Loan decision models for the Jordanian commercial banks

Eletter, Shorouq; Yaseen, Saad G.

doi:10.1504/gber.2017.083960

Cited by 10 publications

(10 citation statements)

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“…However, most studies assumed that misclassification on different classes have a consistent cost. In the real economic world, the cost associated with granting some loans for a customer who defaults on the loan is far greater than the cost (opportunity loss) associated with rejecting some loans from a customer who may have successfully repay the loan [11,[26][27][28][29]. Thus, cost-sensitive learning models for credit scoring need further investigation.…”

Section: A Traditional Credit Scoring Modelsmentioning

confidence: 99%

“…Among the existing modern machine learning methods, NN models are widely used due to the models provide competitive classification ability against other methods. In 2017, Eletter and Yaseen [27] proposed a comparison of predictive results on credit scoring using NN, LDA, and CART based on Jordanian commercial banks dataset. They obtained the NN model provided the highest accuracy and the lowest estimated misclassification cost.…”

Section: A Traditional Credit Scoring Modelsmentioning

confidence: 99%

“…where P(0) and P(1) are the prior probability of non-default and default loans on the test set, respectively. According to the literature [27][28][29][58][59][60], the cost is set to 5 for the misclassification on a defaulter (as a good customer), more costly than the misclassification on a non-defaulter (as a bad customer).…”

Section: Performance Measuresmentioning

confidence: 99%

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

2021

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Most existing studies on credit scoring adapted a concept of classifier ensemble for solving an imbalanced dataset. They apply resampling methods to generate multiple training subsets for constructing multiple base classifiers. However, this approach leads to several problems that degrade the classification performance, such as problems of information loss, model overfitting, and computational cost. Thus, we propose a novel ensemble approach for developing a credit scoring model based on a cost-sensitive neural network, called Cost-sensitive Neural Network Ensemble (CS-NNE). In the proposed approach, multiple class weights are adapted to original training data, enabling the multiple base neural networks to consider imbalanced classes. Following this approach, a high diversity of multiple base classifiers without consequent problems can be achieved. The approach's effectiveness is evaluated on five real-world credit datasets. Among them is a loan-requesting dataset provided by a financial institution in Thailand. The remaining datasets are publicly available and widely used by several existing studies. The experimental results showed that the proposed CS-NNE approach improves the predictive performance over a single neural network based on imbalanced credit datasets, e.g., Thai credit dataset, by achieving 1.36%, 15.67%, and 6.11% Area under the ROC Curve (AUC), Default Detection Rate (DDR), and G-Mean (GM), respectively, and achieving the best Misclassification Cost (MC). The proposed CS-NNE approach can effectively solve a class of imbalance problems and outperform many existing models. The prediction model can well compromise between classes of default (bad credit applicants) and non-default (good credit applicants), whereas existing approaches preferred a class of non-default over default loans (having high specificity and low DDR), resulting in NPL.

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Section: A Traditional Credit Scoring Modelsmentioning

confidence: 99%

Section: A Traditional Credit Scoring Modelsmentioning

confidence: 99%

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

2021

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“…DL emerged with powerful computational models that perform sentiment analysis tasks, including sentiment detection, polarity classification, and sentiment lexicon learning [30]. A typical feedforward neural network comprises three layers: input, hidden, and output layers of fully connected units called neurons [31]. A neuron is the basic computational component of the network.…”

Section: Online Reviewsmentioning

confidence: 99%

Sentiment Analysis towards Actionable Intelligence via Deep Learning

Eletter¹

2020

TEM Journal

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The exponential growth of unstructured data and the ability of businesses to utilize such data in decision-making have led to competitive advantages. The knowledge provided by analyzing unstructured data is crucial for product developers or service providers because it might affect the sustainability of the business. Sentiment analysis is used to gain an understanding of the attitudes, opinions, and emotions expressed within an online review. Naïve Bayes (NB), logistic regression (LR), decision trees (DT), deep learning (DL), and support vector machines (SVM) were used to build a classification model. In the data mining settings, the classification accuracy is the best metric to highlight the best classifier. The DL classifier outperformed other models in terms of accuracy rate. Classifying customers' feelings toward a product or service is critical for providing actionable insights. Utilizing such models will help to analyze huge volumes of reviews, saving both time and costs.

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“…10, No. 1 neurons, nodes or units (Eletter & Yaseen, 2017;Malhotra & Malhotra, 2003;Oreski, Oreski, & Oreski, 2012). The neural network consists essentially of three forms: the input layer, the hidden layer and the output layer.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Credit Risk Classification in Peer-to-Peer Marketplaces: The Nexus of Neural Network Approach

Alexander

Zhongming

Ding

et al. 2020

BER

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Financial innovation in recent years have prominently contributed to the growth of Peer-to-Peer lending marketplaces allowing individual and businesses to secure loans on a common internet-based network. Similar to the ‘bricks and mortar’ banking system, online lending is coupled with the problem of information asymmetry. Borrower risk assessment has henceforth become the major concerns of various platforms that aim to reducing information imbalance towards mitigating credit risk. In this article, authors compared two learning algorithms – Logistic regression and Artificial Neural Network to classify borrowers based on loan repayment schedule. We revealed that both approaches were robust in classifying late borrowers with logistic regression being 0.02% more robust than Neural Network. Regarding variable relative importance, gender is considered the least important variable whereas terms-of-repayment is the most important variable affecting borrowers’ intention to pay off loans. Even though our study contributes to existing literature, it is however not limited to determining factors that may affect lenders’ investment decision in social lending.

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Loan decision models for the Jordanian commercial banks

Cited by 10 publications

References 0 publications

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

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

Sentiment Analysis towards Actionable Intelligence via Deep Learning

Credit Risk Classification in Peer-to-Peer Marketplaces: The Nexus of Neural Network Approach

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