Background:
Large financial companies are perpetually creating and updating customer scoring techniques.
From a risk management view, this research for the predictive accuracy of probability is of vital importance than the traditional binary result of classification, i.e., non-credible and credible customers. The customer's default payment in Taiwan is
explored for the case study.
Objective:
The aim is to audit the comparison between the predictive accuracy of the probability of default with various
techniques of statistics and machine learning.
Method:
In this paper, nine predictive models are compared from which the results of the six models are taken into consideration. Deep learning-based H2O, XGBoost, logistic regression, gradient boosting, naïve Bayes, logit model, and probit
regression comparative analysis is performed. The software tools such as R and SAS (university edition) is employed for
machine learning and statistical model evaluation.
Results:
Through the experimental study, we demonstrate that XGBoost performs better than other AI and ML algorithms.
Conclusion:
Machine learning approach such as XGBoost effectively used for credit scoring, among other data mining and
statistical approaches.
Financial Time Series Forecasting - A Machine Learning Approach
