Financial asset actual interest rate method dynamic accounting system and method

Abstract

The invention discloses a financial asset actual interest rate method dynamic accounting system and method, and relates to the technical field of data processing, and the method comprises the steps: receiving contract information and parameters including cash flow data, interest rate type data and dynamic adjustment event data, and calling an actual interest rate calculation engine to carry out the calculation of an actual interest rate; receiving the transmitted contract information, cash flow data and interest rate type data, and executing a calculation process of an actual interest rate; the calculation process of the actual interest rate is to classify the cash flow data from multi-dimensional features based on a classifier to obtain feature tags; and obtaining an interest rate calculation strategy based on feature tag matching, and calculating an actual interest rate based on the interest rate calculation strategy. Full-automatic calculation of an actual interest rate method is realized, and a complete process from parameter configuration, algorithm implementation, dynamic adjustment to result display is covered.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to a financial asset actual interest rate method dynamic accounting system and method. BACKGROUND

[0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute the prior art.

[0003] As the core method of financial instrument accounting, the systematized implementation of the actual interest rate method has always been an important issue in the field of financial information technology. The mainstream solutions on the market are mainly divided into three categories: general financial software, professional financial accounting system and customized tools based on Excel. These technologies have certain applications in different scenarios, but there are still significant limitations in structural design, calculation principle and functionality.

[0004] Limitations of general financial software: ERP systems represented by SAP and Oracle usually contain basic financial instrument accounting modules, which can handle simple bond investment or loan interest calculation. The technical framework of these systems is based on pre-set accounting standard templates, and the cash flow discounting and carrying cost calculation are realized through parameterized configuration. However, the core problem of such systems lies in the rigidity of their modular design. For example, structural defects: the system uses a static data model, and the contract terms of financial instruments (such as interest rate reset and early repayment) need to be fixed at the initial configuration. If the terms are changed in the middle (such as the modification of interest rate agreed by the borrower and the lender), the system needs to be re-modeled through complex back-end scripts, resulting in a lag in business response. Single calculation principle: only standard cash flow patterns (such as equal principal and interest, and end-of-term repayment) are supported. For complex scenarios with non-fixed cash flows, the system cannot automatically split the main contract and derivative parts, resulting in a deviation of the actual interest rate calculation from the standard requirements. Insufficient scalability: in the context of multiple currencies and multiple accounting standards, the system cannot dynamically recalculate the actual interest rate across currencies, and needs to rely on manual adjustment of exchange rate assumptions, increasing the operational risk.

[0005] Technical bottlenecks of professional financial accounting systems: professional systems for capital markets perform well in derivative pricing, but their application in banking business has adaptability problems: deviation of methodology: such systems are based on market risk measurement models, focusing on fair value measurement rather than the cost method, resulting in a fundamental conflict between the interest amortization logic and the requirements of IFRS9; closed architecture: the system usually uses a black-box calculation engine, and users cannot penetrate to see the actual interest rate iteration solving process, the audit tracking function is weak, and it is difficult to meet the requirements of "Enterprise Accounting Standards No. 39 - Fair Value Measurement" for verifiability; high cost and complexity: professional systems need to rely on proprietary hardware or high-performance computing clusters, with long implementation cycles and high operation and maintenance costs, forming a significant barrier for small and medium-sized financial institutions.

[0006] Inherent defects of Excel tool: Although VBA macro and Power Query enhance the processing capacity of Excel, its defects as a carrier of the actual interest rate method are increasingly prominent:‌Principle error risk: Manual input of cash flow date and amount is prone to data misplacement, and the IRR function may return an incorrect solution when there are non-fixed interval cash flows, and cannot automatically handle the first execution date adjustment;‌Extensibility ceiling: A single file usually only supports the parallel calculation of hundreds of financial instruments, and when dealing with a bank portfolio level of more than ten thousand, it is easy to crash due to memory overflow;‌Version control confusion: Historical versions are scattered on personal terminals, and it is impossible to achieve global consistency management of the calculation process, violating the requirement of the Basic Norms of Internal Control for the centralization of financial processes.

[0007] In summary, the core defects of the prior art can be summarized into the following three categories: (1) Structural rigidity and scenario mismatch: The system architecture fails to fully decouple the business attributes (such as product type, contract terms) of financial instruments from the calculation logic, resulting in the need to restructure underlying code for new products.

[0008] (2) Insufficient scientificity and compliance of calculation method: The actual interest rate solving algorithm has numerical stability defects. Most systems use the bisection method or simple iteration method, which is prone to cumulative calculation errors due to improper convergence threshold settings when faced with ultra-long term or extremely small cash flows.

[0009] (3) Shortcomings in operational efficiency and auditability: Significant calculation delay in batch processing mode. Large banks need to process millions of contracts at the end of the month, and traditional systems use a serial computing architecture that takes several hours, which cannot meet the timeliness requirements of real-time regulatory reporting. SUMMARY

[0010] To overcome the shortcomings of the prior art described above, the present application provides a financial asset actual interest rate method dynamic accounting system and method, which can accurately handle the interest income calculation, carrying cost update and other businesses of loans, bonds, financing leases and other financial products, and has the ability of dynamic adjustment processing and visual result display, which can effectively meet the complex accounting needs of financial institutions.

[0011] To achieve the above purpose, one or more embodiments of the present application provide the following technical solutions: In a first aspect, the present application provides a financial asset actual interest rate method dynamic accounting system, comprising: A business logic processing module for receiving contract information and parameters, the parameters including cash flow data, interest rate type data and dynamic adjustment event data, and calling an actual interest rate calculation engine to calculate the actual interest rate; The actual interest rate calculation engine receives contract information, cash flow data, and interest rate type data transmitted from the business logic processing module, and executes the actual interest rate calculation process. The actual interest rate calculation process is as follows: classifies the cash flow data based on multi-dimensional features using a classifier to obtain feature labels; obtains an interest rate calculation strategy based on feature label matching; and calculates the actual interest rate based on the interest rate calculation strategy.

[0012] In a further technical solution, the business logic processing module performs parameter verification and preprocessing on the parameters to obtain preprocessed cash flow data, interest rate type data, and dynamic adjustment event data.

[0013] A further technical solution involves sending the preprocessed cash flow data to the actual interest rate calculation engine for calculation, and receiving the actual interest rate returned by the actual interest rate calculation engine; based on the actual interest rate and cash flow data, the business logic processing module performs periodic calculations according to the amortization formula to generate a complete amortization table.

[0014] In a further technical solution, the business logic processing module also listens for dynamic adjustment events and triggers the recalculation process of the actual interest rate calculation engine based on the dynamic adjustment events.

[0015] A further technical solution involves the following specific steps for calculating the actual interest rate: A decision tree algorithm is used to construct a classifier, and based on the classifier, cash flow data is classified from the aspects of time features, amount features, and risk features to obtain feature labels; The corresponding interest rate calculation strategy is matched based on the feature labels; when the feature labels are non-fixed intervals and contain credit adjustment terms, a hybrid algorithm strategy of weighted Newton iteration method and adaptive bisection method is used to calculate the internal rate of return.

[0016] In a further technical solution, the hybrid algorithm preferentially uses the weighted Newton iteration method for calculation. If the convergence condition is not met within the preset maximum number of iterations, the adaptive bisection method is used for backoff calculation.

[0017] A further technical solution is that the objective function of the weighted Newton iteration method is expressed as:

[0018] in, For the first Cash flow in the period As a weighting factor, For the initial principal, For cash flow periods, This represents the interest rate.

[0019] Secondly, this invention provides a dynamic accounting method for the effective interest rate of financial assets, comprising: receiving contract information and parameters, the parameters including cash flow data, interest rate type data and dynamic adjustment event data, calling an actual interest rate calculation engine to calculate the actual interest rate; receiving transmitted contract information, cash flow data and interest rate type data, performing an actual interest rate calculation process; the actual interest rate calculation process is to classify the cash flow data from multiple dimensions of features based on a classifier to obtain feature labels; an interest rate calculation strategy is matched based on the feature labels, and the actual interest rate is calculated based on the interest rate calculation strategy.

[0020] In a third aspect, the present application provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the financial asset actual interest rate method dynamic accounting method according to the second aspect.

[0021] In a fourth aspect, the present application provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the financial asset actual interest rate method dynamic accounting method according to the second aspect when executing the program.

[0022] The above one or more technical solutions have the following beneficial effects: The present application solves the problems of rigid structure and multi-scene adaptation, and abstracts contract terms (such as interest rate adjustment and early repayment) into independent event listeners by designing an extensible financial tool meta-model, so as to realize loose coupling of business rules and calculation logic. When a contract change occurs, the system automatically triggers cash flow revaluation and amortization plan update without manual intervention.

[0023] The present application improves the calculation accuracy and compliance with the criteria, introduces an adaptive numerical algorithm, dynamically allocates weights according to the occurrence dates of different cash flows; for example, the weight of recent cash flow is higher, and the weight of long-term cash flow is lower, avoiding excessive fluctuations in the overall calculation result caused by extreme scenarios (such as sudden delay or advance of cash flow); through dynamic adjustment and real-time calibration of date weights, the system can still maintain the stability of numerical solution in extreme cases, avoiding calculation bias caused by traditional fixed weight method. At the same time, through the risk isolation architecture, the credit adjustment and the time value of money are separated and measured, strictly following the dual measurement requirements of IFRS9.

[0024] The present application realizes high performance and audit transparency, adopts a distributed memory calculation framework, performs parallel sharding processing on massive financial tools, shortens the monthly amortization calculation time from hours to minutes. In addition, a full-link audit tracking module is built to record every step of intermediate state from original contract analysis, cash flow prediction, actual interest rate solving to accounting entry generation, supporting penetrating supervision and inspection.

[0025] The system of the present application can significantly improve the accuracy, efficiency and compliance level of financial institutions in the accounting of complex financial instruments, filling the key gaps in the existing technology in dynamic adaptability, algorithm robustness and auditability. BRIEF DESCRIPTION OF DRAWINGS

[0026] The accompanying drawings, which form a part of this specification, are included to provide a further understanding of the present application, illustrate preferred embodiments of the present application, and assist in explaining the present application.

[0027] Figure 1 is a financial asset actual interest rate method dynamic accounting system structure diagram of an embodiment of the present application; Figure 2 is a financial asset actual interest rate method dynamic accounting method flow chart of an embodiment of the present application. DETAILED DESCRIPTION

[0028] It should be noted that the following detailed description is exemplary in nature and is intended to provide further description of the present application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0029] It should be noted that the terms used herein are only intended to describe specific embodiments and are not intended to limit exemplary embodiments according to the present application. As used herein, the singular form is intended to include the plural form unless the context clearly indicates otherwise, and it should also be understood that when the terms "comprise" and / or "include" are used in the specification, they refer to the presence of a feature, step, operation, device, component and / or combinations thereof.

[0030] The embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0031] Embodiment one As shown in Figure 1 The present embodiment discloses a financial asset actual interest rate method dynamic accounting system, comprising: a business logic processing module for receiving contract information and parameters, the parameters including cash flow data, interest rate type data and dynamic adjustment event data, calling an actual interest rate calculation engine to calculate the actual interest rate; an actual interest rate calculation engine for receiving contract information, cash flow data and interest rate type data transmitted by the business logic processing module, executing an actual interest rate calculation process; the actual interest rate calculation process is to classify the cash flow data from multiple dimensions of features based on a classifier to obtain feature labels; to obtain an interest rate calculation strategy based on the feature labels, and to calculate the actual interest rate based on the interest rate calculation strategy; A data management module is configured to store and manage data. An interface service module is configured to perform unified interface management.

[0032] In the embodiment, the dynamic accounting system adopts a hierarchical architecture design, and the business logic processing module, the actual interest rate calculation engine, the data management module and the interface service module work cooperatively.

[0033] In the embodiment, the business logic processing module is responsible for receiving contract information (including contract number, type, term, etc.) and parameters, the parameters including cash flow data (such as the amount of cash flow of each period, date, etc.), interest rate type data (such as fixed interest rate, floating interest rate and related information), and dynamic adjustment event data (such as prepayment amount, interest rate change time, etc.), and calling the actual interest rate calculation engine to complete the calculation of the actual interest rate. The business logic processing module includes parameter verification and preprocessing, internal rate of return (IRR) solving, amortization table period-by-period calculation, dynamic adjustment event processing, and result packaging and returning.

[0034] Specifically, parameter verification and preprocessing: check the legality and format conversion of input parameters. Receive contract information and parameters from the user interface, check the completeness of mandatory fields, verify the legality of numerical values (such as non-negative amount, correct date sequence), and convert data formats (such as date string to timestamp, amount to decimal type). Internal rate of return (IRR) solving: based on the preprocessed cash flow data, call the relevant interface of the actual interest rate calculation engine for calculation. The business logic processing module sends the preprocessed cash flow data to the actual interest rate calculation engine, requests the internal rate of return solving, and receives the actual interest rate result or error information returned by the engine. Amortization table calculation: according to the actual interest rate returned by the calculation engine and the cash flow data, calculate the amortization table (including period number, initial amortization cost, interest income, etc.) according to the amortization formula. Dynamic adjustment event processing: listen to dynamic adjustment events such as prepayment, extension, etc., trigger the recalculation process of the calculation engine. Listen to dynamic adjustment events and receive event parameters, call the actual interest rate calculation engine again, input the adjusted cash flow data and remaining amortization cost, get the adjusted actual interest rate and update the amortization table. Result packaging and return: the calculation engine returns the actual interest rate and amortization table data recalculated to the business logic processing module, which stores these data in the data management module for subsequent query and use. After the data in the data management module is updated, the business logic processing module will update the relevant business records such as accounting vouchers and report data according to the new data. The interface service module reads the updated accounting results from the data management module and sends them to external systems (such as general ledger systems, report systems, etc.) through interfaces, realizing data synchronization and sharing. The business logic processing module packages the updated results into a format understandable by users and displays them to users through the system interface for users to view and confirm.

[0035] Further, dynamic adjustment events are listened to through event triggering and periodic polling. The business logic processing module obtains dynamic adjustment events by real-time listening to event messages inside and outside the system. For example, when contract terms change (such as prepayment, interest rate adjustment, extension, etc.), the relevant system will send event messages, and the business logic processing module can trigger the corresponding processing process after receiving these messages. In addition to event triggering, the business logic processing module can also check whether there are dynamic adjustment events through periodic polling. For example, query whether there are new contract change records in the data management module every certain time, if there are, trigger the corresponding processing process.

[0036] Further, the adjusted parameters (such as the adjusted cash flow data, the remaining carrying cost) can be input by an external system or manually input by a user. When the dynamic adjustment event comes from an external system (such as a credit management system, a risk management system, etc.), the interface service module receives the adjusted parameters sent by the external system and stores them in the data management module, and the business logic processing module reads these parameters from the data management module for subsequent processing. In some cases, the dynamic adjustment event may require the user to manually input the adjusted parameters. For example, the user manually submits a prepayment application on the system interface and inputs the prepayment amount and time, etc. The business logic processing module receives the parameters input by the user and performs legality check and format conversion.

[0037] The data processing flow of the business logic processing module and the actual interest rate calculation engine is as follows: the business logic processing module first performs legality check and format conversion on the received contract information and parameters, and then sends the processed cash flow data to the actual interest rate calculation engine; after the actual interest rate calculation engine completes the actual interest rate calculation, it returns the result to the business logic processing module. When a dynamic adjustment event occurs, the business logic processing module transmits the adjusted cash flow data to the engine again, and the engine returns new results after re-calculation. Through the above technical solution, the business logic processing module is the caller of the actual interest rate calculation engine, and the two form a cooperative relationship of “business instruction - algorithm execution”. The business logic processing module is responsible for processing the business-level logic, such as parameter verification, event listening, etc., while the actual interest rate calculation engine focuses on the execution of core algorithms, and the two cooperate to complete the accounting process of the actual interest rate method.

[0038] The data processing flow of the business logic processing module and the data management module is as follows: the business logic processing module reads the contract basic information, historical amortization data, etc. from the data management module when processing the business. After processing, the generated amortization table, calculation results, etc. are stored in the data management module. Through the above technical solution, the data management module provides data support for the business logic processing module, and the two form a dependent relationship of “data storage - business processing”. The data management module guarantees the integrity and consistency of the data, and the business logic processing module performs business operations based on these data.

[0039] The data processing flow of the business logic processing module and the interface service module is that the business logic processing module interacts with external systems through the interface service module. For example, input data such as contract information and interest rate parameters are obtained from external systems, and output data such as generated accounting vouchers and accounting results are pushed to external systems such as general ledger systems and report systems through interfaces. Through the above technical solution, the interface service module is a bridge between the business logic processing module and the external system, and the two constitute a collaborative relationship of “internal processing - external interaction”. The interface service module realizes the standardized connection between the system and the external system, so that the business logic processing module can focus on internal business processing.

[0040] In the present embodiment, the actual interest rate calculation engine receives standardized cash flow data transmitted by the business logic processing module, including cash flow amounts, dates, initial principal, and dynamically adjusted cash flow data such as cash flow information after prepayment or interest rate change. As a core algorithm module, it is responsible for executing the core mathematical model and calculation process of the actual interest rate method. The actual interest rate calculation engine includes interest rate solving, amortization calculation, dynamic adjustment processing, and abnormality detection and processing.

[0041] Specifically, interest rate solving: the internal rate of return is solved by numerical methods (such as Newton iteration method and dichotomy method). In the Newton iteration method, the objective function and its derivative are constructed according to the cash flow data, and the interest rate value is updated by iteration until the convergence condition is met; the dichotomy method approaches the actual interest rate value by continuously narrowing the interval range. If the Newton iteration method cannot converge, it is automatically switched to the dichotomy method. Amortization calculation: interest income and amortized cost are calculated based on the actual interest rate. Dynamic adjustment processing: according to prepayment, extension, interest rate change and other events, the data of each subsequent period is recalculated. Abnormality detection and processing: abnormal cash flow, interest rate cannot converge, etc. are prompted and processed, and the abnormal information is recorded through log, and the error prompt is returned to the user.

[0042] The flow and algorithm of the actual interest rate calculation engine are as follows: (1) Data reception and preprocessing The business logic processing module transmits contract information, cash flow data, and interest rate type data input by the user to the actual interest rate calculation engine. The engine first performs legality verification on the data, checks for abnormal conditions such as negative cash flow and time sequence error. If the data has a problem, the abnormal information is recorded through log, and an error prompt is returned to the business logic processing module; if the data passes the verification, the cash flow data is sorted according to the time sequence, and the initial principal, cash flow period number and other key data are extracted to prepare for subsequent calculation. (2) Cash flow feature analysis 1) Classify cash flows from multiple dimensions, including time, amount, and risk. Classify cash flows from the time dimension, including fixed interval and non-fixed interval (such as irregular early repayment); classify cash flows from the amount dimension, including equal cash flow, increasing cash flow, decreasing cash flow, and random fluctuation cash flow (such as ABS sub-grade cash flow); classify cash flows from the risk dimension, including with credit adjustment and without credit adjustment (based on IFRS9 standards).

[0043] Use decision tree algorithm to build classifier, train the trained classifier based on historical cash flow data, input cash flow sequence , output feature label (such as non-fixed interval + random fluctuation + credit adjustment).

[0044] 2) Establish a mapping rule library between feature labels and interest rate calculation strategies, and match the corresponding interest rate calculation strategies based on feature labels.

[0045] The mapping rule library is shown in the following table:

[0046] For cash flows with non-fixed interval and credit adjustment, use a hybrid algorithm strategy of weighted Newton iteration and adaptive dichotomy to calculate the internal rate of return (IRR, actual interest rate), the specific steps are as follows: Weighted Newton iteration is preferred: build a target function according to the cash flow data of the financial instrument , the target function represents the net present value of the cash flow as 0. In the traditional Newton iteration method, introduce the cash flow date dynamic weight factor, which is the weighted Newton iteration method, build the weighted target function, represented as:

[0047] Where, is the cash flow of the period, is the weight factor, is the initial principal, is the number of cash flow periods, is the interest rate value.

[0048] , where, is the decay coefficient, is the current date, so that the sensitivity of recent cash flows to interest rate changes is higher, and the weight of future cash flows decays, avoiding iteration shock caused by extreme cash flows (such as long-term delay). Through dynamic adjustment of the weight, the iteration convergence speed is improved in the calculation of financial products with a period of more than 10 years, and the calculation error is further reduced from 0.01% to below 0.005%.

[0049] Derivate the objective function, update the interest rate value through iterative formula In each iteration process, calculate the value of the objective function under the current interest rate value, and determine whether the convergence condition is met. If the convergence condition is met, the current interest rate value is determined as the actual interest rate; if the convergence condition is not met within the preset maximum number of iterations, enter the adaptive bisection method calculation process.

[0050] Bisection method back calculation: determine an initial interval containing the actual interest rate , require and be of opposite signs. Calculate the midpoint of the interval, and calculate the value of . According to the relationship between and , , update the interval: if , then is the actual interest rate; if and are of opposite signs, then the updated interval is ; if and are of opposite signs, then the updated interval is . Repeat the above steps to continuously narrow the interval range until the interval length is less than the set precision requirement, at which point the value of the midpoint of the interval is the approximate actual interest rate.

[0051] Design adaptive bisection method, build interest rate interval prediction model based on historical calculation data, and automatically generate initial search interval according to financial product type (such as loan, bond), remaining term, and interest rate type (fixed / floating). For example: short-term floating interest rate product: the initial interval is set to [LPR-1%, LPR+1%] (example of initial interval, LPR is the loan market quoted interest rate) dynamic contraction. Compared with traditional fixed interval bisection method, the calculation efficiency is improved by 25%.

[0052] (3) Amortization table calculation Based on the calculated actual interest rate, the actual interest rate calculation engine calculates the interest income and amortized cost according to the following formula:

[0053]

[0054] ​From the first phase, the initial capital is taken as the opening balance, the interest income and the closing balance of each period are calculated according to the above formula, and the calculation results are stored as intermediate variables for the calculation of the next period. At the same time, the calculation results of each period are arranged into amortization table data structure, including period number, opening balance, interest income, cash inflow, closing balance and other field information. (4) Dynamic adjustment processing When the system receives the dynamic adjustment event (such as prepayment, extension, interest rate change, and new fee item) passed by the business logic processing module, the actual interest rate calculation engine recalculates the subsequent period data according to the event type. Taking prepayment as an example, the system updates the remaining principal according to the prepayment amount and time, takes the remaining principal after prepayment as the new opening balance, and recalculates the interest income and amortized cost of the subsequent periods. For interest rate change events, the new interest rate value is applied from the change time to recalculate the subsequent data according to the above amortization table calculation process. After the calculation is completed, the updated amortization table data is returned to the business logic processing module. (5) Result output and exception handling The actual interest rate calculation engine encapsulates the final actual interest rate and complete amortization table data into a unified data format and returns it to the business logic processing module. If an abnormal situation occurs during the calculation process, such as a serious cash flow data error that prevents calculation, or both algorithms fail to converge to obtain the actual interest rate, the engine will record detailed exception information, including exception type, occurrence time, related data, etc., and return an exception prompt to the business logic processing module, so that the system can perform corresponding error handling and prompt the user.

[0055] The data processing flow of the actual interest rate calculation engine and the data management module is as follows: During the calculation process, the actual interest rate calculation engine reads cash flow plans, historical interest rate calculation parameters and other data from the business logic processing module. After the calculation is completed, the intermediate calculation results, final actual interest rate and other data are stored in the data management module. Through the above technical solution, the calculation results of the engine need to rely on the data management module for storage and management.

[0056] By the technical solution, the credit adjustment and the time value of money are separated by the algorithm design of the actual interest rate calculation engine and the dynamic adjustment mechanism of the business processing module. Specifically, the actual interest rate calculation engine designs a risk isolation architecture, and when solving the actual interest rate by using a hybrid algorithm strategy (combining the weighted Newton iteration method and the adaptive bisection method), the credit risk factor and the time value of money factor in the cash flow are separated and processed; by independently modeling the credit adjustment item (such as the expected credit loss), it is ensured that the actual interest rate only reflects the time value of money, and the credit risk is processed by a separate measurement module, avoiding mutual interference between the two in the calculation process. The business logic processing module executes a dynamic event-driven mechanism, and when listening to contract change events (such as prepayment and interest rate adjustment), it will trigger the recalculation of the actual interest rate; at this time, the system will process the credit adjustment part (such as the cash flow adjustment caused by the change of the debtor's credit status) and the time value of money part (the discount calculation based on the market interest rate) respectively, to ensure that the credit adjustment does not affect the basic calculation logic of the actual interest rate, and only participates in the amortized cost adjustment as an independent risk measurement factor. The above design strictly follows the requirement of "double measurement" in the accounting standards such as IFRS9, that is, the actual interest rate method accounting and credit loss measurement are separated. By separating the credit adjustment from the actual interest rate calculation, the double confirmation problem of interest income and credit loss is avoided, and the financial accounting result is ensured to meet the regulatory standards.

[0057] In the embodiment, the data management module receives contract information, amortization table data, etc. generated by the business logic processing module; calculation process data, actual interest rate results, etc. generated by the actual interest rate calculation engine; contract change data, external system parameters, etc. received from the external system by the interface service module; provides contract basic information, historical amortization data, etc. for the business logic processing module; provides cash flow plan, historical interest rate calculation parameters, etc. for the actual interest rate calculation engine; and provides accounting results, interface data, etc. that need to be output externally for the interface service module.

[0058] The data management module stores and manages data, supports efficient data reading, writing and querying, ensures data consistency and integrity in the calculation process by establishing appropriate indexes such as indexes on contract numbers, interest start dates, etc., and uses a transaction management mechanism to ensure the atomicity of data operations.

[0059] In the embodiment, the interface service module provides unified interface management and security protection. It provides standard interfaces to support integration with other systems, including: interfacing with the general ledger system to automatically generate accounting vouchers; interfacing with the risk control system to obtain risk exposure data in real time, and using a timing task or message queue to interact with data; and interfacing with the report system to push the calculation results for report analysis, and pushing the calculation results to the report system through a data interface.

[0060] The data processing flow of the interface service module and the data management module is as follows: the data received by the interface service module from an external system, such as contract change information sent by other systems, is stored in the data management module. At the same time, the interface service module reads the data to be output to the outside, such as the accounting result, from the data management module, and sends it to the external system through the interface. Through the above technical solution, the data management module is the intermediate storage node for data interaction between the interface service module and the external system, and the two constitute a “data transfer - storage management” relationship. The data management module ensures the stability and consistency of internal and external data interaction.

[0061] The present application adopts a hybrid IRR calculation algorithm combining Newton iteration method and bisection method, and combines cash flow date weight accurate processing, realizes the calculation error reduction to below 0.001% in the extreme scene of super long term, extremely small cash flow, significantly improves the actual interest rate calculation precision, solves the problem of poor numerical stability of the prior art.

[0062] By designing an extensible financial instrument meta-model, abstracting contract terms into independent event listeners, building a dynamic event-driven mechanism, the business rules and calculation logic are loosely coupled, when the contract terms change, the system automatically triggers the cash flow revaluation and amortization plan update without manual intervention, effectively solving the problem of rigid system structure and poor multi-scenario adaptation capability of the existing system.

[0063] By using a distributed memory computing framework for parallel sharding processing of massive financial instruments, and building a full-link audit tracking module, the month-end amortization calculation time is shortened from hours to less than 15 minutes, and each intermediate state from contract parsing to accounting entry generation is recorded, meeting the regulatory audit requirements, and making up for the shortcomings of low operation efficiency and incomplete audit evidence chain of the prior art.

[0064] In summary, the present application achieves the following technical effects: The calculation efficiency is greatly improved: by using a distributed memory computing framework and parallel sharding processing technology, compared with the traditional serial computing architecture system, the calculation time is shortened from several hours to less than 15 minutes when processing million-level financial contract month-end amortization calculation. At the same time, the system response speed is significantly improved, and the re-calculation time is shortened by an average of 80% when processing dynamic adjustment events (such as prepayment and interest rate change), which can quickly respond to user operations and business changes, meet the real-time regulatory reporting requirements, and greatly improve the operation efficiency of financial institutions.

[0065] Significant improvement in calculation accuracy: The IRR calculation algorithm combines Newton iteration and bisection methods, and accurately handles cash flow date weights. In extreme scenarios such as 30-year terms or extremely small cash flows (such as ABS sub-grades), the calculation error is reduced to below 0.001%, which is tens of times more accurate than traditional algorithms. At the same time, through the risk isolation architecture, the credit adjustment and the time value of money are separated, strictly following the IFRS9 standard, avoiding the double confirmation problem of interest income and credit loss, and ensuring the accuracy and reliability of financial asset or liability accounting results. Outstanding multi-scenario adaptation capability: Through an extensible financial instrument meta-model and a dynamic event-driven mechanism, the system can quickly adapt to loans, bonds, financing leases, and other financial products, as well as fixed interest rates, floating interest rates, and segmented interest rates. For complex cash flow patterns, such as embedded derivative tools and non-fixed cash flows, the system can automatically split the main contract and derivative parts and accurately calculate the actual interest rate. When facing contract term changes, the system automatically triggers cash flow revaluation and amortization plan updates without human intervention. Compared to traditional systems that require re-modeling and development when adding new products or handling term changes, the efficiency is improved by more than 90%. Comprehensive audit and compliance guarantee: The full-link audit tracking module records every intermediate state from the original contract analysis, cash flow prediction, actual interest rate solution to the generation of accounting entries, providing detailed audit evidence chain to meet regulatory requirements such as the "China Certified Public Accountant Audit Standards". The audit traceability efficiency is improved by 70%. The system operation log and version control function achieve global consistency management of the calculation process, effectively preventing operational risks and ensuring that the financial institution's business complies with the "Enterprise Internal Control Basic Standards".

[0066] Reduced operating and maintenance costs: The system uses a general technology framework and standard interfaces, reducing dependence on proprietary hardware and specialized systems, and reducing implementation and maintenance costs. Through automated cash flow modeling, amortization table generation, and dynamic adjustment processing, the number of manual operations is reduced, and labor costs are reduced. According to estimates, after using the system, financial institutions can reduce labor costs by 40% and overall system operating costs by 30% in financial asset accounting-related businesses.

[0067] The actual interest rate method is fully automated, covering the complete process from parameter configuration, algorithm implementation, dynamic adjustment to result display. The system has high flexibility, accuracy, and scalability, effectively supporting the financial institution's financial accounting needs in loan, bond, and lease business scenarios, significantly improving the intelligent level of financial work, and helping enterprises achieve digital transformation.

[0068] Embodiment Two The embodiment discloses a financial asset actual interest rate method dynamic accounting method, comprising: receiving contract information and parameters, the parameters including cash flow data, interest rate type data and dynamic adjustment event data, calling an actual interest rate calculation engine to calculate an actual interest rate; receiving transmitted contract information, cash flow data and interest rate type data, performing an actual interest rate calculation process; the actual interest rate calculation process is to classify the cash flow data from multiple dimensions of features based on a classifier to obtain feature labels; an interest rate calculation strategy is matched based on the feature labels, and an actual interest rate is calculated based on the interest rate calculation strategy.

[0069] Embodiment Three The purpose of this embodiment is to provide a computing device, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method of Embodiment Two when executing the program.

[0070] Embodiment Four The purpose of this embodiment is to provide a computer-readable storage medium, a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to perform the steps of the method of Embodiment Two.

[0071] The steps involved in the devices of Embodiments Three and Four above correspond to the method of Embodiment One, and the specific implementation can be referred to the relevant description part of Embodiment One. The term "computer-readable storage medium" should be understood to include a single medium or multiple media of one or more instruction sets; it should also be understood to include any medium capable of storing, encoding or carrying a set of instructions for execution by a processor and causing the processor to perform any of the methods in the present application.

[0072] Those skilled in the art should understand that each module or step of the present application described above can be realized by a general computer device, alternatively, they can be realized by program code executable by a computing device, so that they can be stored in a storage device for execution by a computing device, or they can be made into individual integrated circuit modules, or a plurality of modules or steps among them can be made into a single integrated circuit module to realize. The present application is not limited to any specific combination of hardware and software.

[0073] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0074] The above describes the specific embodiments of the present application in combination with the drawings, but is not a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications or variations made by those skilled in the art on the basis of the technical solutions of the present application without creative labor are still within the protection scope of the present application.

Claims

1. A financial asset real interest rate method dynamic accounting system, characterized by, The method comprises the following steps: The business logic processing module is used for receiving contract information and parameters, the parameters comprising cash flow data, interest rate type data and dynamic adjustment event data, and calling an actual interest rate calculation engine to calculate the actual interest rate. The actual interest rate calculation engine is used for receiving the contract information, cash flow data and interest rate type data transmitted by the business logic processing module, and executing an actual interest rate calculation process. The actual interest rate calculation process is based on a classifier to classify the cash flow data from multiple dimensional features to obtain feature labels.

2. A system for dynamic accounting of the real interest rate method of a financial asset according to claim 1, characterized in that, An interest rate calculation strategy is matched based on the feature labels, and the actual interest rate is calculated based on the interest rate calculation strategy.

3. A financial asset real rate of interest method dynamic accounting system as defined in claim 2, wherein, The business logic processing module performs parameter verification and preprocessing on the parameters to obtain preprocessed cash flow data, interest rate type data and dynamic adjustment event data. The preprocessed cash flow data is sent to the actual interest rate calculation engine for calculation, and the actual interest rate returned by the actual interest rate calculation engine is received.

4. A system for dynamic accounting of real interest rate method of financial assets according to claim 1, characterized in that, According to the actual interest rate and the cash flow data, the business logic processing module performs period-by-period calculation according to an amortization formula to generate a complete amortization table.

5. A system for dynamic accounting of real interest rate method of financial assets according to claim 1, characterized in that, The business logic processing module also listens to dynamic adjustment events and triggers a recalculation process of the actual interest rate calculation engine based on the dynamic adjustment events. The actual interest rate calculation process is specifically as follows: A decision tree algorithm is used to construct a classifier, and the cash flow data is classified from time features, amount features and risk features based on the classifier to obtain feature labels. An interest rate calculation strategy is matched based on the feature labels.

6. A dynamic accounting system for financial assets using real interest rate approach as claimed in claim 5 wherein, When the feature labels are non-fixed interval time and contain credit adjustment items, a hybrid algorithm strategy of weighted Newton iteration method and adaptive bisection method is used to calculate the internal rate of return.

7. A dynamic accounting system for financial assets actual rate of return methodology as described in claim 6, wherein, The hybrid algorithm preferentially uses the weighted Newton iteration method for calculation. wherein, is the first period cash flow, is the weight factor, is the initial principal, is the number of cash flow periods, is the interest rate value.

8. A method for dynamic accounting of real interest rate of financial assets, characterized in that, If the convergence condition is not met within a preset maximum number of iterations, the adaptive bisection method is used for fallback calculation. The objective function of the weighted Newton iteration method is expressed as: The method comprises the following steps: Receiving contract information and parameters, the parameters comprising cash flow data, interest rate type data and dynamic adjustment event data, and calling an actual interest rate calculation engine to calculate the actual interest rate.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, Receiving the transmitted contract information, cash flow data and interest rate type data, and executing an actual interest rate calculation process.

10. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The actual interest rate calculation process is based on a classifier to classify the cash flow data from multiple dimensional features to obtain feature labels. An interest rate calculation strategy is matched based on the feature labels, and the actual interest rate is calculated based on the interest rate calculation strategy. The program is executed by the processor to realize the steps of the financial asset actual interest rate method dynamic accounting method in claim 8. The processor executes the program to realize the steps of the financial asset actual interest rate method dynamic accounting method in claim 8.

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