Machine Learning Algorithms Based Credit Risk Assessment Models

Analysis of credit scoring is an effective credit risk assessment technique, which is one of the major research fields in the banking sector. Machine learning has a variety of applications in the banking sector and it has been widely used for data analysis. Modern techniques such as machine learning have provided a self-regulating process to analyze the data using classification techniques. The classification method is a supervised learning process in which the computer learns from the input data provided and makes use of this information to classify the new dataset. This research paper presents a comparison of various machine learning techniques used to evaluate the credit risk. A credit transaction that needs to be accepted or rejected is trained and implemented on the dataset using different machine learning algorithms. The techniques are implemented on the German credit dataset taken from UCI repository which has 1000 instances and 21 attributes, depending on which the transactions are either accepted or rejected. This paper compares algorithms such as Support Vector Network, Neural Network, Logistic Regression, Naive Bayes, Random Forest, and Classification and Regression Trees (CART) algorithm and the results obtained show that Random Forest algorithm was able to predict credit risk with higher accuracy

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Multiview Graph Learning for Small- and Medium-Sized Enterprises’ Credit Risk Assessment in Supply Chain Finance

Complexity ◽

10.1155/2021/6670873 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Cong Wang ◽

Fangyue Yu ◽

Zaixu Zhang ◽

Jian Zhang

Keyword(s):

Risk Assessment ◽

Supply Chain ◽

Credit Risk ◽

Quantitative Result ◽

Small Sample ◽

Graph Learning ◽

Credit Risk Assessment ◽

Risk Assessment Models ◽

Supply Chain Finance ◽

Index Systems

In recent years, supply chain finance (SCF) is exploited to solve the financing difficulties of small- and medium-sized enterprises (SMEs). SME credit risk assessment is a critical part in the SCF system. The diffusion of SME credit risk may cause serious consequences, leading the whole supply chain finance system unstable and insecure. Compared with traditional credit risk assessment models, the supply chain relationship, credit condition of SME, and core enterprises should all be considered to rate SME credit risk in SCF. Traditional methods mix all indicators from different index systems. They cannot give a quantitative result on how these index systems work. Furthermore, traditional credit risk assessment models are heavily dependent on the number of annotated SME data. However, it is implausible to accumulate enough credit risky SMEs in advance. In this paper, we propose an adaptive heterogenous multiview graph learning method to tackle the small sample size problem for SMEs’ credit risk forecasting. Three graphs are constructed by using indicators from supply chain operation, SME financial indicator, and nonfinancial indicator individually. All the graphs are integrated in an adaptive manner, providing a quantitative explanation on how the three parts cooperate. The experimental analysis shows that the proposed method has good performance for determining whether SME is risky or nonrisky in SCF. From the perspective of SCF, SME financing ability is still the main factor to determine the credit risk of SME.

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Application of eXtreme gradient boosting trees in the construction of credit risk assessment models for financial institutions

Applied Soft Computing ◽

10.1016/j.asoc.2018.09.029 ◽

2018 ◽

Vol 73 ◽

pp. 914-920 ◽

Cited By ~ 29

Author(s):

Yung-Chia Chang ◽

Kuei-Hu Chang ◽

Guan-Jhih Wu

Keyword(s):

Risk Assessment ◽

Credit Risk ◽

Financial Institutions ◽

Gradient Boosting ◽

Credit Risk Assessment ◽

Risk Assessment Models ◽

Extreme Gradient Boosting

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