Financial Advisory Assistant Platform
Meera Ambre
, Sayali Naik , Rahul Nair , Unmesh Sawant , Aarti Bakshi
Loan default, Credit, Algorithms, Evaluation
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.
"Financial Advisory Assistant Platform", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.5, Issue 5, page no.171 - 176, May-2020, Available :https://ijsdr.org/papers/IJSDR2005029.pdf
Volume 5
Issue 5,
May-2020
Pages : 171 - 176
Paper Reg. ID: IJSDR_191683
Published Paper Id: IJSDR2005029
Downloads: 000347250
Research Area: Engineering
Country: Thane, Maharashtra, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
