Conformance problem processing method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of artificial intelligence, can be applied to business system platforms such as financial science and technology, and discloses a compliance problem processing method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining compliance data which at least comprises compliance problem data and work behavior data; preprocessing the compliance data to generate a training data set; training a pre-established neural network model by adopting the training data set to obtain a fully trained compliance problem processing model; and obtaining a compliance question, and processing the compliance question by using the compliance question processing model to generate a compliance question answer, or obtaining real-time working behavior data of a user, and analyzing the real-time working behavior data by using the compliance question processing model to generate a compliance risk prompt. The technical problem that in the prior art, compliance question answering and compliance risk prompting cannot be well carried out is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of artificial intelligence, in particular to a compliance problem processing method and device, computer equipment and storage medium. BACKGROUND

[0002] In the insurance industry, employees often encounter legal compliance-related problems in their daily work, such as contract clause interpretation, claim compliance judgment, customer information protection, etc. These problems usually need to rely on the legal team for manual solution, which has the following problems:

[0003] Low efficiency: manual solution takes a long time and is difficult to meet the immediate needs of employees.

[0004] Repetitive work: the same or similar problems repeatedly occur, resulting in waste of resources.

[0005] Risk lag: employees may unintentionally touch the compliance boundary in their work, but lack a real-time reminder mechanism, resulting in potential risks.

[0006] Knowledge dispersion: compliance knowledge is scattered among the legal team, making it difficult to form a unified knowledge base and affecting the employees' self-learning and problem-solving abilities.

[0007] Therefore, there is an urgent need for an AI-based compliance question and answer and risk reminder system that can automatically provide compliance feedback based on employee questions and real-time remind employees of compliance risks in their work, improving work efficiency and compliance management level. SUMMARY

[0008] The purpose of the present application is to overcome the above technical deficiencies and provide a compliance problem processing method, device, computer equipment and storage medium to solve the technical problem of not being able to better answer compliance questions and compliance risk prompts in the prior art.

[0009] To achieve the above technical purpose, the present application adopts the following technical solutions:

[0010] In a first aspect, the present application provides a compliance problem processing method, comprising the following steps:

[0011] Obtaining compliance data, wherein the compliance data at least includes compliance problem data and work behavior data;

[0012] Preprocessing the compliance data to generate a training data set;

[0013] Training a pre-established neural network model using the training data set to obtain a trained compliance problem processing model;

[0014] The compliance question is obtained, the compliance question is processed by using the compliance question processing model to generate a compliance question answer, or real-time work behavior data of a user is obtained, and the real-time work behavior data is analyzed by using the compliance question processing model to generate a compliance risk prompt.

[0015] In some embodiments, the compliance data is preprocessed to generate a training data set, including:

[0016] The compliance data is data cleaned to remove invalid data and noise data;

[0017] The cleaned compliance data is labeled, and labels are added to the compliance data to distinguish compliance question data and work behavior data;

[0018] The labeled compliance data is processed by data enhancement, and the enhanced compliance data is divided into a training set and a validation set;

[0019] The training set and the validation set are standardized to generate a training data set.

[0020] In some embodiments, the training data set is used to train a pre-established neural network model to obtain a trained compliance question processing model, including:

[0021] A basic neural network model is established, and the architecture of the basic neural network model is adjusted to generate a neural network model for insurance compliance question answering and risk prompting;

[0022] The training data set is used to train the neural network model in multiple tasks;

[0023] The trained basic neural network model is tested and verified to obtain a compliance question processing model capable of insurance compliance question answering and risk prompting.

[0024] In some embodiments, the compliance question is obtained, and the compliance question is processed by using the compliance question processing model to generate a compliance question answer, including:

[0025] The compliance question is obtained, and the compliance question is analyzed to extract key information in the compliance question;

[0026] The key information is processed by using the compliance question processing model to generate a preliminary compliance question answer;

[0027] The preliminary compliance question answer is verified to generate a compliance question answer.

[0028] In some embodiments, the real-time work behavior of the user is acquired, and the real-time work behavior is analyzed by using the compliance problem processing model to generate a compliance risk prompt, including:

[0029] Acquiring real-time work behavior data of the user, and analyzing the real-time work behavior data to obtain a work behavior mode of the user;

[0030] Detecting the work behavior mode of the user by using the compliance problem processing model to identify a compliance risk;

[0031] Generating a corresponding compliance risk prompt according to the identified compliance risk.

[0032] In some embodiments, after the compliance problem is acquired and the compliance problem processing model is used to process the compliance problem to generate a compliance problem answer, or after the real-time work behavior data of the user is acquired and the compliance problem processing model is used to analyze the real-time work behavior data to generate a compliance risk prompt, the method further includes:

[0033] Acquiring compliance rule data, and organizing and classifying the compliance rule data to establish a compliance rule library;

[0034] Matching and analyzing the compliance problem or the real-time work behavior of the user based on the compliance rule library;

[0035] Generating a compliance problem answer or a compliance risk prompt according to a matching and analyzing result;

[0036] Comparing and integrating the compliance problem answer or the compliance risk prompt to obtain an optimized compliance problem answer or compliance risk.

[0037] In some embodiments, after the compliance problem is acquired and the compliance problem processing model is used to process the compliance problem to generate a compliance problem answer, or after the real-time work behavior data of the user is acquired and the compliance problem processing model is used to analyze the real-time work behavior data to generate a compliance risk prompt, the method further includes:

[0038] Acquiring a feedback result of the user for the compliance problem answer or the compliance risk prompt;

[0039] Optimizing the compliance problem processing model based on the feedback result;

[0040] Re-training and verifying the optimized compliance problem processing model to update the compliance problem processing model.

[0041] In a second aspect, the present application further provides a compliance problem processing device, including:

[0042] The data acquisition module is configured to acquire compliance data, wherein the compliance data at least includes compliance question data and work behavior data.

[0043] The training data set generation module is configured to preprocess the compliance data to generate a training data set.

[0044] The model training module is configured to train a pre-established neural network model by using the training data set to obtain a trained compliance question processing model.

[0045] The question processing module is configured to acquire a compliance question, process the compliance question by using the compliance question processing model to generate a compliance question answer, or acquire real-time work behavior data of a user, analyze the real-time work behavior data by using the compliance question processing model to generate a compliance risk prompt.

[0046] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores computer readable instructions, and the processor executes the computer readable instructions to implement the steps of the compliance question processing method.

[0047] In a fourth aspect, the present application further provides a computer readable storage medium, wherein the computer readable storage medium stores computer readable instructions, and the computer readable instructions are executed by a processor to implement the steps of the compliance question processing method.

[0048] Compared with the prior art, the compliance question processing method, device, computer device and storage medium provided by the present application first acquire compliance data, wherein the compliance data at least includes compliance question data and work behavior data; then preprocess the compliance data to generate a training data set; then train a pre-established neural network model by using the training data set to obtain a trained compliance question processing model; finally acquire a compliance question, process the compliance question by using the compliance question processing model to generate a compliance question answer, or acquire real-time work behavior data of a user, analyze the real-time work behavior data by using the compliance question processing model to generate a compliance risk prompt. The present application realizes intelligent compliance question answering and real-time risk prompting, combines a dynamically updated knowledge base and multi-system integration capability, and constructs an efficient, intelligent and personalized compliance management tool. These technical innovations not only significantly improve the efficiency and accuracy of compliance management, but also provide a comprehensive compliance management solution through data-driven continuous optimization, significantly shorten compliance question answering time through automatic question answering and risk prompting, improve employee work efficiency, reduce employee compliance risks in work through real-time risk prompting and compliance rule matching, and improve the company's compliance management level. BRIEF DESCRIPTION OF DRAWINGS

[0049] In order to more clearly illustrate the solutions in the present application, the drawings needed to be used in the embodiments of the present application will be briefly introduced as follows. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort based on these drawings.

[0050] Figure 1 is an exemplary system architecture diagram to which the present application can be applied;

[0051] Figure 2 is a flow chart of one embodiment of the compliance issue processing method according to the present application;

[0052] Figure 3 is a flow chart of one embodiment of the compliance issue processing method according to the present application; Figure 2 is a flow chart of one specific embodiment of the step S200 shown in

[0053] Figure 4 is a flow chart of one specific embodiment of the step S300 shown in Figure 2

[0054] Figure 5 is a flow chart of one specific embodiment of the step S400 shown in Figure 2

[0055] Figure 6 is a flow chart of another specific embodiment of the step S400 shown in Figure 2

[0056] Figure 7 is a structural schematic diagram of one embodiment of the compliance issue processing apparatus according to the present application;

[0057] Figure 8 is a structural schematic diagram of one embodiment of the computer device according to the present application;

[0058] Figure 9 is a structural schematic diagram of another embodiment of the computer device according to the present application. DETAILED DESCRIPTION

[0059] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the 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.

[0060] ​​​Reference to“an embodiment” herein means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase“in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily all referring to a common set of embodiments, of the application. It is explicitly contemplated that embodiments described herein can be combined with each other in their individual aspects.

[0061] In order to better understand the technical scheme of the present application, the technical scheme in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings.

[0062] The compliance problem processing method based on artificial intelligence provided by the embodiments of the present application can be applied in the application environment of Figure 1 , wherein the client communicates with the server through the network. The server can first acquire compliance data through the client, wherein the compliance data at least includes compliance problem data and work behavior data; then pre-processes the compliance data to generate a training data set; then trains a pre-established neural network model using the training data set to obtain a trained compliance problem processing model; finally acquires a compliance problem, processes the compliance problem using the compliance problem processing model to generate a compliance problem answer, or acquires real-time work behavior data of a user, analyzes the real-time work behavior data using the compliance problem processing model to generate a compliance risk prompt. The present application realizes intelligent compliance question and answer and real-time risk prompting, combines a dynamically updated knowledge base and multi-system integration capability, and constructs an efficient, intelligent, and personalized compliance management tool. These technical innovations not only significantly improve the efficiency and accuracy of compliance management, but also provide a comprehensive compliance management solution through data-driven continuous optimization, significantly shorten compliance problem solving time through automated question and answer and risk prompting, improve employee work efficiency, reduce employee compliance risk in work through real-time risk prompting and compliance rule matching, and improve the company's compliance management level. The client can be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices. The server can be implemented by an independent server or a server cluster composed of multiple servers. The present application will be described in detail below through specific embodiments.

[0063] Please refer to Figure 2 , Figure 2 Fig. 1 shows a flowchart of one embodiment of a compliance problem processing method according to the present application, which is suitable for compliance problem answering or compliance risk monitoring in a financial scenario, and includes steps S100 to S400.

[0064] S100, obtain compliance data, wherein the compliance data at least includes compliance question data and work behavior data.

[0065] In this embodiment, the compliance data at least includes compliance question data and work behavior data, wherein the compliance question data can be obtained by collecting compliance questions raised by employees in the company and corresponding answer records, and the work behavior data can be obtained by collecting behavior data of employees in work (such as contract approval, claim review, etc.), for identifying compliance risks.

[0066] S200, pre-process the compliance data to generate a training data set.

[0067] In this embodiment, in order to facilitate subsequent training of the model, a training data set needs to be generated first, and the compliance data is pre-processed to ensure that the training data set can better train the model, wherein the pre-processing process includes but is not limited to data cleaning, data labeling, data standardization, data enhancement and other processing processes.

[0068] S300, train the pre-established neural network model using the training data set to obtain a trained compliance question processing model.

[0069] In this embodiment, a large model suitable for text understanding and generation tasks is first selected as a base model, and then the model is trained using the training data set, so that the model can adapt to the compliance question and risk prompting tasks.

[0070] S400, obtain a compliance question, process the compliance question using the compliance question processing model to generate a compliance question answer, or obtain real-time work behavior data of a user, analyze the real-time work behavior data using the compliance question processing model to generate a compliance risk prompt.

[0071] In this embodiment, after obtaining the compliance question processing model, the compliance question can be answered, and the compliance risk prompt can also be performed. Specifically, the user can input the compliance question encountered by the user into the model, and the model can automatically output the compliance question answer, or the model can be deployed in an office system, and the model can automatically monitor the daily work behavior of the user to identify whether there is a compliance risk in the daily work of the user.

[0072] In an embodiment of the present invention, compliance data is first obtained, wherein the compliance data includes at least compliance problem data and work behavior data; the compliance data is then preprocessed to generate a training data set; the training data set is then used to train a pre-established neural network model to obtain a fully trained compliance problem processing model; finally, compliance problems are obtained and processed using the compliance problem processing model to generate compliance problem answers, or real-time work behavior data of users is obtained and analyzed using the compliance problem processing model to generate compliance risk alerts. The present invention implements intelligent compliance Q&A and real-time risk alerts, and combines a dynamically updated knowledge base and multi-system integration capabilities to build an efficient, intelligent, and personalized compliance management tool. These technological innovations not only significantly improve the efficiency and accuracy of compliance management, but also provide a comprehensive compliance management solution through data-driven continuous optimization. Through automated Q&A and risk alerts, the time for answering compliance questions is significantly shortened, the work efficiency of employees is improved, and through real-time risk alerts and compliance rule matching, the compliance risks faced by employees at work are reduced, thereby improving the company's compliance management level.

[0073] In some embodiments, see Figure 3 , the step S200 specifically includes:

[0074] S210: Clean and label the compliance data to remove invalid data and noise data;

[0075] S220: Label the cleaned compliance data and add labels to the compliance data to distinguish compliance issue data from work behavior data;

[0076] S230: Perform data augmentation processing on the labeled compliant data, and divide the enhanced compliant data into a training set and a validation set;

[0077] S240: Standardize the training set and the validation set to generate a training data set.

[0078] In this embodiment, data cleansing is used to remove duplicate, invalid, or malformed data. For example, duplicate data refers to records that are identical or partially identical. For example, in a sales order table, there may be multiple identical order records. Duplicate data can be identified by comparing the values ​​of various fields in the records. Completely duplicate records can be deleted directly; partially duplicate records can be merged or the most representative records can be retained based on business needs.

[0079] Data labeling is to semantically label compliance issue data and work behavior data, and mark key compliance terms and risk points.

[0080] Data augmentation is to expand the data set through synonym replacement, sentence rewriting and other technologies to improve the generalization ability of the model. For example, some words in the sentence are replaced by synonyms (such as "happy" is replaced by "happy") through synonym replacement means, or some words in the sentence are randomly deleted to simulate a noisy environment, and the text can also be translated into another language and then translated back to the original language to generate text with similar semantics but different expressions.

[0081] In addition, the embodiment of the present application can also realize field adaptation. According to the characteristics of the insurance industry, the data is processed for field adaptation to increase the adaptation ability of the model.

[0082] In some embodiments, referring to Figure 4 , the step S300 specifically comprises:

[0083] S310, establishing a basic neural network model, adjusting the architecture of the basic neural network model to generate a neural network model for solving insurance compliance problems and risk prompts;

[0084] S320, using the training data set to perform multi-task training on the neural network model;

[0085] S330, testing and verifying the trained basic neural network model to obtain a compliance problem processing model capable of solving insurance compliance problems and risk prompts.

[0086] In this embodiment, first, a large model suitable for text understanding and generation tasks is selected (such as the large model of DeepSeek), and then a fine-tuning architecture suitable for compliance question answering and risk reminding tasks is designed, including an input layer, an encoding layer, a decoding layer, and an output layer. Specifically, the input layer is responsible for converting text data (such as employee questions) into a vector form that the model can process. Usually, the Word Embedding technology is used to convert each word into a fixed-length vector. For example, pre-trained word vectors (such as GloVe or Word2Vec) are used or processed through the embedding layer inside the model. The encoding layer uses the Transformer architecture, which contains multiple layers of self-attention mechanisms (Self-Attention) and feed-forward neural networks (FFN). Each self-attention layer calculates the relationship between words in the input sequence to generate context-dependent representations. The feed-forward neural network further processes the attention output to enhance the model's non-linear representation capabilities. The decoding layer is also based on the Transformer architecture, but it contains self-attention and cross-attention mechanisms. Self-attention is used to process the output sequence of the decoder, and cross-attention is used to focus on the input sequence of the encoder to generate context-dependent answers. The output layer is responsible for converting the output of the decoder into the final prediction result. For question answering tasks, the output layer is usually a linear layer followed by a Softmax function, which generates a probability distribution and selects the word with the highest probability as the output.

[0087] where the self-attention mechanism is used to calculate the dot product of the query (Q), key (K), and value (V) to obtain the attention weight:

[0088] [\text{Attention}(Q,K,V)=\text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V],

[0089] where (d_k) is the dimension of the key.

[0090] The feed-forward neural network includes multiple feed-forward layers, each consisting of two linear transformations and a ReLU activation function, as follows:

[0091] [\text{FFN}(x)=\max(0,W_1x+b_1)W_2+b_2]。

[0092] The loss function uses the cross-entropy loss function to measure the difference between the model output and the true label, as follows:

[0093] [\mathcal{L}=-\frac{1}{N}\sum_{i=1}^{N}\sum_{j=1}^{V}y_{ij}\log p_{ij}],

[0094] where (N) is the batch size, (V) is the vocabulary size, (y_{ij}) is the true label, and (p_{ij}) is the probability predicted by the model.

[0095] During the model training phase, the model is first pre-trained using large-scale general text data to improve its language understanding ability, and then fine-tuned using labeled compliance question data and work behavior data to adapt to the compliance question answering and risk reminding tasks. Moreover, the present application adopts a multi-task learning approach, combining the compliance question answering task and the risk reminding task to improve the comprehensive ability of the model. Specifically, multi-task learning (MTL) is a technique that improves the comprehensive ability of the model by simultaneously learning multiple related tasks. In the present application, the compliance question answering task and the risk reminding task are two closely related core tasks, and through multi-task learning, knowledge sharing and ability transfer between tasks can be achieved, thereby improving the overall performance of the model. The detailed implementation process of multi-task learning is as follows:

[0096] 1. Task definition and data preparation

[0097] 1.1 Task definition

[0098] Compliance question answering task: the model needs to generate accurate and compliant answers based on the employee's questions. For example, answering "Does the claim amount exceeding a certain amount need to be approved?"

[0099] Risk reminding task: the model needs to identify potential compliance risks in real time based on the employee's work behavior data (such as contract approval, claim review, etc.), and provide improvement suggestions.

[0100] 1.2 Data preparation

[0101] Compliance question answering data: including employee questions and corresponding compliance answers, used to train the question answering task.

[0102] Risk reminding data: including employee work behavior data (such as operation records, approval processes, etc.) and corresponding compliance risk labels (such as "violation of approval", "excessive authority operation", etc.), used to train the risk reminding task.

[0103] 2. Model architecture design

[0104] 2.1 Shared encoding layer

[0105] Design a shared encoding layer (such as Transformer encoder) to extract general features of input data.

[0106] The input can be the employee's question (question answering task) or work behavior data (risk reminding task).

[0107] The role of the shared encoding layer is to extract common semantic information related to the two tasks.

[0108] 2.2 Task-Specific Decoding Layer

[0109] Design independent decoding layers for each task:

[0110] Question answering task decoding layer: used to generate natural and fluent answers.

[0111] Risk Alert Task Decoding Layer: used to identify potential risks and generate improvement suggestions.

[0112] Through task-specific decoding layers, the model can be optimized according to the characteristics of different tasks.

[0113] 2.3 Task Embedding (Optional)

[0114] Task embedding is designed for each task to help the model distinguish different tasks.

[0115] Task embeddings can be used as part of the input to guide the model to focus on features of different tasks in the shared encoding layer.

[0116] 3. Loss Function Design

[0117] 3.1 Single-task loss function

[0118] Question answering task loss function: Cross-Entropy Loss is used to measure the difference between the generated answer and the true answer. [

[0120] \mathcal{L}{\text{QA}}=-\frac{1}{N}\sum{i=1}^{N}\sum_{j=1}^{V}y_{ij}\log p_{ij} ]

[0122] Where (N) is the batch size, (V) is the vocabulary size, (y_{ij}) is the probability distribution of the true answer, and (p_{ij}) is the probability distribution generated by the model.

[0123] Risk reminder task loss function: Use binary classification loss (such as binary cross entropy loss) to measure the difference between the model's prediction of risk and the true label. [

[0125] \mathcal{L}{\text{Risk}}=-\frac{1}{M}\sum{k=1}^{M}\left[y_k\log p_k+(1-y_k)\log(1-p_k)\right] ]

[0127] where (M) is the batch size, (y_k) is the true label (0 for no risk, 1 for risky), and (p_k) is the model's predicted risk probability.

[0128] 3.2 Multi-task loss function

[0129] The loss functions of the two tasks are weighted and summed as the final loss function: [\mathcal{L}{\text{Total}}=\alpha\mathcal{L}{\text{QA}}+\beta\mathcal{L}_{\text{Risk}}] where (\alpha) and (\beta) are weight coefficients to balance the loss contributions of the two tasks.

[0130] 4. Model training process

[0131] 4.1 Data input

[0132] Input the employee's question and work behavior data into the model.

[0133] If it is a question-answering task, the input is the employee's question; if it is a risk alert task, the input is the employee's work behavior data.

[0134] 4.2 Shared encoding layer processing

[0135] Extract common features of the input data through the shared encoding layer.

[0136] 4.3 Task-specific decoding layer processing

[0137] According to the task type, input the output of the shared encoding layer into the corresponding decoding layer:

[0138] For the question-answering task, the decoding layer generates an answer.

[0139] For the risk alert task, the decoding layer generates risk prediction and improvement suggestions.

[0140] 4.4 Loss calculation and optimization

[0141] Calculate the total loss according to the loss functions of the two tasks.

[0142] Update the model parameters using an optimizer (such as Adam) to minimize the total loss.

[0143] 5. Model optimization and tuning

[0144] 5.1 Dynamic weight adjustment

[0145] During the training process, the weights of the two tasks ((\alpha) and (\beta)) are dynamically adjusted.

[0146] For example, if the loss of the question and answer task decreases slowly, (\alpha) can be appropriately increased to prioritize the optimization of the question and answer task.

[0147] 5.2 Task distillation (optional)

[0148] If there is a strong correlation between the two tasks, task distillation can be introduced.

[0149] By allowing the model to provide a soft target to the other task while learning one task, the knowledge transfer between tasks is enhanced.

[0150] 5.3 Model compression and acceleration

[0151] Through techniques such as knowledge distillation and quantization compression, the computational complexity of the model is reduced, and the response speed is improved.

[0152] 6. Advantages of multi-task learning

[0153] 6.1 Knowledge sharing

[0154] By sharing the encoding layer, the model can extract common features from both tasks, improving the model's generalization ability.

[0155] For example, the compliance rules learned in the question and answer task can be transferred to the risk alert task.

[0156] 6.2 Data efficiency

[0157] Multi-task learning can make full use of limited data resources, reducing the dependence on labeled data through knowledge transfer between tasks.

[0158] 6.3 Comprehensive ability improvement

[0159] The model can improve its comprehensive ability in compliance question and answer and risk alert tasks by optimizing both tasks simultaneously.

[0160] Through multi-task learning, the model can simultaneously optimize compliance question and answer and risk alert tasks, achieving knowledge sharing and task collaboration. The specific implementation process includes task definition, model architecture design, loss function design, model training and optimization, etc. This method not only improves the comprehensive ability of the model, but also provides comprehensive compliance management solutions for insurance companies.

[0161] In addition, it should be noted that after the model training is completed, the model can also be optimized, for example, by grid search or Bayesian optimization method, the hyperparameters of the model (such as learning rate, batch size, etc.) are adjusted, or by knowledge distillation, quantization compression and other technologies, the calculation complexity of the model is reduced, and the response speed is improved.

[0162] In some embodiments, referring to Figure 5 , the compliance question is obtained, and the compliance question processing model is used to process the compliance question to generate a compliance question answer, including:

[0163] S410, a compliance question is obtained, and the compliance question is parsed to extract key information in the compliance question;

[0164] S420, the compliance question processing model is used to process the key information to generate a preliminary compliance question answer;

[0165] S430, the preliminary compliance question answer is verified to generate a compliance question answer.

[0166] In this embodiment, first, the user input compliance question is parsed, for example, the employee's question is parsed, such as "whether the claim amount exceeding a certain amount needs to be approved?", then the key information is extracted, and the intent of the question is identified through natural language processing technology, to ensure the accuracy of the generated answer, and then the key information is input into the compliance question processing model, and a natural and fluent answer is generated through the compliance question processing model combined with the identified intent, to ensure the accuracy and professionalism of the language. Among them, the natural language processing (NLP) intent recognition is one of the core steps of the task-oriented dialogue system, and the goal is to map the user's natural language question to a predefined intent category (such as "query weather", "book hotel", "play music", etc.), so as to facilitate subsequent task execution or dialogue management.

[0167] In some embodiments, referring to Figure 6 , the real-time work behavior of the user is obtained, and the compliance question processing model is used to analyze the real-time work behavior to generate a compliance risk prompt, including:

[0168] S410', real-time work behavior data of a user is obtained, and the real-time work behavior data is analyzed to obtain a work behavior pattern of the user;

[0169] S420', the compliance question processing model is used to detect the work behavior pattern of the user to identify a compliance risk;

[0170] S430' generates a corresponding compliance risk prompt according to the identified compliance risk.

[0171] In this embodiment, the work behavior data of the employee is collected in real time through the API interface, the potential compliance risk is identified by analyzing the work behavior mode of the employee, and the compliance problem processing model can analyze the work behavior mode of the user and identify abnormal conditions (such as super permission operation and illegal approval) in the work behavior of the employee. After identifying the risk, the employee can be reminded of the potential compliance risk in real time through system notification, email and the like, and specific improvement suggestions (such as "please submit relevant approval materials") can be provided according to the risk type.

[0172] In some embodiments, after the step S400, the method further comprises:

[0173] obtaining compliance rule data, arranging and classifying the compliance rule data, and establishing a compliance rule library;

[0174] matching and analyzing the compliance problem or real-time work behavior of the user based on the compliance rule library;

[0175] generating a compliance problem answer or a compliance risk prompt according to the matching and analysis result;

[0176] comparing and integrating the compliance problem answer or the compliance risk prompt to obtain an optimized compliance problem answer or compliance risk.

[0177] In this embodiment, in order to facilitate answering general questions of the user or identifying some simple behaviors, the application also provides a compliance rule library. The compliance rule library is arranged and classified with respect to laws and regulations related to insurance business, regulatory requirements, company internal policies and the like, and a structured compliance rule library is formed. The compliance rule library can be dynamically updated by accumulation of employee questions and answers, supports subsequent quick retrieval and learning, and improves the adaptability and practicality of the system. For example, when the behavior data is analyzed, the work behavior data of the employee can be converted into a structured format for matching, such as extracting operation type, amount, approver and the like. Then, each behavior data is traversed in the compliance rule library to check whether any rule condition is met. Specifically, condition judgment (such as If-Else) or pattern matching (such as regular expression) can be used for matching. If a violation rule is matched, a risk reminding mechanism is triggered to provide specific improvement suggestions, such as "please submit relevant approval materials".

[0178] In some embodiments, after the step S400, the method further comprises:

[0179] obtaining a feedback result of the user on the compliance problem answer or the compliance risk prompt;

[0180] Based on the feedback result, the compliance problem processing model is optimized;

[0181] The compliance problem processing model after optimization is retrained and verified, and the compliance problem processing model is updated.

[0182] In this embodiment, by collecting the feedback of employees on the answers and risk reminders, the model and rule base can be optimized, and in addition, the generation model and risk identification model are continuously updated according to user feedback and business needs, which can improve the system performance. Specifically, after obtaining the user feedback data, the recommendation strategy can be optimized by adjusting the model parameters, improving the model structure or integrating new features, etc. For example, according to the problems reflected in the feedback data, the parameter settings of the model are adjusted. For example, if the user feedback that the recommendation system recommends is not accurate, the weight parameters in the recommendation algorithm, the similarity calculation method, etc. can be adjusted to improve the accuracy of the recommendation, and the model is retrained and verified to observe whether the performance of the optimized model is improved. The optimization effect can be judged by comparing the model evaluation indexes before and after optimization, such as accuracy, recall rate, F1 value, etc. If the feedback data shows that the existing model structure cannot meet the needs, the model structure can be adjusted. For example, new layers, nodes or modules are added to enhance the expression ability and adaptability of the model, different model architectures and algorithms are tried, and the model that is most suitable for solving the current problem is selected. For example, for image recognition problems, different deep learning architectures such as convolutional neural networks (CNN), recurrent neural networks (RNN), etc. can be tried to compare their performance. Or according to the new problems and needs found in the feedback data, new features are mined and integrated into the model. For example, if the user feedback that the product recommendation is not personalized enough, the user's interest preferences, purchase history and other features can be considered to improve the accuracy of the recommendation, and the new features are preprocessed and selected to ensure their effectiveness and relevance. The feature set can be optimized by feature engineering methods such as feature extraction and feature selection to improve the performance of the model.

[0183] In addition, the system arranged by the method provided in the embodiment of the application can be connected with office systems (such as OA systems, mail systems, etc.) to realize seamless integration, and thus the use of users can be more convenient. For example, by connecting the system arranged by the method provided in the embodiment of the application with a legal management system, the sharing and updating of compliance knowledge can be supported.

[0184] Moreover, the method provided in the application supports compliance Q&A and risk reminders in multiple languages such as English and Chinese, and meets the needs of international business.

[0185] In addition, customized compliance Q&A and risk reminder services can be provided according to the posts and business scenarios of employees to adapt to compliance problem processing of various posts.

[0186] The technical scheme provided by the present application firstly acquires compliance data, wherein the compliance data at least includes compliance problem data and work behavior data; then pre-processes the compliance data to generate a training data set; thereafter trains a pre-established neural network model using the training data set to obtain a training complete compliance problem processing model; finally acquires a compliance problem, processes the compliance problem using the compliance problem processing model to generate a compliance problem answer, or acquires real-time work behavior data of a user, analyzes the real-time work behavior data using the compliance problem processing model to generate a compliance risk prompt. The present application realizes intelligent compliance question answering and real-time risk prompting, combines a dynamically updated knowledge base and multi-system integration capability, and constructs an efficient, intelligent, and personalized compliance management tool. These technical innovations not only significantly improve the efficiency and accuracy of compliance management, but also provide a comprehensive compliance management solution through data-driven continuous optimization, significantly shorten compliance problem solving time through automated question answering and risk prompting, improve employee work efficiency, reduce employee compliance risks in work through real-time risk prompting and compliance rule matching, and improve the company's compliance management level.

[0187] It should be understood that the size of the serial number of each step in the above embodiments does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0188] Another embodiment of the present application provides a compliance problem processing device, which corresponds to the compliance problem processing method in the above embodiments. Please refer to Figure 7 The compliance problem processing device includes a data acquisition module 11, a training data set generation module 12, a model training module 13, and a problem processing module 14. The functions of each functional module are described in detail as follows:

[0189] The data acquisition module 11 is used to acquire compliance data, wherein the compliance data at least includes compliance problem data and work behavior data.

[0190] The training data set generation module 12 is used to pre-process the compliance data to generate a training data set.

[0191] The model training module 13 is used to train a pre-established neural network model using the training data set to obtain a training complete compliance problem processing model.

[0192] The problem processing module 14 is configured to obtain a compliance question, process the compliance question by using the compliance question processing model to generate a compliance question answer, or obtain real-time work behavior data of a user, analyze the real-time work behavior data by using the compliance question processing model to generate a compliance risk prompt.

[0193] In some embodiments, the training data set generation module 12 is specifically configured to:

[0194] perform data cleaning on the compliance data to remove invalid data and noise data;

[0195] perform labeling on the cleaned compliance data to add labels to the compliance data to distinguish compliance question data and work behavior data;

[0196] perform data enhancement processing on the labeled compliance data, and divide the enhanced compliance data into a training set and a validation set;

[0197] perform standardization processing on the training set and the validation set to generate a training data set.

[0198] In some embodiments, the model training module 13 is specifically configured to:

[0199] establish a basic neural network model, adjust the architecture of the basic neural network model, and generate a neural network model for answering insurance compliance questions and providing risk prompts;

[0200] perform multi-task training on the neural network model by using the training data set;

[0201] test and verify the trained basic neural network model to obtain a compliance question processing model capable of answering insurance compliance questions and providing risk prompts.

[0202] In some embodiments, the problem processing module 14 is specifically configured to:

[0203] obtain a compliance question, and parse the compliance question to extract key information in the compliance question;

[0204] process the key information by using the compliance question processing model to generate a preliminary compliance question answer;

[0205] verify the preliminary compliance question answer to generate a compliance question answer.

[0206] In some embodiments, the problem processing module 14 is further configured to:

[0207] obtain real-time work behavior data of a user, and analyze the real-time work behavior data to obtain a work behavior pattern of the user.

[0208] Detecting the working behavior mode of the user by using the compliance problem processing model, and identifying a compliance risk;

[0209] Generating a corresponding compliance risk prompt according to the identified compliance risk.

[0210] In some embodiments, the device further comprises a rule base identification module for:

[0211] Obtaining compliance rule data, arranging and classifying the compliance rule data, and establishing a compliance rule base;

[0212] Matching and analyzing the compliance problem or real-time working behavior of the user based on the compliance rule base;

[0213] Generating a compliance problem answer or a compliance risk prompt according to the matching and analysis result;

[0214] Comparing and integrating the compliance problem answer or the compliance risk prompt to obtain an optimized compliance problem answer or compliance risk.

[0215] In some embodiments, the device further comprises a feedback module for:

[0216] Obtaining a feedback result of the user on the compliance problem answer or the compliance risk prompt;

[0217] Optimizing the compliance problem processing model based on the feedback result;

[0218] Re-training and verifying the optimized compliance problem processing model to update the compliance problem processing model.

[0219] The embodiment of the present application firstly acquires compliance data, wherein the compliance data at least includes compliance problem data and work behavior data; then pre-processes the compliance data to generate a training data set; thereafter trains a pre-established neural network model using the training data set to obtain a trained compliance problem processing model; finally acquires a compliance problem, processes the compliance problem using the compliance problem processing model to generate a compliance problem answer, or acquires real-time work behavior data of a user, analyzes the real-time work behavior data using the compliance problem processing model to generate a compliance risk prompt. The present application realizes intelligent compliance question answering and real-time risk prompting, combines a dynamically updated knowledge base and multi-system integration capability, and constructs an efficient, intelligent and personalized compliance management tool. These technical innovations not only significantly improve the efficiency and accuracy of compliance management, but also provide a comprehensive compliance management solution through data-driven continuous optimization, significantly shorten compliance problem solving time through automated question answering and risk prompting, improve employee work efficiency, reduce employee compliance risks in work through real-time risk prompting and compliance rule matching, and improve the company's compliance management level.

[0220] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by computer readable instructions instructing related hardware, and the computer readable instructions can be stored in a computer readable storage medium. When the program is executed, it can include the processes of the above-mentioned embodiments of each method. The storage medium can be a non-volatile storage medium such as a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0221] It should be understood that although each step in the flowchart of the accompanying drawings is displayed in sequence according to the direction of the arrow, these steps are not necessarily executed in sequence according to the direction of the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and they can be executed in other orders. Moreover, at least part of the steps in the flowchart of the accompanying drawings can include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence is not necessarily sequential, but can be alternately or alternately executed with at least part of other steps or sub-steps or stages of other steps.

[0222] The specific limitations of the compliance problem processing apparatus can refer to the limitations of the compliance problem processing method described above, which will not be repeated here. Each module in the above compliance problem processing apparatus can be implemented by software, hardware and their combination. The above modules can be embedded in or independent of the processor in the computer device in hardware form, or stored in the memory in the computer device in software form, so that the processor calls and executes the operations corresponding to each module.

[0223] In one embodiment, a computer device is provided, which can be a server, and its internal structure diagram can be as shown in Figure 8 The computer device includes a processor, a memory, a network interface and a database connected by a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile and / or volatile storage medium, an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with the external client through the network connection. The computer program is executed by the processor to implement the functions or steps of the server side of the compliance problem processing method based on artificial intelligence.

[0224] In one embodiment, a computer device is provided, which can be a client, and its internal structure diagram can be as shown in Figure 9 The computer device includes a processor, a memory, a network interface, a display screen and an input device connected by a system bus. 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 network interface of the computer device is used to communicate with the external server through the network connection. The computer program is executed by the processor to implement the functions or steps of the compliance problem processing method on the client side.

[0225] In one embodiment, a computer device is provided, which includes a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the following steps when executing the computer program:

[0226] Obtaining compliance data, wherein the compliance data at least includes compliance problem data and work behavior data;

[0227] Preprocessing the compliance data to generate a training data set;

[0228] The pre-established neural network model is trained by using the training data set, and a trained compliance question processing model is obtained.

[0229] The compliance question is obtained, the compliance question processing model is used to process the compliance question, to generate a compliance question answer, or real-time working behavior data of a user is obtained, the compliance question processing model is used to analyze the real-time working behavior data, to generate a compliance risk prompt.

[0230] In one embodiment, a computer readable storage medium is provided, and a computer program is stored on the computer readable storage medium. The computer program is executed by a processor to implement the following steps:

[0231] Compliance data is obtained, wherein the compliance data at least includes compliance question data and working behavior data;

[0232] The compliance data is preprocessed to generate a training data set;

[0233] The pre-established neural network model is trained by using the training data set, and a trained compliance question processing model is obtained;

[0234] The compliance question is obtained, the compliance question processing model is used to process the compliance question, to generate a compliance question answer, or real-time working behavior data of a user is obtained, the compliance question processing model is used to analyze the real-time working behavior data, to generate a compliance risk prompt.

[0235] It should be noted that the functions or steps that the computer readable storage medium or the computer device can implement correspond to the related descriptions of the server side and the client side in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0236] 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. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchl ink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0237] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is exemplified, and in actual application, the above-mentioned functions can be completed by different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above.

[0238] In summary, the compliance problem processing method, device, computer equipment and storage medium provided by the present application first acquire compliance data, wherein the compliance data at least includes compliance problem data and work behavior data; then the compliance data is preprocessed to generate a training data set; then the training data set is used to train the pre-established neural network model to obtain a trained compliance problem processing model; finally, the compliance problem is acquired, the compliance problem processing model is used to process the compliance problem to generate a compliance problem answer, or the real-time work behavior data of the user is acquired, and the compliance problem processing model is used to analyze the real-time work behavior data to generate a compliance risk prompt. The present application realizes intelligent compliance question and answer and real-time risk prompt, combines a dynamically updated knowledge base and multi-system integration capability, and constructs an efficient, intelligent and personalized compliance management tool. These technical innovations not only significantly improve the efficiency and accuracy of compliance management, but also provide a comprehensive compliance management solution through data-driven continuous optimization, significantly shorten the compliance problem solving time through automatic question and answer and risk prompt, improve the work efficiency of employees, reduce the compliance risk of employees in work through real-time risk prompt and compliance rule matching, and improve the compliance management level of the company.

[0239] It should be noted that if non-company software tools or components appear in the embodiments of the present application, they are only used for example introduction and do not represent actual use.

[0240] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method for handling compliance issues, characterized in that: The steps include: Acquiring compliance data, wherein the compliance data includes at least compliance issue data and work behavior data; Preprocessing the compliance data to generate a training data set; Using the training data set to train a pre-established neural network model to obtain a fully trained compliance issue processing model; Obtain compliance issues, and use the compliance issue processing model to process the compliance issues to generate answers to compliance issues, or obtain the user's real-time work behavior data, and use the compliance issue processing model to analyze the real-time work behavior data to generate compliance risk warnings.

2. The compliance issue handling method according to claim 1, characterized in that: The preprocessing of the compliance data to generate a training data set includes: Performing data cleaning on the compliance data to remove invalid data and noise data; Label the cleaned compliance data and add tags to distinguish compliance issue data from work behavior data; Perform data augmentation on the labeled compliant data and divide the augmented compliant data into a training set and a validation set; The training set and the validation set are standardized to generate a training data set.

3. The compliance issue handling method according to claim 1, characterized in that: The training data set is used to train the pre-established neural network model to obtain a fully trained compliance issue processing model, including: Establishing a basic neural network model, adjusting the architecture of the basic neural network model, and generating a neural network model for answering insurance compliance questions and providing risk warnings; Performing multi-task training on the neural network model using the training data set; The trained basic neural network model is tested and verified to obtain a compliance problem handling model that can answer insurance compliance questions and provide risk warnings.

4. The compliance issue handling method according to any one of claims 1 to 3, characterized in that: The obtaining of the compliance question and processing the compliance question using the compliance question processing model to generate an answer to the compliance question includes: Obtaining compliance issues, analyzing the compliance issues, and extracting key information from the compliance issues; Processing the key information using the compliance question processing model to generate preliminary compliance question answers; The preliminary answers to the compliance questions are verified to generate answers to the compliance questions.

5. The compliance issue handling method according to any one of claims 1 to 3, characterized in that: The acquiring of the user's real-time work behavior and analyzing the real-time work behavior using the compliance problem processing model to generate a compliance risk prompt includes: Acquire the user's real-time work behavior data, and analyze the real-time work behavior data to obtain the user's work behavior pattern; Using the compliance issue processing model to detect the user's work behavior pattern and identify compliance risks; Generate corresponding compliance risk alerts based on identified compliance risks.

6. The compliance issue handling method according to claim 1, characterized in that: After obtaining the compliance issue and processing the compliance issue using the compliance issue processing model to generate an answer to the compliance issue, or obtaining the user's real-time work behavior data and analyzing the real-time work behavior data using the compliance issue processing model to generate a compliance risk warning, the method further includes: Obtain compliance rule data, organize and classify the compliance rule data, and establish a compliance rule library; Matching and analyzing the user's compliance issues or real-time work behaviors based on the compliance rule base; Generate compliance question answers or compliance risk alerts based on matching and analysis results; Compare and integrate the answers to the compliance questions or compliance risk prompts to obtain optimized answers to the compliance questions or compliance risks.

7. The compliance issue handling method according to claim 1, characterized in that: After obtaining the compliance issue and processing the compliance issue using the compliance issue processing model to generate an answer to the compliance issue, or obtaining the user's real-time work behavior data and analyzing the real-time work behavior data using the compliance issue processing model to generate a compliance risk warning, the method further includes: Obtain user feedback on answers to compliance questions or compliance risk alerts; Optimizing the compliance issue handling model based on the feedback results; The optimized compliance issue processing model is retrained and verified to update the compliance issue processing model.

8. A compliance issue handling device, characterized in that: include: A data acquisition module, configured to acquire compliance data, wherein the compliance data includes at least compliance issue data and work behavior data; A training data set generation module, configured to pre-process the compliance data to generate a training data set; A model training module, configured to train a pre-established neural network model using the training data set to obtain a fully trained compliance issue processing model; The problem processing module is used to obtain compliance issues and use the compliance problem processing model to process the compliance issues to generate answers to compliance issues, or to obtain the user's real-time work behavior data and use the compliance problem processing model to analyze the real-time work behavior data to generate compliance risk warnings.

9. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the compliance issue handling method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the compliance issue handling method according to any one of claims 1 to 7.

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