scholarly journals An empirical study on P2P loan default prediction model

2020 ◽  
Vol 3 (1) ◽  
Keyword(s):  
Empirical Study ◽  
Prediction Model ◽  
Loan Default ◽  
Default Prediction

A deep metric learning approach for weakly supervised loan default prediction1

2021 ◽  
pp. 1-13
Author(s):  
Kai Zhuang ◽  
Sen Wu ◽  
Xiaonan Gao
Keyword(s):  
Default Risk ◽  
Metric Learning ◽  
Real Life ◽  
Risk Scores ◽  
Loan Default ◽  
Default Prediction ◽  
Heterogeneous Features ◽  
Deep Metric Learning ◽  
Weakly Supervised ◽  
Decision Boundaries

To deal with the systematic risk of financial institutions and the rapid increasing of loan applications, it is becoming extremely important to automatically predict the default probability of a loan. However, this task is non-trivial due to the insufficient default samples, hard decision boundaries and numerous heterogeneous features. To the best of our knowledge, existing related researches fail in handling these three difficulties simultaneously. In this paper, we propose a weakly supervised loan default prediction model WEAKLOAN that systematically solves all these challenges based on deep metric learning. WEAKLOAN is composed of three key modules which are used for encoding loan features, learning evaluation metrics and calculating default risk scores. By doing so, WEAKLOAN can not only extract the features of a loan itself, but also model the hidden relationships in loan pairs. Extensive experiments on real-life datasets show that WEAKLOAN significantly outperforms all compared baselines even though the default loans for training are limited.

scholarly journals Corporate Default Predictions Using Machine Learning: Literature Review

2020 ◽  
Vol 12 (16) ◽  
pp. 6325 ◽  
Author(s):  
Hyeongjun Kim ◽  
Hoon Cho ◽  
Doojin Ryu
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Statistical Models ◽  
Stock Price ◽  
Global Financial Crisis ◽  
Support Vector ◽  
Corporate Default ◽  
Default Prediction ◽  
Future Outcomes ◽  
The Global Financial Crisis

Corporate default predictions play an essential role in each sector of the economy, as highlighted by the global financial crisis and the increase in credit risk. This study reviews the corporate default prediction literature from the perspectives of financial engineering and machine learning. We define three generations of statistical models: discriminant analyses, binary response models, and hazard models. In addition, we introduce three representative machine learning methodologies: support vector machines, decision trees, and artificial neural network algorithms. For both the statistical models and machine learning methodologies, we identify the key studies used in corporate default prediction. By comparing these methods with findings from the interdisciplinary literature, our review suggests some new tasks in the field of machine learning for predicting corporate defaults. First, a corporate default prediction model should be a multi-period model in which future outcomes are affected by past decisions. Second, the stock price and the corporate value determined by the stock market are important factors to use in default predictions. Finally, a corporate default prediction model should be able to suggest the cause of default.

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