AI-driven bank derivative index configuration and decision-making method

Abstract

The invention discloses an AI-driven bank derivative index configuration and decision-making method. The index configuration and decision-making method comprises the following steps: determining general derivative index configuration; generating a derivative index; and verifying the derivative indexes, and establishing a verification mechanism. On the basis of fusing the AI technology, comprehensive construction is carried out from the aspects of database structure design, algorithm module development, user interface development, system deployment and integration and the like, and high efficiency, stability and expansibility of the system are ensured.

Description

Technical Field

[0001] This invention relates to the field of bank indicator configuration and analysis, and in particular to an AI-driven method for configuring and making decisions on bank derivative indicators. Background Technology

[0002] Existing technologies have significant shortcomings in many aspects of bank indicator configuration and analysis, which seriously restricts banks' comprehensive and in-depth understanding and effective management of their own operations.

[0003] Limitations of Basic Indicators: While traditional basic indicators can present an overall overview of banking operations, such as total assets and deposit balances, allowing management to gain a preliminary understanding of the bank's size, these indicators are severely lacking in depth and detail. They cannot uncover the complex internal relationships behind the business, nor can they detect subtle but crucial business trends. For example, a simple loan balance indicator cannot reveal the risk differences between different loan products, the relationship between repayment periods and customer creditworthiness, etc. This leaves bank management lacking sufficiently detailed and accurate information to support decision-making in the face of a complex and ever-changing market environment, making it difficult to grasp the full picture of the business, much like trying to see through a fog.

[0004] The user interface is unfriendly: the existing interface for configuring derivative indicators is cumbersome, filled with technical jargon and complex options, making it extremely unfriendly to non-technical bank managers. Users often need to spend a lot of time learning how to configure indicators and are easily lost in the complex steps, leading to frequent operational errors.

[0005] Limited functionality and lack of flexibility: Existing configuration solutions offer limited functionality, making it difficult to meet the diverse needs of different banks due to variations in business type, market positioning, and development stage. Whether it's a large, comprehensive bank, a small bank focusing on a specific area, or a bank in an expansion or adjustment phase, the limitations of general-purpose functions may prevent them from flexibly customizing derivative indicators based on their own business characteristics.

[0006] Weak data analysis capabilities: Existing solutions have limited data analysis capabilities, failing to fully uncover the potential value behind the data. When processing massive amounts of complex banking business data, traditional methods struggle to accurately identify hidden patterns, trends, and anomalies within the data. For example, in risk assessment, they cannot predict potential risks in a timely and accurate manner, and are slow to respond to dynamic changes in risk. Summary of the Invention

[0007] In view of the above problems, the present invention is proposed to provide an AI-driven method for configuring and deciding on bank derivative indicators to overcome or at least partially solve the above problems.

[0008] According to one aspect of the present invention, an AI-driven method for configuring and deciding on bank derivative indicators is provided, the method comprising: Determine the configuration of general derivative indicators; Generate derivative indicators; Verify the derived metrics and establish a verification mechanism.

[0009] Optionally, determining the configuration of the general derived indicators specifically includes: Intelligent Interactive Interface: The interface is designed to be user-friendly and incorporates AI-driven interactive functions. Users can configure metrics by inputting commands, and NLP technology is used to understand the commands and execute the corresponding operations. The interface provides intelligent prompts and guidance. Based on some information input by the user, machine learning algorithms are used to predict the user's intent and recommend possible metrics, calculation methods and configuration parameters. Intelligent configuration recommendation: Based on machine learning analysis of a large amount of bank data and business scenarios, an intelligent recommendation system is built. When a user enters the configuration page, the system automatically recommends suitable indicator categories, business scopes, and filtering dimension configuration parameters based on multi-dimensional information such as the type, size, and business characteristics of the user's bank and the user's historical configuration habits. Dynamic configuration optimization: Utilizing reinforcement learning algorithms, configuration suggestions are dynamically adjusted based on user feedback on configuration results and real-time changes in business data.

[0010] Optionally, the generation of derived metrics specifically includes: AI-assisted basic generation methods: AI technology is introduced to assist generation methods based on simple arithmetic operations, ratio calculations, and index decomposition. In arithmetic operations, AI algorithms monitor data types and numerical ranges in real time; When calculating ratios, machine learning algorithms are used to analyze historical data to predict possible anomalies such as zero or negative denominators and provide corresponding handling suggestions. During the indicator decomposition process, deep learning technology is used to automatically break down complex indicators and present the decomposition results to users in a visual way to help them understand the intrinsic relationship between indicators. At the same time, data mining-based optimization suggestions are provided to make indicator decomposition more in line with business logic. Intelligent selection and optimization of complex models: For the function of generating derivative indicators based on complex statistical models or risk assessment models, AI is used to achieve intelligent selection and optimization of models.

[0011] Optionally, the use of AI to achieve intelligent selection and optimization of the model specifically includes: Perform multi-dimensional analysis on input data, use machine learning algorithms to evaluate the characteristics, distribution, and nature of business problems, and automatically recommend complex models; By using reinforcement learning algorithms to automatically tune the parameters of the selected model, and continuously optimizing the model performance based on historical data and real-time business feedback, the generated derivative metrics become more accurate and insightful.

[0012] Optionally, the complex model specifically includes: regression analysis, cluster analysis, and risk assessment model.

[0013] Optionally, the verification of derived metrics and the establishment of a verification mechanism specifically include: AI-enhanced data cleaning and verification: Cleaning and verifying the input basic indicator data; Real-time intelligent monitoring and verification: Real-time monitoring and verification of the generated derivative indicators to ensure that the indicators can truly reflect the bank's business operations; Intelligent integration and visualization assistance: The system is deployed in the bank management system to achieve seamless integration with the existing system. AI technology is used to achieve automatic interface adaptation and intelligent data conversion to ensure compatibility with different existing systems. AI technology is used to intelligently recommend visual content, and personalized visual reports and data analysis results are automatically generated based on the user's role, business focus, and historical operating habits.

[0014] Optionally, the data cleaning and verification of the input basic indicator data specifically includes: using deep learning image recognition technology and NLP technology to clean and extract unstructured data, and using machine learning algorithms to detect and repair anomalies in structured data.

[0015] Optionally, the real-time monitoring and verification of the generated derived metrics specifically includes: AI algorithms are used to perform real-time trend analysis and deviation detection on derived indicators, and to compare them with historical data, industry standards, and preset business rules. If abnormal fluctuations or discrepancies with expectations are detected in the indicators, an automatic warning will be issued. Machine learning algorithms will be used to analyze possible causes and provide a detailed diagnostic report to help users adjust the indicator generation process or troubleshoot business issues in a timely manner.

[0016] Optionally, the generation of the derived indicators depends on the basic data. After in-depth calculation and analysis, more in-depth and insightful indicators are produced. Before configuring the derived indicators, the basic data must be complete.

[0017] Optionally, the basic data specifically includes: Basic indicators: As the foundation for calculating derived indicators, they analyze basic indicator data in real time and detect outliers and data deviations through machine learning algorithms; Business definitions: including original definitions, profit-sharing definitions, and performance evaluation definitions. We use AI to build a knowledge graph of business definitions through deep learning of historical business data and definition applications. Filtering Dimensions: Data is divided according to different dimensions such as institution, business line, product, subject, and account to generate multi-dimensional derived indicators; AI clustering algorithm is introduced to deeply mine the massive amount of bank data, discover potential dimension division methods, and provide users with innovative filtering dimension options; Indicator Classification: Indicators are classified into scale, profit and loss, risk, and pricing categories to help clearly organize, understand, and apply complex indicator systems, providing strong support for decision-making, management, and evaluation. Newly generated indicators are automatically classified using Natural Language Processing (NLP) technology, and are categorized by semantic similarity analysis with existing indicator classifications.

[0018] This invention provides an AI-driven method for configuring and deciding on bank derivative indicators. The method includes: determining a general derivative indicator configuration; generating derivative indicators; verifying the derivative indicators; and establishing a verification mechanism. Based on the integration of AI technology, the system is comprehensively constructed from aspects such as database structure design, algorithm module development, user interface development, and system deployment and integration to ensure its efficiency, stability, and scalability.

[0019] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 A flowchart illustrating an AI-driven method for configuring and making decisions on bank derivative indicators, provided as an embodiment of the present invention; Figure 2 Basic data diagrams provided for embodiments of the present invention; Figure 3 This is a functional architecture diagram provided for an embodiment of the present invention. Detailed Implementation

[0022] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0023] The terms "comprising" and "having," and any variations thereof, in the specification, embodiments, claims, and drawings of this invention are intended to cover non-exclusive inclusion, such as including a series of steps or units.

[0024] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0025] like Figure 1 As shown, this invention proposes an AI-driven method for configuring and deciding on bank derivative indicators, including: General Derivative Indicator Configuration Function Design Intelligent Interactive Interface: A user-friendly interface is designed, incorporating AI-driven interactive features. For example, by adding AI interactive functionality, users can configure metrics by inputting commands. The system utilizes NLP technology to understand the commands and execute corresponding operations. The interface provides intelligent prompts and guidance, predicting user intent based on partial user input using machine learning algorithms, and recommending potentially needed metrics, calculation methods, and configuration parameters to improve configuration efficiency.

[0026] Intelligent Configuration Recommendation: Based on machine learning analysis of massive amounts of banking data and business scenarios, an intelligent recommendation system is built. When a user enters the configuration page, the system automatically recommends suitable indicator categories, business definitions, and filtering dimensions based on multi-dimensional information such as the type, size, and business characteristics of the user's bank, as well as the user's historical configuration habits. For example, for banks primarily engaged in retail business, the system prioritizes recommending filtering dimensions and business definitions related to retail customers, as well as indicators suitable for retail business analysis.

[0027] Dynamic configuration optimization: Utilizing reinforcement learning algorithms, the system dynamically adjusts configuration suggestions based on user feedback on configuration results and real-time changes in business data. If a user frequently modifies a configuration parameter, the system learns that the parameter may require more flexible settings and provides a wider range of choices or a more reasonable default value in the next recommendation. As banking operations develop, the system automatically monitors changes in business data patterns and adjusts the recommended configuration scheme in a timely manner to ensure that the configuration always adapts to the bank's business situation.

[0028] Derivative indicator generation methods AI-Assisted Basic Generation Methods: AI technology is introduced to assist generation methods based on simple arithmetic operations, ratio calculations, and indicator decomposition. For example, in arithmetic operations, AI algorithms monitor data types and numerical ranges in real time, automatically adjusting calculation precision to avoid calculation errors caused by precision loss; in ratio calculations, machine learning algorithms are used to analyze historical data, predicting possible anomalies such as zero or negative denominators and providing corresponding handling suggestions; in the indicator decomposition process, deep learning technology is used to automatically break down complex indicators, presenting the decomposition results to users in a visual way to help them understand the inherent relationships between indicators, while providing data mining-based optimization suggestions to make indicator decomposition more in line with business logic.

[0029] Intelligent Complex Model Selection and Optimization: For the function of generating derived indicators based on complex statistical models or risk assessment models, AI is used to achieve intelligent model selection and optimization. The system first performs multi-dimensional analysis on the input data, using machine learning algorithms to evaluate the characteristics, distribution, and nature of the business problem, and automatically recommends the most suitable complex model, such as regression analysis, cluster analysis, and risk assessment models. Then, reinforcement learning algorithms are used to automatically tune the parameters of the selected model, continuously optimizing model performance based on historical data and real-time business feedback, making the generated derived indicators more accurate and insightful. For example, in credit risk assessment models, AI algorithms can automatically adjust model parameters based on the customer data characteristics and risk preferences of different banks, generating risk assessment indicators that better suit the actual situation of the bank.

[0030] Implementation of technical solutions AI-driven database management: Designing a rational database structure to store basic and derived metrics data, ensuring data integrity and security. Utilizing AI technology to optimize database storage and query performance, such as analyzing data access patterns through machine learning algorithms, predicting data usage frequency, and automatically adjusting data storage locations and index structures to improve data read and write efficiency. Simultaneously, using deep learning anomaly detection algorithms to monitor data in the database in real time, promptly identifying abnormal data changes and ensuring data security and integrity.

[0031] Intelligent Algorithm Module Development: Develop algorithm modules to generate various derived metrics, ensuring the accuracy and efficiency of the algorithms. During algorithm development, AI technology is integrated for automated testing and optimization. For example, machine learning algorithms are used to perform automated unit testing on the algorithm modules, simulating various input scenarios to check the accuracy and stability of the algorithms; through data analysis of the algorithm execution process, AI algorithms are used to identify performance bottlenecks and automatically generate optimization suggestions to improve the execution efficiency of the algorithms.

[0032] Intelligent User Interface Development: Develop user interfaces to provide metric configuration and data display functions, facilitating user operation and result viewing. Leverage AI technology to enhance the interactivity and visualization of the user interface. For example, utilize computer vision technology to implement gesture recognition, allowing users to zoom and filter data through gestures; utilize AI image generation technology to automatically generate more intuitive and insightful visualizations based on user-selected data and analysis needs, helping users better understand and analyze data.

[0033] Derivative indicator verification and validation mechanism AI-enhanced data cleaning and verification: The system will perform rigorous data cleaning and verification on the input basic indicator data to ensure the accuracy and integrity of the data. It utilizes deep learning image recognition technology and NLP technology to clean and extract unstructured data, and machine learning algorithms to detect and repair anomalies in structured data. For example, it trains deep learning models to identify data errors in scanned documents, uses clustering algorithms to detect outliers in numerical data, and automatically corrects or prompts the user for further action.

[0034] Real-time intelligent monitoring and verification: The system will monitor and verify the generated derivative indicators in real time to ensure that the indicators accurately reflect the bank's business operations. AI algorithms are used to perform real-time trend analysis and deviation detection on the derivative indicators, comparing them with historical data, industry standards, and preset business rules. If abnormal fluctuations or discrepancies with expectations are detected, the system will automatically issue an alert and use machine learning algorithms to analyze possible causes, providing a detailed diagnostic report to help users adjust the indicator generation process or troubleshoot business issues in a timely manner.

[0035] Intelligent Integration and Visualization Assistance: The system is deployed within the bank's management system, achieving seamless integration with existing systems. AI technology is used to automatically adapt interfaces and intelligently transform data, ensuring compatibility with different existing systems. Rich visualization tools and reporting functions help users intuitively understand and analyze business data, improving overall work efficiency and data sharing capabilities. Simultaneously, AI technology is used to intelligently recommend visualization content, automatically generating personalized visualization reports and data analysis results based on user roles, business focuses, and historical operating habits, enabling different users to quickly access the most valuable information.

[0036] like Figure 3 As shown, the general derivative indicator configuration function design The general derivative indicator configuration function design of this invention aims to create a highly intelligent, user-friendly and flexibly adaptable platform by integrating AI technology, so as to comprehensively meet the diverse management needs and unique business characteristics of different banks.

[0037] Specifically, it covers the following aspects: like Figure 2 As shown, basic data Derivative metrics rely on foundational data and undergo in-depth calculation and analysis to produce more insightful and in-depth indicators. Before configuring derivative metrics, the following foundational data must be complete: Basic Indicators: As the foundation for calculating derived indicators, these indicators must accurately map the characteristics of the target derived indicators, and the data must be reliable and representative. Leveraging an AI-driven data quality monitoring system, basic indicator data is analyzed in real time. Machine learning algorithms detect outliers and data deviations, automatically marking and issuing warnings about potential data quality issues to ensure data accuracy and stability.

[0038] Business Scope: This includes original scope, profit-sharing scope, and performance evaluation scope, providing unified rules and standards for calculating derived indicators and ensuring comparability and consistency among different indicators. Utilizing AI to construct a business scope knowledge graph, through deep learning of historical business data and scope applications, when a user selects a specific business scope, the system intelligently recommends related indicators, calculation methods, and potentially applicable scenario examples, assisting the user in accurately understanding and applying the business scope.

[0039] Filtering Dimensions: Data is segmented based on different dimensions such as institution, business line, product, subject, and account to generate multi-dimensional derived indicators. AI clustering algorithms are introduced to deeply mine massive amounts of bank data, discovering potential dimensional segmentation methods and providing users with innovative filtering dimension options. Simultaneously, based on user behavior analysis and machine learning models, personalized recommendations of commonly used or potentially relevant filtering dimension combinations are made according to users' historical operations and business needs.

[0040] Indicator Classification: Indicators are categorized into categories such as scale, profit and loss, risk, and pricing, facilitating the clear organization, understanding, and application of complex indicator systems, and providing strong support for decision-making, management, and evaluation. Newly generated indicators are automatically classified using Natural Language Processing (NLP) technology, accurately categorized through semantic similarity analysis with existing indicator classifications. Simultaneously, Graph Neural Network (GNN) technology is used to dynamically optimize the indicator classification structure to adapt to the dynamic development and changes in banking operations.

[0041] User interface design Adhering to an intuitive, simple, and modern design philosophy and interaction method, and incorporating AI interaction technology, the system allows users to easily get started and quickly master its usage. The interface mainly includes the main interface, configuration indicator interface, and detailed view interface. Main Interface: Utilizing reinforcement learning algorithms, the interface layout is intelligently adjusted based on user operating habits and business needs. Through user behavior analysis, frequently used functional modules (such as indicator query and configuration) are placed in prominent positions to improve ease of operation. Simultaneously, an intelligent search function is integrated, combining NLP technology, allowing users to quickly locate the required functions or indicator information by inputting their needs in natural language.

[0042] Setting indicator interface: Intelligent Recommendation and Guidance: Utilizing machine learning algorithms, based on partial user input (such as metric type and business scenario description), combined with historical configuration data and industry best practices, the system intelligently recommends complete metric configuration schemes, including basic metrics, business definitions, filtering dimensions, and calculation methods, along with detailed explanations of the recommendations. Simultaneously, a visually guided process assists users in completing the configuration step-by-step, providing clear operation prompts and examples at each step.

[0043] Intelligent Interaction and Verification: Employing a graphical interface, the system offers drag-and-drop and drop-down menu interactions, supports natural language command input for configuration information, and utilizes NLP technology to understand user intent in real time, automatically completing some configuration operations. When inputting parameters, the system uses AI algorithms for real-time verification, not only detecting format errors but also judging parameter rationality based on data logic and business rules, providing immediate intelligent prompts and correction suggestions.

[0044] Personalized configuration assistance: Based on users' historical configuration preferences and business roles, the system provides personalized default parameter values ​​and frequently used options, reducing configuration complexity. It also offers a configuration template function, allowing users to save frequently used configuration schemes for quick reuse, and the system can automatically generate personalized templates based on user behavior.

[0045] Details viewing interface: Intelligent Result Interpretation: This feature not only displays the calculation process and results of indicator generation but also utilizes NLP technology to interpret the results in natural language, analyzing differences between the results and historical data and industry standards, and providing trend predictions and business impact analysis. For example, for derivative indicators reflecting bank asset quality, the system automatically analyzes and provides interpretations such as "The current asset quality indicator has increased by [X]% compared to last month, mainly due to [specific business factors]. It is expected that in the future [time period], with [changes in market factors or business strategies], the indicator may show [trend prediction], which may bring [business impact analysis] to the bank's credit business."

[0046] Visualization and Drill-down Analysis: Utilizing AI image generation technology, the system automatically generates intuitive and rich visualization charts (such as bar charts, line charts, and radar charts) based on the characteristics of the indicator data, showcasing the trends and relationships of indicator changes. It supports data drill-down functionality, allowing users to delve into the details of the underlying data. The system automatically provides related indicator information and analysis, helping users fully understand the business logic behind the data.

[0047] Optimized Results Export and Sharing: Supports exporting and saving results, offering multiple data format options in addition to the common Excel file format to meet the needs of different data analysis tools. Furthermore, integrated sharing functionality allows users to easily share indicator results and analysis reports with team members. The system automatically records sharing history and feedback information, facilitating collaboration and communication.

[0048] Functional layout design Based on AI analysis of user operation processes and business scenarios, the system is laid out according to an efficient and convenient workflow, covering functional modules such as indicator display, query, indicator configuration, modification, deletion, export, and indicator detail viewing. Through intelligent navigation and guidance, users can quickly locate the required operations, and the system provides real-time suggestions for the next steps based on user actions, ensuring users smoothly complete indicator configuration and data viewing tasks. Simultaneously, the system automatically records user operation paths and habits, continuously optimizing the functional layout and improving the user experience.

[0049] 2. Methods for generating derivative indicators By integrating AI technology, the method for generating derived indicators can be further optimized to make it more intelligent, efficient, and accurate, meeting the complex and ever-changing business needs of banks. Indicator Configuration: Leveraging an AI-powered intelligent recommendation system, users can configure derived indicators by selecting basic indicators, derivative indicators, business definitions, and filtering dimensions based on indicator type. Furthermore, the system automatically analyzes and recommends the optimal indicator configuration combination based on natural language descriptions such as user-input business objectives and risk preferences. The system continuously learns from user configuration behavior and business feedback, constantly optimizing recommendation strategies to improve accuracy and relevance. Simultaneously, knowledge graph technology is used to demonstrate the relationships between selected indicators, business definitions, and filtering dimensions, helping users understand the configuration logic and make more informed decisions.

[0050] Basic Arithmetic Operations: Develop advanced algorithm modules to support basic arithmetic operations such as addition, subtraction, multiplication, and division. Introduce an AI dynamic precision control mechanism to automatically adjust the calculation precision based on data type, numerical range, and business needs, ensuring the accuracy and reliability of calculation results. Simultaneously, utilize machine learning algorithms to analyze historical calculation data, predict potential calculation errors (such as overflow, division by zero, etc.), provide early warnings and solutions, and avoid the impact of calculation errors on derived indicators.

[0051] Ratio Calculation: An intelligent ratio calculation algorithm is designed to support common ratio calculations such as growth rate and percentage, including the debt-to-equity ratio and return on assets. For anomalies such as zero or negative denominators, a deep learning model is used for intelligent diagnosis, analyzing the causes (e.g., abnormal business data, sudden changes in the market environment) and providing personalized handling strategies based on the specific reasons. For example, if an error in business data entry leads to an abnormal denominator, the user is prompted to correct the data; if it's caused by changes in the market environment, alternative calculation methods or adjustment suggestions based on the current market situation are provided. Simultaneously, through the analysis of a large amount of historical ratio data, machine learning algorithms are used to discover potential patterns and regularities in ratio changes, providing users with deeper insights into ratio analysis.

[0052] Metric Decomposition: This feature implements a deep learning-based metric decomposition algorithm. Complex metrics are broken down into multiple simpler or sub-metrics for calculation, then summarized or combined to obtain more accurate analysis results. Models such as Recurrent Neural Networks (RNNs) or Long Short-Term Memory Networks (LSTMs) are used to capture long-term dependencies and complex patterns in metric data, enabling more precise decomposition of complex metrics. Simultaneously, reinforcement learning algorithms optimize the decomposition process, continuously adjusting the decomposition strategy based on business feedback to ensure that the decomposed sub-metrics accurately reflect the business essence, improving the quality and reliability of the analysis results. Furthermore, visualization technology is used to demonstrate the hierarchical structure and calculation process of metric decomposition, helping users understand the composition and generation logic of complex metrics.

[0053] Complex Statistical Models or Risk Assessment Models: The system integrates multiple advanced statistical and risk assessment models, such as regression analysis, cluster analysis, and risk assessment models, and combines AI technology to achieve intelligent model selection and optimization. Through multi-dimensional feature analysis of input data, machine learning algorithms automatically recommend the most suitable model. Based on business needs and data characteristics, automatic hyperparameter tuning techniques (such as random search and Bayesian optimization) are used to optimize model parameters, improving model accuracy and generalization ability. Simultaneously, interpretable AI (XAI) technologies, such as Locally Interpretable Model-Independent Explanation (LIME) and SHAP value analysis, are used to interpret model results, helping users understand the model's decision-making process and key influencing factors, enhancing the model's credibility and practicality. Furthermore, the system continuously monitors model performance and automatically adjusts the model or reselects a more suitable model based on new data and business changes, ensuring that the generated derived indicators always possess high insight and practicality.

[0054] The system features flexible configuration parameters, allowing users to customize the generation rules and calculation formulas for derived indicators based on business characteristics and needs. AI technology provides intelligent assistance, such as using NLP to understand user-inputted custom rule descriptions and automatically converting them into system-recognizable calculation formulas; and utilizing machine learning algorithms to analyze user-defined parameters and provide optimization suggestions, ensuring the rationality and effectiveness of parameter settings. Simultaneously, it offers dynamic parameter adjustment functionality. The system monitors changes in business data and indicator results in real time, automatically prompting users to adjust parameters based on preset business rules or machine learning predictions to more accurately reflect business conditions. Furthermore, it records the history of user parameter adjustments and their impact on business performance, providing a reference for subsequent parameter optimization.

[0055] The technical solution of this invention, based on the integration of AI technology, is comprehensively constructed from aspects such as database structure design, algorithm module development, user interface development, and system deployment and integration, ensuring the system's efficiency, stability, and scalability. Database Structure Design: A reasonable database structure is designed to meet the needs of AI data processing, used to store basic and derived indicator data. In addition to traditional tables such as basic indicator tables, derived indicator configuration tables, parameter configuration tables, dimension information tables, and caliber information tables, an AI model training data storage module and a metadata management module are added. A hybrid storage mode combining relational databases and NoSQL databases is adopted, leveraging the advantages of relational databases in data consistency and transaction processing, and the flexibility and scalability of NoSQL databases in handling massive amounts of unstructured and semi-structured data (such as model training data and log data). AI technology is used to optimize database performance; machine learning algorithms are used to analyze data access patterns and automatically adjust the database index structure to achieve efficient data storage and retrieval. Simultaneously, deep learning anomaly detection algorithms are used to monitor the data in the database in real time, promptly detecting abnormal data changes and ensuring data integrity and security.

[0056] Algorithm Module Development: The algorithm module is developed using efficient and stable programming languages ​​and technology stacks such as Python and Java, fully leveraging AI frameworks (such as TensorFlow and PyTorch) to implement various metric generation algorithms and AI functions. During development, emphasis is placed on modularization and encapsulation of algorithms to improve code reusability and maintainability. Comprehensive unit testing and performance optimization are performed on the algorithm module, utilizing automated testing tools (such as pytest and JUnit) combined with AI-driven test case generation technology to ensure the accuracy and stability of the algorithms. Performance analysis tools (such as cProfile and YourKit) are used to perform performance profiling of the algorithms, employing optimization algorithms (such as algorithm optimization and data structure optimization) and parallel computing techniques (such as multithreading and distributed computing) to improve algorithm execution efficiency. Simultaneously, an algorithm version management mechanism is established to record the algorithm's change history and optimization process, facilitating traceability and rollback.

[0057] User Interface Development: Utilizing advanced web front-end technologies (such as Vue and Element-UI) combined with AI interaction technologies, we develop powerful and user-friendly user interfaces. We leverage computer vision technology to achieve natural interaction methods such as gesture recognition and facial expression recognition, enhancing the user experience. With the help of AI image generation and visualization technologies, we automatically generate high-quality, personalized visualization charts and data display interfaces based on user needs, enhancing the intuitiveness and readability of data. Simultaneously, through real-time monitoring and analysis of user interface operations, we utilize machine learning algorithms to optimize interface layout and interaction processes, improving user efficiency and satisfaction. Furthermore, we integrate AI customer service functionality, using NLP technology to answer user questions in real time, providing operational guidance and assistance.

[0058] System Deployment and Integration: Utilizing advanced containerization technologies, such as Docker, combined with a microservice architecture, the system is deployed on stable and reliable cloud platforms, such as Alibaba Cloud and Tencent Cloud, achieving elastic scaling and high availability. Automated deployment tools, such as Kubernetes, enable rapid deployment, updates, and rollbacks, improving deployment efficiency and reliability. For system integration, efficient interfaces are designed and developed to interface with existing systems, including data transmission and authentication / authorization interfaces, achieving seamless integration and ensuring secure, reliable, and efficient data exchange between systems. During deployment, strict compatibility of interfaces and data formats with other systems is ensured, enabling smooth data sharing and interoperability. The system is deployed in a test environment for comprehensive and detailed testing, including functional testing, performance testing, and security testing, ensuring system stability and reliability. After rigorous testing, the system is deployed to the production environment, and its operation is continuously monitored. Faults are promptly addressed and repaired, and the system is regularly updated and optimized to continuously improve performance and functionality, providing users with high-quality and stable services.

[0059] System integration solution: Interface design and development: Design and develop interfaces for interfacing with existing systems, including data transmission interfaces, authentication and authorization interfaces, etc., to achieve seamless integration with existing systems and ensure secure and reliable data exchange between systems.

[0060] Deployment and Testing: During deployment, ensure that the system's interfaces and data formats are compatible with other systems to achieve data sharing and interoperability. Deploy the system in a test environment for comprehensive testing, including functional testing, performance testing, and security testing, to ensure system stability and reliability.

[0061] Deployment and Maintenance: After passing testing, deploy the system to the production environment, monitor system operation, promptly handle and fix faults, and regularly update and optimize system functions.

[0062] Innovative technical ideas and complete implementation solutions The universal derivative indicator configuration function design, the multi-derived indicator generation method, and the comprehensive technical implementation scheme of this invention constitute a complete and innovative technical system. This system breaks through the limitations of traditional indicator configuration systems, innovatively integrating advanced technologies such as artificial intelligence to meet the diverse needs of different banks, bringing entirely new ideas and methods to the field of bank indicator configuration. These core innovations are not only the technical essence of this invention but also a significant driving force for industry development, and therefore deserve comprehensive legal protection to safeguard the uniqueness and innovativeness of this invention.

[0063] Refined technical implementation details The specific programming implementation details are key to the stable and efficient implementation of this invention. From the ingenious design of the database structure to the implementation of complex logic in the algorithm modules, and the precise control of user interface interaction effects, every technical detail embodies the wisdom and hard work of the R&D team. Protecting these technical details can effectively prevent unauthorized copying and plagiarism, ensure that the innovative achievements of this invention are not maliciously misappropriated, maintain a fair competitive environment in the market, and encourage more technological innovation.

[0064] Unique user interface and data display capabilities The user interface and data display functions, as the part directly facing the user, are a key aspect of the value of this invention. Their unique design concept and advanced interactive technology provide users with a convenient, efficient, and highly personalized user experience. Through intelligent interface layout adjustments, natural language interaction, and high-quality data visualization achieved by combining AI technology, users can quickly understand and utilize complex indicator data. Protecting these functions helps maintain a positive user experience with the product, enhances its market competitiveness, and also encourages more companies to focus on user experience innovation, thus promoting the overall improvement of service levels in the industry.

[0065] Beneficial effects: It comprehensively meets the complex and ever-changing business needs of banks, demonstrating significant uniqueness and leadership in the industry, specifically in the following aspects: Flexible and adaptable interface and rich and diverse indicator system A meticulously designed, highly flexible user interface and a comprehensive library of indicator selections form the cornerstone of this invention's ability to configure universal derived indicators, and represent a key difference between this invention and existing technologies. Leveraging AI-driven interface optimization, the system intelligently adjusts the interface layout based on user behavior analysis and machine learning algorithms, prominently displaying frequently used functions and indicators, significantly enhancing ease of use. Simultaneously, utilizing natural language processing technology, users can describe their business needs in natural language, allowing the system to accurately understand and recommend suitable indicator options, achieving a personalized and intelligent indicator configuration experience. This comprehensively meets the diverse needs of different banks in terms of management models, business priorities, and development stages.

[0066] A diverse and intelligent method for generating derivative indicators It provides a comprehensive suite of methods for generating various types of derived indicators, ranging from basic methods based on simple arithmetic operations to advanced methods utilizing complex statistical and risk assessment models. In simple arithmetic operations, AI-driven dynamic precision control and anomaly prediction mechanisms are introduced to ensure high accuracy and stability of the calculation results. For complex models, machine learning algorithms are used to achieve intelligent model selection, automatic parameter optimization, and in-depth interpretation of results, enabling the system to generate more in-depth and insightful derived indicators tailored to various business scenarios. For example, through deep learning of massive amounts of historical and real-time business data, the system can automatically match the most suitable complex model and adjust model parameters in real time according to business changes, providing banks with comprehensive and accurate indicator support for risk assessment and business decision-making.

[0067] Scientific and efficient technical implementation solutions With its well-designed and efficient database structure, high-efficiency algorithm modules developed based on advanced technology stacks, and a user-friendly interface that delivers superior user experience, the system comprehensively ensures high stability and outstanding performance in practical applications. Regarding the database, a hybrid storage model combined with AI-driven performance optimization is employed to achieve efficient data storage, retrieval, and security monitoring. The algorithm modules are developed based on mainstream programming languages ​​and AI frameworks, emphasizing modularity, encapsulation, and performance optimization. Automated testing and AI-assisted optimization ensure the accuracy and efficiency of the algorithms. The user interface integrates cutting-edge web technologies and AI interaction technologies, such as gesture recognition and intelligent visualization, providing users with a convenient, intuitive, and personalized data operation and display experience.

[0068] This invention, by providing a universal bank derivative indicator configuration system and its comprehensive and innovative implementation scheme, successfully breaks through the bottlenecks of existing technologies, offering bank management a powerful, accurate, and efficient indicator configuration tool. It fully demonstrates its innovation and practicality, and in the context of the rapid development of financial technology, it is of great significance for promoting business innovation and improving management efficiency in the banking industry, and is fully protected by law.

[0069] The above specific embodiments further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An AI-driven bank derivative index configuration and decision-making method, characterized in that, The index configuration and decision method comprises: determining a general derivative index configuration; generating derivative indexes; verifying the derivative indexes and establishing a verification mechanism.

2. The AI-driven bank derivative index configuration and decision method according to claim 1, characterized in that, The determination of the general derivative index configuration specifically comprises: an intelligent interaction interface: a user-friendly interface is designed, and AI-driven interaction functions are integrated. The user configures indexes by inputting instructions, uses NLP technology to understand the instructions and performs corresponding operations. The interface provides intelligent prompts and guidance. Based on the partial information input by the user, the machine learning algorithm predicts the user's intention, and recommends the indexes, calculation methods and configuration parameters that may be needed; intelligent configuration recommendation: based on machine learning analysis of a large amount of bank data and business scenarios, an intelligent recommendation system is constructed. When the user enters the configuration page, the system automatically recommends suitable index categories, business scopes, and filtering dimension configuration parameters according to the type, size, business characteristics of the bank to which the user belongs, and the multi-dimensional information of the user's historical configuration habits; dynamic configuration optimization: using reinforcement learning algorithm, the configuration suggestions are dynamically adjusted according to the user's feedback on the configuration results and the real-time changes of business data.

3. The AI-driven bank derivative index configuration and decision method of claim 1, wherein, The generation of derivative indexes specifically comprises: AI-assisted basic generation method: AI technology is introduced to assist in the generation method based on simple arithmetic operations, ratio calculation and index decomposition; In arithmetic operations, AI algorithms monitor data types and value ranges in real time; In ratio calculation, machine learning algorithms are used to analyze historical data to predict possible anomalies such as zero or negative denominators, and provide corresponding processing suggestions. In the process of index decomposition, deep learning technology is used to automatically decompose complex indexes, and the decomposition results are presented to the user in a visual way to help the user understand the internal relationship between indexes. At the same time, optimization suggestions based on data mining are provided to make the index decomposition more consistent with business logic; Intelligent complex model selection and optimization: for the generation function of derivative indexes based on complex statistical models or risk assessment models, AI is used to realize intelligent selection and optimization of models.

4. The AI-driven bank derivative index configuration and decision method according to claim 3, characterized in that, The intelligent selection and optimization of models using AI specifically comprises: Multi-dimensional analysis of input data, using machine learning algorithms to evaluate the characteristics, distribution of data and the nature of business problems, and automatically recommending complex models; Through reinforcement learning algorithm, the parameters of the selected model are automatically optimized, and the model performance is continuously optimized according to historical data and real-time business feedback, so that the generated derivative indexes are more accurate and insightful.

5. The AI-driven bank derivative index configuration and decision method according to claim 4, characterized in that, The complex model specifically comprises: regression analysis, clustering analysis, risk assessment model.

6. The AI-driven bank derivative index configuration and decision method according to claim 1, characterized in that, The verification of derivative indexes and the establishment of a verification mechanism specifically comprises: AI-enhanced data cleaning and verification: data cleaning and verification of input basic index data; Real-time intelligent monitoring and verification: real-time monitoring and verification of generated derivative indexes to ensure that the indexes can truly reflect the business operation of the bank; Intelligent integration and visual assistance: the system is deployed in the bank management system to realize seamless integration with existing systems. AI technology is used to realize automatic adaptation of interfaces and intelligent conversion of data to ensure compatibility with different existing systems; Intelligent recommendation of visual content using AI technology, generating personalized visual reports and data analysis results based on user roles, business focus and historical operation habits.

7. The AI-driven bank derivative index configuration and decision method according to claim 6, characterized in that, The data cleaning and verification of the input basic index data specifically includes: using deep learning image recognition technology and NLP technology to clean and extract unstructured data, and using machine learning algorithms to detect and repair structured data anomalies.

8. The AI-driven bank derivative index configuration and decision method according to claim 6, characterized in that, The real-time monitoring and verification of the generated derivative indicators specifically includes: Real-time trend analysis and deviation detection of derivative indicators using AI algorithms, compared with historical data, industry standards and pre-set business rules; If the indicators show abnormal fluctuations or do not meet expectations, an early warning will be automatically issued, and machine learning algorithms will be used to analyze possible causes and provide detailed diagnostic reports to help users adjust the indicator generation process or troubleshoot business problems in a timely manner.

9. The AI-driven bank derivative index configuration and decision method of claim 1, wherein, The generation of derivative indicators relies on basic data, which is deeply calculated and analyzed to produce more in-depth and insightful indicators; complete the basic data before configuring derivative indicators.

10. The AI-driven bank derivative index configuration and decision method of claim 1, wherein, The basic data specifically includes: Basic indicators: as the foundation of derivative indicator calculation, real-time analysis of basic indicator data, detection of outliers and data bias through machine learning algorithms; Business scope: including original scope, distribution scope, assessment scope, using AI to build business scope knowledge graph, and deep learning of historical business data and scope application; Filter dimensions: divide data by different dimensions such as institutions, lines, products, subjects and accounts to generate multi-dimensional derivative indicators; introduce AI clustering algorithm to deeply mine massive bank data and find potential dimension division methods to provide innovative filtering dimension options for users; Indicator classification: classify indicators into size, profit and loss, risk and pricing categories to help clearly understand and use complex indicator systems, providing strong support for decision-making, management and evaluation; use natural language processing (NLP) technology to automatically classify newly generated indicators based on semantic similarity analysis with existing indicator classifications.

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