Predictive Modelling for Credit Risk Detection using Ensemble Method
Year: 2018
Type: article
Abstract: With the expansion of credit business, the prediction models for taking decision of credit permissions with least risk are becoming more and more admired by banking sectors. The use of Machine Learning (ML) based models has confirmed to be of practical value in resolving a range of banking risk prediction problems. The model for Credit risk prediction seeks to predict feature factors, whether an individual is bad or good applicant for loan or not. Such problems can be better solved using ML. Also, Ensemble classifiers in ML play a key role in prediction problems. The use of Ensemble Methods (EMs) for classification is among the recent areas of research in ML. Many recent researches specify that EMs lead to a major improvement in classification performance by choosing suitable class. For this work, several ML techniques are explored and evaluated on real credit card datasets. Most ML methods have achieved an accuracy of less than 80 percent. Predictive model for Credit Risk Detection based on ensemble technique is proposed. The proposed model is evaluated on basis of various performance metrics and comparison is done with base classifier (learner) resulted in 81 percent prediction accuracy and better correlation coefficient.
Authors: Anand Motwani, Goldi Bajaj, Sushila Mohane
Language: English
Subfield: Accounting
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W2883936697