Financial credit risk prediction in internet finance driven by machine learning

Ma, Xiaofeng; Lv, Shuliang

doi:10.1007/s00521-018-3963-6

Cited by 54 publications

(35 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1-6) and employees (Nos. [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. Based on the features of the management team, we can check company-related information (e.g., the scale of the company) rather than employee-related information.…”

Section: B Data Sourcesmentioning

confidence: 99%

“…Owing to an increase in the amount of data and computing resources, diverse machine learning and deep learning methods have been applied to various domains. Machine learning algorithms have been used to estimate the costs or forecast dangerous events in the economic domain [19][20][21][22]. In addition, previous studies have shown that a machine learning model with a time series can be applied to the stock market [23,24].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning Framework for Multi-Level Classification of Company Revenue

Choi

Kim

et al. 2021

IEEE Access

View full text Add to dashboard Cite

The planning and execution of a business strategy are important aspects of the strategic human resource management of a company. In previous studies, machine learning algorithms were used to determine the main factors correlating employees with company performance. In this study, we introduced a method based on machine-learning algorithms for the classification of company revenue. Both annual and integrated datasets were examined to evaluate the classification performance of the framework under both binary and multiclass conditions. The performance of the proposed method was validated using six evaluation metrics: accuracy, precision, recall, F1-score, receiver operating characteristic curve, and area under the curve. As the experimental results indicate, the XGBoost classifier displayed the best classification performance among the three algorithms (XGBoost classifier, stochastic gradient descent classifier, and logistic regression) used in this study. Moreover, we confirmed the important features of the trained XGBoost model in accordance with variables focusing on human resource management studies. These results demonstrate that the proposed framework has strength in terms of both classification and practical implementation. This study provides novel insights into the relationship between employees and the revenue levels of their employer.

show abstract

Section: B Data Sourcesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning Framework for Multi-Level Classification of Company Revenue

Choi

Kim

et al. 2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…2018), financial modelling and prediction (De Spiegeleer et al . 2018; Ma & Lv 2019), psychology (Holden et al . 2011; Finch et al .…”

Section: Term Meaning Reference(s)mentioning

confidence: 99%

Learning to see the wood for the trees: machine learning, decision trees, and the classification of isolated theropod teeth

2020

View full text Add to dashboard Cite

Taxonomic identification of fossils based on morphometric data traditionally relies on the use of standard linear models to classify such data. Machine learning and decision trees offer powerful alternative approaches to this problem but are not widely used in palaeontology. Here, we apply these techniques to published morphometric data of isolated theropod teeth in order to explore their utility in tackling taxonomic problems. We chose two published datasets consisting of 886 teeth from 14 taxa and 3020 teeth from 17 taxa, respectively, each with five morphometric variables per tooth. We also explored the effects that missing data have on the final classification accuracy. Our results suggest that machine learning and decision trees yield superior classification results over a wide range of data permutations, with decision trees achieving accuracies of 96% in classifying test data in some cases. Missing data or attempts to generate synthetic data to overcome missing data seriously degrade all classifiers predictive accuracy. The results of our analyses also indicate that using ensemble classifiers combining different classification techniques and the examination of posterior probabilities is a useful aid in checking final class assignments. The application of such techniques to isolated theropod teeth demonstrate that simple morphometric data can be used to yield statistically robust taxonomic classifications and that lower classification accuracy is more likely to reflect preservational limitations of the data or poor application of the methods.

show abstract

“…Some scholars find that digital finance challenges financial supervision and increases risks (Chen, Manh, Liu, & Sriboonchitta, 2019; Zhu , 2018). Ma and Shuliang (2019) point out that the continuous development of digital finance makes financial credit risk increasingly difficult to control.…”

Section: Literature Reviewmentioning

confidence: 99%

The impact of digital finance on financial efficiency

et al. 2020

View full text Add to dashboard Cite

This paper quantitatively analyzes the impact of digital finance on financial efficiency. The results show that digital finance has slightly improved the efficiency of the financial sector, but there are significant differences in the impact of provincial efficiency in China. Although financial sector efficiency positively correlates with digital financial efficiency, digital finance gives backward regions disadvantages. The efficiency score and ranking of the financial sector in the eastern region are significantly higher than noneastern region. The progressive effect of digital finance on the efficiency of the financial sector in the eastern region is better than that in the noneastern region.

show abstract

Financial credit risk prediction in internet finance driven by machine learning

Cited by 54 publications

References 14 publications

Machine Learning Framework for Multi-Level Classification of Company Revenue

Machine Learning Framework for Multi-Level Classification of Company Revenue

Learning to see the wood for the trees: machine learning, decision trees, and the classification of isolated theropod teeth

The impact of digital finance on financial efficiency

Contact Info

Product

Resources

About