Articlehybrid2021

Financial Credit Risk Control Strategy Based on Weighted Random Forest Algorithm

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Financial Distress and Bankruptcy Prediction Imbalanced Data Classification Techniques Evaluation and Optimization Models

Abstract

In order to improve the effectiveness of financial credit risk control, a financial credit risk control strategy based on weighted random forest algorithm is proposed. The weighted random forest algorithm is used to classify the financial credit risk data, construct the evaluation index system, and use the analytic hierarchy process to evaluate the financial credit risk level. The targeted risk control strategies are taken according to different risk assessment results. We compared the proposed method with two other methods, and the experimental results show that the proposed method has higher classification accuracy of financial credit data and the risk assessment threshold is basically consistent with the actual results.

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References

2019Forecasting SMEs' credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach 2019Integration of unsupervised and supervised machine learning algorithms for credit risk assessment 2020A Comparative Assessment of Credit Risk Model Based on Machine Learning ——a case study of bank loan data 2019Rule-based credit risk assessment model using multi-objective evolutionary algorithms 2018Predicting credit risk on the basis of financial and non-financial variables and data mining 2020Lean HRM practices in work integration social enterprises: Moving towards social lean production. Evidence from Italian case studies*2019Classification Algorithms in Financial Application: Credit Risk Analysis on Legal Entities 2017Research on the Credit Risk Assessment of Chinese Online Peer-to-peer Lending Borrower on Logistic Regression Model

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