A construction method for intelligent interpretation based on a silver insurance operation analysis index system

Abstract

The present application relates to a kind of based on the construction method of intelligent interpretation of silver insurance operation analysis index system, method includes the following steps: S1, comb silver insurance thread line operation logic and each post role behavior contact, constructs the full-level operation index system covering result index to behavior index;S2, based on the index system, respectively using additive model, multiplication model is completed index dimension and the influence degree of factor resolution, combine IV value screening, regression modeling extraction key behavior index, and fusion industry expert operation experience constructs preliminary intelligent attribution core network;S3, based on historical operation data training preliminary intelligent attribution core model, set index abnormal threshold value embedding intelligent interpretation rule, based on the model of training completion intelligent interpretation, obtain interpretation result;S4, new operation data is regularly included to update model.Compared with prior art, the present application has advantages such as providing strong data reference for operation action.

Description

Technical Field

[0001] This invention relates to the field of data processing and business analysis technology in the insurance industry, and in particular to a method for constructing intelligent interpretation based on a bancassurance business analysis indicator system. Background Technology

[0002] Current business analysis relies heavily on manual interpretation of performance indicators from a single dimension, resulting in low interpretation efficiency and superficial attribution. It can only identify anomalies in performance indicators but cannot quickly locate key driver behaviors that affect performance. Furthermore, it lacks a systematic attribution logic that breaks down the data layer by layer, making it difficult for companies to adjust their business strategies in a timely manner based on the analysis results.

[0003] Low analysis efficiency: Insurance business data indicators are numerous. Relying on manual analysis of indicator trends, chart creation, and anomaly investigation is time-consuming, resulting in a long overall analysis cycle, a heavy workload of basic work, and a slow response speed.

[0004] Deeply understand the difficulties: For business indicators that require drill-down attribution, it is necessary to frequently switch reports and penetrate layer by layer, which is complicated and makes it difficult to quickly locate the root cause of the problem. Summary of the Invention

[0005] The purpose of this invention is to provide a construction method for intelligent interpretation based on the business analysis indicator system of bancassurance. It introduces intelligent diagnostic attribution logic, drills down layer by layer on abnormal data in business indicators, and logically connects result indicators to behavioral indicators, avoiding lengthy data processing processes, and summarizing and analyzing qualitatively and quantitatively to provide strong data reference for business operations.

[0006] The objective of this invention can be achieved through the following technical solutions: A method for intelligent interpretation based on a bancassurance business analysis indicator system, comprising the following steps: S1. Analyze the operational logic of the bancassurance business and the behavioral touchpoints of each position, and build a full-level operational indicator system covering both outcome indicators and behavioral indicators; S2. Based on the aforementioned indicator system, the influence of indicator dimensions and factors is decomposed using additive and multiplicative models, respectively. Key behavioral indicators are extracted by combining IV value screening and regression modeling, and a preliminary intelligent attribution core network is constructed by integrating the business experience of industry experts. S3. Train a preliminary intelligent attribution core model based on historical operating data, set anomaly thresholds for indicators and embed intelligent interpretation rules, perform intelligent interpretation based on the trained model, and obtain the interpretation results. S4. Regularly incorporate new operational data into the updated model.

[0007] Furthermore, the specific steps of S2 are as follows: For the levels in the indicator system that satisfy the additive composition relationship, an additive model is used to calculate the proportion of the change value of each component dimension indicator in the change value of the parent level; A multiplicative model is used for the transformation link indicators in the indicator system to calculate the contribution of each factor to the overall change. The indicators in the indicator system are screened, key behavioral indicators are retained, a subset of key behavioral indicators is obtained, a regression model is constructed, the influence weights are obtained, and expert experience is integrated into the regression model to obtain a preliminary intelligent attribution core network.

[0008] Furthermore, the specific steps for screening the indicators in the indicator system and retaining the key behavioral indicators are as follows: Execution feature binning; Calculate the WOE for each bin. WOE represents the distributional differences of the target variable under different values ​​of a certain feature. Based on WOE, IV is further used for summarization; Based on the IV threshold, key behavioral indicators are retained.

[0009] Furthermore, IV measures the predictive power of a feature for the target variable. Furthermore, the specific implementation of feature binning is as follows: Binning is performed using a decision tree model to find the optimal split point and discretize the continuous variables.

[0010] Furthermore, IV is used to measure the predictive power of a feature for the target variable.

[0011] Furthermore, the specific rules for intelligent interpretation are as follows: Set an abnormal threshold for the indicator. When the performance indicator is abnormal, the model traces back layer by layer along the attribution tree and outputs the influence weight of each level of driving behavior indicator and the interpretation conclusion.

[0012] Furthermore, the full-level operational indicator system includes the correlation logic and hierarchical relationship of the indicators.

[0013] Furthermore, when incorporating new operational data into the update model periodically, update the related logic and hierarchical relationships.

[0014] Furthermore, the results are interpreted as visual charts.

[0015] Compared with the prior art, the present invention has the following beneficial effects: This invention rapidly extracts key operational data from different dimensions, providing a comprehensive overview of the business situation. It clearly reveals the operational logic, breaking down the driving forces from outcome indicators to behavioral indicators, ensuring no overlap or omission. It performs drill-down attribution and quantitative assessment of data fluctuations, providing precise guidance for business decisions. Combined with large-scale model capabilities, it delivers clear summaries and visualizations tailored to different users, helping them quickly identify operational anomalies. Attached Figure Description

[0016] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0017] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0018] This invention proposes a method for intelligent interpretation based on a bancassurance business analysis indicator system. The method includes the following steps: S1. Analyze the operational logic of the bancassurance business and the behavioral touchpoints of each position, and build a full-level operational indicator system covering both outcome indicators and behavioral indicators; S2. Based on the aforementioned indicator system, the influence of indicator dimensions and factors is decomposed using additive and multiplicative models, respectively. Key behavioral indicators are extracted by combining IV value screening and regression modeling, and a preliminary intelligent attribution core network is constructed by integrating the business experience of industry experts. S3. Train a preliminary intelligent attribution core model based on historical operating data, set anomaly thresholds for indicators and embed intelligent interpretation rules, perform intelligent interpretation based on the trained model, and obtain the interpretation results. S4. Regularly incorporate new operational data into the updated model.

[0019] To address the problems of existing technologies, this invention proposes an intelligent interpretation model and its construction method, aiming to: 1. Quickly extract key operational data from different dimensions to provide a comprehensive overview of the business situation.

[0020] 2. Clearly reveal the business logic, from result indicators to behavioral indicators, achieving a thorough breakdown of driving forces without overlap or omission. 3. Drill down to attribute and quantify the fluctuations in data, providing precise guidance for business decisions.

[0021] 4. Leveraging large-scale model capabilities, it provides clear summaries and visualizations tailored to different users, helping them quickly identify operational anomalies.

[0022] The core of this invention lies in introducing intelligent diagnostic attribution logic, which drills down the abnormal data in the business indicators layer by layer, logically connecting the result indicators to the behavioral indicators, avoiding the lengthy data processing process, and summarizing and analyzing qualitatively and quantitatively to provide strong data reference for business operations.

[0023] This invention is constructed through the following steps: 1. Breakdown and analysis of operational indicators: (1) Systematically sort out the operational logic of the banking and insurance sector and the behavioral touchpoints of different roles, and establish an indicator system covering everything from results to behavior.

[0024] (2) Establish the correlation logic and hierarchical relationship of the indicators to provide a basis for subsequent attribution analysis.

[0025] (3) Collect historical performance and behavioral data.

[0026] 2. Core Construction of Attribution Model (1) Dimensional decomposition: The influence is calculated using an additive model: the proportion of the change value of each component dimension item in the change value of the parent level is calculated.

[0027] (2) Index decomposition: The influence is calculated using a multiplicative model: each contact point has a multiplicative relationship with the final transformation result. The contribution of each factor to the overall change is calculated, and the change is decomposed into factors.

[0028] (3) Modeling for the extraction of key behavioral indicators, and screening by calculating the IV value of the behavioral indicators. Feature binning: Binning is performed using a decision tree model to find the optimal split point and discretize the continuous variables; Calculate the Word of Effect (WOE) for each bin. WOE represents the distributional differences of the target variable under different values ​​of a feature. WOE is used to measure the degree of influence of feature values ​​on the target variable.

[0029] IV calculation, based on WOE, further uses IV for aggregation, and utilizes IV to measure the predictive power of a feature on the target variable. The higher the IV value of a feature, the stronger its discriminative power on the target variable.

[0030] To predict the degree to which behavior affects the outcome, a regression model is established to obtain the influence weights.

[0031] Integration of expert experience: Incorporate expert knowledge (such as check-in, channel visits, and department manager accompaniment) into model design, feature selection, and result interpretation.

[0032] (4) Intelligent interpretation rule embedding: Based on historical operating data, the interpretation model is trained and expert experience is used to set the abnormal threshold of indicators. When the performance indicators are abnormal, the model automatically traces back along the attribution tree layer by layer, outputs the influence weight of each level of driving behavior indicators (such as the contribution of a decrease in revenue due to a decrease in conversion rate being 60%) and interpretation conclusions, and forms a visual chart at the same time.

[0033] (5) Dynamic iterative optimization: Regularly incorporate new business data (such as indicators of new business lines), update the correlation matrix and attribution level, and optimize the accuracy of the interpretation model (such as recalculating the correlation of indicators every quarter and adjusting the influence weight algorithm).

[0034] The intelligent diagnostic attribution method of this invention can achieve the following effects: (1) Overview of operating data: A summary of operating data at different levels, including the entire company, channels, and institutions.

[0035] (2) Logical diagnostic attribution: Drill down the abnormal data logically to reveal the main behavioral factors affecting the business data.

[0036] (3) Visual intelligent display: Select effective indicators and display them in a visual manner, providing a clear and concise data interpretation report.

[0037] Compared with the prior art, this invention has the following outstanding innovations and significant beneficial effects: Innovation Point 1: Efficiency Improvement: Replacing manual analysis, the time for attributing abnormal performance indicators is shortened from several days to minutes, quickly locating operational problems.

[0038] Innovation Point 2: Improved Accuracy: From operational data to forward-looking indicators to behavioral indicators, it identifies abnormal data and drills down layer by layer, supports in-depth interpretation of multiple tables, provides users with specific anomaly alerts, accurately locates the root cause of problems, and gives behavioral suggestions based on data.

[0039] Innovation Point 3: Intelligent Interpretation Capability: By integrating report data, it intelligently generates text and chart summaries; by accumulating the business logic of different users, it provides effective interpretation of business data at different levels and depths through a unified analysis framework.

[0040] The core value of this invention lies in: For managers: Provides multi-level operational data summaries to assist in decision-making.

[0041] For planners: Accurately pinpoint abnormal behavior points to assist in developing effective business strategies.

[0042] For frontline staff: It visually demonstrates the specific behaviors that cause abnormal operating data, provides data-driven behavioral suggestions, and promotes improvements in business practices and operational efficiency.

[0043] For the company: It has enabled intelligent interpretation of operations based on data reports, improved the integration path of bancassurance operation analysis and large-scale models, and enhanced the ability to perceive, diagnose, and extract data.

[0044] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for constructing an intelligent interpretation system based on a bancassurance business analysis indicator system, characterized in that, The method includes the following steps: S1. Analyze the operational logic of the bancassurance business and the behavioral touchpoints of each position, and build a full-level operational indicator system covering both outcome indicators and behavioral indicators; S2. Based on the aforementioned indicator system, the influence of indicator dimensions and factors is decomposed using additive and multiplicative models, respectively. Key behavioral indicators are extracted by combining IV value screening and regression modeling, and a preliminary intelligent attribution core network is constructed by integrating the business experience of industry experts. S3. Train a preliminary intelligent attribution core model based on historical operating data, set anomaly thresholds for indicators and embed intelligent interpretation rules, perform intelligent interpretation based on the trained model, and obtain the interpretation results. S4. Regularly incorporate new operational data into the updated model.

2. The method for constructing an intelligent interpretation system based on a bancassurance business analysis indicator system according to claim 1, characterized in that, The specific steps of S2 are as follows: For the levels in the indicator system that satisfy the additive composition relationship, an additive model is used to calculate the proportion of the change value of each component dimension indicator in the change value of the parent level; A multiplicative model is used for the transformation link indicators in the indicator system to calculate the contribution of each factor to the overall change; The indicators in the indicator system are screened, key behavioral indicators are retained, a subset of key behavioral indicators is obtained, a regression model is constructed, the influence weights are obtained, and expert experience is integrated into the regression model to obtain a preliminary intelligent attribution core network.

3. The method for constructing an intelligent interpretation system based on a bancassurance business analysis indicator system according to claim 2, characterized in that, The specific steps for screening indicators in the indicator system and retaining key behavioral indicators are as follows: Execution feature binning; Calculate the WOE for each bin. WOE represents the distributional differences of the target variable under different values ​​of a certain feature. Based on WOE, IV is further used for summarization; Based on the IV threshold, key behavioral indicators are retained.

4. The method for constructing an intelligent interpretation system based on a bancassurance business analysis indicator system according to claim 1, characterized in that, IV measures the predictive power of a feature for a target variable.

5. The method for constructing an intelligent interpretation system based on a bank-insurance business analysis indicator system according to claim 4, characterized in that, The specific binning of execution features is as follows: Binning is performed using a decision tree model to find the optimal split point and discretize the continuous variables.

6. The method for constructing an intelligent interpretation system based on a bancassurance business analysis indicator system according to claim 5, characterized in that, IV is used to measure the predictive power of a feature for a target variable.

7. The method for constructing an intelligent interpretation system based on a banking and insurance business analysis indicator system according to claim 1, characterized in that, The specific rules for intelligent interpretation are as follows: Set an abnormal threshold for the indicator. When the performance indicator is abnormal, the model traces back layer by layer along the attribution tree and outputs the influence weight of each level of driving behavior indicator and the interpretation conclusion.

8. The method for constructing an intelligent interpretation system based on a banking and insurance business analysis indicator system according to claim 1, characterized in that, The full-level operational indicator system includes the correlation logic and hierarchical relationship of the indicators.

9. A method for intelligent interpretation based on a banking and insurance business analysis indicator system as described in claim 8, characterized in that, When incorporating new operational data into the update model periodically, update the related logic and hierarchical relationships.

10. A method for intelligent interpretation based on a banking and insurance business analysis indicator system according to claim 1, characterized in that, The interpretation results are presented as visual charts.

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