Intelligent collection system and method

Abstract

The invention discloses an intelligent collection system and method, and the system comprises a data integration module which is used for collecting and integrating data; the feature engineering module is used for feature extraction and feature selection; the model construction module is used for constructing and optimizing a collection strategy model by using a genetic algorithm or a simulated annealing algorithm; the strategy generation module is used for generating a collection strategy according to the collection strategy model; and the execution and monitoring module is used for monitoring the collection effect in real time and collecting feedback data. According to the method, different collection strategies are coded through a genetic algorithm, and the performance of the strategies in a specific debtor group is evaluated to gradually evolve a more personalized collection strategy; random search can be carried out in a strategy space through a simulated annealing algorithm, local optimum is avoided by receiving a difference solution with a certain probability, and finally personalized collection schemes which are more effective for different debtors are found.

Description

Technical Field

[0001] The present invention belongs to the field of financial technology, and specifically relates to an intelligent debt collection system and method. Background Art

[0002] Currently, debt collection mainly relies on manual or traditional automatic collection methods, which are inefficient and difficult to adapt to complex and changing debt situations. Although there are some collection systems on the market, most of them are based on simple statistical models and lack an in-depth understanding of complex debtor behavior patterns.

[0003] Specifically, the existing debt collection system has the following problems:

[0004] 1. Lack of Personalization:

[0005] Many collection systems adopt a one-size-fits-all approach and do not fully consider the individual differences of debtors, such as economic status, credit history, willingness to repay, etc., which may lead to ineffective collection strategies.

[0006] 2. Over-reliance on historical data:

[0007] Existing systems often predict the future based on past behavior patterns, but in a rapidly changing market environment, such predictions may no longer be accurate.

[0008] 3. Inefficient communication:

[0009] Automated collection emails and phone calls may be seen as spam, causing debtors to ignore or become annoyed, reducing the effectiveness of communication.

[0010] 4. Limited emotional and situational understanding:

[0011] AI-driven systems may have difficulty understanding and responding to debtors’ emotional responses or special situations, such as illness, unemployment, etc., which may exacerbate debtors’ stress.

[0012] 5. Misuse or Abuse of Technology:

[0013] Without proper regulation, collection technology can be abused, leading to harassing collection practices or privacy violations.

[0014] 6. Poor Customer Experience:

[0015] Lack of a humane collection approach may damage customer relationships and affect brand image and customer loyalty in the long run. Summary of the invention

[0016] In view of the above problems, the present invention is proposed to provide an intelligent debt collection system and method that overcomes the above problems or at least partially solves the above problems.

[0017] To achieve the above object, the present invention adopts the following technical solutions:

[0018] An intelligent debt collection system, comprising:

[0019] Data integration module, used to collect and integrate data;

[0020] Feature engineering module for feature extraction and feature selection;

[0021] Model building module, used to build and optimize collection strategy models using genetic algorithms or simulated annealing algorithms;

[0022] A strategy generation module, used to generate a collection strategy based on the collection strategy model;

[0023] The execution and monitoring module is used to monitor the collection effect in real time and collect feedback data.

[0024] Optionally, the system further includes:

[0025] Feedback loop module, which is used to transmit execution results and customer feedback back to the collection strategy model;

[0026] User Interface Module, which provides the interface that enables collectors, analysts, and management to access system functionality and data.

[0027] Optionally, the system further comprises: a compliance and risk management module for ensuring that debt collection complies with laws and regulations;

[0028] The Reporting and Analysis module is used to provide reports, analyze them, and display the performance indicators of collection activities.

[0029] Optionally, the system also includes a security and privacy module for ensuring data security and performing privacy protection.

[0030] Optionally, the data includes basic customer information, transaction history, credit score, and history of collection attempts.

[0031] Optionally, the characteristics include number of days overdue, repayment frequency, and customer response patterns.

[0032] Optionally, the collection strategy includes contact time, communication method and preferential scheme.

[0033] Optionally, the feedback data includes customer response and collection success rate.

[0034] Optionally, the performance indicators include recovery rate and cost-effectiveness ratio.

[0035] The present invention also provides an intelligent debt collection method, which is based on the intelligent debt collection system described in any one of the above items, and includes:

[0036] Prepare data, including the customer's outstanding amount, overdue days, and historical repayment behavior;

[0037] Extract useful features from the data, such as days past due, average payment amount, and number of past collection attempts;

[0038] Design genetic algorithms to optimize collection strategies;

[0039] Training the collection strategy model;

[0040] Apply collection strategies to the actual collection process;

[0041] Collect actual collection effect data and continuously improve the collection strategy model.

[0042] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0043] 1. The present invention encodes different collection strategies (such as collection frequency, communication methods, preferential conditions, etc.) through genetic algorithms, and evaluates the performance of these strategies in specific debtor groups. Through crossover, mutation and selection operations, more personalized collection strategies are gradually evolved; the simulated annealing algorithm can perform random searches in the strategy space, avoid local optimality by accepting differential solutions with a certain probability, and finally find personalized collection plans that are more effective for different debtors.

[0044] 2. The genetic algorithm and simulated annealing model in the present invention are designed to learn from recent interaction data, and the model adapts to market changes and immediate changes in debtor behavior more quickly; the genetic algorithm and simulated annealing model can learn how to interpret the emotions in the debtor's speech and text, and then adjust the collection strategy to communicate in a more gentle or firm manner, depending on the debtor's emotional state.

[0045] 3. The present invention can optimize the tone and method of debt collection to make it more humane and sympathetic, and improve the customer experience by learning the debtor's preferences (such as electronic communication or telephone communication). Combined with the simulated annealing model, the system can dynamically adjust the collection strategy to adapt to the debtor's emotions and life events, such as unemployment or family crisis, so as to find a balance between collection and care. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 A schematic diagram of a smart collection system flow provided in an embodiment of the present application;

[0047] Figure 2A timing diagram of an intelligent debt collection system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0049] Embodiment 1:

[0050] See also Figure 1 and Figure 2 This embodiment provides an intelligent debt collection system, the system comprising:

[0051] Data integration module, used to collect and integrate data.

[0052] Responsible for collecting and integrating data from various sources, including basic customer information, transaction history, credit score, and history of attempted collections, etc. Perform data cleaning and pre-processing to ensure data quality and integrity.

[0053] Feature engineering module for feature extraction and feature selection.

[0054] Extract meaningful features from the raw data, such as the number of days past due, repayment frequency, and customer response patterns. Feature selection determines which features are most important for predicting the likelihood of successful collection.

[0055] Model building module for building and optimizing collection strategy models using genetic algorithms or simulated annealing algorithms.

[0056] The genetic algorithm module may include submodules such as chromosome encoding, fitness function, selection, crossover, mutation, etc. The simulated annealing module may include submodules such as initial strategy setting, neighborhood search, acceptance criteria, cooling plan, etc.

[0057] The strategy generation module is used to generate a collection strategy based on the collection strategy model.

[0058] The collection strategy includes contact time, communication method and preferential scheme.

[0059] The execution and monitoring module is used to monitor the collection effect in real time and collect feedback data.

[0060] Automatically or semi-automatically execute collection strategies, such as sending collection emails, making phone calls, sending text messages, etc., monitor the effectiveness of collection activities in real time, and collect feedback data, including customer response and collection success rate.

[0061] Feedback loop module, which is used to transmit execution results and customer feedback back to the collection strategy model;

[0062] Feed the execution results and customer feedback back to the model for the next round of iterative optimization. Analyze the results to identify which strategies are effective and which need improvement.

[0063] User Interface Module, which provides the interface that enables collectors, analysts, and management to access system functionality and data.

[0064] Provides an intuitive interface that enables collectors, analysts, and management to access system functions and data. Allows manual intervention such as modifying policies, adding notes, handling special cases, etc.

[0065] Compliance and risk management module to ensure collection complies with laws and regulations;

[0066] Ensure that collection activities comply with relevant laws, regulations and internal policies. Monitor potential risks, such as customer complaints, reputation damage, etc.

[0067] The Reporting and Analysis module is used to provide reports, analyze them, and display the performance indicators of collection activities.

[0068] Provides detailed reports and analysis showing performance indicators of collection activities, including recovery rates and cost-effectiveness ratios. Analyzes trends and provides insights for decision makers.

[0069] Security and privacy module, used to ensure data security and privacy protection.

[0070] Ensure data security and privacy protection, comply with GDPR and other regulatory requirements. Encrypt sensitive information and restrict access rights.

[0071] This embodiment encodes different collection strategies (such as collection frequency, communication methods, preferential conditions, etc.) through genetic algorithms, and evaluates the performance of these strategies in specific debtor groups, and gradually evolves more personalized collection strategies through crossover, mutation and selection operations. The simulated annealing algorithm can perform random searches in the strategy space, avoid local optimality by accepting a certain probability of differential solutions, and ultimately find more effective personalized collection solutions for different debtors.

[0072] The genetic algorithm and simulated annealing model in this embodiment are designed to learn from recent interaction data rather than relying solely on historical data, which helps the model adapt more quickly to market changes and immediate changes in debtor behavior; the genetic algorithm and simulated annealing model can learn how to interpret the emotions in the debtor's words and texts, and then adjust the collection strategy to communicate in a more gentle or firm manner, depending on the debtor's emotional state.

[0073] This embodiment can optimize the tone and method of debt collection to make it more humane and sympathetic, and improve the customer experience by learning the debtor's preferences (such as electronic communication or telephone communication). Combined with the simulated annealing model, the system can dynamically adjust the collection strategy to adapt to the debtor's emotions and life events, such as unemployment or family crisis, to find a balance between collection and care.

[0074] Simulated Annealing (SA) is a probability-based optimization algorithm. Its core idea is to find the optimal solution to the problem by simulating the annealing process in physics. During the annealing process, the system starts from a high temperature state and gradually lowers the temperature until it reaches zero temperature. At each temperature state, the system will randomly try a new state. If the energy of the new state is lower than the current state, the new state is accepted; if the energy of the new state is higher than the current state, the new state is accepted according to the temperature and energy difference probability. In this way, the algorithm can gradually approach the optimal solution to the problem.

[0075] Genetic Algorithm (GA) is an optimization algorithm based on natural selection and heredity. Its core idea is to find the optimal solution to a problem by simulating the biological evolution process. In a genetic algorithm, each solution is called an individual, and individuals propagate through selection, crossover, and mutation to adapt to changes in the environment. During the selection process, individuals that are more adaptable to the environment have a greater probability of being selected. During the crossover and mutation process, the characteristics of the individual will change randomly, thus generating new solutions. Through multiple generations of propagation, the algorithm can gradually approach the optimal solution to the problem.

[0076] Embodiment 2:

[0077] Embodiment 2 discloses an intelligent debt collection method, which is based on the intelligent debt collection system described in the above embodiment, and comprises the following steps:

[0078] S1. Prepare data, including the customer's outstanding amount, overdue days, and historical repayment behavior.

[0079] S2. Extract useful features from the data, such as the number of days past due, average repayment amount, and number of past collection attempts.

[0080] S3. Design genetic algorithms to optimize collection strategies.

[0081] 3.1 Parameter Definition

[0082] Chromosome length: Each individual represents a collection strategy.

[0083] Gene code:

[0084] Contact frequency: 1-4 times / week (coded as 1 digit)

[0085] Collection method: Phone (0), SMS (1), Email (2) (code is 2 digits)

[0086] Discount ratio: 0%-50% (code is 6 digits)

[0087] Therefore, a complete chromosome may have 9 bits.

[0088] 3.2. Initialize the population

[0089] Create an initial population of random individuals. Each individual is a string consisting of the above genes.

[0090] 3.3 Fitness Function

[0091] Define a fitness function to measure the effectiveness of the strategy. Assume that the fitness function is calculated as follows:

[0092] Recovery rate: the ratio of actual recovery amount to expected recovery amount.

[0093] Cost: The total expenses incurred during the collection process.

[0094] Fitness score = recovery rate / cost.

[0095] 3.4 Genetic Operation

[0096] Selection: Roulette selection.

[0097] Crossover: Two-point crossover is used.

[0098] Mutation: Randomly flip a gene.

[0099] 3.5 Termination Conditions

[0100] The maximum number of iterations has been reached.

[0101] The best individual remains unchanged for several generations.

[0102] S4. Train the collection strategy model.

[0103] Initialize the population: randomly generate a certain number of individuals.

[0104] Loop iteration:

[0105] Calculate the fitness of each individual. Select the best individual to enter the next generation. Generate new individuals through crossover and mutation.

[0106] Replace the worst individual.

[0107] Check termination conditions.

[0108] S5. Apply the collection strategy to the actual collection process.

[0109] S6. Collect actual collection effect data and continuously improve the collection strategy model.

[0110] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the protection scope of the present invention.

Claims

1. An intelligent debt collection system, characterized in that: The system comprises: Data integration module, used to collect and integrate data; Feature engineering module for feature extraction and feature selection; Model building module, used to build and optimize collection strategy models using genetic algorithms or simulated annealing algorithms; A strategy generation module, used to generate a collection strategy based on the collection strategy model; The execution and monitoring module is used to monitor the collection effect in real time and collect feedback data.

2. The intelligent debt collection system according to claim 1, characterized in that: The system further comprises: Feedback loop module, which is used to transmit execution results and customer feedback back to the collection strategy model; User Interface Module, which provides the interface that enables collectors, analysts, and management to access system functionality and data.

3. The intelligent debt collection system according to claim 1, characterized in that: The system also includes: a compliance and risk management module for ensuring that collection complies with laws and regulations; The Reporting and Analysis module is used to provide reports, analyze them, and display the performance indicators of collection activities.

4. The intelligent debt collection system according to claim 1, characterized in that: The system also includes a security and privacy module for ensuring data security and privacy protection.

5. The intelligent debt collection system according to claim 1, characterized in that: The data includes basic customer information, transaction history, credit score and history of collection attempts.

6. The intelligent debt collection system according to claim 1, characterized in that: The characteristics include number of days past due, frequency of repayments, and customer response patterns.

7. The intelligent debt collection system according to claim 1, characterized in that: The collection strategy includes contact time, communication method and preferential scheme.

8. The intelligent debt collection system according to claim 1, characterized in that: The feedback data includes customer response and collection success rate.

9. The intelligent debt collection system according to claim 3, characterized in that: The performance indicators include recovery rate and cost-effectiveness ratio.

10. An intelligent debt collection method, the method is based on an intelligent debt collection system according to any one of claims 1 to 9, characterized in that: The method comprises: Prepare data, including the customer's outstanding amount, overdue days, and historical repayment behavior; Extract useful features from the data, such as days past due, average payment amount, and number of past collection attempts; Design genetic algorithms to optimize collection strategies; Training the collection strategy model; Apply collection strategies to the actual collection process; Collect actual collection effect data and continuously improve the collection strategy model.

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