Digital post-loan management system

Abstract

The invention discloses a digital post-loan management system which comprises a post-loan data platform, a post-loan business system platform and a post-loan BI platform. A source data pool ODS in the post-loan data platform integrates multi-source data and performs real-time monitoring and early warning; the unified data warehouse DW reorganizes the standardized business data; the label data pool TDM stores and manages label information to assist accurate decision making; the application data pool ADS uses big data and artificial intelligence technology to realize intelligent case division. The post-loan business system platform automatically manages the loan process, and the post-loan BI platform assists decision analysis. According to the system, many problems of existing post-loan management are solved, the risk management level is improved, and the risk is monitored in real time and accurately evaluated; the operation efficiency is improved, and process automation and decision-making intelligence are realized; customer experience is enhanced, and personalized services are provided; supervision requirements are met, and compliance operation is ensured; the method adapts to market changes, promotes financial innovation, and provides powerful support for sustainable development of financial institutions.

Description

Technical Field

[0001] The present invention relates to the technical field of credit finance, and particularly to a digital post-loan management system. Background Art

[0002] Building a digital post-loan management system is an important strategic choice for financial institutions in the current digital era, and its necessity is mainly reflected in the following aspects:

[0003] I. Improving the risk management level

[0004] Real-time monitoring and early warning: The digital post-loan management system can monitor information such as the repayment situation and credit status of borrowers in real time. Through big data analysis and machine learning algorithms, potential risks can be detected in a timely manner and early warnings can be issued, which helps financial institutions take countermeasures quickly and reduce the bad debt rate.

[0005] Refined risk assessment: Compared with traditional manual assessment methods, digital systems can evaluate the credit risks of borrowers more comprehensively and accurately. Through multi-dimensional data analysis, more scientific decision-making support can be provided for financial institutions.

[0006] II. Improving the operation efficiency

[0007] Automated processes: The digital post-loan management system can realize automated management after loan disbursement, including repayment reminders, overdue collection, credit assessment, etc., greatly reducing the manual operation cost and improving work efficiency.

[0008] Intelligent decision-making: Through intelligent analysis models, the system can assist financial institutions in making more accurate and efficient decisions, such as formulating personalized collection plans, adjusting loan amounts, etc., further improving the operation efficiency.

[0009] III. Enhancing the customer experience

[0010] Personalized services: The digital system can provide personalized services according to the repayment situation and credit status of borrowers, such as adjusting the repayment plan, providing preferential interest rates, etc., improving customer satisfaction and loyalty.

[0011] Transparent management: Through the digital platform, financial institutions can disclose the post-loan management situation to borrowers in a timely manner, strengthen communication with borrowers, establish a transparent and mutually trusting cooperation relationship, and enhance the customer experience.

[0012] IV. Meeting regulatory requirements

[0013] Compliance management: The digital post-loan management system can ensure that financial institutions strictly comply with relevant laws, regulations and regulatory requirements during the post-loan management process, reducing compliance risks.

[0014] Audit and Traceability: The system can record the entire process of post-loan management, providing complete audit and traceability basis for regulatory authorities to ensure the compliance operation of financial institutions.

[0015] V. Adapt to Market Changes

[0016] Flexible Adjustment: The digital system has strong flexibility and scalability, and can be quickly adjusted and optimized according to market changes and customer needs to ensure the competitiveness of financial institutions in the fierce market competition.

[0017] Innovation-driven Development: Through the construction of a digital post-loan management system, financial institutions can continuously explore new business models and service methods, promoting the innovation and development of financial products and services.

[0018] In summary, building a digital post-loan management system is an important means for financial institutions to improve risk management level, enhance operational efficiency, improve customer experience, meet regulatory requirements and adapt to market changes. In the digital age, financial institutions should actively embrace digital technologies and promote the digital transformation of post-loan management to achieve sustainable development. Summary of the Invention

[0019] Build a digital post-loan management system to improve the risk management level of financial institutions, enhance operational efficiency, improve customer experience, meet regulatory requirements and adapt to market changes.

[0020] Technical Solution

[0021] Post-loan Data Platform

[0022] Original Data Source Pool ODS

[0023] Definition and Generation Background: It is generated to solve the limitations of traditional databases and data warehouses in data processing. Its data has characteristics such as being subject-oriented, integrated, updatable, and reflecting the current or near-current data state.

[0024] Function

[0025] Data Integration and Sharing: Integrate data from various operational databases and external data sources, and ensure the accuracy and consistency of data through the ETL process, providing comprehensive and real-time data support for post-loan management.

[0026] Real-time Operation Monitoring: Support real-time or near-real-time data updates, facilitating financial institutions to monitor the operation status of loan business in real time, discover potential risks in a timely manner and prevent them.

[0027] Intelligent Analysis and Early Warning: Build a post-loan early warning model based on data, conduct intelligent analysis on the risk status of borrowers, and trigger early warning signals.

[0028] Technical architecture: It is closely integrated with SAS and the credit system. ODS is responsible for basic processing such as data integration, SAS conducts in-depth analysis to build models, and the credit system displays the results and tracks the process.

[0029] Advantages: Improve the real-time performance of data processing, enhance the risk prevention and control ability, and optimize resource allocation.

[0030] Unified data warehouse DW: Reorganize and standardize business data, covering various aspects of data such as customer applications, loans, loan disbursements, repayments, and customer basic information.

[0031] Tag data pool TDM

[0032] Definition and functions: A central database for storing, managing, and applying tag information. Tags are abstract generalizations of borrowers' multi-dimensional data. The main functions include data storage, integration, query, and analysis.

[0033] Tag system construction: Includes tags such as basic information, credit status, behavioral characteristics, and risk warnings, and forms a system through scientific classification and coding.

[0034] Application scenarios: Widely used in risk assessment, collection management, product recommendation, and marketing, etc.

[0035] Technical implementation: Involves technologies such as data collection and cleaning, storage management, analysis and mining, and visualization.

[0036] Application data pool ADS

[0037] Definition and functions: Store, process, and analyze data related to post-loan management, providing data insights and decision support for managers.

[0038] Data sources: Include the internal data of financial institutions themselves and data from external partners and third parties.

[0039] Data processing and analysis technologies

[0040] Big data processing: Process massive data quickly and accurately.

[0041] Artificial intelligence: Use technologies such as machine learning and natural language processing for in-depth mining and intelligent analysis.

[0042] Risk control model: Build a model to monitor and warn of loan risks in real time.

[0043] Data-driven decision-making: Collect and integrate various types of data to form a comprehensive view, providing an objective data basis for intelligent case allocation.

[0044] Machine learning algorithms: Learn the laws of historical data through training models to predict the best collection or case allocation strategies, improving accuracy and efficiency.

[0045] Business Rule and Model Integration: Intelligent case splitting is carried out by combining business rules and requirements to make the results conform to algorithm predictions and actual needs.

[0046] Dynamic Adjustment and Optimization: Continuously iterate and optimize, and update the model and logic in real time according to the progress of debt collection and data accumulation.

[0047] Case Splitting Logic

[0048] Model Training and Evaluation

[0049] Data Preparation: Collect various types of debt collection-related data, clean and process it, and construct features.

[0050] Model Selection: Select appropriate algorithms (such as logistic regression, etc.) according to various factors.

[0051] Model Training: Divide the dataset, tune parameters, and prevent overfitting.

[0052] Model Evaluation: Evaluate the model performance using metrics such as accuracy.

[0053] Intelligent Case Splitting Logic

[0054] Construction of Case Scoring Model: Obtain case features, and select and train and evaluate the model after preprocessing.

[0055] Case Input and Scoring: New cases are input into the model to obtain score values that reflect key information.

[0056] Strategy Selection and Case Distribution: Configure the strategy dictionary, and screen salespersons according to the strategy and score values to distribute cases.

[0057] Key Technologies

[0058] Apply the case splitting strategy and model of the Application Data Pool ADS, and achieve intelligent case splitting through various technical means to improve the accuracy and efficiency of case splitting. At the same time, integrate business rules and be able to dynamically adjust and optimize.

[0059] Beneficial Effects

[0060] Lay a solid foundation for risk control data, and strongly support the improvement of risk control management capabilities. Through the digital post-loan management system, achieve real-time monitoring and early warning, refined risk assessment, and reduce the bad debt rate; automated processes and intelligent decision-making improve operational efficiency; personalized services and transparent management enhance the customer experience; ensure compliance operations and meet regulatory requirements; flexibly adjust to adapt to market changes and promote the development of financial innovation. Specific Implementation Modes

[0061] The present invention will be described in detail below in conjunction with specific embodiments. It should be noted here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments. For some well-known technologies, those skilled in the art can also adopt other alternative methods for implementation; moreover, it is only for a more specific description of the embodiments and is not intended to specifically limit the present invention.

[0062] It should be noted that in the specification, references to "one embodiment", "an embodiment", "exemplary embodiments", "some embodiments", etc. indicate that the described embodiments may include specific features, structures, or characteristics, but not necessarily every embodiment includes such specific features, structures, or characteristics. Additionally, when combining embodiments to describe specific features, structures, or characteristics, implementing such features, structures, or characteristics in combination with other embodiments (whether explicitly described or not) should be within the knowledge of those skilled in the relevant art.

[0063] Generally, terms can be understood at least in part from their use in context. For example, at least in part depending on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in a singular sense, or can be used to describe a combination of features, structures, or characteristics in a plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey a set of exclusive factors, but rather, at least in part depending on the context, can allow for the existence of other factors that may not be explicitly described.

[0064] Embodiment 1

[0065] The digital post-loan management system is divided into three major platforms, namely the post-loan data platform, the post-loan business system platform, and the post-loan BI platform.

[0066] I. Post-loan Data Platform

[0067] 1.1 Source Data Pool ODS

[0068] The source data pool ODS (Operational Data Store) of the digital post-loan management system plays an important role in the risk management and post-loan management of financial institutions. The following is a detailed analysis of ODS in the digital post-loan management system:

[0069] 1.1.1. Definition and Generation Background of ODS

[0070] The ODS system was born to address the limitations of traditional databases and data warehouses in data processing. It mainly targets data that is neither suitable for processing in transactional databases (such as OLTP) nor in analytical databases (such as OLAP). The data in ODS is characterized by being subject-oriented, integrated, updatable, and reflecting the current or near-current data state.

[0071] 1.1.2, The Role of ODS in the Digital Post - loan Management System

[0072] Data Integration and Sharing:

[0073] As the source data pool of the digital post - loan management system, ODS can integrate data from various operational databases and other external data sources. These data include, but are not limited to, borrower information, litigation information, credit records, tax information, and public opinion data, etc.

[0074] Through the ETL (Extract, Transform, Load) process, ODS ensures the accuracy and consistency of data, providing comprehensive and real - time data support for post - loan management.

[0075] Real - time Operational Monitoring:

[0076] The ODS system supports real - time or near - real - time data updates, enabling financial institutions to monitor the operation status of loan business in real time, including borrowers' repayment situations, loan overdue situations, etc.

[0077] This real - time monitoring ability helps financial institutions detect potential risks in a timely manner and take corresponding risk prevention and control measures.

[0078] Intelligent Analysis and Early Warning:

[0079] Based on the data in ODS, financial institutions can build post - loan early warning models to conduct intelligent analysis on borrowers' risk status.

[0080] When borrowers have situations such as repayment overdue, litigation involved, credit abnormality, etc., the early warning model can automatically trigger early warning signals to remind relevant personnel to pay attention and take corresponding management measures.

[0081] 1.1.3, The Technical Architecture of ODS in the Digital Post - loan Management System

[0082] In the digital post - loan management system, ODS is usually closely integrated with other systems (such as SAS, credit systems, etc.) to jointly form a complete technical architecture. Specifically:

[0083] ODS: Responsible for data integration, cleaning, and preliminary processing, providing basic data for subsequent analysis and early warning.

[0084] SAS: Using statistical analysis and data mining technologies, deeply analyze the data in ODS, build early warning models and evaluate risks.

[0085] Credit System: Display the results processed by ODS and SAS, including early warning information, loan status, etc., and track the execution of the post - loan management process.

[0086] 1.1.4. Advantages of ODS in the Digital Post - loan Management System

[0087] Improve the real - time performance of data processing: ODS supports real - time or near - real - time data updates, which helps financial institutions promptly grasp the operation status of loan business.

[0088] Enhance risk prevention and control capabilities: Through intelligent early - warning models and real - time data analysis, financial institutions can detect potential risks in advance and take corresponding prevention and control measures.

[0089] Optimize resource allocation: Based on the data support of ODS, financial institutions can allocate resources more reasonably, improving the efficiency and effectiveness of post - loan management.

[0090] In summary, as the source data pool of the digital post - loan management system, ODS plays an important role in data integration, real - time monitoring, intelligent analysis, and early warning, providing strong support for the risk management and post - loan management of financial institutions.

[0091] 1.2 Unified Data Warehouse DW

[0092] The unified data warehouse reorganizes and standardizes business data

[0093] Business data domain: Data such as customer applications, borrowings, loan disbursements, repayments, write - offs, settlements, fees, loan notes, repayment plans, etc.

[0094] Customer data domain: Data such as customer basic information, contact information, contracts, regions, etc.

[0095] 1.3 Tag Data Pool TDM

[0096] The Tag Data Pool (TDM, Tag Data Management) in the digital post - loan management system is a crucial component in post - loan management. It is of great significance for improving the efficiency, accuracy, and personalized service level of post - loan management. The following is a detailed description of the Tag Data Pool (TDM):

[0097] 1.3.1 Definitions and Functions

[0098] The Tag Data Pool (TDM) is a central database in the digital post - loan management system used to store, manage, and apply tag information. These tag information are usually abstractions and generalizations of multi - dimensional data such as borrower behavior, credit status, repayment ability, etc., which can help financial institutions quickly identify and understand the characteristics and risks of borrowers. The main functions of TDM include:

[0099] Data storage: Centralize and store all tag information related to borrowers, including basic information, credit scores, behavior characteristics, etc.

[0100] Data integration: Integrate label information from different data sources (such as credit systems, risk control systems, external credit investigation agencies, etc.) to form a unified borrower profile.

[0101] Data query: Provide convenient query interfaces to support financial institutions in quickly retrieving and obtaining relevant label information according to business needs.

[0102] Data analysis: Utilize big data analysis and machine learning technologies to deeply mine and analyze label data, providing decision-making support for post-loan management.

[0103] 1.3.2 Label system construction

[0104] The label system in TDM is the cornerstone for constructing the borrower profile and usually includes the following aspects:

[0105] Basic information labels: Such as basic information of the borrower, including name, age, gender, occupation, income, etc.

[0106] Credit status labels: Such as information reflecting the borrower's credit status, including credit scores, overdue records, default times, etc.

[0107] Behavioral characteristic labels: Such as information reflecting the borrower's behavioral characteristics, including consumption habits, repayment behaviors, social network activity levels, etc.

[0108] Risk warning labels: Such as information for early identifying the borrower's risks, including potential risk points, warning levels, etc.

[0109] These labels are organized through scientific classification and coding methods to form a hierarchical label system, facilitating efficient data management and application by financial institutions.

[0110] 1.3.3 Application scenarios

[0111] TDM has a wide range of application scenarios in the digital post-loan management system, including but not limited to:

[0112] Risk assessment: Through comprehensive analysis of the borrower's label information, accurately assess the borrower's credit risk, providing a basis for loan approval and quota adjustment.

[0113] Collection management: Based on the borrower's repayment behavior labels, formulate personalized collection strategies to improve collection efficiency and success rate.

[0114] Product recommendation: Combine the borrower's consumption habits and behavioral characteristic labels to recommend suitable financial products and services to the borrower.

[0115] Marketing: Utilize label information for precision marketing to improve the targeting and effectiveness of marketing activities.

[0116] 1.3.4 Technical implementation

[0117] The technical implementation of TDM usually involves the following aspects:

[0118] Data collection and cleaning: Collect label data from various data sources through API interfaces, data extraction tools, etc., and perform data cleaning and preprocessing to ensure the accuracy and consistency of the data.

[0119] Data storage and management: Adopt storage technologies such as distributed databases and data warehouses to ensure that TDM can efficiently and stably store and manage massive label data.

[0120] Data analysis and mining: Use big data analysis and machine learning algorithms to deeply mine and analyze label data to discover potential risk points and opportunity points.

[0121] Data visualization: Through data visualization technologies, present the analysis results intuitively in the form of charts, dashboards, etc., to facilitate decision-making and monitoring by financial institutions.

[0122] 1.4 Application Data Store ADS

[0123] The Application Data Store (ADS) of the digital post-loan management system is a crucial component in the post-loan management process. It integrates a large amount of data and provides decision-making support for post-loan management through advanced data processing and analysis technologies. The following is a detailed analysis of the Application Data Store (ADS) of the digital post-loan management system:

[0124] 1.4.1 Definition and functions of ADS

[0125] In the digital post-loan management system, ADS (Application Data Store) is mainly responsible for storing, processing, and analyzing various types of data related to post-loan management. These data include but are not limited to customer information, loan information, repayment records, overdue situations, collection records, etc. Through its efficient data processing and analysis capabilities, ADS provides real-time data insights and decision-making support for post-loan management personnel.

[0126] 1.4.2 Data sources of ADS

[0127] Internal data: Data generated by financial institutions themselves, such as customer information, loan contracts, repayment records, etc.

[0128] External data: External data obtained through partners, third-party data service providers, etc., such as credit data, social media data, judicial data, etc.

[0129] 1.4.3 Data processing and analysis technologies of ADS

[0130] Big Data Processing: Utilize big data technologies to process and analyze massive amounts of data quickly and accurately.

[0131] Artificial Intelligence: Through artificial intelligence technologies such as machine learning and natural language processing, deeply mine and intelligently analyze data.

[0132] Risk Control Model: Build a risk control model for post-loan management to monitor and give early warnings of loan risks in real time.

[0133] 1.4.3.1, Data-Driven Decision-Making

[0134] ADS forms a comprehensive data view by collecting, integrating, and analyzing a large amount of data related to post-loan management, such as customer information, loan information, repayment records, and overdue situations. This data provides a solid foundation for intelligent case allocation, enabling case allocation decisions to be based on objective and comprehensive data support rather than subjective judgment or empiricism.

[0135] 1.4.3.2, Machine Learning Algorithms

[0136] The core of intelligent case allocation lies in the application of machine learning algorithms. By training machine learning models, ADS can learn the rules and patterns in historical data, identify the similarities and differences between different cases. During the case allocation process, the model will predict the best collection strategy or case allocation plan based on the characteristics of new cases and historical data. This algorithm-based case allocation method can significantly improve the accuracy and efficiency of case allocation.

[0137] 1.4.3.3, Integration of Business Rules and Models

[0138] In addition to machine learning algorithms, ADS also conducts intelligent case allocation by combining the business rules and actual needs of financial institutions. Business rules are the crystallization of the experience and wisdom accumulated by financial institutions during their long-term operations, and they are of great significance for guiding post-loan management work. ADS will incorporate business rules into machine learning models, making the case allocation results not only conform to algorithm predictions but also meet the actual business needs.

[0139] 1.4.3.4, Dynamic Adjustment and Optimization

[0140] Intelligent case allocation is not a one-time process but a continuous iterative and optimization process. As the collection work progresses and data accumulates continuously, ADS will update the model and data in real time, dynamically adjust and optimize the case allocation logic. This ability of dynamic adjustment enables ADS to continuously adapt to new business scenarios and demand changes, maintaining the timeliness and accuracy of case allocation decisions.

[0141] 1.4.3.5 Case Allocation Logic

[0142] 1.4.3.5.1 Model Training and Evaluation

[0143] Model Training and Evaluation

[0144] 1. Data Preparation

[0145] Data Collection: Collect data related to post-loan collection, including days past due, collection behavior data, historical repayment data, income and liability data, customer basic information, contact information, third-party data, etc.

[0146] Data Cleaning: Handle missing values and outliers, and perform data standardization or normalization to ensure data quality.

[0147] Feature Engineering: Extract and construct features useful for model prediction according to business requirements and data characteristics.

[0148] 2. Model Selection

[0149] Algorithms that may be adopted for the post-loan collection model include logistic regression, decision tree, random forest, gradient boosting trees (such as XGBoost, LightGBM), neural networks, etc. When selecting an algorithm, factors such as the scale of the data, the complexity of the features, and the requirements for model interpretability need to be considered.

[0150] 3. Model Training

[0151] Partition the dataset: Partition the dataset into a training set, a validation set, and a test set, usually in a ratio of 7:2:1 or 6:2:2.

[0152] Parameter Tuning: Use the training set to train the model and adjust the model parameters, such as learning rate, tree depth, number of leaf nodes, etc., through the validation set to optimize the model performance.

[0153] Prevent Overfitting: Adopt techniques such as regularization, pruning, and early stopping to prevent the model from overfitting.

[0154] 4. Model Evaluation

[0155] Evaluation Metrics: Commonly used evaluation metrics for the post-loan collection model include accuracy, recall, F1-score, area under the AUC-ROC curve, etc. Due to the class imbalance problem in the collection scenario (i.e., delinquent customers are in the minority), AUC-ROC and F1-score can more comprehensively evaluate the model performance.

[0156] Performance Verification: Use the test set to verify the generalization ability of the model and ensure that the model can maintain stable and good performance in actual applications.

[0157] Algorithms and Formulas of the Model

[0158] Although various algorithms may be adopted in the post-loan collection model, logistic regression is common in credit scoring and collection prediction due to its simplicity and interpretability. Taking logistic regression as an example, its algorithm and formula are briefly introduced below.

[0159] Logistic Regression Algorithm

[0160] Logistic regression is a generalized linear regression model for binary classification problems. It maps the predicted value of linear regression to the interval (0, 1) through the sigmoid function, thereby obtaining the probability of belonging to a certain class.

[0161] Formula

[0162] Linear regression part: z = θ0 + θ1x1 + θ2x2 +... + θnxn

[0163] Among them, θ0, θ1,..., θn are model parameters, and x1, x2,..., xn are feature variables.

[0164] Sigmoid function: ŷ = 1+e-z1

[0165] Among them, ŷ is the probability of predicting the positive class.

[0166] Loss function (commonly used cross-entropy loss): L(θ) = -m1 ∑i=1m [yi log(ŷi) + (1 - yi) log(1 - ŷi)]

[0167] Among them, m is the number of samples, yi is the true label (0 or 1), and ŷi is the predicted probability.

[0168] Training Process

[0169] The process of training a logistic regression model is to find a set of parameters θ that minimizes the loss function L(θ). This is usually achieved through optimization algorithms such as gradient descent.

[0170] 1.4.3.5.2 Intelligent Case Allocation Logic

[0171] I. Construction of the Case Scoring Model

[0172] 1. Feature acquisition:

[0173] 1. Obtain various features related to the case, such as the case amount, number of periods, hand number, billing date, whether there is a history of repayment, whether there is an installment, credit score, repayment willingness, asset income, education level, occupation, whether the contact information is valid, whether the contact address is valid, etc.

[0174] 2. Feature selection and preprocessing:

[0175] 1. Calculate the correlation between features, and use the obtained correlation to select features to reduce redundancy and improve model efficiency.

[0176] 2. Preprocess the selected features, such as data cleaning, standardization, normalization, etc., to ensure the consistency of model input.

[0177] 3. Model training and evaluation:

[0178] 1. Obtain historical cases as the training set and the test set respectively, and set a score label containing a score value for each historical case.

[0179] 2. According to the selected classification algorithm or regression algorithm (such as logistic regression, decision tree, random forest, etc.), set a hyperparameter group to train the model on the training set.

[0180] 3. Calculate the model parameters and evaluation metric values (such as accuracy, recall, F1 value, etc.) on the test set, and select the model with the optimal evaluation metric value as the case scoring model.

[0181] II. Case Input and Scoring

[0182] 1. Case input:

[0183] 1. When receiving a sub - case task containing a new case, input the new case into the constructed case scoring model.

[0184] 2. Scoring output:

[0185] 1. Obtain the score value of the new case output by the case scoring model, which reflects key information such as the complexity level and risk level of the new case.

[0186] III. Strategy Selection and Case Distribution

[0187] 1. Strategy configuration:

[0188] 1. Set a strategy dictionary, where different key values correspond to different sub - case strategies. These strategies can be flexibly configured according to factors such as business requirements, case characteristics, and the capabilities of salespersons.

[0189] 2. Strategy selection:

[0190] 1. Receive the strategy selected by the user for the new case, or make an automatic selection according to the preset default strategy.

[0191] 3. Salesperson screening and case distribution:

[0192] 1. Construct a list of the experience levels of salespersons, a list of performance ranking segments, and a list of ability score segments, which reflect the professional capabilities, business levels, and processing capabilities of salespersons.

[0193] 2. Based on the strategy and the scoring value of the new case, screen out the eligible salespersons from the salesperson database and distribute the new case to them for processing.

[0194] IV. Algorithm and Formula Examples

[0195] Although the specific algorithms and formulas of the intelligent case distribution logic may vary depending on the actual application scenarios and the selected algorithms, the following is a simplified example of a logistic regression model to illustrate the construction process of the case scoring model:

[0196] Logistic regression model formula:

[0197] P(y=1∣x)=1+e-(w·x+b)1

[0198] Among them, P(y=1∣x) represents the probability that the case belongs to a certain category (such as high risk) given the input feature x; w is the weight vector, indicating the influence degree of each feature on the classification result; b is the bias term; e is the base of the natural logarithm; w·x represents the linear combination of the weight vector and the input feature.

[0199] Model training:

[0200] Train the model with historical case data, that is, find the optimal w and b to minimize the error between the prediction result of the model for the training data and the actual result.

[0201] Model evaluation:

[0202] Use the test set to evaluate the performance of the model, and evaluate the quality of the model by calculating evaluation metrics such as accuracy, recall rate, and F1 value.

[0203] The present invention covers any substitutions, modifications, equivalent methods, and solutions made on the essence and scope of the present invention. To enable the public to have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, and those skilled in the art can fully understand the present invention without the description of these details. In addition, well-known methods, processes, procedures, components, and circuits are not described in detail to avoid unnecessary confusion to the essence of the present invention.

[0204] The above description is only a preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A digital post-loan management system, characterized in that: It includes post-loan data platform, post-loan business system platform and post-loan BI platform. The post-loan data platform includes source data pool ODS, unified data warehouse DW, label data pool TDM and application data pool ADS.

2. The digital post-loan management system according to claim 1 is characterized in that: The source data pool ODS is used to integrate data from various operational databases and external data sources, including borrower information, litigation information, credit records, tax information and public opinion data, etc. It supports real-time or near real-time data updates and ensures data accuracy and consistency through the ETL process. At the same time, a post-loan early warning model is built based on its data to conduct intelligent analysis and early warning of the borrower's risk status.

3. The digital post-loan management system according to claim 1 is characterized in that: The unified data warehouse DW reorganizes and standardizes business data, which includes data such as customer applications, borrowing, lending, repayment, write-offs, settlements, fees, promissory notes, repayment plans, and data in customer data domains such as customer basic information, contact information, contracts, and regions.

4. The digital post-loan management system according to claim 1 is characterized in that: The tag data pool TDM serves as a central database for storing, managing and applying tag information. Its tag information includes basic information tags, credit status tags, behavioral feature tags and risk warning tags. It processes and applies tag data through data collection and cleaning, storage and management, analysis and mining, and data visualization technologies to provide decision support for post-loan management. Application scenarios include risk assessment, collection management, product recommendation and marketing.

5. The digital post-loan management system according to claim 1 is characterized in that: The application data pool ADS stores, processes and analyzes various types of data related to post-loan management. The data sources include data generated by internal financial institutions themselves and data obtained from external sources. Data processing and analysis are carried out through big data processing and artificial intelligence technology, and a post-loan management risk control model is constructed to achieve data-driven decision-making and intelligent case division. The intelligent case division logic includes model training and evaluation, case scoring model construction, case input and scoring, strategy selection and case distribution, and the case division logic can be dynamically adjusted and optimized.

6. The digital post-loan management system according to claim 5 is characterized in that: The model training and evaluation in the application data pool ADS includes data preparation, model selection, model training and model evaluation. Data preparation collects post-loan collection related data and performs cleaning and feature engineering processing; model selection selects a suitable algorithm based on data scale, feature complexity and model interpretability requirements; model training divides the data set, tunes parameters and prevents overfitting; model evaluation uses indicators such as accuracy, recall rate, F1 score, and area under the AUC-ROC curve to evaluate model performance.

7. The digital post-loan management system according to claim 5 is characterized in that: The construction of the case scoring model in the application data pool ADS includes feature acquisition, feature selection and preprocessing, model training and evaluation. Feature acquisition is related to multiple features of the case. Feature selection and preprocessing calculates feature correlation and performs selection and preprocessing. Model training and evaluation trains and evaluates the model through historical cases, and selects the optimal model as the case scoring model. The model can output scoring values ​​according to case inputs, select salesmen based on scoring values ​​and strategies, and distribute cases.

8. The digital post-loan management system according to claim 1 is characterized in that: The post-loan business system platform is used to realize the automated management process after the loan is issued, including repayment reminders, overdue collection, credit assessment and other functions.

9. The digital post-loan management system according to claim 1, characterized in that: The post-loan BI platform is used to provide data analysis and visualization functions to assist financial institutions in making decision analysis.

10. The digital post-loan management system according to any one of claims 1 to 9, characterized in that: The digital post-loan management system can enhance the risk management level of financial institutions, improve operational efficiency, enhance customer experience, meet regulatory requirements and adapt to market changes.