Consumption finance collection system

Abstract

The invention discloses a consumer finance collection system, and aims to improve collection efficiency, reduce labor cost, ensure compliance and improve customer satisfaction. The system is constructed based on four modules, namely an intelligent man-machine cooperation mode, a refined collection process, a law urgency outsourcing case evidence automatic collection module and a customer complaint processing strategy engine. The system realizes automatic quality inspection and intelligent pushing through voice recognition, natural language processing and other technologies, and ensures high efficiency and compliance of collection work. The refined collection module applies data mining and machine learning algorithms to provide personalized collection strategies and enhance the accuracy of customer management. And the law urgency module automatically collects and configures evidences, so that the litigation case processing efficiency is improved. The customer complaint strategy engine automatically identifies and classifies customer complaints through big data and a machine learning technology, and matches an optimal solution strategy. The system integrates intelligent and automatic technologies, significantly improves the efficiency and effect of collection work, and provides an innovative solution for the consumer finance field.

Description

Technical Field

[0001] The present invention relates to the field of financial technology, and in particular to a consumer finance collection system. Background Art

[0002] In the field of modern consumer finance, with the increase in demand for loans, debt collection has become an important part of ensuring capital return and controlling financial risks. However, traditional debt collection methods rely on a large number of manual operations, and there are problems such as low efficiency and difficulty in ensuring compliance. Manual debt collection not only consumes a lot of manpower costs, but is also easily affected by human factors, resulting in inconsistent and non-standard debt collection operations, poor customer experience, and frequent complaints. In addition, due to manual intervention in the traditional debt collection process, there is also a high error rate and potential risks, which makes financial institutions face challenges in debt collection management.

[0003] In order to improve the efficiency of debt collection, intelligent technologies have been gradually introduced into debt collection operations in recent years. Through the application of technologies such as speech recognition, natural language processing, and machine learning, some debt collection processes have been automated. However, the existing intelligent debt collection systems are mostly single-module functions, such as automatic outbound calls and intelligent quality inspection, and have failed to form a complete closed-loop system in data collection, analysis, and debt collection strategy formulation. Existing solutions often lack personalized services and refined management, making it difficult to meet the complex and diverse needs of customers, and have also failed to completely solve the compliance and cost issues in traditional debt collection.

[0004] In addition, with the increase in customer rights awareness, customer complaint handling has also become one of the difficult problems faced by financial institutions. The traditional customer complaint handling process still relies on manual work, which is not only time-consuming, but also has low processing efficiency and cannot achieve rapid response. In response to the above problems, the present invention proposes a consumer finance collection system, which achieves high efficiency, compliance and personalized services in collection operations by constructing an intelligent human-machine collaboration model, a refined collection process, an automatic evidence collection mechanism for outsourced cases, and a customer complaint strategy engine, so as to reduce operating costs and improve customer satisfaction. Summary of the invention

[0005] The present invention relates to a consumer finance collection system, which aims to build efficient and compliant collection capabilities through intelligent means to improve collection efficiency, reduce operating costs, reduce personnel errors and improve customer satisfaction. Based on advanced technologies such as speech recognition, natural language processing, big data analysis and machine learning, the present invention combines automation and intelligent modules to realize a human-machine collaborative collection model, a refined collection process, an automatic evidence collection mechanism for outsourced cases, and a strategy engine for customer complaint handling, thereby creating a full-process intelligent collection system. Specifically, the present invention includes the following main parts:

[0006] 1. Intelligent human-machine collaboration mode

[0007] In traditional collection operations, due to the limitations of manual operations, it is often difficult to ensure the efficiency and compliance of collection. The present invention constructs an intelligent human-machine collaboration mode. First, through voice recognition and natural language processing technology, the collection recording content is quality inspected, and recordings that may contain violations are automatically identified and screened out to ensure the compliance of the collection operation. The intelligent push module assigns the recordings to the corresponding manual review personnel based on the quality inspection results and scores. For recordings with a higher possibility of violations, the system gives priority to pushing them to the manual review team for more rigorous review and processing. After receiving the recordings screened by the system, the manual review team uses the TOP push function to efficiently review and strictly enforce rules, such as Rule 277, to ensure the compliance and accuracy of the collection. This human-machine collaboration mode not only improves the efficiency of the collection operation, but also reduces the risk of illegal operations.

[0008] 2. Refined collection capabilities

[0009] The present invention realizes the refined management and personalized service of collection operations through the independently developed intelligent collection platform. The system first collects customer data through various channels, including basic customer information, loan information, repayment records, etc. Then, the system cleans and organizes the data, and uses data mining and analysis technology to extract key high-quality variables, such as customer credit score, repayment history and overdue days. On this basis, the system uses machine learning algorithms to establish a multidimensional collection model to predict the repayment behavior of customers based on the characteristics and behavior patterns of different customers. Through grouping strategies, the system can formulate diversified collection strategies for different customer groups, including intelligent return visits, intelligent quality inspections, predictive outbound calls, preview outbound calls, three-party calls, consultation transfers and monitoring interventions. This module also supports functions such as intelligent pop-up screens and strategy case division, realizing the personalized and automated deployment of collection strategies, thereby effectively improving the pertinence, effectiveness and customer satisfaction of collection operations.

[0010] 3. Automatic collection of evidence for outsourced cases

[0011] In some collection cases that require legal proceedings, evidence collection is a tedious and time-consuming task. The present invention designs an automatic evidence collection mechanism for outsourced cases of legal collection, which realizes automatic data extraction and aggregation by docking with multiple data sources, including OA systems, ICRM systems, etc. The system has a built-in evidence configuration module to support labeling management of evidence to ensure that each piece of evidence meets legal requirements. The evidence collection process uses OCR recognition and image recognition technology to automatically extract relevant content and upload it to the cloud management platform. At the same time, the system desensitizes sensitive fields to protect customer privacy. The case allocation module supports one-click allocation function, and ensures the accuracy and fairness of the case through double review. Finally, the system encrypts and stores the case folder and provides a physical backup function to ensure data security and prevent data loss. Through this automated mechanism, the collection time of legal collection evidence is shortened from the original 14 days to 1 day, greatly improving the collection efficiency.

[0012] 4. Customer Complaint Handling Strategy Engine

[0013] Customer complaint handling is an important part of improving customer satisfaction and maintaining brand reputation. The present invention designs a customer complaint strategy engine based on big data and machine learning to handle customer complaints in an automated manner. The system first receives the customer's complaint information, pre-processes it, removes noise, extracts key data, builds a complaint classification model, and divides complaints into different types, such as collection, accounting, etc. For each complaint, the system identifies key variables, such as the complaint amount and the number of complaints, and matches the corresponding processing strategy. The strategy engine automatically recommends processing strategies based on the type and characteristics of the complaint, such as fee reduction, adjustment of collection methods, or provision of additional services, and the agent can communicate with the customer based on the strategy recommendations. At the same time, the system monitors the strategy execution process in real time, and optimizes and adjusts the strategy through data analysis and effect evaluation to ensure the accuracy and efficiency of the processing.

[0014] 5. Technical advantages and effects of the system

[0015] The consumer finance collection system of the present invention has many advantages, such as high efficiency and low cost, compliance and security, and personalized services. Through the independently developed intelligent collection platform, combined with advanced data mining and artificial intelligence technologies, the full process of intelligent management and control of the collection business is realized, which significantly improves the collection efficiency and reduces labor costs. The intelligent quality inspection and strategy push functions effectively reduce the risk of violations, and compliance and security are guaranteed. The refined collection strategy and customer profiling functions improve the accuracy of collection operations and customer satisfaction, and the customer complaint strategy engine provides customers with personalized complaint solutions, further enhancing the brand image and market competitiveness.

[0016] Beneficial Effects

[0017] Through the comprehensive application of intelligent and automated technologies, the present invention realizes the efficiency, compliance and personalization of the consumer finance collection system, provides an innovative solution for the consumer finance industry, and effectively solves the problems of low efficiency, difficulty in compliance and high cost in the traditional collection model. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 The overall structure of the system and the human-machine collaboration process diagram of the embodiment of the present invention;

[0020] Figure 2 A schematic diagram of the module composition and system architecture of the intelligent debt collection platform according to an embodiment of the present invention;

[0021] Figure 3 A schematic diagram of a process for automatically collecting evidence for a legal outsourcing case according to an embodiment of the present invention;

[0022] Figure 4 The figure is a flow chart and effect diagram of a customer complaint handling strategy engine according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0024] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0025] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0026] like Figures 1 to 4 Shown

[0027] Example 1

[0028] There are four parts in total to build the ability of intelligent debt collection, namely, establishing an intelligent human-machine collaboration model, refined debt collection, automatic collection of evidence for outsourced cases, and building a customer complaint strategy engine.

[0029] Chapter 1: Establishing an intelligent human-machine collaboration model

[0030] In the field of modern consumer finance, the efficiency and compliance of debt collection operations are crucial. In order to improve the efficiency of debt collection and ensure the compliance of operations, we proposed a debt collection capability building solution based on an intelligent human-machine collaboration model. The following is a detailed description of this model.

[0031] 1. System quality inspection and intelligent push

[0032] System quality inspection: The collection system will first conduct a quality inspection on all recordings, using advanced speech recognition and natural language processing technology to conduct preliminary analysis and classification of the recording content. This step can screen out recordings that may contain violations and provide a basis for subsequent processing.

[0033] Smart push: Based on the results of the system quality inspection, the recordings will be pushed to the corresponding personnel or teams for processing. For example, recordings with violations will be pushed to the manual review team first, while normal recordings may directly enter the next processing flow. In addition, the system will also ensure that each recording is properly processed based on the recording scores and the order of scores from low to high, as well as the push strategy for non-0-score recordings.

[0034] 2. Human-machine collaboration and efficient coordination

[0035] Manual review: During the manual review stage, the collection staff will use the TOP push function to receive recordings that may have problems screened by the system. They will listen to and review these recordings carefully to ensure that each recording complies with the company's collection policies and compliance requirements. At the same time, the manual review team will strictly enforce key business rules such as Rule 277 to ensure the accuracy and compliance of collection operations.

[0036] Efficient collaboration: To ensure the efficiency of human-machine collaboration, we have adopted an online operation mode to achieve multi-party collaboration. Through the close cooperation of system push, recording scoring, model optimization and other links, debt collectors can quickly obtain the required information and make corresponding processing. In addition, the system also supports functions such as proportional push and daily full-volume recording intelligent quality inspection, which further improves the efficiency and accuracy of debt collection operations.

[0037] 3. Management follow-up and continuous optimization

[0038] Management follow-up: During the collection process, the management team will monitor and follow up each link in real time. Once violations or problems are found, they will immediately take corresponding measures to deal with them and urge relevant personnel to make corrections. At the same time, the management team will also regularly review and analyze the collection results to ensure continuous improvement and optimization of the collection process.

[0039] Continuous optimization: In order to continuously improve the efficiency and compliance of debt collection operations, we have taken a variety of optimization measures. For example, we continuously optimize and update the recording scoring model to ensure its accuracy and stability; we regularly train and assess debt collection personnel to improve their professional quality and compliance awareness; we regularly sort out and optimize business rules and processes to ensure that they adapt to market changes and customer needs.

[0040] 4. Refined operations and compliance empowerment

[0041] Refined operations: In debt collection operations, we focus on refined operations and personalized services. Through data analysis and technical means, we can accurately profile and assess the risks of each customer and formulate corresponding debt collection strategies. This not only improves the pertinence and effectiveness of debt collection operations, but also reduces the risk of customer complaints and violations.

[0042] Compliance empowerment: In order to ensure the compliance of debt collection operations, we have taken a series of measures to enable compliance operations. For example, we have established strict compliance systems and processes to ensure that debt collectors comply with relevant laws, regulations and ethical standards when performing debt collection operations; we have conducted compliance training and assessments for debt collectors to improve their compliance awareness and operational skills; and we have established a complaint handling mechanism and a violation punishment mechanism to ensure that violations are corrected and punished in a timely manner.

[0043] Chapter 2: Refined Debt Collection

[0044] In the field of modern consumer finance, the efficiency, compliance and personalized service of debt collection operations are crucial. In order to improve the efficiency of debt collection, reduce operating costs, and ensure compliance and customer satisfaction, we have proposed a refined debt collection capability building solution based on an intelligent platform. The following is a detailed description of the solution.

[0045] 1. Overview of the Intelligent Debt Collection Platform

[0046] This solution realizes intelligent full-process control of debt collection business through the independently developed intelligent debt collection platform with its own intellectual property rights, combined with advanced technologies such as massive data mining, distributed computing and cloud computing. The platform is not only efficient and low-cost, but also uses high-standard safety specifications to ensure the safe and orderly conduct of debt collection business.

[0047] 2. Refining the collection process

[0048] Data Collection

[0049] Data collection is the first step in debt collection. We collect customer information and debt collection data in real time through various channels, such as sensor networks, cameras, business system interfaces, etc. This data includes but is not limited to basic customer information, loan information, repayment records, contact information, etc.

[0050] Data processing

[0051] In the data processing stage, we clean, organize and format the collected data. By using artificial intelligence technologies such as image recognition and natural language processing, we can extract valuable information from massive amounts of data to provide a basis for subsequent analysis and decision-making.

[0052] Data analysis and high-quality variable mining

[0053] Data analysis is a key part of the collection process. We use data mining technology to conduct in-depth analysis of processed data and mine high-quality variables that can distinguish customer quality. These variables include but are not limited to customers' credit scores, repayment history, and number of days overdue. By mining high-quality variables, we can more accurately assess customers' repayment ability and willingness, providing strong support for the formulation of subsequent collection strategies.

[0054] Intelligent analysis and collection model building

[0055] In the intelligent analysis stage, we use machine learning algorithms to build multi-dimensional collection models based on the high-quality variables we have discovered. These models can predict customers’ repayment behavior and collection results based on different customer characteristics and behavior patterns. At the same time, we also use decision engines to group customers so that we can perform collection according to different strategies.

[0056] Intelligent decision-making and collection strategy formulation

[0057] Based on the results of intelligent analysis, we have developed a variety of collection strategies. These strategies include but are not limited to intelligent return visits, intelligent quality inspection, predictive outbound calls, preview outbound calls, three-way calls, consultation transfers, monitoring intervention, etc. Through the intelligent decision-making system, we can flexibly select the most appropriate collection strategy according to different customer situations and collection stages to improve collection efficiency and customer satisfaction.

[0058] Intelligent application and collection system platform

[0059] In the intelligent application stage, we deploy collection strategies into the intelligent collection system, and provide consumer finance institutions with accurate, efficient, and low-cost post-loan technology platforms and outsourcing services through the collection system platform and business-integrated intelligent collection solutions. At the same time, we also use functions such as intelligent pop-up screens and intelligent case division of collection strategies to achieve automation and intelligence in collection operations.

[0060] 3. Platform Features and Advantages

[0061] Independent research and development and independent intellectual property rights

[0062] The intelligent debt collection platform used in this solution is independently developed and has independent intellectual property rights. This ensures the security and reliability of the platform, and also facilitates subsequent upgrades and optimizations.

[0063] High efficiency and low cost

[0064] By using advanced data mining and artificial intelligence technologies, this solution realizes intelligent full-process management and control of debt collection business, which not only improves debt collection efficiency but also reduces operating costs.

[0065] Compliance and Security

[0066] This solution uses high-standard safety regulations to ensure that the collection business is carried out safely and orderly. Through functions such as intelligent quality inspection and monitoring intervention, we can promptly detect and correct violations and ensure the compliance of collection operations.

[0067] Personalized service

[0068] This solution can provide personalized collection services according to different situations and needs of customers, which not only improves customer satisfaction, but also improves the collection effect.

[0069] Chapter 3: Automatic Evidence Collection in Legal Outsourcing Cases

[0070] In the field of modern consumer finance, evidence collection for outsourced legal collection cases is a tedious but important task. In order to improve the efficiency of evidence collection, reduce operating costs, and ensure the accuracy and security of data, we propose an automatic evidence collection solution for outsourced legal collection cases based on intelligent technology. The following is a detailed description of the solution.

[0071] Product functional architecture and process

[0072] In order to solve the above problems, we designed the functional architecture of the legal collection management system and planned the corresponding process. The architecture includes key steps such as case docking, evidence configuration, evidence collection, case allocation and case download.

[0073] Case connection:

[0074] Connect with multiple data sources, including OA systems, ICRM systems, etc., to achieve automatic data extraction and aggregation.

[0075] Carry out whitelist access management for partner institutions to ensure data security and reliability.

[0076] Evidence configuration:

[0077] Specialized personnel will review the evidence to ensure that each piece of evidence complies with legal requirements and business standards.

[0078] Label the evidence to facilitate subsequent retrieval and use.

[0079] Evidence Collection:

[0080] Use intelligent technology to automatically collect evidence materials, such as identifying document content through OCR technology and extracting key information through image recognition technology.

[0081] Upload the collected evidence materials to the cloud and manage and operate them in the cloud desktop environment.

[0082] Sensitive fields are displayed in an anonymized form to protect customer privacy.

[0083] Case Assignment:

[0084] Use the case system to uniformly manage cases and realize one-click allocation function.

[0085] Double review is conducted during the allocation process to ensure the accuracy and fairness of the case.

[0086] Case download:

[0087] Encrypt case folders to ensure data security.

[0088] Provides physical backup function to prevent data loss or damage.

[0089] Technical advantages and effects

[0090] Shorten collection time: Intelligent technology is used to automatically collect evidence materials, reducing the collection time from 14 days to 1 day, a 92% reduction in collection time.

[0091] Reduce security risks: All operating system records reduce the risk of sensitive information leakage and support audit functions. At the same time, sensitive fields are desensitized to protect customer privacy.

[0092] Improve transmission efficiency: The case system is centrally managed, and the one-click allocation function enables cases to be quickly conveyed to relevant personnel, improving transmission efficiency.

[0093] Automatic generation and delivery: The application form is automatically generated and delivered to relevant agencies, reducing the cost and time of manual operations.

[0094] Chapter 4: Customer Complaint Strategy Engine

[0095] In the field of consumer finance, customer complaint handling is a key link in maintaining good customer relationships and improving brand reputation. In order to handle customer complaints efficiently and accurately, we proposed a customer complaint strategy engine based on intelligent technology, which can automatically analyze customer complaint information and quickly match corresponding handling strategies, thereby optimizing the collection process and improving customer satisfaction.

[0096] 1. Overview of Customer Complaint Strategy Engine

[0097] The customer complaint strategy engine is the core component of the entire consumer finance intelligent collection capability. Based on big data analysis and artificial intelligence technology, it implements functions such as complaint classification, strategy matching, and effect evaluation through in-depth mining and analysis of customer complaint information. The engine can automatically handle customer complaints, reduce manual intervention, and improve processing efficiency and quality.

[0098] 2. Workflow of the Customer Complaint Strategy Engine

[0099] Complaint information reception and pre-processing

[0100] When customers express their complaints through phone calls, online chats, etc., the system will first receive and record this information. Then, the complaint information is pre-processed, including noise removal, key information extraction, etc., to provide a basis for subsequent analysis and strategy matching.

[0101] Complaint classification and identification

[0102] Using natural language processing and machine learning technology, we classify and identify the pre-processed complaint information. By building a complaint classification model, we divide the complaint information into different types, such as collection complaints, account complaints, etc. At the same time, we identify the key variable factors in the complaints, such as the complaint amount, number of complaints, etc., to provide a basis for subsequent strategy matching.

[0103] Strategy matching and recommendation

[0104] Based on the results of complaint classification and identification, the customer complaint strategy engine will automatically match the corresponding handling strategy. These strategies include but are not limited to: fee reduction, adjustment of collection methods, provision of additional services, etc. By building a strategy matching model, the system can quickly recommend the most appropriate handling strategy based on the characteristics of complaint information.

[0105] Policy execution and monitoring

[0106] Once the strategy matching is completed, the system will convey the recommended processing strategy to the agent or the automatic execution system. The agent will communicate and negotiate with the customer based on the strategy recommendations and strive to reach a settlement. At the same time, the system will monitor the strategy execution process in real time to ensure the effective implementation of the strategy.

[0107] Effect evaluation and optimization

[0108] After the strategy is implemented for a period of time, the system will evaluate the processing effect. The effectiveness of the strategy is evaluated by collecting and analyzing relevant data, such as customer satisfaction, complaint resolution rate, etc. According to the evaluation results, the strategy is optimized and adjusted to improve the processing effect and customer satisfaction.

[0109] 3. Key technologies of customer complaint strategy engine

[0110] Big data analysis and mining

[0111] The customer complaint strategy engine relies on big data analysis and mining technology to conduct in-depth mining and analysis of massive customer complaint information. By building a data model, it discovers the potential patterns and characteristics in the complaint information, providing strong support for strategy matching and recommendation.

[0112] Natural Language Processing

[0113] Natural language processing technology is used to perform text analysis and sentiment recognition on complaint information. By building text classification models and sentiment analysis models, the system can accurately understand the intention and sentiment tendency of customer complaints, providing an important basis for subsequent strategy matching.

[0114] Machine Learning Algorithms

[0115] Machine learning algorithm is one of the core technologies of the customer complaint strategy engine. By training the machine learning model, the system can automatically learn and identify the characteristics of complaint information, so as to quickly match and recommend the most appropriate handling strategy. At the same time, the machine learning algorithm can also continuously optimize and update the model to adapt to the ever-changing market environment and customer needs.

[0116] 4. Application Effect of Customer Complaint Strategy Engine

[0117] Improve processing efficiency

[0118] The customer complaint strategy engine can automatically handle customer complaints, reduce manual intervention and waiting time, and improve processing efficiency. At the same time, by optimizing the collection process, it can reduce collection costs and improve overall operational efficiency.

[0119] Improve customer satisfaction

[0120] By accurately matching processing strategies, the customer complaint strategy engine can meet the personalized needs of customers and improve customer satisfaction. At the same time, by real-time monitoring and optimizing the strategy execution process, the stability and sustainability of service quality can be ensured.

[0121] Enhance brand influence

[0122] Excellent customer complaint handling capabilities can enhance brand credibility and reputation. Through the efficient operation of the customer complaint strategy engine, companies can handle customer complaints in a timely manner, reduce negative impacts, and enhance brand influence.

[0123] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0124] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A consumer finance collection system, characterized in that: The system includes an intelligent quality inspection module, a manual review module and an intelligent push module. The intelligent quality inspection module analyzes and classifies collection recordings based on speech recognition and natural language processing technology, screens out recordings that may violate regulations and pushes them to the manual review module. The manual review module reviews recordings suspected of violating regulations through the TOP push function to ensure the compliance of collection operations.

2. The consumer finance collection system according to claim 1, characterized in that: The intelligent push module will prioritize non-zero-scoring recordings for manual review based on the scoring results and sorting rules of the recordings, and support proportional push and daily full-volume recording intelligent quality inspection functions to improve the efficiency and accuracy of collection operations.

3. The consumer finance collection system according to claim 1, characterized in that: The system includes a refined collection module, which collects, cleans and analyzes customers' credit scores, repayment records and other data based on data mining, distributed computing and cloud computing technologies. It establishes a multi-dimensional collection model through machine learning algorithms to formulate and implement personalized collection strategies.

4. The consumer finance collection system according to claim 3, characterized in that: The refined collection module includes intelligent analysis and collection model building functions. Based on customer characteristics and behavior patterns, it predicts repayment behavior and groups customers through a decision-making engine, and formulates diversified collection strategies for different groups.

5. The consumer finance collection system according to claim 3, characterized in that: The refined collection module provides intelligent decision-making and collection strategy formulation functions. The strategies include intelligent return visits, intelligent quality inspections, predictive outbound calls, preview outbound calls, three-party calls, consultation transfers and monitoring interventions to adapt to the needs of different customer situations and collection stages.

6. The consumer finance collection system according to claim 1, characterized in that: The system includes an automatic evidence collection module for outsourced legal cases. It automatically connects to multiple data sources, automatically extracts and aggregates customer data, and uses OCR and image recognition technology to collect evidence, thereby realizing the automatic collection and processing of evidence in legal cases.

7. The consumer finance collection system according to claim 6, characterized in that: The automatic evidence collection module for outsourced cases supports evidence configuration, case allocation and case download functions. After the evidence is collected, it is assigned to the relevant institutions with one click through the case management system, and sensitive data is desensitized to protect customer privacy.

8. The consumer finance collection system according to claim 1, characterized in that: The system includes a customer complaint strategy engine module, which uses big data analysis and machine learning technology to classify and identify customer complaints, and matches processing strategies based on the classification results, which are automatically executed or pushed to manual processing.

9. The consumer finance collection system according to claim 8, characterized in that: The customer complaint strategy engine module automatically recommends handling strategies based on the characteristics of customer complaint information, such as complaint amount and number of complaints, and monitors the execution effect in real time to ensure the effective implementation of the strategy and customer satisfaction.

10. The consumer finance collection system according to claim 1, characterized in that: The system has management follow-up and continuous optimization functions. Through real-time monitoring of each link and regular review and analysis, it optimizes the collection process, rules and models, improves collection efficiency and reduces operating costs.

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