An artificial neural network-based ensemble model for credit risk assessment and deployment as a graphical user interface. (2017)

• Record Type: Journal Article
• Authors: Bhattacharjee, Biplab; Sridhar, Amulyashree; Shafi, Muhammad
• Is Part Of: International journal of data mining, modelling and management. Volume 9:Number 2(2017)
• Page Start: 122
• Page End: 141
• Publication Date: 2017
• Subjects: credit risk -- classification -- artificial neural network -- ANN -- graphical user interface -- GUI -- ANN-based ensemble model; Data mining -- Periodicals; Information science -- Periodicals; Databases -- Periodicals; 005.7
• Languages: English
• ISSNs: 1759-1163

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.