ISSN 2394-5125
 

Research Article 


EXPLORATION OF CREDIT RISK BASED ON MACHINE LEARNING TOOLS

Ms Usha Devi, Dr Neera Batra.

Abstract
The Credit risk prediction research domain has been evolving with different predictive models
and these models have been developed using various tools. But the problem is that many of the tools are used in
the wrong situation orwith the wrong data conditions. A systematic review of 62 journals articles published
during 2010 to 2020 has been carried out in this paper. This review paper focuses on performance shown by
elevenpromising and popular tools based on 13 key criterions used in credit risk prediction. It includes the
following machine learning tools: SVM(Support vector machines), MDA(Multiple discriminant
analysis),RS(Rough sets), LR(Logistic regression), ANN(Artificial neural network), CBR( case based
reasoning), DT(Decision tree), GA(Genetic algorithm), KNN(K-Nearest Neighbor), XGBoost algorithm and
DGHNL(Deep Genetic Hierarchical Network of Learners) .Various parameters used so far to identify criterions
include result transparency accuracy, fully deterministic output, , data size capability, data dispersion, variable
types applicable etc. A framework with the help of tables and diagrams has been proposed for the selection of
tools that best fit different situations. It is determined that no single tool is predominantly better than the other
tools. Their performance varies as scenario/situation changes. This review paper contributes towards a detailed
and complete understanding of various tools developed till date for credit risk prediction and their limitations.

Key words: CRPM, BPM, Hybrid tools, Machine learning tools.


 
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Pubmed Style

Ms Usha Devi, Dr Neera Batra. EXPLORATION OF CREDIT RISK BASED ON MACHINE LEARNING TOOLS. JCR. 2020; 7(19): 6427-6449. doi:10.31838/jcr.07.19.739


Web Style

Ms Usha Devi, Dr Neera Batra. EXPLORATION OF CREDIT RISK BASED ON MACHINE LEARNING TOOLS. http://www.jcreview.com/?mno=134940 [Access: August 16, 2021]. doi:10.31838/jcr.07.19.739


AMA (American Medical Association) Style

Ms Usha Devi, Dr Neera Batra. EXPLORATION OF CREDIT RISK BASED ON MACHINE LEARNING TOOLS. JCR. 2020; 7(19): 6427-6449. doi:10.31838/jcr.07.19.739



Vancouver/ICMJE Style

Ms Usha Devi, Dr Neera Batra. EXPLORATION OF CREDIT RISK BASED ON MACHINE LEARNING TOOLS. JCR. (2020), [cited August 16, 2021]; 7(19): 6427-6449. doi:10.31838/jcr.07.19.739



Harvard Style

Ms Usha Devi, Dr Neera Batra (2020) EXPLORATION OF CREDIT RISK BASED ON MACHINE LEARNING TOOLS. JCR, 7 (19), 6427-6449. doi:10.31838/jcr.07.19.739



Turabian Style

Ms Usha Devi, Dr Neera Batra. 2020. EXPLORATION OF CREDIT RISK BASED ON MACHINE LEARNING TOOLS. Journal of Critical Reviews, 7 (19), 6427-6449. doi:10.31838/jcr.07.19.739



Chicago Style

Ms Usha Devi, Dr Neera Batra. "EXPLORATION OF CREDIT RISK BASED ON MACHINE LEARNING TOOLS." Journal of Critical Reviews 7 (2020), 6427-6449. doi:10.31838/jcr.07.19.739



MLA (The Modern Language Association) Style

Ms Usha Devi, Dr Neera Batra. "EXPLORATION OF CREDIT RISK BASED ON MACHINE LEARNING TOOLS." Journal of Critical Reviews 7.19 (2020), 6427-6449. Print. doi:10.31838/jcr.07.19.739



APA (American Psychological Association) Style

Ms Usha Devi, Dr Neera Batra (2020) EXPLORATION OF CREDIT RISK BASED ON MACHINE LEARNING TOOLS. Journal of Critical Reviews, 7 (19), 6427-6449. doi:10.31838/jcr.07.19.739