External customer behavior data-based stop interest bearing method and device, equipment and medium

Abstract

The invention relates to the technical field of financial information and communication, in particular to an external customer behavior data-based file interest bearing method, device and equipment and a medium, and the method comprises the following steps: docking an external data source, acquiring external behavior data of a customer based on the external data source, and integrating the external behavior data with customer account data in a financial platform to form a multi-dimensional customer portrait; establishing a file-based interest bearing rule system based on the multi-dimensional customer portraits; the rule system defines a plurality of interest bearing dimensions, each interest bearing dimension is divided into a plurality of gears, and each gear is associated with an interest rate adjustment coefficient; according to the file-based interest-bearing rule system, acquiring the gear to which the customer belongs in each interest-bearing dimension and the corresponding interest rate adjustment coefficient, and calculating the personalized execution interest rate of the customer through a dynamic interest rate model in combination with a preset weight and a reference interest rate; and on the basis of the personalized execution interest rate, interest calculation is performed in combination with the principal and the storage period of the customer account. The value and risk of the customer are accurately evaluated, the flexibility is high, and differentiated services are provided for the customer.

Description

Technical Field

[0001] This invention relates to the field of financial information and communication technology, and in particular to a method, apparatus, device and medium for calculating interest based on external customer behavior data. Background Technology

[0002] Financial institutions typically base their interest calculation methods on internal account data, such as balance, deposit period, and transaction frequency, employing either fixed interest rates or segmented interest calculation models. For example, time deposits typically have a fixed interest rate based on the deposit period.

[0003] To provide personalized services, some financial institutions have introduced multi-dimensional pricing strategies in related technologies, including adjusting interest rates based on internal indicators such as customer asset size and historical transaction frequency. However, these methods are still limited to relying on internal institutional data; furthermore, interest calculation mechanisms using simple static rules lack dynamic responsiveness, making it difficult to adapt to changes in market interest rates or quickly deploy new interest calculation strategies; it is also difficult to implement personalized interest calculation solutions for different customer groups and scenarios; leading to a decrease in the competitiveness of financial institutions. Summary of the Invention

[0004] This invention provides a method, apparatus, device, and medium for calculating interest based on external customer behavior data, which solves the problems of single data dimension, insufficient flexibility, and lack of differentiated services in related interest calculation methods.

[0005] To achieve the above objectives, this application adopts the following technical solution: Firstly, a method for calculating interest based on external customer behavior data is provided, including: Connect to external data sources, collect customer external behavior data based on external data sources, and integrate it with the internal customer account data of the financial platform to form a multi-dimensional customer profile. Based on the aforementioned multi-dimensional customer profile, a tiered interest calculation rule system is established; the rule system defines multiple interest calculation dimensions, each interest calculation dimension is divided into multiple tiers, and each tier is associated with an interest rate adjustment coefficient; Based on the tiered interest calculation rule system, the customer's tier and corresponding interest rate adjustment coefficient in each interest calculation dimension are obtained. Combined with the preset weights and benchmark interest rate, the customer's personalized execution interest rate is calculated through a dynamic interest rate model. Interest is calculated based on the personalized execution interest rate, combined with the principal and term of the customer's account.

[0006] Secondly, a tiered interest calculation device based on external customer behavior data is provided, comprising: The data access module is used to connect to external data sources, collect external behavioral data of customers based on external data sources, and integrate it with the internal customer account data of the financial platform to form a multi-dimensional customer profile. The tiered interest calculation rule module is used to establish a tiered interest calculation rule system based on the multi-dimensional customer profile. The rule system defines multiple interest calculation dimensions, each interest calculation dimension is divided into multiple tiers, and each tier is associated with an interest rate adjustment coefficient. The interest calculation module is used to obtain the customer's tier and corresponding interest rate adjustment coefficient in each interest calculation dimension according to the tiered interest calculation rule system, and calculate the customer's personalized execution interest rate through a dynamic interest rate model by combining the preset weights and the benchmark interest rate. The interest calculation module is used to calculate interest based on the personalized execution interest rate, combined with the principal and deposit period of the customer's account.

[0007] Thirdly, an electronic device is provided, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the fare calculation method based on external customer behavior data as described in the first aspect.

[0008] Fourthly, a readable storage medium is provided, on which a program or instructions are stored, which, when executed by a processor, implement the steps of the fare calculation method based on external customer behavior data as described in the first aspect.

[0009] The tiered interest calculation method based on external customer behavior data of the present invention has the following advantages: This application constructs a 360-degree, three-dimensional customer profile by introducing external multi-dimensional data and integrating it with internal account data. This allows for a more comprehensive and accurate assessment of a customer's true value, contribution, and potential risks, overcoming the shortcomings of traditional methods that cannot accurately assess customer value and risk due to their single data dimension. It significantly reduces pricing errors and improves the rationality of capital cost and return management. The dynamic interest calculation rule engine built in this application supports flexible configuration across multiple dimensions and tiers, enabling rapid response to market interest rate fluctuations, changes in capital supply and demand, and new product innovation needs, effectively solving the problem of insufficient flexibility in traditional systems. This application can generate highly customized interest rate plans for customer groups with different values ​​and behavioral characteristics, or even individual customers, solving the problem of a lack of differentiated services. By offering precise interest rate discounts, it retains high-value customers and attracts potential customers, effectively enhancing the institution's competitiveness.

[0010] The apparatus, electronic device, and readable storage medium of the present invention and the corresponding method for calculating interest based on external customer behavior data can achieve the same technical effect, and will not be described in detail here to avoid duplication. Attached Figure Description

[0011] Figure 1A schematic flowchart illustrating a tiered interest calculation method based on external customer behavior data, provided for embodiments of this application; Figure 2 A schematic diagram of a dimensional system provided for an embodiment of this application; Figure 3 A schematic diagram illustrating a source of interest rates provided in an embodiment of this application; Figure 4 A schematic diagram illustrating a source of interest rates provided in an embodiment of this application; Figure 5 A schematic flowchart illustrating the determination of the execution rate provided in this application embodiment; Figure 6 A schematic flowchart illustrating the determination of an amount provided in an embodiment of this application; Figure 7 A schematic flowchart illustrating the determination of the number of days provided in this application embodiment; Figure 8 A schematic diagram of a fare calculation device based on external customer behavior data provided in this application embodiment; Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0012] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the technical solutions in the embodiments of this application are clearly described. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art are within the scope of protection of this application.

[0013] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0014] The steps described in the specification and the flowcharts in the accompanying drawings of this invention are not necessarily to be strictly followed according to the step numbers; the execution order of the steps can be changed. Furthermore, certain steps can be omitted, multiple steps can be combined into one step, and / or one step can be broken down into multiple steps.

[0015] This specification provides a method for calculating interest based on external customer behavior data, a device for calculating interest based on external customer behavior data, a computer device, and a computer-readable storage medium. These will be described in detail below with reference to the accompanying drawings and preferred embodiments.

[0016] Tiered interest calculation: Based on pre-set rules, customers are categorized into different tiers, and interest is calculated according to the interest rate corresponding to each tier.

[0017] Please see Figure 1 This application provides a method for calculating interest based on external customer behavior data, such as... Figure 1 As shown, it includes: Step S1: Connect to external data sources, collect external behavioral data of customers based on external data sources, and integrate it with the internal customer account data of the financial platform to form a multi-dimensional customer profile.

[0018] The external behavioral data includes at least one of the following: consumption behavior, investment and financial management behavior, and online activity behavior data.

[0019] In this step, external data sources refer to data sources outside of financial institutions. External customer behavior data refers to data related to financial behavior generated by customers outside of financial institutions, such as consumption records at other merchants and investment behavior on third-party wealth management platforms. By collecting multi-dimensional data on customer consumption behavior, investment preferences, and online activities from external data sources and integrating and correlating this data with the internal customer profile data of financial institutions, the problem of single data dimensions is solved, thereby enabling accurate assessment of customer value and risk.

[0020] The customer profile dimensions include: customer value level, account activity level, and depth of business cooperation. Customer value level can be categorized as A / B / C class customers; account activity level can be determined based on average monthly transaction frequency; and depth of business cooperation can be determined based on cross-product holding rate.

[0021] In some possible implementations, clustering algorithms can be used to automatically mine customer profile dimensions, which may also include exclusive dimensions for high-frequency trading customers.

[0022] Step S2: Based on the multi-dimensional customer profile, establish a tiered interest calculation rule system; the rule system defines multiple interest calculation dimensions, each interest calculation dimension is divided into multiple tiers, and each tier is associated with an interest rate adjustment coefficient.

[0023] The interest calculation dimensions include: product dimension, institution / region dimension, and customer dimension.

[0024] This step involves dividing the interest rate into tiers based on multiple dimensions and assigning a corresponding interest rate adjustment factor to each tier, thus supporting the interpretability and configurability of the interest calculation rules. The interest rate adjustment factor is also known as the floating rate; see [link to relevant documentation]. Figure 2 For example, if a VIP employee of a branch deposits a one-year fixed deposit, the interest rate may be increased by 3% (or even multiple floating rates) on top of the bank's internal interest rate. The final result is calculated using the formula "Executive Interest Rate = Internal Interest Rate + Internal Interest Rate × Floating Rate".

[0025] Intra-bank interest rates are derived from benchmark interest rates and market interest rates. See [link / reference] Figure 3 Intra-bank interest rates are set by financial institutions themselves, derived from benchmark interest rates (market-standard rates) and market interest rates (market indicators such as LIBOR / SHIBOR); they support regional / institutional differentiation.

[0026] Step S3: Based on the tiered interest calculation rule system, obtain the customer's tier and corresponding interest rate adjustment coefficient in each interest calculation dimension. Combined with the preset weights and benchmark interest rate, calculate the customer's personalized execution interest rate through a dynamic interest rate model.

[0027] The dynamic interest rate model performs the following calculations: Execution rate = Benchmark rate × Σ(Dimension coefficient × Weight) + Adjusted rate; Among them, the benchmark interest rate is the central bank's benchmark or market interest rate; the dimension coefficient is determined by the customer's tier; the weight is the proportion of each dimension's impact on the interest rate (configurable); the interest rate is adjusted to cope with market fluctuations or special scenarios.

[0028] In this step, a pre-built dynamic interest rate model is used to integrate the coefficients and weights of various dimensions to dynamically calculate the personalized execution interest rate for the customer. By combining the static benchmark interest rate with dynamic customer characteristics and market factors, the interest rate can be generated flexibly and automatically. By adjusting the interest rate, it can quickly respond to market fluctuations, and by adjusting the weights, it can respond to strategy changes, thereby improving the speed of product innovation and market response.

[0029] In the specific implementation process, see Figure 4 Interest rates can be uniformly allocated "by product," such as a bank-wide uniform interest rate for a certain deposit product, or personalized "by account," such as individual adjustments for VIP customers. After determining the product / account, the fixed interest rate and tiered interest rates are distinguished, and finally, the applicable interest rate is calculated by combining them with the floating interest rate.

[0030] Step S4: Calculate interest based on the personalized execution interest rate, combined with the principal and deposit period of the customer's account.

[0031] Furthermore, the interest calculation includes both compound interest and simple interest methods. Compound interest: Interest = Principal × [(1 + Applicable interest rate / number of periods)^number of periods - 1] Simple interest: Interest = Principal × Execution rate × Deposit period.

[0032] This step calculates the final interest using compound or simple interest formulas based on the calculated personalized execution rate. This method ultimately achieves accurate and personalized interest results, ensuring seamless integration between the theoretical model and actual business operations, and directly improving the rationality of capital pricing.

[0033] In practice, it is necessary to determine the interest calculation amount and the tiered amount. The interest calculation amount is the base for calculating interest, and the tiered amount is the base used to match interest rate tiers. See [link / reference]. Figure 5 Depending on the type of amount, data is obtained from individual accounts or calculated using rules. For example, current balance and previous day's balance are obtained from individual accounts, while the average daily balance is calculated using rules: Average Daily Balance = Accumulated Amount ÷ Number of Days.

[0034] Corresponding to the above amounts, it is necessary to determine the number of days for interest calculation and the number of days for matching the interest tier. The number of days for interest calculation is the actual number of days for interest calculation, and the number of days for matching the interest tier is the base number of days for matching the interest rate tier. See [link to relevant documentation]. Figure 6 Different calculation methods can be selected based on the business scenario. The calculation types for interest calculation days include actual days, monthly benchmark of 30 days, etc.; the calculation types for tiered days include deposit period days, actual days, accrual days, and interest calculation days obtained from the transaction model, etc. Specific business can be abstracted to the tiered day type for expansion.

[0035] In some possible implementations, the interest calculation employs a progressive differential interest method, specifically: The customer's account balance is divided into tiers based on the amount, and interest is calculated for each tier according to the applicable interest rate. Finally, the interest from each tier is added together to obtain the total interest.

[0036] Example: Customer B has a balance of 110,000 (difference based on tier). Interest = (30000 × 0.35% + 20000 × 0.5% + 50000 × 1.2% + 10000 × 2.1%) / 360 Further, see Figure 7 The execution interest rate is determined based on the tiered amount and scenario factor.

[0037] When the tiered amount and the hierarchical amount have the same hierarchical rules, only the tiered amount needs to be configured; when the tiered amount and the hierarchical amount have different hierarchical rules, the tiered amount and the hierarchical amount need to be configured according to the actual business requirements. When the hierarchical amount has no value, the amount is hierarchically stratified according to the tiered amount.

[0038] In the above specific implementation process, this application also configures a microservice architecture, including module interaction processes, such as interest rate fluctuations implemented through RbRateElementChain; and extended interface design such as the day calculation interface IRbCalcIntDaysAssemble.

[0039] This application integrates external multi-dimensional data, such as consumer behavior, investment preferences, and online activities, with internal account data to construct a 360-degree comprehensive customer profile. This allows for a more comprehensive and accurate assessment of a customer's true value, contribution, and potential risks, overcoming the shortcomings of traditional methods that cannot accurately assess customer value and risk due to their single data dimension. It significantly reduces pricing errors and improves the rationality of capital cost and return management. The dynamic interest calculation rule engine built in this application supports flexible configuration across multiple dimensions and tiers. Combined with a microservice architecture, the system can quickly respond to market interest rate fluctuations, changes in capital supply and demand, and new product innovation needs, effectively solving the problem of insufficient flexibility in traditional systems. This application can generate highly customized interest rate plans for customer groups with different values ​​and behavioral characteristics, or even individual customers, solving the problem of a lack of differentiated services. By offering precise interest rate discounts, it retains high-value customers and attracts potential customers, effectively enhancing the institution's competitiveness.

[0040] Data shows that this application: external data reduced interest rate error by ≥15%, improving the rationality of funding pricing; the launch cycle of new interest-bearing products was shortened by 7 days, enabling rapid response to market changes; and customized interest rate schemes reduced the attrition rate by ≥12%.

[0041] See Figure 8 Corresponding to the above-described embodiment of the interest calculation method based on external customer behavior data, this application embodiment provides an interest calculation device based on external customer behavior data, comprising: The data access module 1001 is used to connect to external data sources, collect external behavioral data of customers based on external data sources, and integrate it with the internal customer account data of the financial platform to form a multi-dimensional customer profile. The tiered interest calculation rule module 1002 is used to establish a tiered interest calculation rule system based on the multi-dimensional customer profile; the rule system defines multiple interest calculation dimensions, each interest calculation dimension is divided into multiple tiers, and each tier is associated with an interest rate adjustment coefficient; The interest calculation processing module 1003 is used to obtain the customer's tier and corresponding interest rate adjustment coefficient in each interest calculation dimension according to the tiered interest calculation rule system, and calculate the customer's personalized execution interest rate through a dynamic interest rate model by combining the preset weights and benchmark interest rate. The interest calculation module 1004 is used to calculate interest based on the personalized execution interest rate, combined with the principal and deposit period of the customer's account.

[0042] The above-mentioned interest calculation device based on external customer behavior data implements the steps and processes of the above-mentioned interest calculation method based on external customer behavior data, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0043] See Figure 9 Corresponding to the above embodiments of the method for calculating interest based on external customer behavior data, this application provides an electronic device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps and processes of the above embodiments of the method for calculating interest based on external customer behavior data, and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0044] The memory 1009 can be used to store software programs and various data. The memory 1009 may primarily include a first storage area for storing programs or instructions and a second storage area for storing data. The first storage area may store the operating system, application programs or instructions required for at least one function (such as sound playback function, image playback function, etc.). Furthermore, the memory 1009 may include volatile memory or non-volatile memory, or both. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (SLDRAM), and direct memory bus RAM (DRRAM). The memory 1009 in the embodiments of this application includes, but is not limited to, these and any other suitable types of memory.

[0045] The processor 1010 may include one or more processing units; optionally, the processor 1010 integrates an application processor and a modem processor, wherein the application processor mainly handles operations involving the operating system, user interface, and applications, and the modem processor mainly handles wireless communication signals, such as a baseband processor. It is understood that the aforementioned modem processor may also not be integrated into the processor 1010.

[0046] Corresponding to the above-described embodiment of the method for calculating interest based on external customer behavior data, this application embodiment also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the steps and processes of the above-described embodiment of the method for calculating interest based on external customer behavior data, and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0047] The processor is the processor in the electronic device described in the above embodiments of this application. The readable storage medium includes a computer-readable storage medium, such as a computer read-only memory (ROM), random access memory (RAM), a magnetic disk, or an optical disk.

[0048] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0049] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0050] It is understood that the embodiments of this application have been described above in conjunction with the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. As those skilled in the art will know, various changes or equivalent substitutions can be made to these features and embodiments without departing from the spirit and scope of the invention. Furthermore, those skilled in the art, under the guidance or instruction of this application, can modify these features and embodiments to adapt to specific situations and materials without departing from the spirit and scope of the invention. Therefore, this invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are within the protection scope of this invention.

Claims

1. A tiered interest calculation method based on external customer behavior data, characterized in that: include: Connect to external data sources, collect customer external behavior data based on external data sources, and integrate it with the internal customer account data of the financial platform to form a multi-dimensional customer profile. Based on the aforementioned multi-dimensional customer profile, a tiered interest calculation rule system will be established. The rule system defines multiple interest calculation dimensions, each interest calculation dimension is divided into multiple tiers, and each tier is associated with an interest rate adjustment coefficient; Based on the tiered interest calculation rule system, the customer's tier and corresponding interest rate adjustment coefficient in each interest calculation dimension are obtained. Combined with the preset weights and benchmark interest rate, the customer's personalized execution interest rate is calculated through a dynamic interest rate model. Interest is calculated based on the personalized execution interest rate, combined with the principal and term of the customer's account.

2. The method for calculating interest based on external customer behavior data according to claim 1, characterized in that, The external behavioral data includes at least one of the following: consumer behavior, investment and financial management behavior, and online activity behavior data; Customer profile dimensions include: customer value level, account activity level, and depth of business cooperation.

3. The method for calculating interest based on external customer behavior data according to claim 1, characterized in that, The interest calculation dimensions include: product dimension, institution / region dimension, and customer dimension.

4. The method for calculating interest based on external customer behavior data according to claim 1, characterized in that, The dynamic interest rate model performs the following calculations: Execution rate = Benchmark rate × Σ(Dimension coefficient × Weight) + Adjusted rate; Among them, the benchmark interest rate is the central bank's benchmark or market interest rate; the dimension coefficient is determined by the customer's tier; the weight is the proportion of each dimension's impact on the interest rate; and the interest rate adjustment is used to cope with market fluctuations or special scenarios.

5. The method for calculating interest based on external customer behavior data according to claim 1, characterized in that, The interest calculation includes both compound interest and simple interest methods. Compound interest: Interest = Principal × [(1 + Applicable interest rate / number of periods)^number of periods - 1] Simple interest: Interest = Principal × Execution rate × Deposit period.

6. The method for calculating interest based on external customer behavior data according to claim 1, characterized in that, The interest calculation adopts the differential progressive interest method, specifically: The customer's account balance is divided into tiers based on the amount, and interest is calculated for each tier according to the applicable interest rate. Finally, the interest from each tier is added together to obtain the total interest.

7. The method for calculating interest based on external customer behavior data according to claim 6, characterized in that, The execution interest rate is determined based on the tiered amount and the scenario factor.

8. A tiered interest calculation device based on external customer behavior data, characterized in that, include: The data access module is used to connect to external data sources, collect external behavioral data of customers based on external data sources, and integrate it with the internal customer account data of the financial platform to form a multi-dimensional customer profile. The tiered interest calculation rule module is used to establish a tiered interest calculation rule system based on the multi-dimensional customer profile. The rule system defines multiple interest calculation dimensions, each interest calculation dimension is divided into multiple tiers, and each tier is associated with an interest rate adjustment coefficient; The interest calculation module is used to obtain the customer's tier and corresponding interest rate adjustment coefficient in each interest calculation dimension according to the tiered interest calculation rule system, and calculate the customer's personalized execution interest rate through a dynamic interest rate model by combining the preset weights and the benchmark interest rate. The interest calculation module is used to calculate interest based on the personalized execution interest rate, combined with the principal and deposit period of the customer's account.

9. An electronic device, characterized in that, The electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the parking fee calculation method based on external customer behavior data as described in any one of claims 1 to 8.

10. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of the fare calculation method based on external customer behavior data as described in any one of claims 1 to 8.

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