Dynamic adjusting enterprise credit risk assessment method

Abstract

The invention discloses a dynamic adjusting enterprise credit risk assessment method comprising the steps: capturing the news on the set news acquisition website through the web crawler according to the preset period; extracting the enterprise entity from the news and marking the enterprise entity name in the news text; performing emotion annotation on a certain number of news including the enterprise entity; training the classifier of the news emotion by the machine learning model by using the data annotated by experts; classifying the acquired non- annotated news data by using the trained model; calculating a cumulative score based on the number of positive, negative and neutral news items associated with one enterprise entity; integrating the news quantity and the emotion type distribution and calculating the overall score value of the news public opinion; using the weighted summation method to calculate the overall credit risk score with combination of the financial indicators andthe public opinion indicators, wherein the score can be updated dynamically based on real-time news; and outputting the credit score and the corresponding credit rating.

Description

technical field [0001] The invention relates to the field of data analysis and processing, in particular to a dynamically adjusted enterprise credit risk assessment method. Background technique [0002] The credit level of an enterprise is an important factor considered when banks and investment institutions make loans or invest. However, the current immature development of my country's credit market lacks unified credit record standards and objective risk assessment norms, resulting in the lack of credit for a large number of enterprises. Such a market situation, on the one hand, seriously hinders the investment in honest companies, leading to financing difficulties, high costs, and hindered business development; on the other hand, it also creates a heavy burden of non-performing assets on banks and investment institutions, reducing their Profit and market competitiveness. Establishing a fair and objective enterprise credit evaluation model is of great significance for re...

AI Technical Summary

Problems solved by technology

This solution has the following shortcomings: due to the long update cycle of financial indicators, and various possible situations such as data delay, incompleteness, and inauthenticity, the update of credit scores is slow, and it cannot objectively and comprehensively reflect the corporate risk at a certain point in time state

The introduction of additional risk factors such as public opinion provides investors with the possibility of comprehensively judging the risk status of enterprises. However, due to the large amount of public opinion data and the rapid update, it is impossible for most people to obtain complete information in a timely manner, even if these data are available. How much impact it has on enterprise risk still needs to rely on the subjective experience judgment of investors, and there are many possibilities of missed judgment or misjudgment

The lack of quantitative indicators leads to the lack of objective fairness and practical operability of such methods

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