CN110782335A - Method, device and storage medium for processing credit data based on artificial intelligence - Google Patents
Method, device and storage medium for processing credit data based on artificial intelligence Download PDFInfo
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Abstract
The application relates to the field of artificial intelligence, and provides a method, a device and a storage medium for processing credit data based on artificial intelligence, wherein the method comprises the following steps: the method comprises the steps of respectively determining problem types matched with overdue types according to overdue types corresponding to target users in a loan hasty receiving list, selecting target problems matched with the problem types from a question bank according to the problem types, determining hasty receiving channels of the target users according to the overdue types, establishing session connection with terminals bound with the target users through the hasty receiving channels, converting the target problems into first media streams according to response logic of the target problems, and sending the first media streams to the terminals of the target users to be in session with the terminals. And carrying out semantic analysis on the second media stream of the target user according to a voice recognition technology, extracting audio data related to overdue payment from the second media stream, converting the audio data related to overdue payment into character information, and inputting the character information and the collection prompting tag into a collection prompting platform. This scheme can improve and urge receipts work efficiency.
Description
Technical Field
The present application relates to the field of artificial intelligence technologies, and in particular, to a method, an apparatus, and a storage medium for processing credit data based on artificial intelligence.
Background
At present, domestic consumption finance is developing vigorously, and meanwhile, the number of cases of overdue loans is increasing continuously. For users who have loans in overdue period, the current collection urging method is mainly that customer service staff urge collection in the modes of telephone, short message, mail or letter and the like. Specifically, the catalytic recovery process is as follows: when the call is made by using the collection urging platform, a collection urging member needs to communicate with a debtor to confirm various information, and the confirmed information is filled in an operation interface of the collection urging platform. Requiring the input of a lot of manpower.
However, in the process of communicating between the acquirer and the debtor, if the voice recognition in the telephone communication by the acquirer is not accurate, the acquirer may understand the wrong intention of the debtor, or may enter a wrong action code due to factors such as reducing attention caused by frequent repetitive recording, and finally enter a wrong intention of the debtor. Because the prompt collector needs to fill in a lot of information such as action codes, prompt collection remarks and the like when filling in the confirmed information on the operation interface of the prompt collector, even if the prompt collector understands the correct voice intention, the prompt collector can easily input wrong information, and the operation is time-consuming and labor-consuming.
Disclosure of Invention
The application provides a credit data processing method, a credit data processing device and a credit data processing storage medium based on artificial intelligence, which can solve the problems of low efficiency of manual collection and easy error in the prior art.
In a first aspect, the present application provides a method for processing credit data based on artificial intelligence, the method comprising:
obtaining and traversing loan information of a plurality of users;
screening multiple overdue loan data from the user loan information according to preset screening conditions, classifying the overdue loan data according to overdue types, counting target users corresponding to the overdue loan data, and generating a loan payment list comprising multiple target users;
determining problem types matched with the overdue types of the target users according to the overdue types corresponding to the target users in the loan hasty receiving list, selecting target problems matched with the problem types from a question bank according to the problem types corresponding to the target users, and determining the hasty receiving channels of the target users in the loan hasty receiving list according to the overdue types;
establishing session connection with terminals bound with target users through the collection prompting channel, converting the target problems into first media streams according to response logic of the target problems, and sending the first media streams corresponding to the target problems to the terminals of the target users so as to carry out session with the terminals of the target users; the response logic is a response rule for prompting the receiving platform to send a media stream of a target question to a terminal of a target user according to a preset response mode so as to have conversation with the terminal;
receiving a second media stream sent by a terminal of a target user, carrying out semantic analysis on the second media stream according to a voice recognition technology, and extracting audio data related to overdue payment from the second media stream;
converting audio data related to overdue payment into character information, and inputting the character information and a collection urging label matched with the character information into the collection urging platform;
and generating a loan acceptance record according to the text information and the acceptance label and storing the loan acceptance record.
In some possible designs, the parsing the second media stream according to a speech recognition technique to extract data related to a past due payment from the second media stream includes:
performing sound framing on the second media stream by adopting a moving window function to obtain a plurality of sections of audio data, wherein each section of audio data is a frame;
respectively carrying out waveform transformation on each section of audio data to obtain a multi-dimensional vector corresponding to each section of audio data, wherein the multi-dimensional vector is an acoustic feature;
recognizing a pronunciation sequence with the maximum matching probability with each multi-dimensional vector by adopting an acoustic model;
finding out a character string sequence with the maximum matching probability with the pronunciation sequence by adopting a language model;
performing semantic understanding (e.g., context understanding) on the sequence of strings to identify core words in the second media stream;
matching preset keywords with core words in the second media stream to obtain the audio data related to overdue payment; the preset keywords refer to keywords agreeing to payment or having payment tendency.
In some possible designs, after receiving the second media stream sent by the terminal of the target user, before performing semantic analysis on the second media stream according to the speech recognition technology, the method further includes:
measuring the playing decibel of the second media stream;
and if the playing decibel of the second media stream is lower than a preset decibel, increasing the playing decibel of the second media stream so that the playing decibel of the second media stream is not less than the preset decibel, wherein the preset decibel refers to a lowest decibel value which meets the requirement that a machine carries out semantic recognition on the second media stream.
In some possible designs, after receiving the second media stream sent by the terminal of the target user, before performing semantic analysis on the second media stream according to the speech recognition technology, the method further includes:
analyzing data in the second media stream;
if the second media stream is determined to contain the noise data through analysis, performing voice enhancement processing on the voice signal of the second media stream by adopting the following formula to obtain an optimal voice signal:
min i m i z e W T(k)R-1W(k)+(X(k)-X^(k|k-1))TΘ-1(X(k)-X^(k|k-1))+λ||v(k)||1
subjiect to Y(k)＝CX(k)+n(k)+v(k)
wherein, x (k) and n (k) are variables, x (k) is an optimal estimation of a state value in a kalman filtering algorithm, n (k) is an estimation of gaussian noise, Θ is a covariance matrix of gaussian noise, and v (k) is sparse noise;
filtering the noise data from the second media stream using a Kalman filtering algorithm.
In some possible designs, after receiving the second media stream sent by the terminal of the target user, before performing semantic analysis on the second media stream according to the speech recognition technology, the method further includes:
measuring the playing speed of the second media stream;
and if the playing speed of the second media stream is higher than the preset playing speed, reducing the playing speed of the second media stream so as to enable the playing speed of the second media stream not to be higher than the preset playing speed.
In some possible designs, the reducing the play-out speed of the second media stream includes:
reducing the play speed of the second media stream by framing or windowing
Dividing the voice signal of the second media stream into a plurality of short segments by adopting an overlapped segmentation mode, wherein the voice signal of the second media stream comprises a plurality of video frames, and the overlapped part of the previous frame and the next frame is frame shift;
and weighting the short segments by adopting a movable window with limited length so as to reduce the playing speed of the second media stream.
In some possible designs, after the extracting the audio data related to the overdue payment from the second media stream and before the converting the audio data related to the overdue payment into the text information, the method further includes:
performing lie detection analysis on the second media stream;
if the target user is determined to have false answers, selecting a reinforcement question according to a question corresponding to the false answers, wherein the reinforcement question is used for guiding the target user to answer questions with multiple dimensions;
sending the reinforcement problem to the target user according to a problem guide mode;
receiving an answer to the reinforcement question by a target user;
and performing semantic analysis on the answer of the target user aiming at the reinforcement question, and replacing the false answer in the data related to overdue repayment with the result of the semantic analysis.
In a second aspect, the present application provides an apparatus for processing credit data based on artificial intelligence, having the function of implementing the method for processing credit data based on artificial intelligence provided in correspondence with the first aspect described above. The functions can be realized by hardware, and the functions can also be realized by executing corresponding software by hardware. The hardware or software includes one or more modules corresponding to the above functions, which may be software and/or hardware.
In one possible design, the apparatus includes:
the receiving and sending module is used for acquiring loan information of a plurality of users;
the processing module is used for traversing the plurality of user loan information acquired by the transceiving module; screening multiple overdue loan data from the user loan information according to preset screening conditions, classifying the overdue loan data according to overdue types, counting target users corresponding to the overdue loan data, and generating a loan payment list comprising multiple target users; determining problem types matched with the overdue types of the target users according to the overdue types corresponding to the target users in the loan hasty receiving list, selecting target problems matched with the problem types from a question bank according to the problem types corresponding to the target users, and determining the hasty receiving channels of the target users in the loan hasty receiving list according to the overdue types; establishing session connection with terminals bound with each target user through the collection prompting channel, converting the target problems into first media streams according to response logic of the target problems, and sending the first media streams corresponding to the target problems to the terminals of the target users through the receiving and sending modules so as to carry out session with the terminals of the target users; the response logic is a response rule for prompting the receiving platform to send a media stream of a target question to a terminal of a target user according to a preset response mode so as to have conversation with the terminal;
the processing module is further used for receiving a second media stream sent by the terminal of the target user through the transceiving module, carrying out semantic analysis on the second media stream according to a voice recognition technology, and extracting audio data related to overdue repayment from the second media stream; converting audio data related to overdue payment into character information, and inputting the character information and a collection urging label matched with the character information into the collection urging platform; and generating a loan acceptance record according to the text information and the acceptance label and storing the loan acceptance record.
In some possible designs, the processing module is specifically configured to:
performing sound framing on the second media stream by adopting a moving window function to obtain a plurality of sections of audio data, wherein each section of audio data is a frame;
respectively carrying out waveform transformation on each section of audio data to obtain a multi-dimensional vector corresponding to each section of audio data, wherein the multi-dimensional vector is an acoustic feature;
recognizing a pronunciation sequence with the maximum matching probability with each multi-dimensional vector by adopting an acoustic model;
finding out a character string sequence with the maximum matching probability with the pronunciation sequence by adopting a language model;
performing semantic understanding (e.g., context understanding) on the sequence of strings to identify core words in the second media stream;
matching preset keywords with core words in the second media stream to obtain the audio data related to overdue payment; the preset keywords refer to keywords agreeing to payment or having payment tendency.
In some possible designs, after the transceiver module receives the second media stream transmitted by the terminal of the target user, the processing module is further configured to, before performing semantic analysis on the second media stream according to a speech recognition technique:
measuring the playing decibel of the second media stream;
and if the playing decibel of the second media stream is lower than a preset decibel, increasing the playing decibel of the second media stream so that the playing decibel of the second media stream is not less than the preset decibel, wherein the preset decibel refers to a lowest decibel value which meets the requirement that a machine carries out semantic recognition on the second media stream.
In some possible designs, the processing module is further configured to, after the transceiver module receives the second media stream transmitted by the terminal of the target user, before semantically analyzing the second media stream according to a speech recognition technique:
analyzing data in the second media stream;
if the second media stream is determined to contain the noise data through analysis, performing voice enhancement processing on the voice signal of the second media stream by adopting the following formula to obtain an optimal voice signal:
min i m i z e W T(k)R-1W(k)+(X(k)-X^(k|k-1))TΘ-1(X(k)-X^(k|k-1))+λ||v(k)||1
subjiect to Y(k)＝CX(k)+n(k)+v(k)
wherein, x (k) and n (k) are variables, x (k) is an optimal estimation of a state value in a kalman filtering algorithm, n (k) is an estimation of gaussian noise, Θ is a covariance matrix of gaussian noise, and v (k) is sparse noise;
filtering the noise data from the second media stream using a Kalman filtering algorithm.
In some possible designs, after the transceiver module receives the second media stream transmitted by the terminal of the target user, the processing module is further configured to, before performing semantic analysis on the second media stream according to a speech recognition technique:
measuring the playing speed of the second media stream;
and if the playing speed of the second media stream is higher than the preset playing speed, reducing the playing speed of the second media stream so as to enable the playing speed of the second media stream not to be higher than the preset playing speed.
In some possible designs, the processing module is specifically configured to:
reducing the play speed of the second media stream by framing or windowing
Dividing the voice signal of the second media stream into a plurality of short segments by adopting an overlapped segmentation mode, wherein the voice signal of the second media stream comprises a plurality of video frames, and the overlapped part of the previous frame and the next frame is frame shift;
and weighting the short segments by adopting a movable window with limited length so as to reduce the playing speed of the second media stream.
In some possible designs, after the processing module extracts the audio data related to the overdue payment from the second media stream, before the processing module converts the audio data related to the overdue payment into text information, the processing module is further configured to:
performing lie detection analysis on the second media stream;
if the target user is determined to have false answers, selecting a reinforcement question according to a question corresponding to the false answers, wherein the reinforcement question is used for guiding the target user to answer questions with multiple dimensions;
sending the reinforcement problem to the target user according to a problem guide mode;
receiving, by the transceiver module, an answer to the reinforcement question by a target user;
and performing semantic analysis on the answer of the target user aiming at the reinforcement question, and replacing the false answer in the data related to overdue repayment with the result of the semantic analysis.
A further aspect of the application provides a computer device comprising at least one connected processor, memory and transceiver, wherein the memory is configured to store program code and the processor is configured to invoke the program code in the memory to perform the method of the first aspect.
A further aspect of the present application provides a computer storage medium comprising instructions which, when run on a computer, cause the computer to perform the method of the first aspect described above.
Compared with the prior art, in the embodiment of the application, the problem types matched with the overdue types of the target users are respectively determined according to the overdue types corresponding to the target users in the loan collection prompting list, the target problems matched with the problem types are selected from the question bank according to the problem types corresponding to the target users, the collection prompting channels of the target users in the loan collection prompting list are respectively determined according to the overdue types, conversation connection is established with the terminals bound with the target users through the collection prompting channels, the target problems are converted into first media streams according to the response logic of the target problems, and the first media streams corresponding to the target problems are sent to the terminals of the target users to be in conversation with the terminals of the target users. And carrying out semantic analysis on the second media stream of the target user according to a voice recognition technology, extracting audio data related to overdue payment from the second media stream, converting the audio data related to overdue payment into character information, and inputting the character information and an acceptance prompting label matched with the character information into the acceptance prompting platform. Therefore, after the scheme is adopted, the machine communicates with the target user and confirms various basic information, the collection prompting work efficiency is improved, the collection prompting cost is reduced, whether the machine is tired or not is not required to be considered, the accuracy rate of identifying the real voice intention of the target user can be improved, the loan collection prompting record is automatically generated, a collector is not required to fill in a lot of information, the collection prompting becomes intelligent, the productivity is liberated, and the user experience is improved.
Drawings
FIG. 1 is a schematic flow chart of a method for processing credit data based on artificial intelligence in an embodiment of the application;
FIG. 2 is a schematic structural diagram of an apparatus for processing credit data based on artificial intelligence in an embodiment of the present application;
fig. 3 is a schematic structural diagram of a computer device in an embodiment of the present application.
The implementation, functional features and advantages of the objectives of the present application will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. The terms "first," "second," and the like in the description and in the claims of the present application and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that the embodiments described herein may be practiced otherwise than as specifically illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or modules is not necessarily limited to those steps or modules explicitly listed, but may include other steps or modules not explicitly listed or inherent to such process, method, article, or apparatus, and such that a division of modules presented in this application is merely a logical division that may be implemented in an actual application in a different manner, such that multiple modules may be combined or integrated into another system, or some features may be omitted, or may not be implemented.
The application provides a method, a device and a storage medium for processing credit data based on artificial intelligence. The scheme is executed by a collection urging platform, and Artificial Intelligence (AI) is deployed in the collection urging platform.
Referring to fig. 1, a method of processing credit data based on artificial intelligence in an embodiment of the application is described below, the method comprising:
101. and obtaining and traversing loan information of a plurality of users.
The user loan information comprises a borrower name, an identity card number, a face image, a mail box, a mobile phone number, a loan product, a borrowing date, a borrowing amount and a repayment period.
102. And screening a plurality of items of overdue loan data from the user loan information according to preset screening conditions, classifying the overdue loan data according to overdue types, counting target users corresponding to the overdue loan data, and generating a loan hastening list comprising a plurality of target users.
The preset screening conditions can comprise the amount of outstanding money, the number of outstanding days higher than a preset limit, the amount of outstanding money close to a payment date and the remaining payment date.
The overdue types include risk users, potential risk users, old dependent users, unleashed users, and old dependent users. Wherein, the risk user refers to a user with overdue repayment days more than 90 days. A potentially risky user refers to a user who has not more than 60 days of overdue repayment. The old user refers to a user with the accumulated number of historical overdue repayment reaching 4 times in 6 months. The lost user refers to a user who cannot be contacted according to the contact telephone number in the incoming case. A legacy user refers to a user who has not been paid until a specified payment time. In order to distinguish the overdue users, an overdue type identifier is respectively set for each overdue user. For example: the potential risk user corresponds to a potential risk type identifier L, the risk user corresponds to an overdue type identifier H, the old user corresponds to an overdue type identifier F, the lost user corresponds to an overdue type identifier M, and the old user corresponds to an overdue type identifier L.
For the overdue target user handling scheme, reference may be made to the handling manner of the overdue target user in the present application, which is not described herein again.
103. And respectively determining problem types matched with the overdue types of the target users according to the overdue types corresponding to the target users in the loan collection list, selecting target problems matched with the problem types from a question bank according to the problem types corresponding to the target users, and respectively determining collection channels of the target users in the loan collection list according to the overdue types.
The collection urging channel comprises a telephone, a network communication account, a mailbox, a home address, a working address and a court.
For example, for a potentially risky user, the target question may be "mr. li, hello, your shenzhen nan shan bank tail number 6222342100009094 credit card overdue 22090.23 yuan, please repay before 2019-5-20 days as soon as possible, if there is no objection and agree to a confirmation for repayment".
For a user who is very old, the target question can be 'good you, when you can pay at all, and if not, the overdue information is declared to a credit center and is disclosed nationwide or a court summons is submitted to you'.
For the lost-contact user, the target question can be 'you are good, you take a service before 2019-8-20 days for repayment, and if the payment is not paid, the court is applied for freezing the personal real estate and funds in all financial accounts under the name'.
104. And establishing session connection with terminals bound with each target user through the collection prompting channel, converting the target problems into first media streams according to response logic of the target problems, and sending the first media streams corresponding to the target problems to the terminals of the target users so as to carry out session with the terminals of the target users.
The response logic is a response rule for prompting the receiving platform to send a media stream of a target question to a terminal of a target user according to a preset response mode so as to be conversed with the terminal.
The first media stream may be voice data or video data, and both the voice data and the video data are AI-simulated voice, and the format of the first media stream is not limited in the present application.
In some embodiments, the answer to target question logic to convert the target question into the first media stream comprises:
and coding the target problem according to the response logic of the target problem to obtain a first media stream.
105. And receiving a second media stream sent by the terminal of the target user, carrying out semantic analysis on the second media stream according to a voice recognition technology, and extracting audio data related to overdue payment from the second media stream.
In some embodiments, the parsing the second media stream according to the speech recognition technique to extract data related to the overdue payment from the second media stream includes:
performing sound framing on the second media stream by adopting a moving window function to obtain a plurality of sections of audio data, wherein each section of audio data is a frame;
respectively carrying out waveform transformation on each section of audio data to obtain a multi-dimensional vector corresponding to each section of audio data, wherein the multi-dimensional vector is an acoustic feature;
recognizing a pronunciation sequence with the maximum matching probability with each multi-dimensional vector by adopting an acoustic model;
finding out a character string sequence with the maximum matching probability with the pronunciation sequence by adopting a language model;
performing semantic understanding (e.g., context understanding) on the sequence of strings to identify core words in the second media stream;
and matching preset keywords with core words in the second media stream to obtain the audio data related to overdue payment.
The preset keywords refer to keywords agreeing to payment or having payment tendency. For example, the preset keywords include "good", "OK", "can", and "and so on will still" and so on, which carry keywords for agreeing to repayment. If the audio data contains the keywords of 'good', 'OK', 'can' and the like which carry the one-way payment approving keywords, the audio data indicates that the overdue user approves the payment, and the overdue urging result is willing to pay. If the voice data does not contain keywords for agreeing to repayment, such as 'good', 'OK', 'can' and the like, the voice data indicates that overdue users do not agree to repayment, and the overdue users are unwilling to repayment as overdue urging results.
In some embodiments, the preset keywords may be implemented by using a pre-trained Hidden Markov Model (HMM) model, and the HMM model may be used to perform keyword recognition on the audio data. The HMM model is a pre-trained model which is stored in the collection platform and is used for identifying whether preset keywords such as 'good', 'OK', 'can', 'and' will still exist in the call voice. In this embodiment, whether the pre-trained HMM model identifies the preset keyword exists in the audio data or not can improve the identification accuracy.
In some embodiments, to further ensure the accuracy of semantic analysis, after receiving the second media stream sent by the terminal of the target user, before performing semantic analysis on the second media stream according to a speech recognition technology, the playback decibel of the second media stream may be increased, the playback speed of the second media stream may be reduced, and noise in the second media stream may be removed. The following are introduced separately:
(1) improving the playing decibel of the second media stream
Measuring the playing decibel of the second media stream;
and if the playing decibel of the second media stream is lower than the preset decibel, increasing the playing decibel of the second media stream so as to enable the playing decibel of the second media stream not to be smaller than the preset decibel.
The preset decibel is the lowest decibel value which meets the requirement that the machine carries out semantic recognition on the second media stream.
Even if the speaking volume of the target user is low or far away from the microphone, the received second media stream cannot be clearly and accurately analyzed due to the low volume of the recorded speaking of the user (namely, the second media stream is converted to be sent to the collection platform). Therefore, after the decibel of the second media stream is improved, the accuracy and the definition of semantic recognition can be enhanced.
(2) Reducing the playback speed of the second media stream
Measuring the playing speed of the second media stream;
and if the playing speed of the second media stream is higher than the preset playing speed, reducing the playing speed of the second media stream so as to enable the playing speed of the second media stream not to be higher than the preset playing speed.
Even if the speaking speed of the target user is fast, the received second media stream cannot be clearly and accurately analyzed due to the fact that the recorded speaking speed of the user (namely, the second media stream is converted to be sent to the collection urging platform) is fast. Therefore, after the playing speed of the second media stream is reduced, the accuracy and the definition of semantic recognition can be enhanced.
In some embodiments, the playing speed of the second media stream may be reduced by framing or windowing. Taking the second media stream as an example of a speech signal, the speech signal has a short-time stationarity (the speech signal can be considered to be approximately constant within 10-30 ms), then framing the speech signal refers to dividing the speech signal into short segments for processing, such as an overlapping and segmenting method, an overlapping portion of a previous frame and a next frame is called a frame shift, and a ratio of the frame shift to the frame length is generally 0-0.5. For example, the number of frames per second is about 33 to 100 frames, the frame length is 25ms, and the frame shift is 10 ms. The framing can be implemented by weighting with movable finite-length windows.
(3) Removing noise in the second media stream
Analyzing data in the second media stream;
and if the second media stream contains noise data through analysis, filtering the noise data from the second media stream.
Even if noise exists around the target user when speaking, the received second media stream cannot be clearly and accurately analyzed because the noise data is included in the recorded user speech (i.e. converted into the second media stream and sent to the collection platform). It can be seen that after the noise data in the second media stream is removed, semantic enhancement can be achieved, that is, the accuracy and definition of semantic recognition can be enhanced.
The noise may be filtered using a kalman (Kronecker) filtering algorithm. The manner in which the noise data is analyzed and the manner in which the noise data is filtered are not limited in this application.
For example, the statistical properties of the speech signal and the noise data p (k) & noise data n (k) are:
E(p(k))＝q,E(n(k))＝r
E(p(k)p(j)T)＝Qδkj,E(n(k)n(j)T)＝Rδkj
wherein q and r are mean values of noise p (k) and n (k), respectively; q and R are the covariance of the noise data p (k) & noise data n (k), respectively, and δ kj is the Kronecker function. In this application, the speech enhancement refers to estimating the optimal speech signal x (k) on the premise that the speech signal y (k) is known, and the optimal speech signal x (k) may also be referred to as optimal estimation. An optimization formula for a speech signal is as follows:
min i m i z e W T(k)R-1W(k)+(X(k)-X^(k|k-1))TΘ-1(X(k)-X^(k|k-1))+λ||v(k)||1
subjiect to Y(k)＝CX(k)+n(k)+v(k)
wherein, x (k) and n (k) are variables, x (k) is the optimal estimation of the state value in the Kronecker filtering algorithm, that is, n (k) is the estimation of gaussian noise, Θ is the covariance matrix of gaussian noise, v (k) is sparse noise, and the optimal estimation x (k) of the speech signal is obtained by solving the above optimization formula.
106. And converting the audio data related to overdue payment into character information, and inputting the character information and the collection prompting label matched with the character information into the collection prompting platform.
The acceptance urging label can comprise promised on-schedule payment (for example, connection-is oneself-promised payment), application of delayed payment (for example, connection-is oneself-promised delayed payment), powerless payment (for example, connection-user family-powerless payment), and the like.
In some embodiments, after the extracting the audio data related to the overdue payment from the second media stream and before the converting the audio data related to the overdue payment into the text information, the method further comprises:
performing lie detection analysis on the second media stream;
if the target user is determined to have false answers, selecting a reinforcement question according to a question corresponding to the false answers, wherein the reinforcement question is used for guiding the target user to answer questions with multiple dimensions;
sending the reinforcement problem to the target user according to a problem guide mode;
receiving an answer to the reinforcement question by a target user;
and performing semantic analysis on the answer of the target user aiming at the reinforcement question, and replacing the false answer in the data related to overdue repayment with the result of the semantic analysis.
Therefore, through the lie detection analysis of the second media stream of the target user, whether the target user hides the real repayment capacity and the repayment willingness or not can be deeply excavated, the mastering degree of the repayment willingness checked on the real repayment capacity of the target user can be improved, and therefore the probability of overdue repayment and the risk of dealing with potential reimbursement are reduced.
107. And generating a loan acceptance record according to the text information and the acceptance label and storing the loan acceptance record.
The loan collection record refers to a whole-course record of every collection payment, and is used for analysis, update and management of collection staff, for example, the collection record is conveniently retrieved according to conditions, or the collection level is distributed to the target user. The payment acceleration grade is determined according to the time exceeding the repayment date or the overdue loan amount, the overdue time is longer, the payment acceleration grade of the overdue loan data is higher, the loan amount is larger, and the payment acceleration grade of the overdue loan data is higher.
In some embodiments, after the loan collection urging record is generated, a user image (for example, in a knowledge graph form) can be drawn for each target user according to the collection urging record, so that a collection urging person can more intuitively analyze the characteristics of the target user, such as the repayment ability and the repayment credit.
Compared with the prior art, in the embodiment of the application, the problem types matched with the overdue types of the target users are respectively determined according to the overdue types corresponding to the target users in the loan collection prompting list, the target problems matched with the problem types are selected from the question bank according to the problem types corresponding to the target users, the collection prompting channels of the target users in the loan collection prompting list are respectively determined according to the overdue types, conversation connection is established with the terminals bound with the target users through the collection prompting channels, the target problems are converted into first media streams according to the response logic of the target problems, and the first media streams corresponding to the target problems are sent to the terminals of the target users to be in conversation with the terminals of the target users. And carrying out semantic analysis on the second media stream of the target user according to a voice recognition technology, extracting audio data related to overdue payment from the second media stream, converting the audio data related to overdue payment into character information, and inputting the character information and an acceptance prompting label matched with the character information into the acceptance prompting platform. Therefore, after the scheme is adopted, the machine communicates with the target user and confirms various basic information, the collection prompting work efficiency is improved, the collection prompting cost is reduced, whether the machine is tired or not is not required to be considered, the accuracy rate of identifying the real voice intention of the target user can be improved, the loan collection prompting record is automatically generated, a collector is not required to fill in a lot of information, the collection prompting becomes intelligent, the productivity is liberated, and the user experience is improved.
The technical features mentioned in the embodiment or implementation manner corresponding to fig. 1 are also applicable to the embodiments corresponding to fig. 2 and fig. 3 in the present application, and the details of the following similarities are not repeated.
A method for processing credit data based on artificial intelligence in the present application is described above, and an apparatus for performing the above method for processing credit data based on artificial intelligence is described below.
Fig. 2 is a schematic diagram of an apparatus 20 for processing credit data based on artificial intelligence, which can be applied to voice intention recognition, such as a machine-to-human voice or video call. The apparatus 20 in the embodiments of the present application is capable of implementing steps corresponding to the method of processing credit data based on artificial intelligence as performed in the embodiment corresponding to fig. 1 above. The functions implemented by the apparatus 20 may be implemented by hardware, or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above functions, which may be software and/or hardware. The apparatus 20 may include a transceiver module 201 and a processing module 202, and the processing module 202 and the obtaining module 201 may refer to operations performed in the embodiment corresponding to fig. 1, which are not described herein again. The processing module 202 may be used to control transceiving, obtaining, and the like operations of the transceiving module 201.
In some embodiments, the transceiver module 201 may be configured to obtain loan information of a plurality of users;
the processing module 202 may be configured to traverse the plurality of user loan information acquired by the transceiver module 201; screening multiple overdue loan data from the user loan information according to preset screening conditions, classifying the overdue loan data according to overdue types, counting target users corresponding to the overdue loan data, and generating a loan payment list comprising multiple target users; determining problem types matched with the overdue types of the target users according to the overdue types corresponding to the target users in the loan hasty receiving list, selecting target problems matched with the problem types from a question bank according to the problem types corresponding to the target users, and determining the hasty receiving channels of the target users in the loan hasty receiving list according to the overdue types; establishing session connection with terminals bound with each target user through the collection prompting channel, converting the target problem into a first media stream according to response logic of the target problem, and sending the first media stream corresponding to the target problem to the terminal of the target user through the transceiver module 201 so as to perform session with the terminal of the target user; the response logic is a response rule for prompting the receiving platform to send a media stream of a target question to a terminal of a target user according to a preset response mode so as to have conversation with the terminal;
the processing module 202 is further configured to receive a second media stream sent by the terminal of the target user through the transceiver module, perform semantic analysis on the second media stream according to a speech recognition technology, and extract audio data related to overdue repayment from the second media stream; converting audio data related to overdue payment into character information, and inputting the character information and a collection urging label matched with the character information into the collection urging platform; and generating a loan acceptance record according to the text information and the acceptance label and storing the loan acceptance record.
In some embodiments, the processing module 202 is specifically configured to:
performing sound framing on the second media stream by adopting a moving window function to obtain a plurality of sections of audio data, wherein each section of audio data is a frame;
respectively carrying out waveform transformation on each section of audio data to obtain a multi-dimensional vector corresponding to each section of audio data, wherein the multi-dimensional vector is an acoustic feature;
recognizing a pronunciation sequence with the maximum matching probability with each multi-dimensional vector by adopting an acoustic model;
finding out a character string sequence with the maximum matching probability with the pronunciation sequence by adopting a language model;
performing semantic understanding (e.g., context understanding) on the sequence of strings to identify core words in the second media stream;
matching preset keywords with core words in the second media stream to obtain the audio data related to overdue payment; the preset keywords refer to keywords agreeing to payment or having payment tendency.
In some embodiments, after the transceiver module 201 receives the second media stream transmitted by the terminal of the target user, before performing semantic analysis on the second media stream according to a speech recognition technology, the processing module 202 is further configured to:
measuring the playing decibel of the second media stream;
and if the playing decibel of the second media stream is lower than a preset decibel, increasing the playing decibel of the second media stream so that the playing decibel of the second media stream is not less than the preset decibel, wherein the preset decibel refers to a lowest decibel value which meets the requirement that a machine carries out semantic recognition on the second media stream.
In some embodiments, after the transceiver module 201 receives the second media stream transmitted by the terminal of the target user, before performing semantic analysis on the second media stream according to a speech recognition technology, the processing module 202 is further configured to:
analyzing data in the second media stream;
if the second media stream is determined to contain the noise data through analysis, performing voice enhancement processing on the voice signal of the second media stream by adopting the following formula to obtain an optimal voice signal:
min i m i z e W T(k)R-1W(k)+(X(k)-X^(k|k-1))TΘ-1(X(k)-X^(k|k-1))+λ||v(k)||1
subjiect to Y(k)＝CX(k)+n(k)+v(k)
wherein, x (k) and n (k) are variables, x (k) is an optimal estimation of a state value in a kalman filtering algorithm, n (k) is an estimation of gaussian noise, Θ is a covariance matrix of gaussian noise, and v (k) is sparse noise;
filtering the noise data from the second media stream using a Kalman filtering algorithm.
In some embodiments, after the transceiver module receives the second media stream transmitted by the terminal of the target user, the processing module 202, before performing semantic analysis on the second media stream according to a speech recognition technology, is further configured to:
measuring the playing speed of the second media stream;
and if the playing speed of the second media stream is higher than the preset playing speed, reducing the playing speed of the second media stream so as to enable the playing speed of the second media stream not to be higher than the preset playing speed.
In some embodiments, the processing module 202 is specifically configured to:
reducing the play speed of the second media stream by framing or windowing
Dividing the voice signal of the second media stream into a plurality of short segments by adopting an overlapped segmentation mode, wherein the voice signal of the second media stream comprises a plurality of video frames, and the overlapped part of the previous frame and the next frame is frame shift;
and weighting the short segments by adopting a movable window with limited length so as to reduce the playing speed of the second media stream.
In some embodiments, after the processing module 202 extracts the audio data related to the overdue payment from the second media stream, before the processing module converts the audio data related to the overdue payment into the text information, the processing module is further configured to:
performing lie detection analysis on the second media stream;
if the target user is determined to have false answers, selecting a reinforcement question according to a question corresponding to the false answers, wherein the reinforcement question is used for guiding the target user to answer questions with multiple dimensions;
sending the reinforcement problem to the target user according to a problem guide mode;
receiving, by the transceiver module, an answer to the reinforcement question by a target user;
and performing semantic analysis on the answer of the target user aiming at the reinforcement question, and replacing the false answer in the data related to overdue repayment with the result of the semantic analysis.
It should be noted that the physical device corresponding to the transceiver module 201 shown in fig. 2 is the transceiver shown in fig. 3, and the transceiver can implement part or all of the functions of the transceiver module 201, or implement the same or similar functions as the transceiver module 201.
The physical device corresponding to the processing module 202 shown in fig. 2 is the processor shown in fig. 3, and the processor can implement part or all of the functions of the processing module 202, or implement the same or similar functions as the processing module 202.
The apparatus 20 in the embodiment of the present application is described above from the perspective of a modular functional entity, and a computer device is described below from the perspective of hardware, as shown in fig. 3, and includes: a processor, a memory, a transceiver (which may also be an input-output unit, not identified in fig. 3), and a computer program stored in the memory and executable on the processor. For example, the computer program may be a program corresponding to the method of processing credit data based on artificial intelligence in the embodiment corresponding to fig. 1. For example, when the computer device implements the functionality of the apparatus 20 as shown in FIG. 2, the processor, when executing the computer program, implements the steps in the method for processing credit data based on artificial intelligence performed by the apparatus 20 in the embodiment corresponding to FIG. 2 described above; alternatively, the processor implements the functions of the modules in the apparatus 20 according to the embodiment corresponding to fig. 2 when executing the computer program. For another example, the computer program may be a program corresponding to the method of processing credit data based on artificial intelligence in the embodiment corresponding to fig. 1.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like which is the control center for the computer device and which connects the various parts of the overall computer device using various interfaces and lines.
The memory may be used to store the computer programs and/or modules, and the processor may implement various functions of the computer device by running or executing the computer programs and/or modules stored in the memory and calling data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, video data, etc.) created according to the use of the cellular phone, etc. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The transceivers may also be replaced by receivers and transmitters, which may be the same or different physical entities. When the same physical entity, may be collectively referred to as a transceiver. The transceiver may be an input-output unit.
The memory may be integrated in the processor or may be provided separately from the processor.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM), and includes several instructions for enabling a terminal (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present application.
The embodiments of the present application have been described above with reference to the drawings, but the present application is not limited to the above-mentioned embodiments, which are only illustrative and not restrictive, and those skilled in the art can make many changes and modifications without departing from the spirit and scope of the present application and the protection scope of the claims, and all changes and modifications that come within the meaning and range of equivalency of the claims are to be embraced within their scope.
Claims (10)
1. A method for processing credit data based on artificial intelligence, the method comprising:
obtaining and traversing loan information of a plurality of users;
screening multiple overdue loan data from the user loan information according to preset screening conditions, classifying the overdue loan data according to overdue types, counting target users corresponding to the overdue loan data, and generating a loan payment list comprising multiple target users;
determining problem types matched with the overdue types of the target users according to the overdue types corresponding to the target users in the loan hasty receiving list, selecting target problems matched with the problem types from a question bank according to the problem types corresponding to the target users, and determining the hasty receiving channels of the target users in the loan hasty receiving list according to the overdue types;
establishing session connection with terminals bound with target users through the collection prompting channel, converting the target problems into first media streams according to response logic of the target problems, and sending the first media streams corresponding to the target problems to the terminals of the target users so as to carry out session with the terminals of the target users; the response logic is a response rule for prompting the receiving platform to send a media stream of a target question to a terminal of a target user according to a preset response mode so as to have conversation with the terminal;
receiving a second media stream sent by a terminal of a target user, carrying out semantic analysis on the second media stream according to a voice recognition technology, and extracting audio data related to overdue payment from the second media stream;
converting audio data related to overdue payment into character information, and inputting the character information and a collection urging label matched with the character information into the collection urging platform;
and generating a loan acceptance record according to the text information and the acceptance label and storing the loan acceptance record.
2. The method of claim 1, wherein the parsing the second media stream according to a speech recognition technique to extract data related to a past due payment from the second media stream comprises:
performing sound framing on the second media stream by adopting a moving window function to obtain a plurality of sections of audio data, wherein each section of audio data is a frame;
respectively carrying out waveform transformation on each section of audio data to obtain a multi-dimensional vector corresponding to each section of audio data, wherein the multi-dimensional vector is an acoustic feature;
recognizing a pronunciation sequence with the maximum matching probability with each multi-dimensional vector by adopting an acoustic model;
finding out a character string sequence with the maximum matching probability with the pronunciation sequence by adopting a language model;
performing semantic understanding (e.g., context understanding) on the sequence of strings to identify core words in the second media stream;
matching preset keywords with core words in the second media stream to obtain the audio data related to overdue payment; the preset keywords refer to keywords agreeing to payment or having payment tendency.
3. The method of claim 1, wherein after receiving the second media stream sent by the terminal of the target user and before performing semantic analysis on the second media stream according to the speech recognition technology, the method further comprises:
measuring the playing decibel of the second media stream;
and if the playing decibel of the second media stream is lower than a preset decibel, increasing the playing decibel of the second media stream so that the playing decibel of the second media stream is not less than the preset decibel, wherein the preset decibel refers to a lowest decibel value which meets the requirement that a machine carries out semantic recognition on the second media stream.
4. The method according to any of claims 1-3, wherein after receiving the second media stream sent by the terminal of the target user, before semantically analyzing the second media stream according to the speech recognition technology, the method further comprises:
analyzing data in the second media stream;
if the second media stream is determined to contain the noise data through analysis, performing voice enhancement processing on the voice signal of the second media stream by adopting the following formula to obtain an optimal voice signal:
minimize WT(k)R-1W(k)+(X(k)-X^(k|k-1))TΘ-1(X(k)-X^(k|k-1))+λ||v(k)||1
subjiect to Y(k)＝CX(k)+n(k)+v(k)
wherein, x (k) and n (k) are variables, x (k) is an optimal estimation of a state value in a kalman filtering algorithm, n (k) is an estimation of gaussian noise, Θ is a covariance matrix of gaussian noise, and v (k) is sparse noise;
filtering the noise data from the second media stream using a Kalman filtering algorithm.
5. The method according to any of claims 1-3, wherein after receiving the second media stream sent by the terminal of the target user, before semantically analyzing the second media stream according to the speech recognition technology, the method further comprises:
measuring the playing speed of the second media stream;
and if the playing speed of the second media stream is higher than the preset playing speed, reducing the playing speed of the second media stream so as to enable the playing speed of the second media stream not to be higher than the preset playing speed.
6. The method of claim 5, wherein reducing the playback speed of the second media stream comprises:
reducing the play speed of the second media stream by framing or windowing
Dividing the voice signal of the second media stream into a plurality of short segments by adopting an overlapped segmentation mode, wherein the voice signal of the second media stream comprises a plurality of video frames, and the overlapped part of the previous frame and the next frame is frame shift;
and weighting the short segments by adopting a movable window with limited length so as to reduce the playing speed of the second media stream.
7. The method of any of claims 1-3, wherein after extracting audio data related to an overdue payment from the second media stream and before converting the audio data related to the overdue payment into text information, the method further comprises:
performing lie detection analysis on the second media stream;
if the target user is determined to have false answers, selecting a reinforcement question according to a question corresponding to the false answers, wherein the reinforcement question is used for guiding the target user to answer questions with multiple dimensions;
sending the reinforcement problem to the target user according to a problem guide mode;
receiving an answer to the reinforcement question by a target user;
and performing semantic analysis on the answer of the target user aiming at the reinforcement question, and replacing the false answer in the data related to overdue repayment with the result of the semantic analysis.
8. An apparatus for processing credit data based on artificial intelligence, the apparatus comprising:
the receiving and sending module is used for acquiring loan information of a plurality of users;
the processing module is used for traversing the plurality of user loan information acquired by the transceiving module; screening multiple overdue loan data from the user loan information according to preset screening conditions, classifying the overdue loan data according to overdue types, counting target users corresponding to the overdue loan data, and generating a loan payment list comprising multiple target users; determining problem types matched with the overdue types of the target users according to the overdue types corresponding to the target users in the loan hasty receiving list, selecting target problems matched with the problem types from a question bank according to the problem types corresponding to the target users, and determining the hasty receiving channels of the target users in the loan hasty receiving list according to the overdue types; establishing session connection with terminals bound with each target user through the collection prompting channel, converting the target problems into first media streams according to response logic of the target problems, and sending the first media streams corresponding to the target problems to the terminals of the target users through the receiving and sending modules so as to carry out session with the terminals of the target users; the response logic is a response rule for prompting the receiving platform to send a media stream of a target question to a terminal of a target user according to a preset response mode so as to have conversation with the terminal;
the processing module is further used for receiving a second media stream sent by the terminal of the target user through the transceiving module, carrying out semantic analysis on the second media stream according to a voice recognition technology, and extracting audio data related to overdue repayment from the second media stream; converting audio data related to overdue payment into character information, and inputting the character information and a collection urging label matched with the character information into the collection urging platform; and generating a loan acceptance record according to the text information and the acceptance label and storing the loan acceptance record.
9. A computer device, the device comprising:
at least one processor, memory, and transceiver;
wherein the memory is configured to store program code and the processor is configured to invoke the program code stored in the memory to perform the method of any of claims 1-7.
10. A computer storage medium comprising instructions which, when run on a computer, cause the computer to perform the method of any one of claims 1-7.
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| CN116153330A (en) * | 2023-04-04 | 2023-05-23 | 杭州度言软件有限公司 | Intelligent telephone voice robot control method |
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