INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15
In today’s world the banking sector is facing a tremendous increase and issue regarding the non-performing loans/assets from the their customers which results in jeopardizing effect on the growth of the institute in banking world. In world, where technology is advancing daily, is have been easy for companies to store the huge data of the customers which represent their behavior. With the help of the data collected from a leading credit provided to unbanked population, we have done loan default prediction. In this paper we will see how the loan default prediction is done using four different machine learning algorithms named Naive Bayes’ Theorem, Deep Learning using four and five layers, Logistic regression and Gradient boosting. The algorithm model evaluation is done using confusion matrix, Receiving Operating Characteristic charts, Cumulative charts, etc. The evaluation also has important metrics as accuracy, sensitivity, precision, etc. After comparing the performances of the algorithm, we save the model to the disk using Pythons pickle model and make use of it for predicting the new data. This paper provides basis to find the risky customers from the bunch of applicants.
Keywords:
Loan default, Credit, Algorithms, Evaluation
Cite Article:
"Financial Advisory Assistant Platform", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.5, Issue 5, page no.171 - 176, May-2020, Available :http://www.ijsdr.org/papers/IJSDR2005029.pdf
Downloads:
000337072
Publication Details:
Published Paper ID: IJSDR2005029
Registration ID:191683
Published In: Volume 5 Issue 5, May-2020
DOI (Digital Object Identifier):
Page No: 171 - 176
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631
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