CN114827358A - Post-credit intelligent collection system based on big data wind control and AI voice recognition technology - Google Patents
Post-credit intelligent collection system based on big data wind control and AI voice recognition technology Download PDFInfo
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Abstract
The invention relates to a post-loan intelligent collection system based on big data wind control and AI voice recognition technology, which comprises: the receiving module receives a plurality of bill information generated based on a loan event, the setting module sets a pre-collection period and an overdue collection period according to time information, the dividing module divides three first collection queues according to transaction amount, the first collection module sends collection information to corresponding initiators according to the sequence of collection tasks in any first collection queue, the counting module counts the performance of the initiators in the pre-collection period, the adjusting module adjusts the collection modes in the collection tasks in the overdue collection periods according to the performance in the pre-collection periods, and the second collection module sends the collection information to the corresponding initiators according to the sequence of the adjusted collection tasks. The hastening mode of the hastening task in the overdue hastening period is adjusted through the pre-hastening period performance, so that the hastening success rate is improved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a post-credit intelligent collection system based on big data wind control and AI voice recognition technology.
Background
With the vigorous development of personal retail credit business, financial products are gradually enriched and have different product characteristics, and the products have the development tendency that the amount of money tends to decrease in a single amount of money and the number of strokes increases, so that great challenge is provided for post-loan management, and the requirement that the amount of money is small and the number of strokes is increased when the post-loan is urged to be received is difficult to meet only by means of manual electrocatalysis.
Patent application No. 202110206824.4 discloses an intelligent hasten-to-accept robot system comprising: the robot cluster module is used as an execution main body of the collection job to implement the collection task and establish communication with the client; the operation standardization module is used as a strategy convergence pool and is used for driving and coordinating all the robot operations in the robot cluster module by configuring a collection strategy; the full-voice interaction module is used for assisting the robot cluster module to communicate with the client for multiple times based on a preset collection target and a conversation strategy after the client hooks off the phone, continuously identifying the intention of the conversation responded by the client, switching the corresponding scene by the robot cluster module, and selecting a reasonable conversation strategy for communication to promote collection service; and the collection and portrait drawing module is used for generating customer portrait by depending on customer data and providing the customer portrait for the whole system to configure relevant collection and collection strategies and opinions by using the customer portrait.
In the prior art, when the collection prompting operation is carried out, a collection prompting technical strategy of a relevant scene is configured for the robot only according to the picture of a client, and the collection prompting mode is single, so that the collection prompting success rate of the client is low.
Disclosure of Invention
Therefore, the invention provides an intelligent post-loan collection system based on big data wind control and AI voice recognition technology, which can solve the problem of low collection success rate.
In order to achieve the above object, the present invention provides a post-loan intelligent collection system based on big data wind control and AI voice recognition technology, comprising:
the receiving module is used for receiving a plurality of pieces of bill information generated based on the loan event, wherein the bill information comprises the time information of the loan event, the sponsor of the loan event, the transaction amount of the loan event and the repayment mode;
the setting module is used for setting a pre-payment-urging period and an overdue payment-urging period according to time information, wherein the pre-payment-urging period is arranged before the overdue payment-urging period, calculating the listed payment time according to the time information and the payment mode, setting the payment time between the pre-payment-urging period and the overdue payment-urging period at any time, and calculating the listed payment amount according to the transaction amount and the payment mode;
the division module divides three first collection urging queues according to the transaction amount when the time information and the repayment mode are the same and the transaction amount is different, each collection urging queue at least comprises one collection urging task, and a collection urging task sequencing sequence is generated according to the transaction amount for any first collection urging queue, wherein the collection urging tasks comprise an initiator and a collection urging mode;
the first collection prompting module sends collection prompting information to the corresponding sponsor in a corresponding collection prompting mode according to the sequence of collection prompting tasks in any first collection prompting queue;
the statistic module is used for counting the performance of the initiator in the pre-hasten receiving period according to the repayment condition of the initiator and the voice state of the initiator;
the adjusting module adjusts an induced shrinkage mode in an induced shrinkage task in an overdue induced shrinkage period according to the performance in the pre-induced shrinkage period to generate four second induced shrinkage queues;
and the second collection prompting module sends collection prompting information to the corresponding sponsor in a corresponding collection prompting mode according to the sequence of collection prompting tasks in any second collection prompting queue.
Further, when three first collection queues are generated according to the transaction amount, the three first collection queues correspond to different collection modes, namely a manual collection mode, an AI collection mode and a short message collection mode;
when three first hasten queues are divided according to transaction amount, a first grade amount B1 and a second grade amount B2 are set, the transaction amount for any initiator is set to be A1,
when the transaction amount A1 is larger than or equal to the first-level amount B1, dividing a collection task corresponding to the transaction amount into a first collection queue with a collection mode being a manual collection mode;
when the first-grade amount B1 is larger than the transaction amount A1 and is not less than the second-grade amount B2, dividing the collection task corresponding to the transaction amount into a first collection queue of which the collection mode is an AI collection mode;
when the transaction amount A1 is less than the second-level amount B2, dividing the collection task corresponding to the transaction amount into a first collection queue with a collection mode being a short message collection mode.
The invention further provides an intelligent post-credit collection system based on big data wind control and AI voice recognition technology, which further comprises a screening module respectively connected with the dividing module and the first collection module, wherein after the dividing module divides the first collection queue and before the first collection module does not send a collection message, the screening module screens collection tasks in any first collection queue by two screening methods, wherein the first screening method screens according to the account balance of an initiator, when the account balance of the initiator is more than or equal to the current payment amount, the collection tasks corresponding to the initiator are screened from any first collection queue, and when the account balance of the initiator is less than the current payment amount, the collection tasks corresponding to the initiator are left in any first collection queue.
Further, when the screening module screens the collection tasks in any first collection queue through two screening methods, wherein the second screening method screens the collection tasks according to the preset collection time, the collection tasks in any first collection queue are divided into a first collection task and a second collection task according to the collection sequence, when the collection mode corresponding to any first collection queue is the manual collection mode,
when the first collection prompting task and the second collection prompting task are respectively distributed to two workers to be executed, if the sponsor of the first collection prompting task or the sponsor of the second collection prompting task completes repayment before the preset collection prompting time, the first collection prompting task or the second collection prompting task is screened from the first collection prompting queue;
when the first collection prompting task and the second collection prompting task are simultaneously distributed to a worker to be executed, if the sponsor of the first collection prompting task or the sponsor of the second collection prompting task completes repayment before the preset collection prompting time, the first collection prompting task or the second collection prompting task is removed from the first collection prompting queue, and if the sponsor of the second collection prompting task completes repayment before the first collection prompting task is completed, the second collection prompting task is screened from the first collection prompting queue;
and when the collection mode corresponding to any first collection urging queue is an AI collection urging mode or a short message collection urging mode, the AI collection urging mode or the short message collection urging mode has multi-task processing capacity, and if the sponsor of the first collection urging task or the sponsor of the second collection urging task completes repayment before the preset collection urging time, the first collection urging task or the second collection urging task is screened out of the first collection urging queue.
Further, when counting the performance of the initiator in the pre-collection period, judging the performance of the initiator in the pre-collection period according to the payment condition of the initiator, when the initiator does not complete the payment, if the collection urging mode of the collection urging task corresponding to the initiator is the short message collection urging mode, not performing grading on the initiator, and if the collection urging mode of the collection urging task corresponding to the initiator is the manual collection urging mode or the AI collection urging mode, performing grading on the initiator.
Further, when the sponsor is graded, the sponsor is graded according to the voice state of the sponsor, the grading is divided into three grades, namely, a first grade, a second grade and a third grade, wherein the first grade represents that the sponsor attitude is poor, the second grade represents that the sponsor attitude is good, the third grade represents that the sponsor attitude is good, the voice state is the speed of speech, the statistical module comprises a speed measuring unit and a statistical unit, the speed of speech S is measured by the speed measuring unit, the standard speed of speech ranges from a preset speed of speech C1 to a preset speed of speech C2,
when the speed of speech S of the initiator is larger than or equal to the preset speed of speech C2, representing that the attitude of the initiator is poor, counting the expression of the initiator in the pre-hastening receiving period as one level;
when the speed of speech S of the initiator is less than or equal to the preset speed of speech C1 and less than the preset speed of speech C2, the attitude of the initiator is indicated to be good, and the expression of the initiator in the pre-hastening receiving period is counted to be two levels;
and when the speech speed S of the initiator is less than the preset speech speed C1, the attitude of the initiator is superior, and the expression of the initiator in the pre-hastening period is counted into three levels.
Furthermore, when the hastening mode of the hastening task in the overdue hastening period is adjusted according to the performance in the pre-hastening period, four second hastening queues are generated, correspond to different hastening modes and are respectively a letter hastening mode, a manual hastening mode, an AI hastening mode and a short message hastening mode,
when the hastening mode of the first hastening task is a manual hastening mode, if the initiator of the first hastening task shows a first level in the pre-hastening period, the hastening mode of the first hastening task is adjusted to a letter hastening mode, if the initiator of the first hastening task shows a second level in the pre-hastening period, the hastening mode of the first hastening task is still the manual hastening mode, and if the initiator of the first hastening task shows a third level in the pre-hastening period, the hastening mode of the first hastening task is adjusted to a short message hastening mode;
when the hastening mode of the first hastening task is an AI hastening mode, if the initiator of the first hastening task shows a first level in the pre-hastening period, the hastening mode of the first hastening task is adjusted to an artificial hastening mode, if the initiator of the first hastening task shows a second level in the pre-hastening period, the hastening mode of the first hastening task is still the AI hastening mode, and if the initiator of the first hastening task shows a third level in the pre-hastening period, the hastening mode of the first hastening task is adjusted to a short message hastening mode;
when the collection mode of the first collection task is the short message collection mode, the collection mode of the first collection task is adjusted to be the AI collection mode after overdue.
Further, when the hasty mode is a short message hasty mode or a letter hasty mode, the text content required by the short message hasty mode or the file content required by the letter hasty mode of the overdue sponsor is automatically generated according to a preset template.
The intelligent post-loan collection system based on big data wind control and AI voice recognition technology further comprises a risk assessment module, wherein the risk assessment module is used for assessing the risk of the performance in the overdue collection period after the second collection module finishes collection, and outputting the risk assessment result in the overdue collection period.
Further, when performing risk assessment on the performance in the overdue payment acceleration period, if the initiator of the first payment acceleration task completes payment within the preset overdue payment acceleration time, the initiator of the first payment acceleration task is evaluated as a low-risk initiator, if the initiator of the first payment acceleration task does not complete payment within the preset overdue payment acceleration time, the initiator of the first payment acceleration task is evaluated as a high-risk initiator, and the second payment acceleration task of the high-risk initiator enters the first payment acceleration queue of the manual payment acceleration mode of the payment acceleration mode in advance of the preset time.
Compared with the prior art, the invention has the advantages that the receiving module receives a plurality of bill information generated based on loan events, the setting module sets a pre-payment-urging period and an overdue payment-urging period according to the time information in the bill information, then calculates the listed payment time according to the time information and the payment mode, any payment time is set between the pre-payment-urging period and the overdue payment-urging period, calculates the listed payment amount according to the transaction amount and the payment mode, then the dividing module divides three first payment-urging queues according to the transaction amount when the time information and the payment mode are the same and the transaction amount is different, the first payment-urging module sends payment-urging information to a corresponding initiator in a corresponding payment-urging mode according to the sequence of the payment-urging tasks in any first payment-urging queue to complete the tasks of the pre-payment-urging period, if the initiator does not pay at the payment time, then the statistics module counts the performance of the pre-harvest cycle, the adjustment module adjusts the harvest mode in the harvest tasks in the overdue harvest cycle according to the performance in the pre-harvest cycle to generate four second harvest queues, and finally the second harvest module sends out harvest information to corresponding promoters according to the harvest sequence in the second harvest queues to complete the overdue harvest cycle tasks, and adjusts the harvest mode of the harvest tasks in the overdue harvest cycle according to the performance in the pre-harvest cycle to further improve the success rate of harvest.
Particularly, the transaction amount of any initiator is classified according to the first-level amount and the second-level amount, the collection urging mode of the collection urging task corresponding to any initiator is determined, the collection urging mode is determined by classifying the transaction amount, the collection urging mode is richer, different grades correspond to different collection urging modes, and the collection urging success rate is improved.
Particularly, the screening module screens the payment prompting task with enough payment from the first payment prompting queue after the division module divides the first payment prompting queue and before the first payment prompting module does not send a payment prompting message according to the account balance of the sponsor, so that the payment is automatically paid in the payment time, the payment prompting task amount is reduced, and the work efficiency of payment prompting is improved.
Particularly, the screening module screens according to preset collection prompting time after the first collection prompting queue is divided by the dividing module and before the first collection prompting module does not send a collection prompting message, and if a sponsor of a collection prompting task completes repayment before the preset collection prompting time, the collection prompting task is removed from the first collection prompting queue, so that the collection prompting task amount is reduced, the collection prompting work efficiency is improved, and the experience of the sponsor is improved.
Particularly, when counting the performance of the initiator in the pre-receiving period, judging the performance of the initiator in the pre-receiving period according to the repayment condition of the initiator, when the initiator does not complete the repayment, if the collection urging mode of the collection urging task corresponding to the initiator is the short message collection urging mode, not grading the initiator, if the collection urging mode of the collection urging task corresponding to the initiator is the manual collection urging mode or the AI collection urging mode, grading the initiator, and by grading the performance of the initiator in the pre-receiving period, the late-adjustment and adjustment of the collection urging mode in the collection urging task in the overdue collection urging period can be realized, and the success rate of collection can be improved.
Particularly, when the voice is prompted to be received manually and AI is prompted to be received, the voice speed of the voice state of the initiator is graded through conversation with the initiator to form the representation of the pre-prompting receiving period, and the voice state of the initiator is graded, so that when the initiator is overdue, the prompt receiving mode in the prompt receiving task in the overdue prompting receiving period can be adjusted through the representation of the initiator in the pre-prompting receiving period, and the success rate of prompt receiving can be improved.
Particularly, when the hastening modes of hastening tasks in an overdue hastening period are adjusted in time according to the performance level of the initiator in the pre-hastening period, the hastening modes with the performance level of one level are all raised by one level, the hastening modes with the performance level of two levels are kept unchanged, the hastening modes with the performance level of three levels are all lowered by one level, and when short message hastening is carried out, the hastening modes of the hastening tasks in a hastening queue are adjusted according to the performance level of the initiator so that the hastening modes of the hastening tasks in the hastening queue can be adjusted, and the success rate of hastening can be improved.
Particularly, when the short message collection mode is the short message collection mode or the letter collection mode, the text content required by the short message collection mode of the overdue sponsor or the file content required by the letter collection mode is automatically generated according to the preset template so as to be directly sent by short messages or printed for mailing, the workload of manually writing the text content required by the short message collection mode or the file content required by the letter collection mode is reduced, and the working efficiency is improved.
Particularly, after the overdue hastening period, the risk evaluation module carries out risk evaluation on the performance in the overdue hastening period and outputs the performance score in the overdue hastening period, the sponsor with high risk score carries out a manual hastening mode in advance when carrying out a pre-hastening period in the next period, and the success rate of hastening and the efficiency of hastening work are improved through the risk evaluation.
Particularly, by performing risk assessment on the performance in the overdue payment acceleration period, whether the sponsor of the first payment acceleration task completes payment within the preset overdue payment acceleration time or not is judged as a low-risk sponsor, the sponsor of the first payment acceleration task does not complete payment is judged as a high-risk sponsor, the high-risk sponsor enters a payment acceleration mode into a first payment acceleration queue of an artificial payment acceleration mode in the preset time in advance, and the success rate of payment acceleration and the efficiency of payment acceleration work are improved through the risk assessment.
Drawings
Fig. 1 is a schematic structural diagram of a post-loan intelligent collection system based on big data wind control and AI voice recognition technologies according to an embodiment of the present invention.
Detailed Description
In order that the objects and advantages of the invention will be more clearly understood, the invention is further described below with reference to examples; it should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only for explaining the technical principle of the present invention, and do not limit the scope of the present invention.
It should be noted that in the description of the present invention, the terms of direction or positional relationship indicated by the terms "upper", "lower", "left", "right", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings, which are only for convenience of description, and do not indicate or imply that the device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and thus, should not be construed as limiting the present invention.
Furthermore, it should be noted that, in the description of the present invention, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
Referring to fig. 1, the intelligent post-credit collection system based on big data wind control and AI voice recognition technology according to an embodiment of the present invention includes:
the receiving module 110 receives a plurality of pieces of receipt information generated based on the loan event, wherein the receipt information includes time information of the loan event, an initiator of the loan event, a transaction amount of the loan event and a repayment mode;
the setting module 120 is used for setting a pre-payment-urging period and an overdue payment-urging period according to the time information, wherein the pre-payment-urging period is set before the overdue payment-urging period, calculating the listed payment time according to the time information and the payment mode, setting the payment time between the pre-payment-urging period and the overdue payment-urging period at any time, and calculating the listed payment amount according to the transaction amount and the payment mode;
the dividing module 130 divides three first receiving-urging queues according to the transaction amount when the time information and the repayment mode are the same and the transaction amount is different, each receiving-urging queue at least comprises a receiving-urging task, and a receiving-urging task sequencing sequence is generated according to the transaction amount for any first receiving-urging queue, wherein each receiving-urging task comprises an initiator and a receiving-urging mode;
the first collection prompting module 140 sends collection prompting information to the corresponding sponsor in a corresponding collection prompting mode according to the sequence of collection prompting tasks in any first collection prompting queue;
the statistic module 150 is used for counting the performance of the sponsor in the pre-hasten receiving period, and the performance in the pre-hasten receiving period is according to the repayment condition of the sponsor and the voice state of the sponsor;
the adjusting module 160 adjusts the hastening mode in the hastening task in the overdue hastening period according to the performance in the pre-hastening period to generate four second hastening queues;
the second hastening module 170 sends hastening information to the corresponding sponsor in the corresponding hastening mode according to the sequence of hastening tasks in any second hastening queue.
Specifically, the embodiment of the invention receives a plurality of bill information generated based on a loan event through a receiving module, a setting module sets a pre-charging period and an overdue charging period according to time information in the plurality of bill information, then calculates a listed repayment time according to the time information and a repayment mode, any repayment time is set between the pre-charging period and the overdue charging period, calculates a listed repayment amount according to a transaction amount and a repayment mode, then a dividing module divides three first hasten receiving queues according to the transaction amount when the time information and the repayment mode are the same and the transaction amount is different, the first hasten receiving module sends hasten receiving information to a corresponding initiator in a corresponding hasten receiving mode according to the sequence of the hasten receiving tasks in any first hasten receiving queue to complete the tasks of the pre-charging period, if the initiator does not repay at the repayment time, then a counting module counts the performance in the pre-charging period, secondly, the adjusting module adjusts the hastening and receiving mode in the hastening and receiving tasks in the overdue hastening and receiving period according to the performance in the pre-hastening and receiving period to generate four second hastening and receiving queues, and finally the second hastening and receiving module sends hastening and receiving information to corresponding promoters according to the hastening and receiving sequence in the second hastening and receiving queues to complete the tasks in the overdue hastening and receiving period.
Specifically, the list repayment time is calculated according to the time information and the repayment mode, the repayment mode is a stage condition, the list repayment time is the repayment time of each period, the list repayment amount is calculated according to the transaction amount and the repayment mode, and the list repayment amount is the repayment amount of each period.
Specifically, the embodiment of the invention calculates the column repayment time through the time information and the repayment mode, so that a pre-payment-urging period and an overdue urging period can be set according to the column repayment time, pre-payment urging is carried out before the repayment date, if the repayment is not completed before the repayment date, overdue urging is carried out, and the step-by-step payment urging is carried out on the column repayment date, so that the success rate of the debt urging can be improved, the column repayment amount is calculated through the transaction amount and the repayment mode, and the screening of the debt urging tasks according to the column repayment amount can reduce the amount of the debt urging and improve the work efficiency of the debt urging.
Specifically, when three first collection queues are generated according to the transaction amount, the three first collection queues correspond to different collection modes, namely a manual collection mode, an AI collection mode and a short message collection mode;
when three first hasten queues are divided according to transaction amount, a first grade amount B1 and a second grade amount B2 are set, the transaction amount for any initiator is set to be A1,
when the transaction amount A1 is larger than or equal to the first-level amount B1, dividing a collection task corresponding to the transaction amount into a first collection queue with a collection mode being a manual collection mode;
when the first-grade amount B1 is larger than the transaction amount A1 and is not less than the second-grade amount B2, dividing the collection task corresponding to the transaction amount into a first collection queue of which the collection mode is an AI collection mode;
when the transaction amount A1 is less than the second-level amount B2, dividing the collection task corresponding to the transaction amount into a first collection queue with a collection mode being a short message collection mode.
Specifically, the transaction amount of any initiator is classified according to the first-level amount and the second-level amount, the collection urging mode of the collection urging task corresponding to any initiator is determined, the collection urging mode is determined by classifying the transaction amount, the collection urging mode is richer, different grades correspond to different collection urging modes, and the collection urging success rate is improved.
Specifically, as shown in fig. 1, the system for intelligently urging receipt after credit based on big data air control and AI voice recognition technology according to the embodiment of the present invention further includes a screening module 180, which is respectively connected to the dividing module and the first urging receiving module, and after the dividing module divides the first urging receiving queue and before the first urging receiving module does not send an urging message, the screening module screens the urging receiving tasks in any first urging receiving queue through two screening methods, where the first screening method screens according to the account balance of the initiator, when the account balance of the initiator is greater than or equal to the current repayment amount, the urging receiving tasks corresponding to the initiator are screened from any first urging receiving queue, and when the account balance of the initiator is less than the current repayment amount, the urging receiving tasks corresponding to the initiator are left in any first urging receiving queue.
Specifically, in the embodiment of the present invention, the screening module screens the payment prompting task from the first payment prompting queue after the division module divides the first payment prompting queue and before the first payment prompting module does not send the payment prompting message according to the account balance of the sponsor, so that the payment prompting task with the account balance of the sponsor sufficient for payment is screened out from the first payment prompting queue, and the payment is automatically made at the time of payment, which reduces the amount of the payment prompting task and improves the work efficiency of payment prompting.
Specifically, when the screening module screens the collection tasks in any first collection queue through two screening methods, wherein the second screening method screens the collection tasks according to the preset collection time, the collection tasks in any first collection queue are divided into a first collection task and a second collection task according to the collection sequence, when the collection mode corresponding to any first collection queue is the manual collection mode,
when the first collection prompting task and the second collection prompting task are respectively distributed to two workers to be executed, if the sponsor of the first collection prompting task or the sponsor of the second collection prompting task completes repayment before the preset collection prompting time, the first collection prompting task or the second collection prompting task is screened from the first collection prompting queue;
when the first collection prompting task and the second collection prompting task are simultaneously distributed to a worker to be executed, if the sponsor of the first collection prompting task or the sponsor of the second collection prompting task completes repayment before the preset collection prompting time, the first collection prompting task or the second collection prompting task is removed from the first collection prompting queue, and if the sponsor of the second collection prompting task completes repayment before the first collection prompting task is completed, the second collection prompting task is screened from the first collection prompting queue;
and when the collection mode corresponding to any first collection urging queue is an AI collection urging mode or a short message collection urging mode, the AI collection urging mode or the short message collection urging mode has multi-task processing capacity, and if the sponsor of the first collection urging task or the sponsor of the second collection urging task completes repayment before the preset collection urging time, the first collection urging task or the second collection urging task is screened out of the first collection urging queue.
Specifically, the screening module screens the first collection urging queue according to the preset collection urging time after the division module divides the first collection urging queue and before the first collection urging module does not send the collection urging message, and if the sponsor of the collection urging task completes repayment before the preset collection urging time, the collection urging task is removed from the first collection urging queue, so that the collection urging task amount is reduced, the collection urging work efficiency is improved, and the experience of the sponsor is improved.
Specifically, when counting the performance of the initiator in the pre-collection period, the performance of the initiator in the pre-collection period is judged according to the payment condition of the initiator, when the initiator does not complete payment, if the collection urging mode of the collection urging task corresponding to the initiator is the short message collection urging mode, the initiator is not graded, and if the collection urging mode of the collection urging task corresponding to the initiator is the manual collection urging mode or the AI collection urging mode, the initiator is graded.
Specifically, when the performance of the initiator in the pre-collection period is counted, the performance of the initiator in the pre-collection period is judged according to the repayment condition of the initiator, when the initiator does not complete the repayment, if the collection urging mode of the collection urging task corresponding to the initiator is the short message collection urging mode, the initiator is not subjected to grade division, if the collection urging mode of the collection urging task corresponding to the initiator is the manual collection urging mode or the AI collection urging mode, the initiator is subjected to grade division, and the performance of the initiator in the pre-collection urging period is subjected to grade division, so that the collection urging mode in the collection urging task in the post-adjustment and adjustment period can be realized, and the collection urging success rate can be improved.
Specifically, when the sponsor is graded, the sponsor is graded according to the voice state of the sponsor, the grading is divided into three grades, namely, a first grade, a second grade and a third grade, wherein the first grade represents that the sponsor attitude is poor, the second grade represents that the sponsor attitude is good, the third grade represents that the sponsor attitude is good, the voice state is the speed of speech, the statistical module comprises a speed measuring unit and a statistical unit, the speed of speech S is measured by the speed measuring unit, the standard speed of speech ranges from a preset speed of speech C1 to a preset speed of speech C2,
when the speed of speech S of the initiator is larger than or equal to the preset speed of speech C2, representing that the attitude of the initiator is poor, counting the expression of the initiator in the pre-hastening receiving period as one level;
when the speed of speech S of the initiator is less than or equal to the preset speed of speech C1 and less than the preset speed of speech C2, the attitude of the initiator is indicated to be good, and the expression of the initiator in the pre-hastening receiving period is counted to be two levels;
and when the speech speed S of the initiator is less than the preset speech speed C1, the attitude of the initiator is superior, and the expression of the initiator in the pre-hastening period is counted into three levels.
Specifically, because the speed and the volume of speech are affected by the emotional fluctuation of the person, the speed of speech of the initiator is compared with the preset normal speed of speech to grade the performance of the initiator in the pre-hastening and receiving period, and the volume of the initiator is compared with the preset normal volume to grade the performance of the initiator in the pre-hastening and receiving period.
Specifically, in the embodiment of the invention, when the manual collection and the AI collection are performed, the voice speed of the voice state of the initiator is graded through the dialogue with the initiator to form the expression of the pre-collection period, and the voice state of the initiator is graded, so that when the initiator is overdue, the collection mode in the collection task in the overdue collection period can be adjusted through the expression of the initiator in the pre-collection period, and the success rate of collection can be improved.
Specifically, when the hastening mode of the hastening task in the overdue hastening period is adjusted according to the performance in the pre-hastening period, four second hastening queues are generated, correspond to different hastening modes and are respectively a letter hastening mode, a manual hastening mode, an AI hastening mode and a short message hastening mode,
when the hastening mode of the first hastening receiving task is a manual hastening receiving mode, if the behavior of the initiator of the first hastening receiving task in the pre-hastening receiving period is a first level, the hastening receiving mode of the first hastening receiving task is adjusted to be a letter hastening receiving mode, if the behavior of the initiator of the first hastening receiving task in the pre-hastening receiving period is a second level, the hastening receiving mode of the first hastening receiving task is still a manual hastening receiving mode, and if the behavior of the initiator of the first hastening receiving task in the pre-hastening receiving period is a third level, the hastening receiving mode of the first hastening receiving task is adjusted to be a short message hastening receiving mode;
when the hastening mode of the first hastening task is an AI hastening mode, if the initiator of the first hastening task shows a first level in the pre-hastening period, the hastening mode of the first hastening task is adjusted to an artificial hastening mode, if the initiator of the first hastening task shows a second level in the pre-hastening period, the hastening mode of the first hastening task is still the AI hastening mode, and if the initiator of the first hastening task shows a third level in the pre-hastening period, the hastening mode of the first hastening task is adjusted to a short message hastening mode;
when the collection mode of the first collection task is the short message collection mode, the collection mode of the first collection task is adjusted to be the AI collection mode after overdue.
Specifically, according to the performance level of the initiator in the pre-collection period, when the collection mode of the collection task in the overdue collection period is adjusted in time, the collection modes with the performance level of one level are all increased by one level, the collection modes with the performance level of two levels are kept unchanged, the collection modes with the performance levels of three levels are all decreased by one level, and when short message collection is performed, the collection modes are increased to the AI collection mode as long as the initiator is overdue, so that the collection modes of the collection tasks in the collection queue are adjusted according to the performance level of the initiator, and the success rate of collection can be improved.
Specifically, when the message collection mode is a short message collection mode or a letter collection mode, text content required by the message collection mode of the overdue sponsor or file content required by the letter collection mode is automatically generated according to a preset template.
Specifically, when the message collection mode is the short message collection mode or the letter collection mode, the text content required by the message collection mode of the overdue sponsor or the file content required by the letter collection mode is automatically generated according to the preset template so as to be directly sent by short messages or printed for mailing, the workload of manually writing the short message collection mode or the letter collection mode is reduced, and the working efficiency is improved.
Specifically, as shown in fig. 1, the intelligent post-loan collection system based on big data wind control and AI voice recognition technology according to the embodiment of the present invention further includes a risk assessment module 190, where the risk assessment module performs risk assessment on the performance in the overdue collection period after the second collection module completes collection, and outputs a risk assessment result in the overdue collection period.
Specifically, in the embodiment of the invention, after the overdue hastening period, the risk evaluation module carries out risk evaluation on the performance in the overdue hastening period and outputs the performance score in the overdue hastening period, the sponsor with high risk score carries out a manual hastening mode in advance when carrying out a pre-hastening period in the next period, and the success rate of hastening and the efficiency of hastening work are improved through the risk evaluation.
Specifically, when performing risk assessment on performance in an overdue payment acceleration period, if an initiator of a first payment acceleration task completes payment within a preset overdue payment acceleration time, the initiator of the first payment acceleration task is evaluated as a low-risk initiator, if the initiator of the first payment acceleration task does not complete payment within the preset overdue payment acceleration time, the initiator of the first payment acceleration task is evaluated as a high-risk initiator, and a second payment acceleration task of the high-risk initiator enters a first payment acceleration queue of an artificial payment acceleration mode which is an acceleration mode in advance of a preset time.
Specifically, when the payment mode of the sponsor is in stages, risk assessment is carried out after the overdue hastening period of the first period, and the risk assessment result influences the hastening of the second period.
Specifically, according to the embodiment of the invention, by performing risk assessment on the performance in the overdue payment acceleration period, whether the sponsor of the first payment acceleration task completes payment within the preset overdue payment acceleration time or not is judged as a low-risk sponsor, the sponsor who completes payment is judged as a high-risk sponsor, the high-risk sponsor enters the manual payment acceleration mode into the first payment acceleration queue of the payment acceleration mode in advance at the preset time, and the success rate of payment acceleration and the efficiency of payment acceleration are improved through the risk assessment.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention; various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The utility model provides a receive system is urged to intelligence after loan based on big data wind accuse and AI speech recognition technique which characterized in that includes:
the receiving module is used for receiving a plurality of pieces of bill information generated based on the loan event, wherein the bill information comprises the time information of the loan event, the sponsor of the loan event, the transaction amount of the loan event and the repayment mode;
the setting module is used for setting a pre-payment-urging period and an overdue payment-urging period according to time information, wherein the pre-payment-urging period is arranged before the overdue payment-urging period, calculating the listed payment time according to the time information and the payment mode, setting the payment time between the pre-payment-urging period and the overdue payment-urging period at any time, and calculating the listed payment amount according to the transaction amount and the payment mode;
the division module divides three first collection urging queues according to the transaction amount when the time information and the repayment mode are the same and the transaction amount is different, each collection urging queue at least comprises one collection urging task, and a collection urging task sequencing sequence is generated according to the transaction amount for any first collection urging queue, wherein the collection urging tasks comprise an initiator and a collection urging mode;
the first collection prompting module sends collection prompting information to the corresponding sponsor in a corresponding collection prompting mode according to the sequence of collection prompting tasks in any first collection prompting queue;
the statistic module is used for counting the performance of the initiator in the pre-hasten receiving period according to the repayment condition of the initiator and the voice state of the initiator;
the adjusting module adjusts an induced shrinkage mode in an induced shrinkage task in an overdue induced shrinkage period according to the performance in the pre-induced shrinkage period to generate four second induced shrinkage queues;
and the second collection prompting module sends collection prompting information to the corresponding sponsor in a corresponding collection prompting mode according to the sequence of collection prompting tasks in any second collection prompting queue.
2. The intelligent post-loan collection system based on big data wind control and AI voice recognition technology as claimed in claim 1, wherein the dividing module divides three first collection queues according to the transaction amount, the three first collection queues corresponding to different collection modes, which are a manual collection mode, an AI collection mode and a short message collection mode;
when three first hasten queues are divided according to transaction amount, a first grade amount B1 and a second grade amount B2 are set, the transaction amount for any initiator is set to be A1,
when the transaction amount A1 is larger than or equal to the first-level amount B1, dividing a collection task corresponding to the transaction amount into a first collection queue with a collection mode being a manual collection mode;
when the first-grade amount B1 is larger than the transaction amount A1 and is not less than the second-grade amount B2, dividing the collection task corresponding to the transaction amount into a first collection queue of which the collection mode is an AI collection mode;
when the transaction amount A1 is less than the second-level amount B2, dividing the collection task corresponding to the transaction amount into a first collection queue with a collection mode being a short message collection mode.
3. The intelligent post-credit collection system based on big data wind control and AI voice recognition technology as claimed in claim 2, further comprising a screening module connected to the partitioning module and the first collection module, respectively, wherein after the partitioning module partitions the first collection queue and before the first collection module does not send a collection message, the screening module screens collection tasks in any first collection queue by two screening methods, wherein the first screening method screens according to the balance of the account of the sponsor, when the balance of the account of the sponsor is greater than or equal to the repayment amount of the current time, the collection tasks corresponding to the sponsor are screened from any first collection queue, and when the balance of the account of the sponsor is less than the repayment amount of the current time, the collection tasks corresponding to the sponsor are left in any first collection queue.
4. The intelligent post-loan collection system based on big data wind control and AI voice recognition technology as claimed in claim 3, wherein when the screening module screens the collection tasks in any first collection queue by two screening methods, wherein the second screening method screens the collection tasks according to the preset collection time, the collection tasks in any first collection queue are divided into a first collection task and a second collection task according to the collection sequence, when the collection mode corresponding to any first collection queue is the manual collection mode,
when the first collection prompting task and the second collection prompting task are respectively distributed to two workers to be executed, if the sponsor of the first collection prompting task or the sponsor of the second collection prompting task completes repayment before the preset collection prompting time, the first collection prompting task or the second collection prompting task is screened from the first collection prompting queue;
when the first collection prompting task and the second collection prompting task are simultaneously distributed to a worker to be executed, if the sponsor of the first collection prompting task or the sponsor of the second collection prompting task completes repayment before the preset collection prompting time, the first collection prompting task or the second collection prompting task is removed from the first collection prompting queue, and if the sponsor of the second collection prompting task completes repayment before the first collection prompting task is completed, the second collection prompting task is screened from the first collection prompting queue;
and when the collection mode corresponding to any first collection urging queue is an AI collection urging mode or a short message collection urging mode, the AI collection urging mode or the short message collection urging mode has multi-task processing capacity, and if the sponsor of the first collection urging task or the sponsor of the second collection urging task completes repayment before the preset collection urging time, the first collection urging task or the second collection urging task is screened out of the first collection urging queue.
5. The intelligent post-loan collection system based on big data wind control and AI voice recognition technology as claimed in claim 4, wherein the statistics module determines the performance of the sponsor in the pre-collection period according to the repayment status of the sponsor when counting the performance of the sponsor in the pre-collection period, and does not grade the sponsor if the collection mode of the collection task corresponding to the sponsor is the short message collection mode when the sponsor does not complete the repayment, and grades the sponsor if the collection mode of the collection task corresponding to the sponsor is the manual collection mode or the AI collection mode.
6. The system of claim 5, wherein the statistic module is configured to grade the sponsor according to the voice status of the sponsor into three grades, namely, a first grade, a second grade and a third grade, wherein the first grade represents that the sponsor has bad attitude, the second grade represents that the sponsor has good attitude, the third grade represents that the sponsor has good attitude, the voice status is speech speed, the statistic module comprises a speed measuring unit and a statistic unit, the speech speed S is measured by the speed measuring unit, the standard speech speed is in a range from C1 to C2,
when the speed of speech S of the initiator is larger than or equal to the preset speed of speech C2, representing that the attitude of the initiator is poor, counting the expression of the initiator in the pre-hastening receiving period as one level;
when the speed of speech S of the initiator is less than or equal to the preset speed of speech C1 and less than the preset speed of speech C2, the attitude of the initiator is indicated to be good, and the expression of the initiator in the pre-hastening receiving period is counted to be two levels;
and when the speech speed S of the initiator is less than the preset speech speed C1, the attitude of the initiator is superior, and the expression of the initiator in the pre-hastening period is counted into three levels.
7. The intelligent post-loan collection system based on big data wind control and AI voice recognition technology as claimed in claim 6, wherein the adjustment module generates four second collection queues when adjusting the collection hastening mode of the collection hastening task in the overdue collection period according to the performance in the pre-collection period, the four second collection queues correspond to different collection hastening modes, which are respectively a letter collection hastening mode, a manual collection hastening mode, an AI collection hastening mode and a short message collection hastening mode,
when the hastening mode of the first hastening task is a manual hastening mode, if the initiator of the first hastening task shows a first level in the pre-hastening period, the hastening mode of the first hastening task is adjusted to a letter hastening mode, if the initiator of the first hastening task shows a second level in the pre-hastening period, the hastening mode of the first hastening task is still the manual hastening mode, and if the initiator of the first hastening task shows a third level in the pre-hastening period, the hastening mode of the first hastening task is adjusted to a short message hastening mode;
when the hastening mode of the first hastening task is an AI hastening mode, if the initiator of the first hastening task shows a first level in the pre-hastening period, the hastening mode of the first hastening task is adjusted to an artificial hastening mode, if the initiator of the first hastening task shows a second level in the pre-hastening period, the hastening mode of the first hastening task is still the AI hastening mode, and if the initiator of the first hastening task shows a third level in the pre-hastening period, the hastening mode of the first hastening task is adjusted to a short message hastening mode;
when the collection mode of the first collection task is the short message collection mode, the collection mode of the first collection task is adjusted to be the AI collection mode after overdue.
8. The intelligent post-credit collection system based on big data wind control and AI voice recognition technology as claimed in claim 7, wherein when the collection mode is a short message collection mode or a letter collection mode, the text content or the file content required by the short message collection mode for the overdue sponsor is automatically generated according to a preset template.
9. The intelligent post-loan collection system based on big data wind control and AI voice recognition technology as claimed in claim 8, further comprising a risk assessment module that assesses the risk of performance during the overdue collection period after the second collection module completes collection, and outputs the assessment result of risk during the overdue collection period.
10. The intelligent post-loan collection system based on big data wind control and AI voice recognition technology as claimed in claim 9, wherein when the risk assessment module performs risk assessment on the performance within the overdue collection period, if the sponsor of the first collection task completes the collection within the preset overdue collection time, the sponsor of the first collection task is evaluated as a low risk sponsor, and if the sponsor of the first collection task does not complete the collection within the preset overdue collection time, the sponsor of the first collection task is evaluated as a high risk sponsor, and the second collection task of the high risk sponsor is advanced into the first collection queue of the collection mode, i.e. the manual collection mode, at the preset time.
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