Default Prediction
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2021 ◽  
pp. 106334
Author(s):  
Ralf Kellner ◽  
Maximilian Nagl ◽  
Daniel Rösch

2022 ◽  
Vol 59 ◽  
pp. 101536
Author(s):  
Kunpeng Yuan ◽  
Guotai Chi ◽  
Ying Zhou ◽  
Hailei Yin

Risks ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 159
Author(s):  
Sunghwa Park ◽  
Hyunsok Kim ◽  
Janghan Kwon ◽  
Taeil Kim

In this paper, we use a logit model to predict the probability of default for Korean shipping companies. We explore numerous financial ratios to find predictors of a shipping firm’s failure and construct four default prediction models. The results suggest that a model with industry specific indicators outperforms other models in predictive ability. This finding indicates that utilizing information about unique financial characteristics of the shipping industry may enhance the performance of default prediction models. Given the importance of the shipping industry in the Korean economy, this study can benefit both policymakers and market participants.


2021 ◽  
pp. 097226292110362
Author(s):  
Shilpa Shetty H. ◽  
Theresa Nithila Vincent

The unprecedented pandemic COVID-19 has impacted businesses across the globe. A significant jump in the credit default risk is expected. Credit default is an indicator of financial distress experienced by the business. Credit default often leads to bankruptcy filing against the defaulting company. In India, the Insolvency and Bankruptcy Code (IBC) is the law that governs insolvency and bankruptcy. As reported by the Insolvency and Bankruptcy Board of India (IBBI), the number of companies filing for bankruptcy under IBC is on a rise, and the industrial sector has witnessed the maximum number of bankruptcy filings. The present article attempts to develop a credit default prediction model for the Indian industrial sector based on a sample of 164 companies comprising an equal number of defaulting and nondefaulting companies. A total of 120 companies are used as training samples and 44 companies as the testing samples. Binary logistic regression analysis is employed to develop the model. The diagnostic ability of the model is tested using receiver operating characteristic curve, area under the curve and annual accuracy. According to the study, return on assets, current ratio, debt to total assets ratio, sales to working capital ratio and cash flow to total assets ratio is statistically significant in predicting default. The findings of the study have significant implications in lending and investment decisions.


Author(s):  
Upasana Mukherjee ◽  
Vandana Thakkar ◽  
Shawni Dutta ◽  
Utsab Mukherjee ◽  
Samir Kumar Bandyopadhyay

The growth of regularly generated data from many financial activities has significant implications for every corner of financial modelling. This study has investigated the utilization of these continuous growing data by a means of an automated process. The automated process can be developed by using Machine learning based techniques that analyze the data and gain experience from the underlying data. Different important domains of financial fields such as Credit card fraud detection, bankruptcy detection, loan default prediction, investment prediction, marketing and many more can be modelled by implementing machine learning methods. Among several machine learning based techniques, the use of parametric and non-parametric based methods are approached by this research. Two parametric models namely Logistic Regression, Gaussian Naive Bayes models and two non-parametric methods such as Random Forest, Decision Tree are implemented in this paper. All the mentioned models are developed and implemented in the field of Credit card fraud detection, bankruptcy detection, loan default prediction. In each of the aforementioned cases, the comparative study among the classification techniques is drawn and the best model is identified. The performance of each classifier on each considered domain is evaluated by various performance metrics such as accuracy, F1-score and mean squared error. In the credit card fraud detection model the decision tree classifier performs the best with an accuracy of 99.1% and, in the loan default prediction and bankruptcy detection model, the random forest classifier gives the best accuracy of  97% and 96.84% respectively.


Author(s):  
Samir Bandyopadhyay ◽  
Vandana Thakkar ◽  
Upasana Mukherjee ◽  
Shawni Dutta

The growth of regularly generated data from many financial activities has significant implications for every corner of financial modeling. This study has investigated the utilization of these continuous growing data by a means of an automated process. The automated process can be developed by using Machine learning based techniques that analyze the data and gain experience from the underlying data. Different important domains of financial fields such as Credit card fraud detection, bankruptcy detection, loan default prediction, investment prediction, marketing and many other financial models can be modeled by implementing machine learning models. Among several machine learning based techniques, the use of parametric and non-parametric based methods are approached by this research. Two parametric models namely Logistic Regression, Gaussian Naive Bayes models and two non-parametric methods such as Random Forest, Decision Tree are implemented in this paper. All the mentioned models are developed and implemented in the field of Credit card fraud detection, bankruptcy detection, loan default prediction. In each of the aforementioned cases, the comparative study among the classification techniques is drawn and the best model is identified. The performance of each classifier on each considered domain is evaluated by various performance metrics such as accuracy, recall, precision, F1-score and mean squared error. In the credit card fraud detection model the decision tree classifier performs the best with an accuracy of 99.1% and, in the loan default prediction and bankruptcy detection model, the random forest classifier gives the best accuracy of 97% and 96.84% respectively.


2021 ◽  
Vol 33 (6) ◽  
pp. 1-18
Author(s):  
Jun Hou ◽  
Qianmu Li ◽  
Yaozong Liu ◽  
Sainan Zhang

As an important global policy guide to promote economic transformation and upgrading, the upsurge of E-Commerce has been continuously upgraded with continuous breakthroughs in information technology. In recent years, China’s e-commerce consumer credit has developed well, but due to its short time of production and insufficient experience for reference, credit risk, fraud risk, and regulatory risk continue to emerge. Aiming at the problem of E-Commerce Consumer Credit default analysis, this paper proposes a Fusion Enhanced Cascade Model (FECM). This model learns feature data of credit data by fusing multi-granularity modules, and incorporates random forest and GBDT trade-off variance and bias methods. The paper compares FECM and gcForest on multiple data sets, to prove the applicability of FECM in the field of E-commerce credit default prediction. The research results of this paper are helpful to the risk control of financial development, and to construct a relatively stable financial space for promoting the construction and development of E-Commerce.


Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-1
Author(s):  
Ying Chen ◽  
Ruirui Zhang


2021 ◽  
Vol 33 (6) ◽  
pp. 0-0

As an important global policy guide to promote economic transformation and upgrading, the upsurge of E-Commerce has been continuously upgraded with continuous breakthroughs in information technology. In recent years, China’s e-commerce consumer credit has developed well, but due to its short time of production and insufficient experience for reference, credit risk, fraud risk, and regulatory risk continue to emerge. Aiming at the problem of E-Commerce Consumer Credit default analysis, this paper proposes a Fusion Enhanced Cascade Model (FECM). This model learns feature data of credit data by fusing multi-granularity modules, and incorporates random forest and GBDT trade-off variance and bias methods. The paper compares FECM and gcForest on multiple data sets, to prove the applicability of FECM in the field of E-commerce credit default prediction. The research results of this paper are helpful to the risk control of financial development, and to construct a relatively stable financial space for promoting the construction and development of E-Commerce.


Author(s):  
Mohammad Zoynul Abedin ◽  
M. Kabir Hassan ◽  
Imran Khan ◽  
Ivan F. Julio

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