Bank-insurance data docking method and system

Abstract

The invention belongs to the technical field of insurance data processing, and particularly relates to a bank-insurance data docking method and system. According to the bank-insurance data docking method, standardized definition is carried out on fields, with the same meaning but different names, of a bank and an insurance through a standard field information base, automatic recognition of a matching module is facilitated, and matching and docking of data are completed. The automatic capturing module and the matching module replace manual export, comparison and table modification operations, so that the data processing time is greatly shortened, and the efficiency is improved. For personalized fields, manual intervention can be carried out through the alarm module, secondary recognition is carried out manually, and finally matching of full-quantity fields is achieved. The log module can completely record the whole process operation information from data capture to message generation, a clear and traceable data link is provided for management and troubleshooting, and the subsequent operation and maintenance difficulty is reduced.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of insurance data processing, and specifically relates to a bank-insurance data docking method and system. BACKGROUND

[0002] When conducting business and data analysis, insurance companies often need to interact with banks for a large amount of data, such as insurance information input, policy verification, claim data transmission, customer information synchronization, etc. Due to differences in business specifications and system development standards between banks and insurance companies, there is significant incompatibility between the interface data of the two parties. For example, the naming method adopted in the bank form is "name", while the naming method adopted by the insurance company is "customer name". Similar differentiating fields include "ID card number" and "ID card number", "date" and "time", etc. The above differentiating fields cause the systems of the two parties to be unable to smoothly dock.

[0003] In the prior art, the data docking between banks and insurance companies mainly adopts a manual processing method: the staff needs to export forms from the systems of the two parties, manually compares different interface fields, manually unifies and matches the differentiating fields with different names in the systems of the two parties, and finally generates an interactive message using the unified data.

[0004] The above-mentioned manual docking method has many defects: first, it is low in efficiency, requires a large amount of manpower and time cost, and is difficult to meet the real-time needs of insurance business; second, it is easily affected by human factors, resulting in errors such as missing and mismatching fields, which affects the progress of business; third, it lacks a unified standard field reference, and the docking rules are not systematic, with low standardization; fourth, there is no real-time monitoring mechanism, and problems in the docking process are difficult to trace back, making subsequent troubleshooting and management difficult. SUMMARY

[0005] Based on the above technical problems, the application provides a bank-insurance data docking method and system to solve the technical problems of low efficiency, errors, inability to achieve standardized processing, and difficulty in tracing back caused by manual docking in the prior art.

[0006] To achieve the above-mentioned purposes, the technical solution adopted by the application is: In a first aspect, the application provides a bank-insurance data docking method, comprising the following steps: S1: establishing a standard field information library to unify differentiating terms with the same meaning but different names in the bank and insurance system; S2: automatically reading the original data table output by the bank and insurance interface through a data grabbing module, extracting the name and data type information of the interface field in the original data table, and recording the source of the interface field; S3: Comparing the interface field obtained in S2 with the standard field information library, and unifying the differentiated terms in the bank-side original data table and the insurance-side original data table into standard field names respectively through accurate matching or fuzzy matching; S4: Writing the personalized field that cannot be matched with the standard field information library into an alarm module, and transferring into work analysis through the alarm module; S5: Based on the standard field names completed in S3, matching the data values of the bank side and the insurance side; S6: Generating a data docking table according to the matching result, generating a standard message in a preset format and sending it to a target system, and simultaneously writing the whole-process operation into a log module in real time for recording.

[0007] In a possible implementation manner, the standard field information library in step S1 comprises standard field names, data types, field descriptions and value ranges.

[0008] In a possible implementation manner, the data grabbing module in step S2 supports timing grabbing and / or real-time triggered grabbing, and the source of the interface field comprises the bank side or the insurance side.

[0009] In a possible implementation manner, in step S3, the fuzzy matching adopts a semantic analysis algorithm to identify synonymous or near-synonymous fields.

[0010] In a possible implementation manner, the alarm module in step S4 comprises an alarm information storage unit, a notification unit and a manual processing interaction unit, and the notification unit supports multi-channel alarm notification.

[0011] In a possible implementation manner, the notification unit sends alarm information to the manual processing interaction unit through email, system pop-up window or short message.

[0012] In a possible implementation manner, the standard field information library supports dynamic updating.

[0013] Compared with the prior art, the bank-insurance data docking method provided by the present application has the following beneficial effects: The bank-insurance data docking method provided by the present application standardizes the fields with the same meaning but different names of the bank and the insurance through the standard field information library, so as to facilitate the automatic identification of the matching module and complete the matching and docking of the data. Moreover, the standard field information library provides a unified and authoritative field naming and format specification for the data docking, which helps to improve the accuracy and standardization degree of the data docking.

[0014] Through the automatic grabbing module of S2 step and the programmed matching and conversion of S3-S5 steps, the manual export, comparison and modification of the table are replaced, the data processing time is greatly shortened, and the efficiency is improved. For personalized fields, manual intervention can be realized through the alarm module, secondary identification is performed by manual, and finally the matching of full-quantity fields is realized. The log module can record the whole process operation information from data grabbing to message generation, provide clear and traceable data link for management and problem troubleshooting, and reduce the difficulty of subsequent operation and maintenance.

[0015] In a second aspect, the application provides a bank-insurance data docking system, comprising a data grabbing module, a standard field information library, an alarm module, a matching module and a log module; The data grabbing module is used to automatically read the original data table of the bank side and the insurance side interface, and extract the interface field information; The standard field information library is used to store the standard field and the field matching corresponding rule, and provide the basis for field comparison and matching; The alarm module is used to receive the personalized field and the field matching exception information, send an alarm to the administrator and support manual processing interaction; The matching module is used to match the data values of the bank side and the insurance side, and generate a data docking table; The log module is used to record the operation information of the whole process of data docking, including field grabbing, field comparison, matching alarm, field matching and message generation result.

[0016] The bank-insurance data docking system provided by the application is used to realize the bank-insurance data docking method described above, has the same technical effects, and will not be described here. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only some embodiments of the application, and for those skilled in the art, without creative labor, can also obtain other drawings according to these drawings.

[0018] Figure 1 A flowchart of a bank-insurance data docking method provided by the embodiments of the application; DETAILED DESCRIPTION

[0019] In order to make the technical problems, technical solutions and beneficial effects of the application more clear, the following will be further described in detail by combining with the drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the application, and are not used to limit the application.

[0020] 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.

[0021] A bank-insurance data docking method and system provided by an embodiment of the present application is described below.

[0022] In a first aspect, as Figure 1 indicated, the present application provides a bank-insurance data docking method, comprising the following steps: S1: Establish a standard field information library to unify the differentiated terms with the same meaning but different names in the bank and insurance system. The standard field information library contains the entered standard field name, data type, field description and value range, and supports dynamic updating, and can be added, deleted, modified and inquired according to the needs.

[0023] S2: The data extraction module automatically reads the original data table output by the bank and insurance interface, extracts the name and data type information of the interface field in the original data table, and records the source of the interface field. The data extraction module can use existing data acquisition technology, such as RPA. RPA technology combines the standard field information library to standardize the definition of the fields with different names agreed by the bank and the insurance company, which can automatically identify and automate a large amount of repetitive work in this work, liberate a large number of human resources and have lower failure rate.

[0024] The data extraction module supports timed extraction and / or real-time triggered extraction, and the source of the interface field includes the bank or the insurance company. The data extraction module has multiple trigger modes, for example, it can be triggered to extract data at a predetermined time and automatically extract data when the predetermined time is reached. Alternatively, it can also be triggered in real time by events, which is manually started by the administrator on the interface.

[0025] S3: Compare the interface field obtained in S2 with the standard field information library, and unify the differentiated terms in the bank original data table and the insurance original data table into standard field names through accurate matching or fuzzy matching. Accurate matching means that the term is exactly the same as the preset word, and fuzzy matching uses a semantic analysis algorithm to automatically identify synonymous or near-synonymous fields.

[0026] S4: Write the personalized fields that cannot be matched with the standard field information library into the alarm module, and transfer them to the analysis module through the alarm module. The alarm module includes an alarm information storage unit, a notification unit and a manual processing interaction unit, and the notification unit supports multi-channel alarm notification When encountering personalized fields that cannot be automatically processed, the system will not stall or exit with an error, but will expose the problem to the administrator in a timely and accurate manner through the alarm module, and provide a convenient processing entry, ensuring that the docking process can continue to execute after manual intervention, and ensuring the core business.

[0027] The alarm module includes an alarm information storage unit, a notification unit, and a human-machine interaction unit, enabling efficient human-machine collaboration. The notification unit ensures alarm information delivery through one or more channels, such as email, system pop-ups, and SMS, preventing omissions. Multi-channel parallel notifications ensure that administrators receive alarms in the most convenient way, regardless of their working status (at a computer or on the move). In particular, SMS notifications overcome terminal limitations, achieving near real-time delivery, thus significantly shortening anomaly response time and creating conditions for rapid problem resolution.

[0028] The manual processing interaction unit standardizes and digitizes processing actions, making the processing process recordable and reviewable, thereby improving the efficiency and management level of the manual processing links themselves.

[0029] S5: The matching module matches the data values ​​of the bank and the insurance company based on the standard field names completed in S3. The matching module can read the raw data table output by the interface, assimilate the differing fields into standard fields, and match the data values ​​of the bank and the insurance company.

[0030] S6: Generate a data docking table based on the matching results, generate standard messages in a preset format and send them to the target system, and record the entire process operation in real time in the log module.

[0031] In step S4, alarms can be categorized as needed. Different alarm levels (urgent, important, and general) can be set based on the severity of the anomaly's impact (such as missing key fields or batch matching failure). For the same alarms that occur repeatedly within a short period of time, they can be packaged and then sent again to avoid sending a large number of alarm messages at the same time.

[0032] In step S4, a message template is defined for each channel for different alarm types. Variables (such as alarm ID, anomaly details, and occurrence time) can be embedded in the template to achieve personalized notifications. Notifications can be sent one-to-one or in groups.

[0033] In step S6, the log module consists of a log collection submodule, a log storage submodule, a log query submodule, and a log monitoring submodule. The log collection submodule collects operation logs from each module, and the collected data is stored in the log storage submodule. Log data supports compressed storage to reduce storage space usage. The log query submodule has a user-friendly interface; log information can be queried through this submodule when needed.

[0034] In a second aspect, the embodiments of the present application further provide a bank-insurance data docking system, comprising a data capturing module, a standard field information library, an alarm module, a matching module and a log module; the data capturing module is configured to automatically read original data tables of interfaces of a bank side and an insurance side, and extract interface field information; the standard field information library is configured to store standard fields and field matching corresponding rules, and provide a basis for field comparison and matching; the alarm module is configured to receive individualized fields and field matching exception information, send alarms to administrators and support manual processing interaction; the matching module is configured to match data values of the bank side and the insurance side, and generate a data docking table; and the log module is configured to record operation information of a whole process of data docking, including field capturing, field comparison, matching alarm, field matching and message generation results.

[0035] The bank-insurance data docking system provided by the embodiments of the present application is used to implement the bank-insurance data docking method described above, has the same technical effects, and will not be described here.

[0036] It can be understood that the parts in the above embodiments can be freely combined or deleted to form different combined embodiments, and the specific content of each combined embodiment will not be described here. After the description, it can be considered that the present application has described each combined embodiment, and can support different combined embodiments.

[0037] The above only describes the preferred embodiments of the present application, and does not limit the present application. Any modification, equivalent replacement and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for bank-insurance data integration, characterized in that, Includes the following steps: S1: Establish a standard field information database to unify differentiated terms with the same meaning but different names in the banking and insurance systems; S2: Automatically read the raw data tables output by the bank and insurance company interfaces through the data capture module, extract the names and data types of the interface fields in the raw data tables, and record the source of the interface fields; S3: Compare the interface fields obtained in S2 with the standard field information database, and unify the differentiated terms in the bank's original data table and the insurance's original data table into standard field names through exact matching or fuzzy matching; S4: Write personalized fields that cannot be matched with the standard field information database into the alarm module, and transfer them to the working analysis through the alarm module; S5: Based on the standard field names completed in S3, match the data values ​​of the bank and the insurer; S6: Generate a data docking table based on the matching results, generate standard messages in a preset format and send them to the target system, and record the entire process operation in real time in the log module.

2. The bank-insurance data docking method according to claim 1, characterized in that, The standard field information database mentioned in step S1 includes standard field names, data types, field descriptions, and value ranges.

3. The bank-insurance data docking method according to claim 1, characterized in that, The data capture module in step S2 supports timed capture and / or real-time triggered capture, and the source of the interface fields includes banks or insurance companies.

4. The bank-insurance data docking method according to claim 1, characterized in that, In step S3, fuzzy matching uses a semantic analysis algorithm to identify synonyms or near-synonyms.

5. The bank-insurance data docking method according to claim 1, characterized in that, The alarm module in step S4 includes an alarm information storage unit, a notification unit, and a manual processing interaction unit. The notification unit supports alarm notifications from multiple channels.

6. The bank-insurance data docking method according to claim 5, characterized in that, The notification unit sends alarm information to manual personnel via email, system pop-up, or SMS.

7. The bank-insurance data docking method according to claim 1, characterized in that, The standard field information database supports dynamic updates.

8. A bank-insurance data docking system, characterized in that, It includes a data capture module, a standard field information database, an alarm module, a matching module, and a logging module; The data capture module is used to automatically read the original data tables from the bank and insurance company interfaces and extract the interface field information; The standard field information database is used to store standard fields and corresponding field matching rules, providing a basis for field comparison and matching; The alarm module is used to receive personalized fields and field matching anomaly information, send alarms to the administrator, and support manual handling interaction; The matching module is used to match data values ​​between the bank and the insurance company and generate a data docking table. The log module is used to record operation information for the entire data integration process, including field capture, field comparison, matching alarms, field matching, and message generation results.

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