Authors :
Anushi Jain; Shivangi Gupta; Mandeep Singh Narula
Volume/Issue :
Volume 7 - 2022, Issue 5 - May
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3xBo0iN
DOI :
https://doi.org/10.5281/zenodo.6658793
Abstract :
The goal of this research is to develop a
model for forecasting loan defaults. This type of
strategy is unavoidable since bad loans are a critical
problem in the financial sector. To address this issue,
a literature analysis has been conducted to study the
significant factors that lead up to and solve this
problem. Dense Neural Network with Dropout (ANN
with Deep Learning), XGBoost, Random Forest,
Logistics Regression, and Support Vector Classifier
are the approaches utilized. We have compared the
models' accuracies, performance, and confusion
matrix measures during the experimental phase. The
best approach has been chosen, described, and
suggested based on these factors. Our final results are
based on the number of defaulters predicted and
actualized, while we have also suggested a model if we
prefer institutional research that prioritized accuracy,
performance, and speed.
Keywords :
Credit Score, Logistic Regression, XGBoost.
The goal of this research is to develop a
model for forecasting loan defaults. This type of
strategy is unavoidable since bad loans are a critical
problem in the financial sector. To address this issue,
a literature analysis has been conducted to study the
significant factors that lead up to and solve this
problem. Dense Neural Network with Dropout (ANN
with Deep Learning), XGBoost, Random Forest,
Logistics Regression, and Support Vector Classifier
are the approaches utilized. We have compared the
models' accuracies, performance, and confusion
matrix measures during the experimental phase. The
best approach has been chosen, described, and
suggested based on these factors. Our final results are
based on the number of defaulters predicted and
actualized, while we have also suggested a model if we
prefer institutional research that prioritized accuracy,
performance, and speed.
Keywords :
Credit Score, Logistic Regression, XGBoost.
