CN112288585A - Insurance business actuarial data processing method and device and electronic equipment - Google Patents
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Abstract
One or more embodiments of the present specification provide an insurance business actuarial data processing method, the processing method including reading insurance business data from a table of a distributed column-oriented database in a predetermined format and converting the insurance business data into business entity objects; processing the business entity object by using a parallel computing engine to obtain a actuarial index; according to a preset check rule, performing compliance check on the attribute value of the business entity object, and repairing the non-compliant insurance business data found in the compliance check; the insurance business data which passes the compliance check or is repaired is saved to a data warehouse based on a distributed system architecture; and performing insurance service actuarial and data summarization according to the actuarial index and the insurance service data stored in the data warehouse. The embodiment of the invention detects and repairs the processed data through distributed extraction and parallel computing processing, consumes less time for extracting, detecting and repairing the data, and improves the data summarizing efficiency.
Description
Technical Field
One or more embodiments of the present disclosure relate to the field of data processing technologies, and in particular, to a method and an apparatus for processing insurance business actuarial data, and an electronic device.
Background
The actuarial calculation is to apply the knowledge and principle of mathematics, statistics, finance, insurance and population to solve the items needing calculation in the business insurance and various social security services, such as the measurement of death rate, the construction of life table, the setting of rate, the calculation of reserve money and the distribution of business surplus, so as to ensure the stability and safety of the actuarial operation.
At present, single-thread single-unit processing and manual processing are mainly adopted for processing short-risk actuarial data in insurance to summarize the data, and the processing time is too long, so that the efficiency of data summarization is low.
Disclosure of Invention
In view of the above, one or more embodiments of the present disclosure are directed to a method, an apparatus, and an electronic device for processing insurance business actuations, so as to solve the problem of long processing time for big data in short-risk actuations.
In view of the above objects, one or more embodiments of the present specification provide an insurance business actuarial data processing method, including:
reading insurance business data from a table of a distributed column-oriented database in a preset format and converting the insurance business data into business entity objects;
processing the business entity object by using a parallel computing engine to obtain a actuarial index;
according to a preset check rule, performing compliance check on the attribute value of the business entity object, and repairing the non-compliant insurance business data found in the compliance check;
saving insurance business data which passes the compliance check or is repaired to a data warehouse based on a distributed system architecture;
and performing insurance service actuations and data summarization according to the actuarial indexes and insurance service data stored in the data warehouse.
As an alternative embodiment, the distributed column-oriented database is an HBase database.
As an alternative embodiment, the parallel computing engine is a MapReduce or Spark computing engine.
As an alternative embodiment, the data warehouse is a Hive library based on Hadoop.
As an optional implementation, the actuarial indicator includes at least one of: group/individual logo, death warranty, serious warranty, settled claims, and pending claims.
As an optional implementation, the repairing the non-compliant insurance service data includes: and restoring the non-compliant insurance business data by utilizing a pre-trained random forest model.
As an alternative embodiment, the non-compliant insurance service data includes a mandatory information field with blank space or insurance service data with filling content in a format not in accordance with the requirements of the information field.
As an alternative embodiment, the non-compliant insurance service data includes insurance service data for which the effective date and the end date are not proper.
Corresponding to the obtaining method, an embodiment of the present invention further provides an insurance business actuarial data processing apparatus, including:
the reading module is used for reading insurance business data from a table of a distributed column-oriented database in a preset format and converting the insurance business data into business entity objects;
the processing module is used for processing the business entity object by using a parallel computing engine to obtain a actuarial index;
the checking and repairing module is used for carrying out compliance checking on the attribute value of the business entity object according to a preset checking rule and repairing the non-compliant insurance business data found in the compliance checking;
the storage module is used for storing the insurance business data which passes the compliance check or is repaired to a data warehouse based on a distributed system architecture;
and the actuarial and summary module is used for performing actuarial and data summary on the insurance business according to the actuarial index and the insurance business data stored in the data warehouse.
Corresponding to the obtaining method, the embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored on the memory and executable by the processor, and the processor implements the method when executing the computer program.
As can be seen from the foregoing, in the insurance business actuarial data processing method, the insurance business actuarial data processing device, and the electronic device provided in one or more embodiments of the present disclosure, data is read from the distributed database and converted into the business entity object, and the business entity object is processed by using the parallel computing engine, so that efficiency is improved in comparison with single-thread processing, detection and repair of a machine learning algorithm are performed on the processed data, time consumed for extraction, detection, and repair of the data is reduced, and efficiency of data summarization is improved.
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In order to more clearly illustrate one or more embodiments or prior art solutions of the present specification, the drawings that are needed in the description of the embodiments or prior art will be briefly described below, and it is obvious that the drawings in the following description are only one or more embodiments of the present specification, and that other drawings may be obtained by those skilled in the art without inventive effort from these drawings.
FIG. 1 is a flow diagram of an insurance business actuarial data processing method according to one or more embodiments of the present disclosure;
FIG. 2 is a schematic block diagram of an insurance business actuarial data processing apparatus according to one or more embodiments of the present disclosure;
fig. 3 is a schematic structural diagram of an electronic device according to one or more embodiments of the present disclosure.
Detailed Description
To make the objects, technical solutions and advantages of the present disclosure more apparent, the present disclosure is further described in detail below with reference to specific embodiments.
In order to achieve the above object, an embodiment of the present invention provides an insurance business actuarial data processing method, including:
reading insurance business data from a table of a distributed column-oriented database in a preset format and converting the insurance business data into business entity objects;
processing the business entity object by using a parallel computing engine to obtain a actuarial index;
according to a preset check rule, performing compliance check on the attribute value of the business entity object, and repairing the non-compliant insurance business data found in the compliance check;
saving insurance business data which passes the compliance check or is repaired to a data warehouse based on a distributed system architecture;
and performing insurance service actuations and data summarization according to the actuarial indexes and insurance service data stored in the data warehouse.
In the embodiment of the invention, for insurance service data, data is extracted in a distributed manner in a preset format and converted into an entity object, the entity object is processed by using parallel operation to obtain an actuarial index, the entity object is detected according to a preset rule, data which is not in compliance is repaired, the compliance data or the repaired data is stored in a data warehouse, and insurance service actuarial and data summarization are carried out on the actuarial index and the data stored in the data warehouse to obtain a data summary table. In the embodiment of the invention, the distributed extraction and parallel computation processing are adopted, so that the processing efficiency is improved compared with the single-thread processing efficiency, the detection and the repair of the machine learning algorithm are carried out on the processed data, the time consumed for the extraction, the detection and the repair of the data is short, and the data summarizing efficiency is improved.
Referring to fig. 1, an embodiment of the present invention provides a method for processing actuarial data of insurance services, including:
s100, reading insurance business data from a table of the distributed column-oriented database in a preset format, and converting the insurance business data into business entity objects.
As an alternative embodiment, the distributed column-oriented database is an HBase database.
Alternatively, yaml was used to extract the data from the HBase database and parse the data.
And S200, processing the business entity object by using a parallel computing engine to obtain a actuarial index.
As an alternative embodiment, the parallel computing engine is a MapReduce or Spark computing engine.
Optionally, MapReduce is used for processing, data is classified in a Map stage, each type of data is processed independently to generate a specific index, and results processed in the Map stage are summarized in a Reduce stage.
As an optional implementation, the actuarial indicator includes at least one of: group/individual logo, death warranty, serious warranty, settled claims, and pending claims.
S300, according to a preset check rule, performing compliance check on the attribute value of the business entity object, and repairing the non-compliant insurance business data found in the compliance check.
As an alternative embodiment, the non-compliant insurance service data includes a mandatory information field with blank space or insurance service data with filling content in a format not in accordance with the requirements of the information field.
Optionally, the type of policy is filled in incorrectly, and the amount of money under different types is mixed up.
As an optional implementation, the repairing the non-compliant insurance service data includes: and restoring the non-compliant insurance business data by utilizing a pre-trained random forest model.
As an alternative embodiment, the non-compliant insurance service data includes insurance service data for which the effective date and the end date are not proper.
Optionally, the training method of the effective date model of the random forest model includes:
acquiring a training set, wherein the training set comprises data which does not contain an effective date and only contains a relevant field of the effective date and data which contains the effective date and the relevant field;
inputting the training data in the training set into the random forest model, and performing iterative computation until the difference value between the predicted effective date and the actual effective date is less than a threshold value;
and obtaining the trained random forest model with the effective date.
Optionally, the training method of the ending date model of the random forest model includes:
acquiring a training set, wherein the training set comprises data which does not contain an expiration date and only contains an expiration date related field and data which contains an expiration date and a related field;
inputting the training data in the training set into the random forest model, and performing iterative computation until the difference value between the predicted termination date and the actual termination date is less than a threshold value;
and obtaining a trained random forest model of the termination date.
The principle of the random forest regression algorithm is as follows:
step 1, randomly extracting m sample points from a training sample set S to obtain a new S1, S2 and S3.
And 2, training the CART regression tree by using a sub-training set, wherein in the training process, a segmentation rule of each node is to randomly select k features from all the features, select an optimal segmentation point from the k features and divide left and right subtrees.
And 3, generating a plurality of CART regression tree models according to the second step.
And 4, the final prediction result of each CART regression tree is the mean value of the leaf nodes to which the sample points are located.
And 5, the final prediction result of the random forest is the average value of the prediction results of all CART regression trees.
S400, storing the insurance business data which passes the compliance check or is repaired to a data warehouse based on a distributed system architecture.
As an alternative embodiment, the data warehouse is a Hive library based on Hadoop.
The structured data file can be mapped into a database table through the hive data warehouse tool.
And S500, performing insurance service actuarial and data summarization according to the actuarial index and the insurance service data stored in the data warehouse.
It should be noted that the method of one or more embodiments of the present disclosure may be performed by a single device, such as a computer or server. The method of the embodiment can also be applied to a distributed scene and completed by the mutual cooperation of a plurality of devices. In such a distributed scenario, one of the devices may perform only one or more steps of the method of one or more embodiments of the present disclosure, and the devices may interact with each other to complete the method.
Based on any one of the above-described embodiments of the insurance business actuarial data processing method, the present invention further provides an insurance business actuarial data processing apparatus, as shown in fig. 2, including:
the reading module 10 is used for reading insurance business data from a table of a distributed column-oriented database in a preset format and converting the insurance business data into business entity objects;
the processing module 20 is configured to process the business entity object by using a parallel computing engine to obtain a actuarial index;
the checking and repairing module 30 is configured to perform compliance checking on the attribute value of the business entity object according to a predetermined checking rule, and repair the non-compliant insurance business data found in the compliance checking;
the storage module 40 is used for saving the insurance business data which passes the compliance check or is repaired to a data warehouse based on a distributed system architecture;
and the actuarial and summary module 50 is used for performing actuarial and data summary on the insurance services according to the actuarial indexes and the insurance service data stored in the data warehouse.
In the embodiment of the invention, for insurance service data, data is extracted in a distributed manner in a preset format and converted into an entity object, the entity object is processed by using parallel operation to obtain an actuarial index, the entity object is detected according to a preset rule, data which is not in compliance is repaired, the compliance data or the repaired data is stored in a data warehouse, and insurance service actuarial and data summarization are carried out on the actuarial index and the data stored in the data warehouse to obtain a data summary table. In the embodiment of the invention, the distributed extraction and parallel computation processing are adopted, so that the processing efficiency is improved compared with the single-thread processing efficiency, the detection and the repair of the machine learning algorithm are carried out on the processed data, the time consumed for the extraction, the detection and the repair of the data is short, and the data summarizing efficiency is improved.
It is to be noted that unless otherwise defined, technical or scientific terms used in one or more embodiments of the present specification should have the ordinary meaning as understood by those of ordinary skill in the art to which this disclosure belongs. The word "comprising" or "comprises", and the like, means that the element or item listed before the word covers the element or item listed after the word and its equivalents, but does not exclude other elements or items.
Based on any one of the above embodiments of the insurance business actuarial data processing method, the present invention further provides an insurance business actuarial data processing electronic device, as shown in fig. 3, including: a processor 1010, a memory 1020, an input/output interface 1030, a communication interface 1040, and a bus 1050. Wherein the processor 1010, memory 1020, input/output interface 1030, and communication interface 1040 are communicatively coupled to each other within the device via bus 1050.
The processor 1010 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits, and is configured to execute related programs to implement the technical solutions provided in the embodiments of the present disclosure.
The Memory 1020 may be implemented in the form of a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, or the like. The memory 1020 may store an operating system and other application programs, and when the technical solution provided by the embodiments of the present specification is implemented by software or firmware, the relevant program codes are stored in the memory 1020 and called to be executed by the processor 1010.
The input/output interface 1030 is used for connecting an input/output module to input and output information. The i/o module may be configured as a component in a device (not shown) or may be external to the device to provide a corresponding function. The input devices may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output devices may include a display, a speaker, a vibrator, an indicator light, etc.
The communication interface 1040 is used for connecting a communication module (not shown in the drawings) to implement communication interaction between the present apparatus and other apparatuses. The communication module can realize communication in a wired mode (such as USB, network cable and the like) and also can realize communication in a wireless mode (such as mobile network, WIFI, Bluetooth and the like).
It should be noted that although the above-mentioned device only shows the processor 1010, the memory 1020, the input/output interface 1030, the communication interface 1040 and the bus 1050, in a specific implementation, the device may also include other components necessary for normal operation. In addition, those skilled in the art will appreciate that the above-described apparatus may also include only those components necessary to implement the embodiments of the present description, and not necessarily all of the components shown in the figures.
Those of ordinary skill in the art will understand that: the discussion of any embodiment above is meant to be exemplary only, and is not intended to intimate that the scope of the disclosure, including the claims, is limited to these examples; within the spirit of the present disclosure, features from the above embodiments or from different embodiments may also be combined, steps may be implemented in any order, and there are many other variations of different aspects of one or more embodiments of the present description as described above, which are not provided in detail for the sake of brevity.
It is intended that the one or more embodiments of the present specification embrace all such alternatives, modifications and variations as fall within the broad scope of the appended claims. Therefore, any omissions, modifications, substitutions, improvements, and the like that may be made without departing from the spirit and principles of one or more embodiments of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (10)
1. An insurance business actuarial data processing method is characterized by comprising the following steps:
reading insurance business data from a table of a distributed column-oriented database in a preset format and converting the insurance business data into business entity objects;
processing the business entity object by using a parallel computing engine to obtain a actuarial index;
according to a preset check rule, performing compliance check on the attribute value of the business entity object, and repairing the non-compliant insurance business data found in the compliance check;
saving insurance business data which passes the compliance check or is repaired to a data warehouse based on a distributed system architecture;
and performing insurance service actuations and data summarization according to the actuarial indexes and insurance service data stored in the data warehouse.
2. The actuarial data processing method of claim 1, wherein the distributed column-oriented database is an HBase database.
3. The insurance business actuarial data processing method according to claim 1 or 2, wherein the parallel computing engine is a MapReduce or Spark computing engine.
4. The insurance business actuarial data processing method according to claim 1 or 2, wherein the data warehouse is a Hive library based on Hadoop.
5. The insurance service actuarial data processing method according to claim 1 or 2, wherein the actuarial indicator comprises at least one of: group/individual logo, death warranty, serious warranty, settled claims, and pending claims.
6. The insurance service actuarial data processing method according to claim 1 or 2, wherein the repairing the non-compliant insurance service data comprises: and restoring the non-compliant insurance business data by utilizing a pre-trained random forest model.
7. The insurance business actuarial data processing method of claim 6, wherein the non-compliant insurance business data includes a mandatory information field having a blank space or insurance business data having a filling content format that does not conform to the requirements of the information field.
8. The insurance business actuarial data processing method according to claim 6, wherein the non-compliant insurance business data includes insurance business data whose effective date and end date are inappropriate.
9. An insurance business actuarial data processing apparatus, comprising:
the reading module is used for reading insurance business data from a table of a distributed column-oriented database in a preset format and converting the insurance business data into business entity objects;
the processing module is used for processing the business entity object by using a parallel computing engine to obtain a actuarial index;
the checking and repairing module is used for carrying out compliance checking on the attribute value of the business entity object according to a preset checking rule and repairing the non-compliant insurance business data found in the compliance checking;
the storage module is used for storing the insurance business data which passes the compliance check or is repaired to a data warehouse based on a distributed system architecture;
and the actuarial and summary module is used for performing actuarial and data summary on the insurance business according to the actuarial index and the insurance business data stored in the data warehouse.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable by the processor, characterized in that the processor implements the method according to any of claims 1 to 8 when executing the computer program.
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