Hybrid Model for Credit Risk Prediction: An Application of Neural Network Approaches

Guotai Chi · Mohammad Shamsu Uddin · Mohammad Zoynul Abedin · Kunpeng Yuan

47 citations0/0 files storedInternational Journal of Artificial Intelligence Tools

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Financial Distress and Bankruptcy Prediction Neural Networks and Applications Imbalanced Data Classification Techniques

Abstract

Credit risk prediction is essential for banks and financial institutions as it helps them to evade any inappropriate assessments that can lead to wasted opportunities or monetary losses. In recent times, the hybrid prediction model, a combination of traditional and modern artificial intelligence (AI) methods that provides better prediction capacity than the use of single techniques, has been introduced. Similarly, using conventional and topical artificial intelligence (AI) technologies, researchers have recommended hybrid models which amalgamate logistic regression (LR) with multilayer perceptron (MLP). To investigate the efficiency and viability of the proposed hybrid models, we compared 16 hybrid models created by combining logistic regression (LR), discriminant analysis (DA), and decision trees (DT) with four types of neural network (NN): adaptive neurofuzzy inference systems (ANFISs), deep neural networks (DNNs), radial basis function networks (RBFs) and multilayer perceptrons (MLPs). The experimental outcome, investigation, and statistical examination express the capacity of the planned hybrid model to develop a credit risk prediction technique different from all other approaches, as indicated by ten different performance measures. The classifier was authenticated on five real-world credit scoring data sets.

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References

1968FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY 2015Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research 2000Neural network credit scoring models 2005Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters 2006Credit scoring with a data mining approach based on support vector machines 2017A framework for sensitivity analysis of decision trees 2007Using neural network ensembles for bankruptcy prediction and credit scoring 2007Recent developments in consumer credit risk assessment

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