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.
Highlights
In recent years, with the rapid development of Internet finance, online credit has developed very rapidly and its participants are becoming more and more diversified
For the traditional statistical methods, only a single feature or a small number of features can study the relationship between user loan risk. erefore, it is a challenge to the traditional statistical methods [4]
In order to solve the uncertainty of human factors in the process of manual audit, a method to judge whether to make a loan according to detailed rules is proposed by financial institutions. is method gives a conclusion according to the rules, so that the credit auditor can judge whether to lend money according to clear indicators
Summary
With the rapid development of Internet finance, online credit has developed very rapidly and its participants are becoming more and more diversified. In order to control the financial credit risk more effectively, a financial credit risk control strategy based on weighted random forest algorithm is proposed. 2. Related Work e study in [12] proposed a financial credit risk assessment method based on particle swarm optimization algorithm. E work in [13] proposed the financial credit risk assessment method of stack noise reduction self-coding network, fully considered the correlation between data features, improved the stack noise reduction self-coding neural network model, introduced the truncated Karhunen lo eve expansion as the noise input term, and eliminated the noise data in the financial credit risk data to obtain more effective evaluation results. E work in [18] used a loan dataset of a commercial bank to test five different machine learning algorithms including KNN, DT, RF, NB, and logistic regression for credit risk assessment. E work in [13] proposed the financial credit risk assessment method of stack noise reduction self-coding network, fully considered the correlation between data features, improved the stack noise reduction self-coding neural network model, introduced the truncated Karhunen lo eve expansion as the noise input term, and eliminated the noise data in the financial credit risk data to obtain more effective evaluation results. e study in [14] proposed a financial credit risk assessment method based on xgbfs, which uses a series of data preprocessing methods and embedded feature selection method xgbfs (xgboost feature selection) to reduce the user’s credit data dimension, train the xgboost assessment model, and realize the user’s credit risk assessment. e work in [15] proposed that integration of supervised and unsupervised machine learning strategies will produce better results than using only one of them. ey proposed a system for credit risk assessment by integrating the supervised and unsupervised learning strategies at the consensus stage and the dataset clustering stage. e study in [16] concluded that traditional approaches used for forecasting credit risks are not well suited to help the financial institutions and they need ML-based techniques for forecasting credit risk. ey proposed a hybrid ensemble machine learning approach by incorporating two classic machine learning approaches, the RS (random subspace), and multiboosting. e work in [17] used the standard probit algorithm along with various machine learning algorithms like neural networks and KNN. ey achieved a lower error rate with the machine learning techniques as compared to the classic methods used for credit risk assessment. e work in [18] used a loan dataset of a commercial bank to test five different machine learning algorithms including KNN, DT, RF, NB, and logistic regression for credit risk assessment. eir results show that the random forest algorithm performs better than the other algorithms tested
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