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CN-122022973-A - Business risk analysis method and device, electronic equipment and storage medium

CN122022973ACN 122022973 ACN122022973 ACN 122022973ACN-122022973-A

Abstract

The application discloses a business risk analysis method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence or other related fields, wherein the method comprises the steps of acquiring target multi-source data related to target financial business from N financial data sources, wherein N is a positive integer; inputting target multi-source data into a preset target model, and outputting a data analysis result in a natural language form, wherein the preset target model is a large model which is adjusted in advance according to the financial field direction and is used for analyzing risk data relevance of the target multi-source data and expressing the risk data relevance in the natural language form, and filling a preset report template with the data analysis result in the natural language form to obtain a business risk analysis report of the target financial business. The application solves the technical problem of low accuracy of business risk report caused by insufficient data integration analysis in the related technology.

Inventors

  • WANG XIANLEI

Assignees

  • 中国工商银行股份有限公司

Dates

Publication Date
20260512
Application Date
20260130

Claims (11)

  1. 1. A method for analyzing business risk, comprising: acquiring target multi-source data related to target financial business from N financial data sources, wherein N is a positive integer; inputting the target multi-source data into a preset target model, and outputting a data analysis result in a natural language form, wherein the preset target model is a large model which is adjusted in advance according to the financial field direction and is used for analyzing the risk data relevance of the target multi-source data and expressing the risk data relevance in the natural language form; and filling a preset report template by using the data analysis result in the natural language form to obtain a business risk analysis report of the target financial business.
  2. 2. The method of analysis of claim 1, wherein the target multi-source data comprises structured data and unstructured data, the method further comprising, prior to inputting the target multi-source data into a pre-set target model: performing first preprocessing on the structured data, wherein the first preprocessing comprises data cleaning processing and data structure standardization processing; And performing second preprocessing on the unstructured data, wherein the second preprocessing comprises natural language processing and entity recognition processing.
  3. 3. The method of claim 1, wherein the financial data sources include an internal database of a financial system, a data call interface within the financial system rights, an external data platform pre-authorized to the financial system, and the step of obtaining target multi-source data related to a target financial transaction from N financial data sources includes: extracting first related data related to the target financial business from the internal database; Accessing the data calling interface in the financial system authority to request second related data related to the target financial business in the latest updating period; Receiving a data push message pre-authorized to the external data platform of the financial system to obtain third related data related to the target financial business; and integrating the first related data, the second related data and the third related data to obtain target multi-source data related to the target financial business.
  4. 4. The method of claim 1, wherein the predetermined target model is constructed by: Acquiring a natural language processing frame serving as a basic model, and acquiring a financial domain corpus provided in advance by a financial system; Selecting a model optimization algorithm and a learning rate dynamic adjustment strategy to obtain a model iterative adjustment strategy; Training the basic model based on the financial domain corpus and the model iteration adjustment strategy, and adjusting model parameters in an iteration process; stopping iterative training until the identification accuracy of the basic model to the risk association degree of the historical business data in the financial field corpus exceeds a preset threshold value, and obtaining the target model.
  5. 5. The method according to claim 1, wherein the step of inputting the target multi-source data into a preset target model and outputting the data analysis result in the form of natural language comprises: analyzing a risk association rule of structured data in the target multi-source data through a multi-mode data fusion algorithm preset by the target model, and decoding potential risk features of unstructured data in the target multi-source data to obtain a risk feature analysis result; And converting the risk characteristic analysis result into text description by applying a natural language generation technology preset by the target model to obtain a data analysis result in the natural language form.
  6. 6. The analysis method according to claim 1, wherein the step of filling a preset report template with the data analysis result in the natural language form to obtain a business risk analysis report of the target financial business comprises: filling structured data in the data analysis result into a first region to be filled in the preset report template; for unstructured data in the data analysis result, carrying out format arrangement on the unstructured data according to the text display requirement of the preset report template, and filling the arrangement result into a second area to be filled; And after all the contents in the data analysis result are integrated, obtaining the business risk analysis report of the target financial business.
  7. 7. The method of claim 1, wherein after obtaining a business risk analysis report for the target financial business, the method further comprises: Sending the business risk analysis report to an interaction interface; Collecting feedback information of a user on the business risk analysis report through the interactive interface; and updating the business risk analysis report according to the feedback information.
  8. 8. A business risk analysis device, comprising: The acquisition unit is used for acquiring target multi-source data related to target financial business from N financial data sources, wherein N is a positive integer; The analysis unit is used for inputting the target multi-source data into a preset target model and outputting a data analysis result in a natural language form, wherein the preset target model is a large model which is adjusted in advance according to the direction of the financial field and is used for analyzing the risk data relevance of the target multi-source data and expressing the risk data relevance in the natural language form; And the filling unit is used for filling a preset report template by using the data analysis result in the natural language form to obtain a business risk analysis report of the target financial business.
  9. 9. A computer readable storage medium, characterized in that the computer readable storage medium comprises a stored computer program, wherein the computer program, when run, controls a device in which the computer readable storage medium is located to perform the method of analyzing business risk according to any of claims 1 to 7.
  10. 10. An electronic device comprising one or more processors and a memory for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of business risk analysis of any of claims 1-7.
  11. 11. A computer program product comprising computer instructions which, when executed by a processor, implement the steps of the business risk analysis method of any one of claims 1 to 7.

Description

Business risk analysis method and device, electronic equipment and storage medium Technical Field The invention relates to the technical field of artificial intelligence or other related fields, in particular to a business risk analysis method and device, electronic equipment and storage medium. Background With the rapid development of the field of financial science and technology, particularly in general financial business management, the generation and analysis of post-business risk reports are in a key period of technical transformation. For a long time, financial institutions have relied on methods that combine traditional template filling systems with rules engines to monitor and evaluate the health of loan portfolio services. However, the inherent mode of relying on a single structured data processing manner cannot meet the business requirements of increasingly diversified current financial data types and complicated business scenarios. On the one hand, a static template filling system can only process limited structured data, but cannot deeply mine the inherent links between data, especially those key information hidden in unstructured text, so that risk reports lack deep insight. On the other hand, although the rule engine makes up the defect of the template system to a certain extent and can perform preliminary analysis on data based on preset logic, the solidified analysis framework is difficult to cope with complex and changeable business scenes, particularly when cross-mechanism and multi-product combination analysis is encountered, the rules engine always catches the forepart and breaks the elbows, and the rule engine is also ungainly about risk signals in unstructured data, so that the generated risk reports always have information blind areas, and the accuracy of business decision is reduced. The prior art has obvious defects in the process of data integration analysis, and cannot effectively integrate multi-source data from the inside and the outside of a financial institution, so that the defect of the data integration analysis not only limits the depth and the breadth of a risk report, but also causes low report generation efficiency, heavy manual dependence and difficulty in reflecting market dynamics and customer demand changes in real time. In view of the above problems, no effective solution has been proposed at present. Disclosure of Invention The application mainly aims to provide a business risk analysis method and device, electronic equipment and storage medium, which at least solve the technical problem of low business risk reporting accuracy caused by insufficient data integration analysis in the related technology. In order to achieve the above object, according to one aspect of the present application, there is provided a business risk analysis method, including obtaining target multi-source data related to a target financial business from N financial data sources, where N is a positive integer, inputting the target multi-source data into a preset target model, and outputting a data analysis result in a natural language form, where the preset target model is a large model previously adjusted according to a financial domain direction, analyzing risk data relevance of the target multi-source data, and expressing the risk data relevance in a natural language form, and filling a preset report template with the data analysis result in the natural language form to obtain a business risk analysis report of the target financial business. Further, the target multi-source data comprises structured data and unstructured data, and before the target multi-source data is input into a preset target model, the method further comprises the steps of performing first preprocessing on the structured data, wherein the first preprocessing comprises data cleaning processing and data structure standardization processing, and performing second preprocessing on the unstructured data, wherein the second preprocessing comprises natural language processing and entity identification processing. The financial data source comprises an internal database of a financial system, a data calling interface in the financial system authority and an external data platform preauthorized to the financial system, wherein the step of acquiring target multi-source data related to a target financial business from N financial data sources comprises the steps of extracting first related data related to the target financial business from the internal database, accessing the data calling interface in the financial system authority, requesting second related data related to the target financial business in the latest updating period, receiving a data pushing message preauthorized to the external data platform of the financial system to obtain third related data related to the target financial business, and integrating the first related data, the second related data and the third related data to obtain the target multi-source data