  Since the 1990s, there have been significant advances in the technology space
and the e-Commerce area, leading to an exponential increase in demand for
cashless payment solutions. This has led to increased demand for credit cards,
bringing along with it the possibility of higher credit defaults and hence
higher delinquency rates, over a period of time. The purpose of this research
paper is to build a contemporary credit scoring model to forecast credit
defaults for unsecured lending (credit cards), by employing machine learning
techniques. As much of the customer payments data available to lenders, for
forecasting Credit defaults, is imbalanced (skewed), on account of a limited
subset of default instances, this poses a challenge for predictive modelling.
In this research, this challenge is addressed by deploying Synthetic Minority
Oversampling Technique (SMOTE), a proven technique to iron out such imbalances,
from a given dataset. On running the research dataset through seven different
machine learning models, the results indicate that the Light Gradient Boosting
Machine (LGBM) Classifier model outperforms the other six classification
techniques. Thus, our research indicates that the LGBM classifier model is
better equipped to deliver higher learning speeds, better efficiencies and
manage larger data volumes. We expect that deployment of this model will enable
better and timely prediction of credit defaults for decision-makers in
commercial lending institutions and banks.

Risk Management;Machine Learning;Statistical Finance