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  Published Paper Details:

  Paper Title

MACHINE LEARNING CLASSIFICATION AND DEEP LEARNING BASED CREDIT PREDICTION AND RISK ANALYSIS ON LOANS DATA.

  Authors

  Ashutosh Khushwaha,  Aditya Kardile,  Mohd. Zaib Nawab,  Dr. Vinod Wadne

  Keywords

Keywords: Machine Learning, loan, credit, Imbalance data, prediction, etc.

  Abstract


Estimation or assessment of default a debt could be a crucial method that ought to be allotted by banks to assist them to assess if a loan somebody may be a defaulter at a later section in order that they method the applying and judge whether or not to approve the loan or not. Banks have already been creating an attempt to try to this through utilization of FICO score and credit Reports. In this project, using Machine learning techniques, we focus on dealing with imbalance data problem to enhance the performance of loan default and loan approves prediction. Artificial intelligence will facilitate modernize the standards for loan applications and permit banks to try to higher loan management. Equifax, one of the three major credit bureaus, determined through a recent research study that financial institutions sometimes deny good borrowers based on these FICO criteria artificial intelligence can help modernize the criteria for loan applications and allow banks to do better loan management

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2105225

  Paper ID - 206771

  Page Number(s) - c181-c183

  Pubished in - Volume 9 | Issue 5 | May 2021

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Ashutosh Khushwaha,  Aditya Kardile,  Mohd. Zaib Nawab,  Dr. Vinod Wadne,   "MACHINE LEARNING CLASSIFICATION AND DEEP LEARNING BASED CREDIT PREDICTION AND RISK ANALYSIS ON LOANS DATA.", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.9, Issue 5, pp.c181-c183, May 2021, Available at :http://www.ijcrt.org/papers/IJCRT2105225.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


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ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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