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

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.

Modeling credit risk with a multi‐stage hybrid model: An alternative statistical approach

“…This research study established a new PCA and ANN/logistic hybrid model and confirmed that this model is more efficient than LR or ANN baseline models. Recently, Chi et al (2019) performed a comprehensive hybrid study and compared 16 hybrid models combining LR, discriminant analysis (DA), and DT with four techniques: a NN, an adaptive neuro-fuzzy inference system, deep neural network, radial basis function network, and multilayer perceptron (MLP). They also recommended LR and MLP as the best combination among other baseline and hybrid models.…”

“…Recent findings have confirmed the superiority of GB and RF approaches over frequently used AI methods such as, NN and SVM (Cheng et al, 2018;Jiang & Jones, 2018;Jones, 2017;Jones et al, 2015Jones et al, , 2017Uddin, Chi, Al Janabi, et al, 2020), but no evidence exists of hybrid or ensemble models that can be used to improve the prediction accuracy. However, the existing literature (in Table 2) reports on the deficiencies of single models and the superior capacity of hybrid classifiers compared to individual models (see, for example, Arifovic & Gencay, 2001;Blanco et al, 2001;Caigny et al, 2018;Chen & Li, 2010;Chi et al, 2019;Hamadani et al, 2013;Liang et al, 2015;Li et al, 2016;Oreski et al, 2012;Oreski & Oreski, 2014). To fill this research gap and because we are highly motivated by the superior capabilities of GB and the RF methods, compared to other modern and sophisticated AI approaches, in this study, we introduce a novel hybrid model by combining industry-standard logistic regression (LR) with GB (TreeNet ® ) and RF models.…”

A Novel Method for Credit Scoring Based on Cost-Sensitive Neural Network Ensemble

“…Traditional credit scoring models have been developed using statistical or machine learning methods, such as Support Vector Machine (SVM) [14,15], Logistic Regression (LR) [16][17][18] , Decision Tree (DT) [4,19], and Neural Network (NN) [6,[20][21][22][23][24] based on a single or ensemble approach. [25] obtained that boosting method outperforms competitive state-of-the-art classification algorithms based on extracted features from phone usage data and individual's app usage behaviors.…”

An optimized decomposition integration framework for carbon price prediction based on multi-factor two-stage feature dimension reduction

“…In contrast, AI models represented by artificial neural networks (ANN) do not need to satisfy statistical assumptions and can effectively learn the nonlinear relationships in data (Abedin et al, 2021 ). Nowadays, AI models are widely used in the field of time series forecasting, such as credit risk prediction forecasting (Abedin et al, 2018 ; Chi et al, 2019 ), energy supply forecasting (Sun et al, 2022 ), and wind speed forecasting (Li et al, 2022a , 2022b ). Due to carbon prices’ nonlinear and nonstationary characteristics, AI models have better prediction performance and adaptability than statistical methods.…”