Resource processing notification information processing method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of artificial intelligence or financial science and technology, in particular to a processing method for resource processing notification information. The method comprises the steps of performing semantic analysis processing on a feedback text of an account based on historical resource processing notification information through a large language model to obtain an emotion feature and a behavior feature corresponding to the feedback text; performing behavior prediction processing on the account according to the emotion features and the behavior features to obtain a predicted interaction behavior and default risk information of the account; the predicted interaction behavior is used for representing an interaction behavior to be triggered by the account in a future time period; the default risk information is used for representing a resource loss risk generated after the account does not execute the resource processing operation; and obtaining target resource processing notification information for the account according to the predicted interaction behavior and the default risk information. By adopting the method, the determination accuracy of the target resource processing notification information can be improved.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and in particular to a method, apparatus, computer equipment, storage medium and computer program product for processing resource processing notification information. Background Art

[0002] With the development of the economy, the intensification of market competition and the diversification of financial products, the number of enterprises and individuals exposed to financial resource products has generally increased, and the phenomenon of enterprises and individuals failing to return resources on time has also increased. Therefore, the importance of financial institutions conducting resource processing notification activities for overdue resources has become increasingly prominent. Resource processing notification is an important means for financial institutions to protect resources and control financial risks, and it is also an important means to ensure the liquidity of financial institutions' resources.

[0003] The traditional method of determining resource processing notification information usually adopts a fixed resource processing notification template, such as resource processing notification information in the form of telephone or text message. The telephone voice content or text message text content lacks personalization and flexibility, resulting in a low accuracy rate in determining resource processing notification information. Summary of the invention

[0004] Based on this, it is necessary to provide a method, apparatus, computer device, computer-readable storage medium and computer program product for processing resource processing notification information that can improve the accuracy of determining resource processing notification information in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for processing resource processing notification information. The method comprises:

[0006] Through a large language model, semantic analysis is performed on the feedback text of the account based on the historical resource processing notification information to obtain the emotional features and behavioral features corresponding to the feedback text; the emotional features are used to characterize the emotional attitude features of the account towards the historical resource processing notification information; the behavioral features are used to characterize the interactive behavior features generated by the account in response to the historical resource processing notification information;

[0007] According to the emotional characteristics and the behavioral characteristics, the account is subjected to behavioral prediction processing to obtain predicted interactive behaviors and default risk information of the account; the predicted interactive behaviors are used to characterize the interactive behaviors that will be triggered by the account in a future time period; the default risk information is used to characterize the resource loss risk generated after the account fails to perform resource processing operations;

[0008] According to the predicted interaction behavior and the default risk information, target resource processing notification information for the account is obtained.

[0009] In one embodiment, a large language model is used to perform semantic analysis on the feedback text of the account based on the historical resource processing notification information to obtain the emotional features and behavioral features corresponding to the feedback text, including:

[0010] Using the large language model, performing keyword extraction processing on the feedback text based on the historical resource processing notification information to obtain keywords in the feedback text;

[0011] According to the keywords, sentiment analysis is performed on the feedback text to obtain the sentiment features;

[0012] According to the keywords, context extraction processing is performed on the feedback text to obtain behavioral characteristics of the account.

[0013] In one embodiment, before performing keyword extraction processing on the feedback text based on the historical resource processing notification information by using the large language model to obtain keywords in the feedback text, the method further includes:

[0014] Acquire the original feedback text and the feedback data to be processed associated with the account; the original feedback text is structured data; the feedback data to be processed is unstructured data;

[0015] Converting the feedback data to be processed from an unstructured form to a structured form to obtain converted feedback text;

[0016] The original feedback text and the converted feedback text are preprocessed to obtain the feedback text.

[0017] In one embodiment, the account is subjected to behavior prediction processing according to the emotional characteristics and the behavioral characteristics to obtain the predicted interactive behavior and default risk information of the account, including:

[0018] Obtain historical interaction behaviors of the account;

[0019] Performing behavior prediction processing on the account according to the emotional features, the behavioral features, and the historical interactive behaviors to obtain predicted interactive behaviors of the account;

[0020] Default risk information of the account is obtained according to the predicted interaction behavior and resource information corresponding to the account.

[0021] In one embodiment, obtaining target resource processing notification information for the account according to the predicted interaction behavior and the default risk information includes:

[0022] generating a user profile of the account according to the emotional characteristics, the behavioral characteristics, the predicted interactive behavior, and the default risk information;

[0023] According to the user portrait, target resource processing notification information for the account is obtained.

[0024] In one embodiment, after obtaining target resource processing notification information for the account according to the user portrait, the method further includes:

[0025] Feedback notification content information in the target resource processing notification information to the account according to the notification time and notification method in the target resource processing notification information;

[0026] Collecting actual interaction behaviors of the account triggered by the target resource processing notification information in a future time period;

[0027] According to the actual interaction behavior, the target resource processing notification information is updated to obtain updated resource processing notification information for the account.

[0028] In a second aspect, the present application also provides a device for processing resource processing notification information. The device comprises:

[0029] A text analysis module is used to perform semantic analysis on the feedback text of the account based on the historical resource processing notification information through a large language model to obtain the emotional features and behavioral features corresponding to the feedback text; the emotional features are used to characterize the emotional attitude features of the account towards the historical resource processing notification information; the behavioral features are used to characterize the interactive behavior features generated by the account in response to the historical resource processing notification information;

[0030] A behavior prediction module is used to perform behavior prediction processing on the account according to the emotional characteristics and the behavior characteristics to obtain predicted interaction behavior and default risk information of the account; the predicted interaction behavior is used to characterize the interaction behavior that will be triggered by the account in a future time period; the default risk information is used to characterize the resource loss risk generated after the account fails to perform a resource processing operation;

[0031] The information generation module is used to obtain target resource processing notification information for the account according to the predicted interaction behavior and the default risk information.

[0032] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0033] Through a large language model, semantic analysis is performed on the feedback text of the account based on the historical resource processing notification information to obtain the emotional features and behavioral features corresponding to the feedback text; the emotional features are used to characterize the emotional attitude features of the account towards the historical resource processing notification information; the behavioral features are used to characterize the interactive behavior features generated by the account in response to the historical resource processing notification information;

[0034] According to the emotional characteristics and the behavioral characteristics, the account is subjected to behavioral prediction processing to obtain predicted interactive behaviors and default risk information of the account; the predicted interactive behaviors are used to characterize the interactive behaviors that will be triggered by the account in a future time period; the default risk information is used to characterize the resource loss risk generated after the account fails to perform resource processing operations;

[0035] According to the predicted interaction behavior and the default risk information, target resource processing notification information for the account is obtained.

[0036] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0037] Through a large language model, semantic analysis is performed on the feedback text of the account based on the historical resource processing notification information to obtain the emotional features and behavioral features corresponding to the feedback text; the emotional features are used to characterize the emotional attitude features of the account towards the historical resource processing notification information; the behavioral features are used to characterize the interactive behavior features generated by the account in response to the historical resource processing notification information;

[0038] According to the emotional characteristics and the behavioral characteristics, the account is subjected to behavioral prediction processing to obtain predicted interactive behaviors and default risk information of the account; the predicted interactive behaviors are used to characterize the interactive behaviors that will be triggered by the account in a future time period; the default risk information is used to characterize the resource loss risk generated after the account fails to perform resource processing operations;

[0039] According to the predicted interaction behavior and the default risk information, target resource processing notification information for the account is obtained.

[0040] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0041] Through a large language model, semantic analysis is performed on the feedback text of the account based on the historical resource processing notification information to obtain the emotional features and behavioral features corresponding to the feedback text; the emotional features are used to characterize the emotional attitude features of the account towards the historical resource processing notification information; the behavioral features are used to characterize the interactive behavior features generated by the account in response to the historical resource processing notification information;

[0042] According to the emotional characteristics and the behavioral characteristics, the account is subjected to behavioral prediction processing to obtain predicted interactive behaviors and default risk information of the account; the predicted interactive behaviors are used to characterize the interactive behaviors that will be triggered by the account in a future time period; the default risk information is used to characterize the resource loss risk generated after the account fails to perform resource processing operations;

[0043] According to the predicted interaction behavior and the default risk information, target resource processing notification information for the account is obtained.

[0044] The above-mentioned resource processing notification information processing method, device, computer equipment, storage medium and computer program product, through a large language model, perform semantic analysis on the account's feedback text based on historical resource processing notification information to obtain the emotional characteristics and behavioral characteristics corresponding to the feedback text; the emotional characteristics are used to characterize the account's emotional attitude characteristics towards the historical resource processing notification information; the behavioral characteristics are used to characterize the interactive behavior characteristics generated by the account in response to the historical resource processing notification information; based on the emotional characteristics and behavioral characteristics, the account is subjected to behavioral prediction processing to obtain the account's predicted interactive behavior and default risk information; the predicted interactive behavior is used to characterize the interactive behavior that will be triggered by the account in a future time period; the default risk information is used to characterize the resource loss risk generated after the account fails to perform resource processing operations; based on the predicted interactive behavior and default risk information, the target resource processing notification information for the account is obtained.

[0045] Beneficial effects of the present application: by leveraging the advantages of a large language model in understanding text semantics and emotional intent, analyzing the emotional features and behavioral features of an account on historical resource processing notification information, capturing the account's emotions and behavioral intentions, and being able to more accurately predict the account's next predicted interactive behavior and the account's default risk information, so as to generate personalized target resource processing notification information based on the account's predicted interactive behavior and default risk information, that is, when generating the target resource processing notification information, the account's next predicted interactive behavior and the account's default risk information are comprehensively considered, making the target resource processing notification information ultimately generated more accurate, thereby effectively improving the accuracy of the generated target resource processing notification information. Moreover, by improving the accuracy of the generated target resource processing notification information, it is beneficial to improve the execution efficiency of the resource processing operations associated with the target resource processing notification information. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 An application environment diagram of a method for processing resource processing notification information in one embodiment;

[0047] Figure 2 A flowchart of a method for processing resource processing notification information in one embodiment;

[0048] Figure 3 A flowchart of steps for obtaining emotional features and behavioral features corresponding to a feedback text in one embodiment;

[0049] Figure 4 A schematic diagram of a flow chart of the steps of updating target resource processing notification information in one embodiment;

[0050] Figure 5 It is a flowchart of a method for processing resource processing notification information in another embodiment;

[0051] Figure 6 It is a flowchart of a method for processing resource processing notification information in another embodiment;

[0052] Figure 7 It is a structural block diagram of a processing device for resource processing notification information in one embodiment;

[0053] Figure 8 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0055] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations. The acquisition, transmission, storage, use, and processing of data in the technical solution of this application are in compliance with relevant provisions of national laws and regulations.

[0056] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned, and they should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0057] The method for processing resource processing notification information provided in the embodiment of the present application can be applied to the following examples: Figure 1 In the application environment shown. Among them, the server 101 communicates with the user terminal 102 through the network. The data storage system can store the data that the service terminal 102 needs to process. The data storage system can be integrated on the server 102, or it can be placed on the cloud or other network servers. The server 101 refers to a computer system or program that provides computer resources, services or functions to financial institutions. The user terminal 102 refers to the computer device logged in by the account. Among them, the server 101 can be implemented by an independent server or a server cluster composed of multiple servers, and of course it can also be implemented by various personal computers, laptops and other terminal devices. The user terminal 102 can be but is not limited to various personal computers, laptops, smart phones, tablets, Internet of Things devices and portable wearable devices. There is an account logged in on the user terminal.

[0058] In one embodiment, Figure 2 As shown, a method for processing resource processing notification information is provided, and the method is applied to Figure 1 The server in the example is used as an example to illustrate, including the following steps:

[0059] Step S201: semantically analyze the feedback text of the account based on the historical resource processing notification information through a large language model to obtain the emotional features and behavioral features corresponding to the feedback text.

[0060] Among them, the emotional characteristics are used to characterize the emotional attitude characteristics of the account towards the historical resource processing notification information; the behavioral characteristics are used to characterize the interactive behavior characteristics of the account in response to the historical resource processing notification information.

[0061] Among them, a large language model (LLM) is a language model composed of a deep learning model (such as a neural network) with many parameters (usually billions of weights or more). A large language model can generate text or understand the meaning of text.

[0062] Among them, the account refers to the account of an individual or enterprise that has overdue borrowed resources. Feedback text refers to the information text fed back by the account regarding historical resource processing notification information. Historical resource processing notification information refers to resource processing notification information previously sent to the account. Resource processing notification information refers to information that notifies the account to perform resource processing; for example, resource processing notification information can be policy information for notifying the account to return the borrowed resources in a timely manner. In this case, the feedback text represents the communication content fed back by the account regarding the received resource processing notification information. In actual applications, resource processing notification information can be collection strategy information.

[0063] Specifically, the server can collect information about the feedback of the account on the historical resource processing notification information from multiple channels, and then the server obtains the feedback information of the account, and then converts the feedback information in various forms (such as telephone recordings, voice, etc.) into text form, and then the server obtains the feedback text of the account; wherein, the channels include telephone recordings, emails, text messages, letters and other social media channels. The server pre-trains the large language model; the server inputs the feedback text into the large language model, so as to perform semantic analysis and processing of the feedback text in terms of emotion and behavior through the large language model, and outputs the emotion features and behavior features corresponding to the feedback text.

[0064] Step S202, performing behavior prediction processing on the account based on the emotional characteristics and behavioral characteristics to obtain the predicted interactive behavior and default risk information of the account.

[0065] Among them, the predicted interactive behavior is used to characterize the interactive behavior that the account will trigger in the future time period; the default risk information is used to characterize the resource loss risk generated after the account fails to perform resource processing operations.

[0066] The future time period refers to a period of time after the account receives the target resource processing notification information. Interaction behaviors include complaints and repayment of owed resources.

[0067] Specifically, the server combines emotional characteristics, behavioral characteristics, and the account's historical interaction behavior to determine the account's attitude toward resource processing notifications, predict the account's possible future behavioral responses to resource processing notifications, and the risks that will arise after the account fails to perform resource processing operations, and then outputs the account's predicted interaction behavior in future time periods and the account's default risk information.

[0068] Step S203, obtaining target resource processing notification information for the account according to the predicted interaction behavior and default risk information.

[0069] The target resource processing notification information includes notification method, notification time and notification content.

[0070] Specifically, the server uses predicted interactive behavior and default risk information to analyze the behavioral preference characteristics of the account, and uses a large language model to learn and adapt to the account's behavioral changes. Based on the analysis data and learning results, it adjusts and optimizes the historical resource processing notification information, and finally outputs the latest target resource processing notification information for the account.

[0071] In the above-mentioned method for processing resource processing notification information, a semantic analysis is performed on the feedback text of the account based on the historical resource processing notification information through a large language model to obtain the emotional features and behavioral features corresponding to the feedback text; the emotional features are used to characterize the emotional attitude features of the account towards the historical resource processing notification information; the behavioral features are used to characterize the interactive behavior features generated by the account in response to the historical resource processing notification information; based on the emotional features and behavioral features, the account is subjected to behavioral prediction processing to obtain the predicted interactive behavior and default risk information of the account; the predicted interactive behavior is used to characterize the interactive behavior that will be triggered by the account in the future time period; the default risk information is used to characterize the resource loss risk generated after the account fails to perform the resource processing operation; based on the predicted interactive behavior and default risk information, the target resource processing notification information for the account is obtained. The above method has the following beneficial effects: by leveraging the advantages of the large language model in understanding text semantics and emotional intentions, analyzing the emotional characteristics and behavioral characteristics of the account on the historical resource processing notification information, capturing the account's emotions and behavioral intentions, and being able to more accurately predict the account's next predicted interactive behavior and the account's default risk information, so as to generate personalized target resource processing notification information based on the account's predicted interactive behavior and default risk information, that is, when generating the target resource processing notification information, the account's next predicted interactive behavior and the account's default risk information are comprehensively considered, so that the target resource processing notification information finally generated is more accurate, thereby effectively improving the accuracy of the generated target resource processing notification information. Moreover, by improving the accuracy of the generated target resource processing notification information, it is beneficial to improve the execution efficiency of the resource processing operation associated with the target resource processing notification information.

[0072] In one embodiment, Figure 3 As shown, in the above step S201, a semantic analysis is performed on the feedback text of the account based on the historical resource processing notification information through a large language model to obtain the emotional features and behavioral features corresponding to the feedback text, which specifically include the following contents:

[0073] Step S301 : extracting keywords from the feedback text based on the historical resource processing notification information through a large language model to obtain keywords in the feedback text.

[0074] Specifically, the server inputs the feedback text of the notification information processed based on the historical resources into the large language model, performs in-depth text analysis on the feedback text through the large language model, and the in-depth text analysis includes keyword extraction, topic identification and context understanding, and outputs the keywords in the feedback text. It should be noted that the output keywords include those directly extracted from the feedback text through keyword extraction processing, those identified from the feedback text through topic identification processing, and also include keywords generated by the large language model based on the context understanding of the feedback text.

[0075] Step S302: Perform sentiment analysis on the feedback text according to the keywords to obtain sentiment features.

[0076] Specifically, the feedback text is subjected to sentiment analysis based on the keywords output in the above step S301 through the large language model to obtain sentiment features. The sentiment features are used to reflect the sentiment attitude (such as positive, negative or neutral) in the feedback text. For example, the sentiment features can reflect the feedback attitude, return intention and overdue reason of the account.

[0077] Step S303: Perform context extraction on the feedback text according to the keywords to obtain the behavior characteristics of the account.

[0078] Specifically, the context extraction capability of the large language model is used to continue to extract the context of the feedback text based on the keywords output in the above step S301 to analyze the behavior of the account in response to the historical resource processing notification information, and then the large language model outputs the behavior characteristics of the account. Among them, the behavior characteristics are used to reflect the interactive behavior of the account in response to the historical resource processing notification information. For example, the behavior characteristics can reflect the account's complaint behavior, repayment behavior, breach of contract behavior, etc.

[0079] In this embodiment, the feedback text is subjected to keyword extraction processing through a large language model to obtain the keywords in the feedback text, and then the feedback text is subjected to sentiment analysis processing and behavior analysis processing based on the keywords, thereby achieving a multi-faceted analysis of the feedback text and providing a reliable processing basis for the subsequent steps of behavior prediction processing.

[0080] In one embodiment, in the above step S301, keyword extraction processing is performed on the feedback text based on the historical resource processing notification information through a large language model to obtain the keywords in the feedback text, and the method further includes: obtaining the original feedback text and the feedback data to be processed associated with the account; the original feedback text belongs to structured data; the feedback data to be processed belongs to unstructured data; converting the feedback data to be processed from an unstructured form to a structured form to obtain converted feedback text; and preprocessing the original feedback text and the converted feedback text to obtain the feedback text.

[0081] Specifically, the server can collect feedback information on historical resource processing notifications associated with the account from multiple channels (such as telephone recordings, emails, text messages, letters, etc.). The feedback information includes original feedback texts that belong to structured data and unstructured data that are to be processed. In actual applications, the original feedback texts that belong to structured data include: 1) resource processing notification records, including customer number, resource processing notification method, contact object, contact method, contact result, customer response, and promised repayment date / amount; 2) resource processing notification text and letter data, including whether the sending is successful and customer response; 3) judicial resource processing notification data, including judicial stage, customer response, etc.; 4) outsourced resource processing notification data, which is similar to resource processing notification records. Unstructured data that are to be processed include: 1) robot resource processing notification voice data; 2) manual telephone resource processing notification voice data; 3) various agreement pictures and other non-formatted data. It should be noted that the above original feedback texts and unprocessed feedback data are all information and data authorized by the account or fully authorized by all parties to ensure the information security of the account.

[0082] Furthermore, the server converts the feedback data to be processed from an unstructured form to a structured form, and can convert the feedback data to be processed in voice form into data in text form, so that the server obtains the converted feedback text; then the original feedback text and the converted feedback text are preprocessed, which can be a text denoising process, and finally the feedback text is obtained.

[0083] In this embodiment, by converting the collected feedback data to be processed into a converted feedback text in a structured form, and then preprocessing the original feedback text and the converted feedback text, the text quality of the processed feedback text is effectively improved, which helps to improve the accuracy of the subsequently generated target resource processing notification information.

[0084] In one embodiment, the above step S202 performs behavior prediction processing on the account based on the emotional characteristics and behavioral characteristics to obtain the account's predicted interactive behavior and default risk information, which specifically includes the following contents: obtaining the account's historical interactive behavior; performing behavior prediction processing on the account based on the emotional characteristics, behavioral characteristics and historical interactive behavior to obtain the account's predicted interactive behavior; obtaining the account's default risk information based on the predicted interactive behavior and the resource information corresponding to the account.

[0085] Among them, historical interaction behavior refers to the interaction behavior triggered between the account and the server in the historical time period.

[0086] Specifically, the server collects the historical interaction behaviors of the account; the server predicts the behavior of the account in the future time period based on the account's recent emotional characteristics, behavioral characteristics and historical interaction behaviors, and obtains the account's predicted interaction behaviors; the server can also analyze the resource loss risks that will arise after the account fails to perform resource processing operations based on the resource information corresponding to the account, and further improve and correct its resource loss risks in combination with the predicted interaction behaviors, and finally output the account's default risk information.

[0087] In this embodiment, the predicted interactive behavior of the account is predicted by combining the emotional characteristics, behavioral characteristics and historical interactive behaviors; then, the default risk information of the account is obtained based on the predicted interactive behavior and the resource information corresponding to the account, thereby achieving reasonable prediction of the predicted interactive behavior of the account and accurate assessment of the default risk information, and providing reliable data support for the subsequent generation of personalized target resource processing notification information.

[0088] In one embodiment, the above step S203 obtains target resource processing notification information for the account based on the predicted interaction behavior and default risk information, specifically including the following contents: generating a user portrait of the account based on emotional characteristics, behavioral characteristics, predicted interaction behavior and default risk information; obtaining target resource processing notification information for the account based on the user portrait.

[0089] Specifically, the server can combine the sentiment and behavior characteristics output by the large language model, as well as the predicted interaction behavior and default risk information obtained in the above step S202, to formulate a detailed user portrait for the account; wherein the user portrait includes preferences, communication style, and historical behavior patterns. Based on the user portrait, the server obtains target resource processing notification information for the account, which can be input into the large language model, such as the user portrait, predicted interaction behavior, and default risk information, so that the large language model can learn the sentiment, behavior, preference, and other characteristics of the account, adapt to the behavior changes of the account in the future time period, and take default risk information into consideration, and finally output the target resource processing notification information adapted to the account characteristics and behavior changes.

[0090] In this embodiment, the user portrait of the account is used to generate personalized target resource processing notification information for the account, which effectively improves the accuracy of the generated target resource processing notification information.

[0091] In one embodiment, Figure 4 As shown, in the above step S203, after obtaining the target resource processing notification information for the account according to the predicted interaction behavior and the default risk information, it also includes:

[0092] Step S401: Feedback notification content information in the target resource processing notification information to the account according to the notification time and notification method in the target resource processing notification information.

[0093] Specifically, the server feeds back the notification content information in the target resource processing notification information to the account according to the notification time and notification method in the target resource processing notification information, for example, the notification content information may be sent to the account through a notification method such as SMS, phone call, or email according to the notification time. The user end logged in to the account may display the notification content information, such as displaying SMS, playing phone voice, or displaying email.

[0094] Step S402: collecting the actual interaction behaviors of the account triggered by the target resource processing notification information in the future time period.

[0095] Specifically, after receiving the notification content information, if the account triggers actual interactive behavior based on the notification content information within a preset future time period, such as triggering a complaint behavior, a reply confirmation behavior, a satisfactory and positive review behavior, etc., the actual interactive behavior will be transmitted to the server.

[0096] Step S403: updating the target resource processing notification information according to the actual interaction behavior to obtain updated resource processing notification information for the account.

[0097] Specifically, the server inputs the actual interaction behavior into the large language model, so as to further update the target resource processing notification information based on the actual interaction behavior through the large language model, and output the updated resource processing notification information for the account; the server can also feed back the updated notification content information in the updated resource processing notification information to the account according to the updated notification time and updated notification method in the updated resource processing notification information. The server can also collect the target resource processing notification information and actual interaction behavior of each account, generate a detailed analysis report and display it, so that the server managers can view the analysis report and use the analysis report to further optimize the large language model.

[0098] In this embodiment, the target resource processing notification information is updated through the actual interaction behavior of the account triggered by the target resource processing notification information in the future time period, and the updated resource processing notification information for the account is obtained. The target resource processing notification information can be continuously adjusted and optimized with the accumulation of time and actual interaction behavior, thereby further improving the accuracy of the generated resource processing notification information, helping to promote the account's cooperation and response willingness to resource processing, and improving the success rate and effect of resource processing.

[0099] In one embodiment, Figure 5As shown, another method for processing resource processing notification information is provided, and this method is applied to Figure 1 The server in the example is used as an example to illustrate, including the following steps:

[0100] Step S501, obtaining the original feedback text and the feedback data to be processed associated with the account; the original feedback text belongs to structured data; the feedback data to be processed belongs to unstructured data.

[0101] Step S502: convert the feedback data to be processed from an unstructured form to a structured form to obtain a converted feedback text; and pre-process the original feedback text and the converted feedback text to obtain a feedback text.

[0102] Step S503: Using a large language model, keyword extraction is performed on the feedback text based on the historical resource processing notification information to obtain keywords in the feedback text.

[0103] Step S504: perform sentiment analysis on the feedback text according to the keywords to obtain sentiment features.

[0104] Step S505: Perform context extraction on the feedback text according to the keywords to obtain the behavior characteristics of the account.

[0105] Step S506, performing behavior prediction processing on the account based on the emotional characteristics and behavioral characteristics to obtain the account's predicted interactive behavior and default risk information.

[0106] Step S507, generating a user profile of the account based on the emotional characteristics, behavioral characteristics, predicted interactive behaviors and default risk information; and obtaining target resource processing notification information for the account based on the user profile.

[0107] Step S508: Feedback notification content information in the target resource processing notification information to the account according to the notification time and notification method in the target resource processing notification information.

[0108] Step S509, collecting the actual interaction behavior of the account triggered by the target resource processing notification information in the future time period; updating the target resource processing notification information according to the actual interaction behavior to obtain updated resource processing notification information for the account.

[0109] The above-mentioned method for processing resource processing notification information can achieve the following beneficial effects: by leveraging the advantages of the large language model in understanding text semantics and emotional intentions, analyzing the emotional characteristics and behavioral characteristics of the account on historical resource processing notification information, capturing the account's emotions and behavioral intentions, and being able to more accurately predict the account's next predicted interactive behavior and the account's default risk information, so as to generate personalized target resource processing notification information based on the account's predicted interactive behavior and default risk information, that is, when generating the target resource processing notification information, the account's next predicted interactive behavior and the account's default risk information are comprehensively considered, so that the target resource processing notification information finally generated is more accurate, thereby effectively improving the accuracy of the generated target resource processing notification information. Moreover, by improving the accuracy of the generated target resource processing notification information, it is beneficial to improve the execution efficiency of the resource processing operations associated with the target resource processing notification information.

[0110] In order to more clearly illustrate the method for processing resource processing notification information provided by the embodiment of the present disclosure, the method for processing resource processing notification information is specifically described below with a specific embodiment. Figure 6 As shown, another method for processing resource processing notification information is provided, which can be applied to Figure 1 The server in the , specifically includes the following contents:

[0111] The server collects the interaction behaviors and feedback information of overdue accounts from multiple channels (phone recordings, emails, text messages, letters, and other social media channels), and processes the feedback information by standardizing the text, removing noise data, identifying key information, and obtaining the account's feedback text.

[0112] The server performs natural language processing / text analysis on the feedback text through a large language model to deeply analyze the reasons for account overdue, account complaint willingness, account repayment ability and willingness, and obtain analysis results.

[0113] The server predicts the predicted interaction behavior of the account in the future time period based on the analysis results and the historical interaction behavior of the account. The target resource processing notification information is generated based on the analysis results and the predicted interaction behavior. The server can also continuously adjust and optimize the target resource processing notification information based on the actual interaction behavior generated by the account based on the target resource processing notification information. In addition, the resource processing notification experience of excellent seats in financial institutions can be extracted to further adjust the target resource processing notification information and obtain updated resource processing notification information.

[0114] In this embodiment, the following beneficial effects can be achieved: a large language model is used to more accurately judge the debtor's emotional state and possible behavioral trends, which helps to formulate target resource processing notification information that is more in line with actual conditions, and continuously adjusts and optimizes the target resource processing notification information based on actual interactive behaviors, thereby ensuring that the target resource processing notification information can be continuously improved with the accumulation of time and experience, greatly improving the accuracy of the target resource processing notification information, and helping to promote the account's cooperation and response willingness to resource processing, thereby improving the success rate of resource processing and the effect of resource processing.

[0115] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0116] Based on the same inventive concept, the embodiment of the present application also provides a processing device for resource processing notification information for implementing the processing method for resource processing notification information involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of one or more processing devices for resource processing notification information provided below can refer to the limitations of the processing method for resource processing notification information above, and will not be repeated here.

[0117] In one embodiment, Figure 7 As shown, a device 700 for processing resource processing notification information is provided, comprising: a text analysis module 701, a behavior prediction module 702 and an information generation module 703, wherein:

[0118] The text analysis module 701 is used to perform semantic analysis on the feedback text of the account based on the historical resource processing notification information through a large language model to obtain the emotional features and behavioral features corresponding to the feedback text; the emotional features are used to characterize the emotional attitude features of the account towards the historical resource processing notification information; the behavioral features are used to characterize the interactive behavior features generated by the account in response to the historical resource processing notification information.

[0119] The behavior prediction module 702 is used to perform behavior prediction processing on the account based on the emotional characteristics and behavioral characteristics to obtain the account's predicted interactive behavior and default risk information; the predicted interactive behavior is used to characterize the interactive behavior that the account will trigger in the future time period; the default risk information is used to characterize the resource loss risk generated after the account fails to perform resource processing operations.

[0120] The information generation module 703 is used to obtain target resource processing notification information for the account according to the predicted interaction behavior and default risk information.

[0121] In one embodiment, the text analysis module 701 is also used to perform keyword extraction processing on the feedback text based on the historical resource processing notification information through a large language model to obtain keywords in the feedback text; perform sentiment analysis processing on the feedback text based on the keywords to obtain sentiment features; perform context extraction processing on the feedback text based on the keywords to obtain behavioral features of the account.

[0122] In one embodiment, the processing device 700 for resource processing notification information also includes a preprocessing module, which is used to obtain original feedback text and feedback data to be processed associated with the account; the original feedback text belongs to structured data; the feedback data to be processed belongs to unstructured data; the feedback data to be processed is converted from an unstructured form to a structured form to obtain a converted feedback text; the original feedback text and the converted feedback text are preprocessed to obtain a feedback text.

[0123] In one embodiment, the behavior prediction module 702 is also used to obtain the historical interaction behavior of the account; perform behavior prediction processing on the account based on the emotional characteristics, behavioral characteristics and historical interaction behavior to obtain the predicted interaction behavior of the account; and obtain the default risk information of the account based on the predicted interaction behavior and the resource information corresponding to the account.

[0124] In one embodiment, the information generation module 703 is also used to generate a user profile of the account based on the emotional characteristics, behavioral characteristics, predicted interactive behaviors and default risk information; and obtain target resource processing notification information for the account based on the user profile.

[0125] In one embodiment, the resource processing notification information processing device 700 also includes an information update module, which is also used to feed back the notification content information in the target resource processing notification information to the account according to the notification time and notification method in the target resource processing notification information; collect the actual interaction behavior of the account triggered by the target resource processing notification information in a future time period; update the target resource processing notification information according to the actual interaction behavior to obtain updated resource processing notification information for the account.

[0126] Each module in the processing device for processing resource processing notification information can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0127] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 8 As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for processing resource processing notification information is implemented. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0128] Those skilled in the art will understand that Figure 8 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0129] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0130] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0131] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0132] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0133] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0134] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for processing resource processing notification information, characterized in that: The method comprises: Through a large language model, semantic analysis is performed on the feedback text of the account based on the historical resource processing notification information to obtain the emotional features and behavioral features corresponding to the feedback text; the emotional features are used to characterize the emotional attitude features of the account towards the historical resource processing notification information; the behavioral features are used to characterize the interactive behavior features generated by the account in response to the historical resource processing notification information; According to the emotional characteristics and the behavioral characteristics, the account is subjected to behavioral prediction processing to obtain predicted interactive behaviors and default risk information of the account; the predicted interactive behaviors are used to characterize the interactive behaviors that will be triggered by the account in a future time period; the default risk information is used to characterize the resource loss risk generated after the account fails to perform resource processing operations; According to the predicted interaction behavior and the default risk information, target resource processing notification information for the account is obtained.

2. The method according to claim 1, characterized in that The feedback text of the account based on the historical resource processing notification information is semantically analyzed by the large language model to obtain the emotional features and behavioral features corresponding to the feedback text, including: Using the large language model, performing keyword extraction processing on the feedback text based on the historical resource processing notification information to obtain keywords in the feedback text; According to the keywords, sentiment analysis is performed on the feedback text to obtain the sentiment features; According to the keywords, context extraction processing is performed on the feedback text to obtain behavioral characteristics of the account.

3. The method according to claim 2, characterized in that Before performing keyword extraction processing on the feedback text based on the historical resource processing notification information by using the large language model to obtain keywords in the feedback text, the method further includes: Acquire the original feedback text and the feedback data to be processed associated with the account; the original feedback text is structured data; the feedback data to be processed is unstructured data; Converting the feedback data to be processed from an unstructured form to a structured form to obtain converted feedback text; The original feedback text and the converted feedback text are preprocessed to obtain the feedback text.

4. The method according to claim 1, characterized in that: The performing behavior prediction processing on the account according to the emotion characteristics and the behavior characteristics to obtain the predicted interaction behavior and default risk information of the account includes: Obtain historical interaction behaviors of the account; According to the emotional features, the behavioral features and the historical interactive behaviors, the account is subjected to behavioral prediction processing to obtain predicted interactive behaviors of the account; Default risk information of the account is obtained according to the predicted interaction behavior and resource information corresponding to the account.

5. The method according to claim 1, characterized in that The obtaining target resource processing notification information for the account according to the predicted interaction behavior and the default risk information includes: generating a user profile of the account according to the emotional characteristics, the behavioral characteristics, the predicted interactive behavior, and the default risk information; According to the user portrait, target resource processing notification information for the account is obtained.

6. The method according to claim 5, characterized in that After obtaining target resource processing notification information for the account according to the user portrait, the method further includes: Feedback notification content information in the target resource processing notification information to the account according to the notification time and notification method in the target resource processing notification information; Collecting actual interaction behaviors of the account triggered by the target resource processing notification information in a future time period; According to the actual interaction behavior, the target resource processing notification information is updated to obtain updated resource processing notification information for the account.

7. A device for processing resource processing notification information, characterized in that: The device comprises: A text analysis module is used to perform semantic analysis on the feedback text of the account based on the historical resource processing notification information through a large language model to obtain the emotional features and behavioral features corresponding to the feedback text; the emotional features are used to characterize the emotional attitude features of the account towards the historical resource processing notification information; the behavioral features are used to characterize the interactive behavior features generated by the account in response to the historical resource processing notification information; A behavior prediction module is used to perform behavior prediction processing on the account according to the emotional characteristics and the behavior characteristics to obtain predicted interaction behavior and default risk information of the account; the predicted interaction behavior is used to characterize the interaction behavior that will be triggered by the account in a future time period; the default risk information is used to characterize the resource loss risk generated after the account fails to perform a resource processing operation; The information generation module is used to obtain target resource processing notification information for the account according to the predicted interaction behavior and the default risk information.

8. The device according to claim 7, characterized in that The text analysis module is also used to perform keyword extraction processing on the feedback text based on the historical resource processing notification information through the large language model to obtain keywords in the feedback text; perform sentiment analysis processing on the feedback text based on the keywords to obtain the sentiment features; and perform context extraction processing on the feedback text based on the keywords to obtain the behavioral features of the account.

9. The device according to claim 8, characterized in that The device for processing resource processing notification information also includes a preprocessing module, which is used to obtain original feedback text and feedback data to be processed associated with the account; the original feedback text belongs to structured data; the feedback data to be processed belongs to unstructured data; convert the feedback data to be processed from an unstructured form to a structured form to obtain a converted feedback text; and preprocess the original feedback text and the converted feedback text to obtain the feedback text.

10. The device according to claim 7, characterized in that The behavior prediction module is also used to obtain the historical interaction behavior of the account; perform behavior prediction processing on the account based on the emotional characteristics, the behavioral characteristics and the historical interaction behavior to obtain the predicted interaction behavior of the account; and obtain the default risk information of the account based on the predicted interaction behavior and the resource information corresponding to the account.

11. The device according to claim 7, characterized in that The information generation module is also used to generate a user portrait of the account based on the emotional characteristics, the behavioral characteristics, the predicted interactive behavior and the default risk information; and obtain target resource processing notification information for the account based on the user portrait.

12. The device according to claim 11, characterized in that The resource processing notification information processing device also includes a policy updating module, which is also used to feed back notification content information in the target resource processing notification information to the account according to the notification time and notification method in the target resource processing notification information; Collecting actual interaction behaviors of the account triggered by the target resource processing notification information in a future time period; According to the actual interaction behavior, the target resource processing notification information is updated to obtain updated resource processing notification information for the account.

13. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

15. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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