Papers by Dr. Mohammad Shamsu Uddin, Assistant Professor, BTM
Artificial Intelligence and Islamic Finance, 2021
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The Journal of Risk Model Validation, 2021
This paper examines which hybridization strategy is more suitable for credit risk assessment in t... more This paper examines which hybridization strategy is more suitable for credit risk assessment in the dynamic financial world. As such, we use extensive new data sets and develop different hybrid models by combining traditional statistical and modern artificial intelligence methods based on classification and clustering feature selection approaches. We find that a multilayer perceptron (MLP) combined with discriminant analysis or logistic regression (LR) can significantly improve classification accuracy compared with other single and hybrid classifiers. In particular, the findings of our empirical analysis, statistical significance test and expected cost of misclassification test confirm the superiority of the clustering-based LR C MLP hybrid classifier in improving prediction accuracy in maximum performance criteria. To check the efficiency and viability of the proposed model, we use three imbalanced data sets: Chinese farmer credit, Chinese small and medium-sized enterprise (SME) credit and German credit. We also use Australian credit data for further authentication and a robustness check. The first two data sets are private and high dimensional, whereas the second two are mostly used, publicly available and low dimensional. Thus, our findings are relevant for many areas of credit risk, such as SME, farmer and consumer credit risk modeling.
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International Journal of Finance & Economics, 2020
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International Journal on Artificial Intelligence Tools, 2019
Credit risk prediction is essential for banks and financial institutions as it helps them to evad... more 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 (...
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The Journal of Risk Model Validation, 2020
The purpose of this study is to introduce a gradient-boosting model that is robust to high-dimens... more The purpose of this study is to introduce a gradient-boosting model that is robust to high-dimensional data and can produce a strong classifier by combining the predictors of many weak classifiers for credit default risk prediction. Therefore, this method is recommended for practical applications. This study compares the gradient-boosting model with four other well-known classifiers, namely, a classification and regression tree (CART), logistic regression (LR), multivariate adaptive regression splines (MARS) and a random forest (RF). Six real-world credit data sets are used for model validation. The performance of each model is compared using six performance measures, and a receiver operating characteristics (ROC) curve is plotted for the best classifiers of each data set. The empirical findings confirm that the gradient-boosting model is reliable and efficient across all of the performance criteria. In addition, LR and CART exhibit superior performances. The contributions of this study have theoretical and practical implications, as credit default risk prediction is a complicated and always contemporary issue.
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Asian Business Review, 2020
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ABC Research Alert, 2017
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International Journal of Knowledge Engineering and Data Mining, 2019
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Economic Research-Ekonomska Istraživanja, 2021
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Papers by Dr. Mohammad Shamsu Uddin, Assistant Professor, BTM