer voice data. If there is light voice in customer voice data, you can determine the current Suspected customers are suspected of fraud, and if there is no light voice in the customer's voice data, it will not be processed.
  • the background sound anti-fraud strategy is to identify the background sound in the customer's voice data, determine the type of background sound of the current nuclear customer's environment, and use the recognized background sound category and the current nuclear customer's pre-recorded background category as By comparison, if the category of the background sound recognized is different from the pre-recorded background category of the current nuclear customer, it can be determined that the current nuclear customer is suspected of fraud.
  • the voice emotion anti-fraud strategy is to perform voice emotion recognition on the customer's voice data, determine the emotion category of the current nuclear customer, and determine whether the emotion category is the preset emotion category, such as Nervous, if the emotion category is the preset emotion category, it can be determined that the current nuclear customer is suspected of fraud, and if the emotion category is not the preset emotion category, it will not be processed.
  • the preset emotion category such as Nervous
  • Step S103 according to the customer's voice data and the target anti-fraud strategy, perform an anti-fraud identification operation to obtain the anti-fraud identification result of the current nuclear customer.
  • the credit anti-fraud system after determining the customer voice data and the target anti-fraud strategy, performs an anti-fraud identification operation based on the customer's voice data and the target anti-fraud strategy to obtain the anti-fraud identification result of the current nuclear customer.
  • the target anti-fraud strategy as voiceprint anti-fraud strategy, voice anti-fraud strategy, light voice anti-fraud strategy, background sound anti-fraud strategy and voice emotion anti-fraud strategy as examples:
  • the credit anti-fraud system first implements the voiceprint anti-fraud strategy for current nuclear customers to obtain the first anti-fraud recognition result of the current nuclear customers, that is, the credit anti-fraud system performs voiceprint feature recognition on the customer's voice data to obtain several voices Voiceprint feature, and input a number of voiceprint feature numbers into the voiceprint model to obtain the voiceprint of the current core user, and then determine whether the voiceprint is the same as the pre-recorded voiceprint of the current client, if the voiceprint is the same as the current client’s voiceprint If the pre-recorded voiceprints are the same, the first anti-fraud identification result of the current nuclear customer is that there is no suspect of non-self fraud. If the voiceprint is different from the pre-recorded voiceprint of the current customer, the first nuclear customer is determined to be the first The result of anti-fraud identification is suspected of non-self fraud;
  • the voice anti-fraud strategy on the current nuclear customer to obtain the second anti-fraud recognition result of the current nuclear customer, that is, the credit anti-fraud system performs voice recognition on the customer's voice data, converts the customer's voice data into text, and converts the text Match with the pre-entered text of the current nuclear body customer. If the text matches the pre-entered text of the current nuclear body customer, the second anti-fraud recognition result of the current nuclear body customer is determined to be that there is no suspected information fraud. If the pre-entered text of the current nuclear customer does not match, it is determined that the second anti-fraud identification result of the current nuclear customer is suspected of information fraud;
  • the credit anti-fraud system performs light speech recognition on the customer's voice data to determine whether the customer's voice data There is voice data that includes soft-sound sound spectrum features. If there is no voice data that includes soft-sound sound spectrum features in the customer's voice data, the third anti-fraud recognition result of the current nuclear customer is determined to be that there is no soft-spoofing suspect. If there is voice data in the customer's voice data that contains soft voice spectrum characteristics, the third anti-fraud recognition result of the current core customer is determined to be suspected of soft fraud;
  • the background sound anti-fraud strategy on the current nuclear customer to obtain the fourth anti-fraud recognition result of the current nuclear customer, that is, the credit anti-fraud system performs background sound recognition on the customer's voice data to obtain the current nuclear customer's location The background sound category of the environment, and compare the background sound category with the pre-recorded background sound category of the current nuclear customer. If the background sound category is the same as the pre-recorded background sound category of the current nuclear customer, determine the current nuclear customer The fourth anti-fraud recognition result is that there is no suspect of background sound fraud. If the background sound category is different from the pre-recorded background sound category of the current nuclear body customer, the fourth anti-fraud recognition result of the current nuclear body customer is determined to be the presence of background sound Suspected of fraud;
  • the voice emotion anti-fraud strategy is implemented on the current nuclear customer, and the fifth anti-fraud recognition result of the current nuclear customer is obtained, that is, the credit anti-fraud system performs voice emotion recognition on the customer's voice data to obtain the voice emotion of the current nuclear customer Category, and determine whether the voice sentiment category is a preset category, such as nervousness, if the voice sentiment category is a preset category, it is determined that there is a suspected emotional fraud in the fifth anti-fraud recognition result of the current nuclear customer, if the voice emotion If the category is not the preset category, it is determined that there is no suspected emotional fraud in the fifth anti-fraud identification result of the current nuclear customer.
  • the voice sentiment category is a preset category, such as nervousness
  • the results of anti-fraud identification include one or more of non-self fraud suspects, no information fraud suspects, no soft fraud suspects, no background sound fraud suspects, and no emotional fraud suspects, and non-self fraud suspects, There is a combination of one or more of suspected information fraud, suspected soft fraud, suspected background sound fraud, and suspected emotional fraud.
  • the credit anti-fraud system can also simultaneously execute voiceprint anti-fraud strategy, voice anti-fraud strategy, light voice anti-fraud strategy, background voice anti-fraud strategy and voice emotional anti-fraud strategy.
  • the invention when the credit verification instruction is detected, the invention initiates an outgoing call to the corresponding terminal device, and when the outgoing call is connected, receives the customer voice data sent by the terminal device during the call, and then According to the risk level in the credit verification command, determine the target anti-fraud strategy, and perform the corresponding anti-fraud identification operation based on the customer's voice data and the target anti-fraud strategy to obtain the current anti-fraud identification result of the nuclear verification customer,
  • the corresponding anti-fraud strategy is selected, and based on the customer's voice data and anti-fraud strategy, the corresponding anti-fraud identification operation is performed to accurately and comprehensively identify the anti-fraud Fraud, effectively improve the recognition rate and coverage of fraud, and reduce credit risk.
  • step S103 a second embodiment of the credit anti-fraud method of the present invention is proposed.
  • the difference from the foregoing embodiment is that after step S103, it further includes:
  • Step S104 According to the anti-fraud identification result, determine whether the current nuclear customer has suspected fraud
  • the credit anti-fraud system determines whether the current nuclear client is suspected of fraud based on the anti-fraud identification result, that is, the anti-fraud identification of the current nuclear client Does the result include one or more of suspected non-person fraud, suspected information fraud, suspected soft fraud, suspected background audio fraud, and suspected emotional fraud?
  • the current anti-fraud identification result of the nuclear user includes one or more of non-personal fraud suspects, information fraud suspects, soft fraud suspects, background sound fraud suspects, and emotional fraud suspects
  • step S105 if the current nuclear customer has a suspicion of fraud, the risk level of the current nuclear customer is adjusted according to the anti-fraud identification result, and a fraud risk reminder operation is performed.
  • the credit anti-fraud system adjusts the current nuclear identity customer's risk level based on the anti-fraud identification result, that is, increases the current nuclear identity customer's risk level, and executes a fraud risk alert operating. Specifically, based on the anti-fraud identification result, the current fraudulent customer's fraud index is determined, and it is determined whether the fraudulent index is greater than or equal to a preset threshold. Increase the risk level by one level. Among them, the higher the risk level, the greater the suspicion of fraud.
  • the risk level includes six levels: S level, A level, B level, C level, D level, and E level, and S level ⁇ A level ⁇ B level ⁇ C Level ⁇ D level ⁇ E level, that is, credit customers with risk level S are not suspected of fraud, credit customers with risk level A are extremely low, and credit customers with risk level B are low Credit customers with a rating of grade C have a higher suspicion of fraud, those with a risk rating of D have a higher suspicion of fraud, and those with a risk rating of E have a higher suspicion of fraud.
  • the above-mentioned preset threshold can be set by a person skilled in the art based on actual conditions, which is not specifically limited in this embodiment.
  • the way to determine the fraud index is to obtain each type of fraud suspect contained in the anti-fraud identification result (non-self fraud suspect, information fraud suspect, soft fraud suspect, background sound fraud suspect and voice emotional fraud suspect), and query the pre-stored
  • the mapping relationship table of fraud suspect types and fraud index obtain the fraud index corresponding to the included fraud suspect type, and accumulate the fraud index corresponding to each fraud suspect type, and the total fraud index obtained is the fraud index of the current nuclear customer.
  • the mapping relationship table between the fraud suspect type and the fraud index can be set by a person skilled in the art based on actual conditions, which is not specifically limited in this embodiment.
  • the mapping relationship between the suspected fraud type and the fraud index is shown in the following table: Type of suspected fraud Fraud Index Not suspected of fraud 30 Suspected information fraud 30 Softly suspected of fraud 15 Suspected background sound fraud 15 Voice Emotional Fraud Suspected 10
  • the current fraud index of the nuclear self-identity customers is 30; when the anti-fraud identification result contains non-person fraud suspects and softly suspected fraud, the current fraudulent customer's fraud index is 45 ; When the anti-fraud identification result includes light fraud suspects, background sound fraud suspects, and voice emotional fraud suspects, the current fraud index of the self-identity customers is 40; when the anti-fraud identification results include non-personal fraud suspects and information fraud suspects, the current nuclear The fraud index of personal customers is 60.
  • the present invention determines that the current nuclear customer is suspected of fraud based on the anti-fraud identification result, the risk level of the current nuclear customer is increased, and a fraud risk reminder operation is performed to reduce the risk of dishonesty.
  • a third embodiment of the credit anti-fraud method of the present invention is proposed, which differs from the foregoing embodiment in that after step S104, it further includes:
  • step A when the triggered verification instruction of the current nuclear body client is detected, the verification operation of the verification body is performed in the credit verification call according to the preset verification problem tree.
  • the electric auditor may manually trigger the current nuclear customer's nuclear addition instruction, or an intelligent robot may automatically trigger the current nuclear identity customer's nuclear addition instruction, when the credit anti-fraud system detects
  • the core addition question broadcast operation is performed on the outgoing call according to the preset core addition question tree, that is, each time the current answer option selected by the customer based on the current core addition question is received
  • the current answer option determine whether there is a corresponding next added nuclear question in the added nuclear question tree, if there is a corresponding next added nuclear question in the added nuclear question tree, then according to the current answer option, in the credit core
  • the corresponding next core addition problem in the core addition problem tree is broadcast.
  • the execution of the core addition problem broadcast operation is stopped.
  • the answer options of the core-adding question A are A1 and A2, respectively, and the next core-adding question corresponding to the answer option A1 in the core decision tree is the core-adding question 1, and the next option corresponding to the answer option A2 in the core decision tree
  • the core addition question is the core addition question 2, when the answer option is the core addition question A's answer option A1, the next core addition question is the core addition question 1, otherwise the answer option is the core addition question A's answer option A2,
  • the next nuclear issue is nuclear issue 2.
  • the present invention can add a check to the current nucleus verification customer to further improve the accuracy rate of anti-fraud identification and the nucleus verification accuracy.
  • the invention also provides a credit anti-fraud system.
  • FIG. 4 is a schematic diagram of functional modules of the first embodiment of the credit anti-fraud system of the present invention.
  • the credit anti-fraud system includes:
  • the obtaining module 101 is used to obtain the current risk level of the customer of the nuclear body when the credit body call is monitored, and obtain the customer voice data collected during the credit body phone call;
  • the strategy determination module 102 is used to determine a target anti-fraud strategy based on the risk level, wherein the target anti-fraud strategy is a voiceprint anti-fraud strategy, a voice anti-fraud strategy, a light voice anti-fraud strategy, and a background voice anti-fraud strategy And one or more of voice emotion anti-fraud strategies;
  • the anti-fraud module 103 is configured to perform an anti-fraud identification operation according to the customer voice data and the target anti-fraud strategy to obtain the anti-fraud identification result of the current nuclear customer.
  • policy determination module 102 is also used to:
  • mapping relationship table obtains a policy identification code corresponding to the risk level, and determine an anti-fraud strategy corresponding to the policy identification code as a target anti-fraud strategy.
  • the credit anti-fraud system also includes:
  • the judging module is used for judging whether the current nuclear customer is suspected of fraud based on the anti-fraud identification result
  • the risk level adjustment module is used to adjust the current risk level of the current nuclear client based on the anti-fraud identification result if the current nuclear client is suspected of fraud;
  • the execution module is used to perform fraud risk reminding operations.
  • risk level adjustment module is also used to:
  • the anti-fraud identification result determine the fraud index of the current nuclear customer, and determine whether the fraud index is greater than or equal to a preset threshold;
  • the fraud index is greater than or equal to a preset threshold, the risk level of the current nuclear customer is increased by one level.
  • the credit anti-fraud system also includes:
  • the core adding module is configured to perform an operation for reporting an additional core issue during the credit core call according to the preset core issue question tree when the triggered core core instruction of the current core entity is detected.
  • the core adding module is also used to:
  • the corresponding next added core question in the added core question tree is broadcasted in the credit core body call according to the current answer option;
  • an embodiment of the present invention further proposes a computer-readable storage medium on which a credit anti-fraud program is stored.
  • a credit anti-fraud program is executed by a processor, the following steps are performed:
  • the target anti-fraud strategy is voiceprint anti-fraud strategy, voice anti-fraud strategy, light voice anti-fraud strategy, background sound anti-fraud strategy and voice emotion anti-fraud strategy.
  • an anti-fraud identification operation is performed to obtain the anti-fraud identification result of the current nuclear customer.
  • mapping relationship table obtains a policy identification code corresponding to the risk level, and determine an anti-fraud strategy corresponding to the policy identification code as a target anti-fraud strategy.
  • the current nuclear customer's risk level is adjusted according to the anti-fraud identification result, and a fraud risk reminder operation is performed.
  • the anti-fraud identification result determine the fraud index of the current nuclear customer, and determine whether the fraud index is greater than or equal to a preset threshold;
  • the fraud index is greater than or equal to a preset threshold, the risk level of the current nuclear customer is increased by one level.
  • the core adding issue broadcast operation is performed in the credit core body call according to the preset core adding question tree.
  • the corresponding next added core question in the added core question tree is broadcasted in the credit core body call according to the current answer option;
  • the methods in the above embodiments can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware, but in many cases the former is better Implementation.
  • the technical solution of the present invention can be embodied in the form of a software product in essence or part that contributes to the existing technology, and the computer software product is stored in a storage medium (such as ROM/RAM) as described above , Disk, CD-ROM), including several instructions to enable a terminal device (which can be a mobile phone, computer, server, air conditioner, or network equipment, etc.) to perform the methods described in various embodiments of the present invention.

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Abstract

La présente invention concerne un procédé anti-fraude sur crédit, consistant : lors de la surveillance d'un appel de vérification d'identité liée à un crédit, à obtenir un niveau de risque d'un client dont l'identité est en cours de vérification et à obtenir des données vocales de client acquises pendant l'appel de vérification d'identité liée au crédit ; à déterminer une stratégie anti-fraude cible selon le niveau de risque, la stratégie anti-fraude cible étant une stratégie anti-fraude à empreinte vocale, une stratégie anti-fraude vocale, une stratégie anti-fraude vocale légère, une stratégie anti-fraude par bruit de fond et/ou une stratégie anti-fraude par émotion vocale ; et à réaliser une opération d'identification anti-fraude selon les données vocales de client et la stratégie anti-fraude cible, afin d'obtenir un résultat d'identification anti-fraude du client dont l'identité est en cours de vérification. La présente invention concerne également un système et un dispositif anti-fraude sur crédit et un support d'informations lisible par ordinateur. La présente invention peut améliorer le taux de reconnaissance et le taux de couverture de fraude et réduire le risque lié à un crédit.
PCT/CN2019/079495 2018-11-27 2019-03-25 Procédé anti-fraude sur crédit, système, dispositif et support d'informations lisible par ordinateur WO2020107756A1 (fr)

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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111738747A (zh) * 2020-06-24 2020-10-02 中诚信征信有限公司 一种反欺诈决策的方法及装置
CN112579864A (zh) * 2020-12-25 2021-03-30 北京明朝万达科技股份有限公司 反诈骗处理方法和装置
CN112862009A (zh) * 2021-03-29 2021-05-28 中信银行股份有限公司 一种基于流式计算的实时反欺诈数据处理方法及系统
CN113051058A (zh) * 2021-04-06 2021-06-29 浙江百应科技有限公司 一种反欺诈智能决策引擎的调度系统及方法
CN113506018A (zh) * 2021-07-26 2021-10-15 中国工商银行股份有限公司 线上作业处理方法、装置及系统
CN114445088A (zh) * 2022-01-13 2022-05-06 内蒙古蒙商消费金融股份有限公司 一种欺诈行为的判定方法、装置、电子设备和存储介质

Families Citing this family (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110532365A (zh) * 2019-09-02 2019-12-03 中国工商银行股份有限公司 信息确定方法、装置、系统、电子设备和介质
CN110852761B (zh) * 2019-10-11 2023-07-04 支付宝(杭州)信息技术有限公司 制定反作弊策略的方法、装置及电子设备
CN110718228B (zh) * 2019-10-22 2022-04-12 中信银行股份有限公司 语音分离方法、装置、电子设备及计算机可读存储介质
CN111275445B (zh) * 2020-01-15 2023-08-22 支付宝实验室(新加坡)有限公司 一种数据的处理方法、装置及设备
CN111339859A (zh) * 2020-02-17 2020-06-26 出门问问信息科技有限公司 一种多模态风险控制方法、装置以及计算机可读存储介质
CN112419040A (zh) * 2020-10-31 2021-02-26 国家电网有限公司 一种信贷反欺诈识别方法、装置和存储介质
CN112365341A (zh) * 2020-11-26 2021-02-12 四川享宇金信金融科技有限公司 信贷机构反欺诈方法、装置、设备和存储介质
CN112800272A (zh) * 2021-01-18 2021-05-14 德联易控科技(北京)有限公司 识别保险理赔欺诈行为的方法及装置
CN113592626A (zh) * 2021-07-29 2021-11-02 中国工商银行股份有限公司 反欺诈信用卡申请信息校验方法及装置
CN113852724A (zh) * 2021-09-24 2021-12-28 重庆富民银行股份有限公司 基于ai语音的异常行为防控方法

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108805695A (zh) * 2018-05-29 2018-11-13 成都启发分期科技有限公司 汽车金融业务风险评估方法
CN108806695A (zh) * 2018-04-17 2018-11-13 平安科技（深圳）有限公司 自更新的反欺诈方法、装置、计算机设备和存储介质
CN108882242A (zh) * 2018-06-08 2018-11-23 国家计算机网络与信息安全管理中心 基于声纹识别和意图理解技术的反诈骗系统的自学习方法

Family Cites Families (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8336085B2 (en) * 2004-11-15 2012-12-18 Microsoft Corporation Tuning product policy using observed evidence of customer behavior
CN102035649B (zh) * 2009-09-29 2013-08-21 国际商业机器公司 认证方法和装置
CN103886495A (zh) * 2013-09-30 2014-06-25 上海本家空调系统有限公司 一种基于网络交易的监控方法及系统
US20150142446A1 (en) * 2013-11-21 2015-05-21 Global Analytics, Inc. Credit Risk Decision Management System And Method Using Voice Analytics
US9875347B2 (en) * 2014-07-31 2018-01-23 Nok Nok Labs, Inc. System and method for performing authentication using data analytics
CN105005901A (zh) * 2015-07-09 2015-10-28 厦门快商通信息技术有限公司 一种面向金融领域的交易欺诈检测系统与方法
CN107679046A (zh) * 2016-08-01 2018-02-09 上海前隆信息科技有限公司 一种欺诈用户的检测方法及装置
CN107958215A (zh) * 2017-11-23 2018-04-24 深圳市分期乐网络科技有限公司 一种防欺诈识别方法、装置、服务器及存储介质
CN107945015B (zh) * 2018-01-12 2021-05-11 深圳壹账通智能科技有限公司 人机问答审核方法、装置、设备及计算机可读存储介质

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108806695A (zh) * 2018-04-17 2018-11-13 平安科技（深圳）有限公司 自更新的反欺诈方法、装置、计算机设备和存储介质
CN108805695A (zh) * 2018-05-29 2018-11-13 成都启发分期科技有限公司 汽车金融业务风险评估方法
CN108882242A (zh) * 2018-06-08 2018-11-23 国家计算机网络与信息安全管理中心 基于声纹识别和意图理解技术的反诈骗系统的自学习方法

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111738747A (zh) * 2020-06-24 2020-10-02 中诚信征信有限公司 一种反欺诈决策的方法及装置
CN112579864A (zh) * 2020-12-25 2021-03-30 北京明朝万达科技股份有限公司 反诈骗处理方法和装置
CN112579864B (zh) * 2020-12-25 2022-09-16 北京明朝万达科技股份有限公司 反诈骗处理方法和装置
CN112862009A (zh) * 2021-03-29 2021-05-28 中信银行股份有限公司 一种基于流式计算的实时反欺诈数据处理方法及系统
CN113051058A (zh) * 2021-04-06 2021-06-29 浙江百应科技有限公司 一种反欺诈智能决策引擎的调度系统及方法
CN113506018A (zh) * 2021-07-26 2021-10-15 中国工商银行股份有限公司 线上作业处理方法、装置及系统
CN114445088A (zh) * 2022-01-13 2022-05-06 内蒙古蒙商消费金融股份有限公司 一种欺诈行为的判定方法、装置、电子设备和存储介质

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