
International Journal For Multidisciplinary Research
E-ISSN: 2582-2160
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Impact Factor: 9.24
A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal
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Machine Learning for Credit Scoring Evaluation: a Survey
Author(s) | Baidyanath Sou |
---|---|
Country | India |
Abstract | Financial Risk Management (FRM) is a critical component of any organization’s financial success. It helps to protect financial health and long-term growth by identifying and mitigating financial risk. As the risk analysis heavily depends on information-deriving decision making, Machine learning is a promising field for new methods and technologies. In recent decades we have seen increasing adoption of Machine Learning methods for various risk management tasks. Credit Scoring is one of the risk factors involving creditors inability to perform their contractual obligations. This research article examines several scientific literature articles and conference proceedings from reputed databased and found out machine learning methods are applied to predict credit scoring gave promising results than traditional statistical methods. |
Keywords | Financial Risk, Credit Scoring, Machine Learning |
Field | Computer |
Published In | Volume 2, Issue 5, September-October 2020 |
Published On | 2020-10-31 |
Cite This | Machine Learning for Credit Scoring Evaluation: a Survey - Baidyanath Sou - IJFMR Volume 2, Issue 5, September-October 2020. DOI 10.36948/ijfmr.2020.v02i05.10691 |
DOI | https://doi.org/10.36948/ijfmr.2020.v02i05.10691 |
Short DOI | https://doi.org/gtbtkz |
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E-ISSN 2582-2160

CrossRef DOI is assigned to each research paper published in our journal.
IJFMR DOI prefix is
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