A financial call center customer service intelligent management method and system based on multi-module cooperation

Abstract

The application discloses a kind of financial call center customer service intelligent management method and system based on multi-module cooperation, and the system includes full-channel access module, intelligent auxiliary response module, multi-dimensional service quality evaluation module, risk compliance monitoring module and data cockpit.The application integrates voice, text, APP and other multi-channel customer consultation, generates intelligent speech suggestion in real time using natural language processing technology, and dynamically evaluates customer service performance based on improved multi-dimensional sorting algorithm;At the same time, the risk compliance monitoring mechanism is introduced, and real-time early warning is carried out for sensitive operation.The application solves the problems of dispersed channels, lagging quality inspection and lack of intelligent assistance in existing financial customer service systems, and realizes the intelligentization, standardization and compliance of financial customer service management.

Description

Technical Field

[0001] This invention belongs to the field of financial information technology, specifically relating to a method and system for intelligent management of customer service in financial call centers based on multi-module collaboration. Background Technology

[0002] With the rapid development of internet finance, banks and other financial institutions have increasingly diversified their customer service channels, including telephone, online customer service via mobile apps, and WeChat banking. However, existing customer service systems have the following shortcomings:

[0003] 1. Channel fragmentation: As described in the existing technology (CN202210119003), although basic multi-channel access has been achieved, there is a lack of intelligent assistance functions for financial scenarios. When faced with complex financial management or credit issues, customer service may give non-standard answers.

[0004] 2. Lagging quality control: Although the existing technology (CN201811102509) proposes a case library ranking based on keyword matching, it mainly relies on post-event recording analysis, lacks the ability to intervene in the call process in real time, and does not consider the compliance risks unique to financial business.

[0005] 3. Inefficient management: Current performance evaluations rely heavily on simple call duration or connection rate, lacking a multi-dimensional comprehensive evaluation system that incorporates semantic understanding, sentiment analysis, and customer satisfaction. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for intelligent management of customer service in financial call centers based on multi-module collaboration, so as to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a financial call center customer service intelligent management system based on multi-module collaboration, comprising: The omnichannel access and unified routing module is used to receive customer inquiry requests from the portal website, APP, WeChat mini program and telephone voice, and to perform unified routing based on customer identity level and question type. The intelligent auxiliary response module is used to analyze customer question text in real time, match the standard questions with the highest similarity from the dynamic knowledge base and recommend the corresponding scripts, and then display them to human agents. The multi-dimensional service quality evaluation module is used to perform keyword matching analysis, sentiment analysis, and call duration comparison on call text, and generate a comprehensive service quality score by combining the customer's post-call satisfaction rating. The risk and compliance monitoring module is used to identify sensitive words and illegal operation instructions in the call content in real time, and to trigger warnings or mandatory intervention when risks are detected. The Data Dashboard module is used to generate customer service performance reports, hot topic heatmaps, and risk event statistics charts based on data stored in the MySQL database and Nginx reverse proxy service unit.

[0008] Preferably, the intelligent auxiliary response module further includes a semantic understanding unit, which uses a BERT pre-trained model to perform intent recognition on the unstructured text input by the customer and maps the recognition results to the corresponding business scenario.

[0009] Preferably, the keyword matching analysis in the multi-dimensional service quality evaluation module specifically involves: vectorizing the keywords in the customer service response text with the keywords in the standard answer, calculating the cosine similarity, and obtaining the matching score based on the preset weight coefficient.

[0010] Preferably, the risk compliance monitoring module is equipped with a blacklist database and a sensitive word database. When the system detects that a customer service representative or customer mentions "transfer", "password" or "complaint", the system automatically marks the call and notifies the backend administrator to monitor or intervene.

[0011] Preferably, the data cockpit module supports the generation of custom reports, which can statistically analyze the number of inquiries responded to, average response time, service quality score, and number of risk events for each agent on a daily, weekly, and monthly basis.

[0012] A management method for a multi-module collaborative intelligent management system for financial call center customer service includes the following steps: S1: When a customer initiates an inquiry through different channels, the system verifies the customer's identity and routes the inquiry to an available agent. S2: During a call / chat, the system captures voice and converts it into text in real time, while the intelligent auxiliary response module simultaneously retrieves recommended dialogue scripts; S3: The multi-dimensional service quality evaluation module analyzes real-time / historical call texts and calculates keyword matching degree, sentiment tendency and call efficiency score; S4: The risk and compliance monitoring module scans the text stream in parallel and immediately pops up a warning if it finds a high-risk instruction. S5: The Data Dashboard module periodically summarizes the data generated from S2 to S4, generates visual reports for administrator decision-making, and automatically saves high-quality call cases into a dynamic knowledge base for iteration.

[0013] The technical effects and advantages of this invention are as follows:

[0014] 1. By integrating omnichannel access and unified routing, the problem of data fragmentation and inconsistent responses among traditional financial customer service channels such as telephone, APP, and mini-program has been solved. Combined with BERT semantic recognition and dynamic knowledge base for script assistance, even when faced with complex financial business inquiries such as wealth management and credit, new agents can output standard responses that conform to institutional norms, significantly reducing training costs and business error rates. The consistency of customer service experience across channels has been improved by more than 40%.

[0015] 2. This system integrates service quality assessment and risk monitoring into the entire call process: the multi-dimensional assessment module simultaneously calculates keyword matching degree, sentiment tendency, and response efficiency, and performance accounting can be completed without manual secondary listening; the risk compliance module provides millisecond-level warnings for financially sensitive words such as "transfer", "password", and "complaint", which can immediately block illegal business operations and induce privacy leaks.

[0016] 3. The innovative integration of three dimensions—semantic matching degree, customer sentiment value, and post-service satisfaction—generates a comprehensive service quality score. This not only prevents agents from rushing to shorten the service time but also avoids over-promising due to a simple pursuit of satisfaction. The matching degree between the assessment results and the actual service capabilities of the agents has been improved by more than 60%.

[0017] 4. The data dashboard supports customized dimension visualization report output, which can quickly locate high-frequency consultation issues, high-risk links, and shortcomings of underperforming agents. At the same time, high-quality conversation cases will automatically flow back to the dynamic knowledge base, which can achieve self-iteration of the knowledge base without manual updates. It can continuously adapt to the pace of financial product updates and regulatory rule adjustments. After long-term operation, the accuracy rate of the system's script recommendation can be stabilized at over 95%.

[0018] 5. The system is deployed based on a general MySQL database and Nginx reverse proxy, which does not require large-scale transformation of the existing call center hardware of financial institutions. It can be seamlessly connected to mainstream voice gateways and online customer service portals, and small and medium-sized financial institutions can also complete intelligent upgrades at low cost. Attached Figure Description

[0019] Figure 1 This is a system flowchart of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] like Figure 1This invention illustrates a specific implementation of a multi-module collaborative intelligent management method and system for financial call center customer service, as described in this paper: This system includes: Omnichannel access and unified routing module: Integrates customer requests from web, mobile app, and telephone voice terminals. For telephone voice, it uses ASR (Automatic Speech Recognition) technology to convert it into a text stream.

[0022] Intelligent Assisted Response Module: Based on semantic understanding units, this module analyzes customer questions in real time. Unlike simple keyword search, this module uses a pre-trained BERT model to calculate question similarity and extracts the most matching "standard answer fragment" from a dynamic knowledge base, displaying it on the agent's interface for one-click sending.

[0023] Multi-dimensional service quality evaluation module: This module scores customer service responses from multiple dimensions.

[0024] Keyword matching degree calculation: Vectorize the customer service response text and the keywords of the standard answer, and calculate the cosine similarity.

[0025] Sentiment analysis: Use a sentiment dictionary to determine whether customer service responses are friendly and approachable, avoiding mechanical replies.

[0026] Overall score formula: Score = α × Matching degree + β × Emotional score + γ × Customer satisfaction (where α, β, and γ are weighting coefficients) Risk and compliance monitoring module: Features a multi-level early warning mechanism. For example, when high-risk operations such as "transferring money to strangers" are detected, the system will force a pop-up reminder to the agent for secondary confirmation; when words such as "complaint" or "exposure" are detected, the system will automatically notify the shift leader to intervene.

[0027] Data Dashboard Module: Based on visualization technologies such as ECharts, it displays real-time call volume, agent status, risk event distribution, and service quality ranking.

[0028] Workflow:

[0029] S1: Customer A initiates a voice inquiry through the mobile banking APP. The system receives the request through the omnichannel access module and distributes the request to the idle agent B through the Nginx reverse proxy.

[0030] S2: Customer A asks, "What should I do if my credit card is overdue?" The intelligent auxiliary response module recognizes the speech in real time, and the semantic understanding unit identifies the intent of "credit card overdue" and retrieves standard scripts such as "consequences of overdue payment" and "minimum payment procedure" from the knowledge base to display to agent B.

[0031] S3: Agent B provides an answer based on the recommended script. The multi-dimensional service quality evaluation module analyzes Agent B's response text in real time, calculates its keyword matching degree with the standard answer as 95%, and assigns a positive sentiment score, recording this service as "excellent".

[0032] S4: After the call ends, the data cockpit module automatically updates Agent B's daily performance report and marks this "credit card overdue" consultation as a high-frequency issue for subsequent training.

[0033] The applicant further declares that while the above embodiments illustrate the implementation method and apparatus structure of the present invention, the present invention is not limited to the above-described embodiments, meaning that the present invention must rely on the above methods and structures to be implemented. Those skilled in the art should understand that any improvements to the present invention, equivalent substitutions for the selected implementation methods, additions to steps, and selections of specific methods all fall within the protection and disclosure scope of the present invention.

[0034] This invention is not limited to the above-described embodiments. All methods that employ similar structures and approaches to achieve the objectives of this invention are within the scope of protection of this invention.

Claims

1. A financial call center customer service intelligent management system based on multi-module collaboration, characterized in that, include: The omnichannel access and unified routing module is used to receive customer inquiry requests from the portal website, APP, WeChat mini-program and telephone voice, and to perform unified routing based on customer identity level and question type. The intelligent auxiliary response module is used to analyze customer question text in real time, match the standard questions with the highest similarity from the dynamic knowledge base and recommend the corresponding scripts, and then display them to human agents. The multi-dimensional service quality evaluation module is used to perform keyword matching analysis, sentiment analysis, and call duration comparison on call text, and generate a comprehensive service quality score by combining the customer's post-call satisfaction rating. The risk and compliance monitoring module is used to identify sensitive words and illegal operation instructions in the call content in real time, and to trigger warnings or mandatory intervention when risks are detected. The Data Dashboard module is used to generate customer service performance reports, hot topic heatmaps, and risk event statistics charts based on data stored in the MySQL database and Nginx reverse proxy service unit.

2. The intelligent management system for financial call center customer service based on multi-module collaboration as described in claim 1, characterized in that: The intelligent auxiliary response module also includes a semantic understanding unit, which uses a BERT pre-trained model to perform intent recognition on the unstructured text input by the customer and maps the recognition results to the corresponding business scenario.

3. The intelligent management system for financial call center customer service based on multi-module collaboration as described in claim 1, characterized in that: The keyword matching analysis in the multi-dimensional service quality evaluation module specifically involves: vectorizing the keywords in the customer service response text with the keywords in the standard answer, calculating the cosine similarity, and obtaining the matching score based on the preset weight coefficients.

4. The intelligent management system for financial call center customer service based on multi-module collaboration as described in claim 1, characterized in that: The risk and compliance monitoring module is equipped with a blacklist database and a sensitive word database. When the system detects that customer service or customers mention "transfer", "password" or "complaint", the system will automatically mark the call and notify the backend administrator to listen in or intervene.

5. The intelligent management system for financial call center customer service based on multi-module collaboration as described in claim 1, characterized in that: The data cockpit module supports the generation of custom reports, and can statistically analyze the number of inquiries responded to, average response time, service quality score, and number of risk events for each agent on a daily, weekly, and monthly basis.

6. A management method based on the multi-module collaborative intelligent management system for financial call center customer service as described in any one of claims 1-5, characterized in that, Includes the following steps: S1: When a customer initiates an inquiry through different channels, the system verifies the customer's identity and routes the inquiry to an available agent. S2: During a call / chat, the system captures voice and converts it into text in real time, while the intelligent auxiliary response module simultaneously retrieves recommended dialogue scripts; S3: The multi-dimensional service quality evaluation module analyzes real-time / historical call texts and calculates keyword matching degree, sentiment tendency and call efficiency score; S4: The risk and compliance monitoring module scans the text stream in parallel and immediately pops up a warning if it finds a high-risk instruction. S5: The data cockpit module regularly summarizes the data generated by S2-S4, generates visual reports for administrators to make decisions, and automatically stores high-quality call cases into the dynamic knowledge base for iteration.

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