Machine learning in banking risk management: A literature review
M Leo, S Sharma, K Maddulety - Risks, 2019 - mdpi.com
There is an increasing influence of machine learning in business applications, with many
solutions already implemented and many more being explored. Since the global financial …
solutions already implemented and many more being explored. Since the global financial …
Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research
Many years have passed since Baesens et al. published their benchmarking study of
classification algorithms in credit scoring [Baesens, B., Van Gestel, T., Viaene, S …
classification algorithms in credit scoring [Baesens, B., Van Gestel, T., Viaene, S …
Computational approaches and data analytics in financial services: A literature review
D Andriosopoulos, M Doumpos… - Journal of the …, 2019 - Taylor & Francis
The level of modeling sophistication in financial services has increased considerably over
the years. Nowadays, the complexity of financial problems and the vast amount of data …
the years. Nowadays, the complexity of financial problems and the vast amount of data …
A study on predicting loan default based on the random forest algorithm
L Zhu, D Qiu, D Ergu, C Ying, K Liu - Procedia Computer Science, 2019 - Elsevier
Recently, with the advance of electronic commerce and big data technology, P2P online
lending platforms have brought opportunities to businessmen, but at the same time, they are …
lending platforms have brought opportunities to businessmen, but at the same time, they are …
Two-stage consumer credit risk modelling using heterogeneous ensemble learning
M Papouskova, P Hajek - Decision support systems, 2019 - Elsevier
Modelling consumer credit risk is a crucial task for banks and non-bank financial institutions
to support decision-making on granting loans. To model the overall credit risk of a consumer …
to support decision-making on granting loans. To model the overall credit risk of a consumer …
Loan default prediction by combining soft information extracted from descriptive text in online peer-to-peer lending
Predicting whether a borrower will default on a loan is of significant concern to platforms and
investors in online peer-to-peer (P2P) lending. Because the data types online platforms use …
investors in online peer-to-peer (P2P) lending. Because the data types online platforms use …
A robust support vector regression model for electric load forecasting
Electric load forecasting is a crucial part of business operations in the energy industry.
Various load forecasting methods and techniques have been proposed and tested. With …
Various load forecasting methods and techniques have been proposed and tested. With …
Multi-view ensemble learning based on distance-to-model and adaptive clustering for imbalanced credit risk assessment in P2P lending
Credit risk assessment is a crucial task in the peer-to-peer (P2P) lending industry. In recent
years, ensemble learning methods have been verified to perform better in default prediction …
years, ensemble learning methods have been verified to perform better in default prediction …
Graph convolutional network-based credit default prediction utilizing three types of virtual distances among borrowers
Abstract Machine learning models have been actively utilized to quantitatively predict the
default probability based on the personal information obtained from loan applicants …
default probability based on the personal information obtained from loan applicants …
Mining semantic soft factors for credit risk evaluation in peer-to-peer lending
ABSTRACT While Peer-to-Peer (P2P) lending is rapidly growing, it is also accompanied by
high credit risk due to information asymmetry. Besides conventional hard information, soft …
high credit risk due to information asymmetry. Besides conventional hard information, soft …