Title: An artificial neural network-based ensemble model for credit risk assessment and deployment as a graphical user interface

Authors: Biplab Bhattacharjee; Amulyashree Sridhar; Muhammad Shafi

Addresses: School of Management Studies, National Institute of Technology, Calicut, India ' Department of Computer Science, RV College of Engineering, RV Vidyanikethan Post, Mysuru Road, Bengaluru, India ' School of Management Studies, National Institute of Technology, Calicut, India

Abstract: Credit risk is a common threat to the financial industry since improper management of credit risk leads to heavy financial losses in banking and non-banking sectors. Data mining approaches have been employed in the past to assess the credit risk. This study utilises the German credit dataset sourced from UCI machine learning repository for generating an artificial neural network-based ensemble learning model for credit risk assessment. Eleven data mining algorithms have been applied on an open source tool Weka for performing credit ratings on the German credit dataset using supervised learning approach. The performance of each algorithm was evaluated, and algorithms with the most diverse false positive and false negative results and that are highly accurate were selected for generating an ensemble model. The predicted outcomes of the top five ranked algorithms were fed into a feed-forward artificial neural network by employing an 'nnet' package in R. The artificial neural network-based ensemble model attained an accuracy of 98.98%, performing better than the individual component algorithms. Based on this ANN-based ensemble model, an interactive graphical user interface was further developed in R. The user-friendly graphical user interface can be used by financial organisations as a decision-support system for assessing the credit risk.

Keywords: credit risk; classification; artificial neural network; ANN; graphical user interface; GUI; ANN-based ensemble model.

DOI: 10.1504/IJDMMM.2017.085643

International Journal of Data Mining, Modelling and Management, 2017 Vol.9 No.2, pp.122 - 141

Received: 29 Aug 2015
Accepted: 11 May 2016

Published online: 28 Jul 2017 *

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