Enterprise risk quantitative evaluation and tracking system based on machine learning

Abstract

An enterprise risk quantitative evaluation and tracking system based on machine learning is a system integrating risk factor identification, risk evaluation, risk tracking and early warning, and specifically comprises a risk quantitative evaluation subsystem, an investment project value evaluation subsystem, a bond risk quantitative evaluation subsystem and a risk early warning tracking subsystem.The biggest characteristic of the system is that the system is based on data of an origin parameter big data platform, potential risk factors possibly existing in a target enterprise are automatically recognized and judged through a machine learning algorithm, a risk assessment model suitable for the target enterprise is automatically called by analyzing basic information of a company, risk quantitative assessment and real-time tracking and early warning of the target enterprise are achieved, and a reliable and practical tool is provided for investors.

Description

technical field [0001] The invention relates to the field of financial tools, in particular to a machine learning-based enterprise risk quantitative assessment and tracking system. Background technique [0002] status quo: [0003] In the financial industry, equity investment is the most common and common business activity. Scientific and professional equity investment needs to collect a large amount of macro data, industry data, financial data and non-financial data of enterprises over the years, and all data will be evaluated and analyzed by professionals, and then investment evaluation reports and conclusions will be formed. The management of corporate equity investment generally includes several stages: pre-investment investigation, investment evaluation and post-investment monitoring. [0004] The disadvantages of the current traditional method are: [0005] 1. Accuracy [0006] The current traditional method mainly relies on the experience of internal experts to ma...

AI Technical Summary

Problems solved by technology

[0006] The current traditional method mainly relies on the experience of internal experts to make credit risk judgments, and has low requirements for the timeliness and representativeness of macro and industry data, which will lead to deviations between the evaluation results and the actual situation of the enterprise

[0007] 2. Isolation between various links

[0010] The models of current institutions are mostly established based on traditional bank risk assessment models. The setting of factors in the model and the parameters of factors are all established by experts based on experience and cannot be adjusted according to the actual situation. The flexibility and usability of the model are poor.

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