Credit Risk Analysis Using a Reliability-Based Neural Network Ensemble Model

Lai, Kin Keung; Yu, Lean; Wang, Shouyang; Zhou, Ligang

doi:10.1007/11840930_71

Cited by 57 publications

(31 citation statements)

References 20 publications

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“…However, some hybrid AI techniques, which integrate two or more single classification methods or cluster and classification methods have shown higher predictability than individual methods. Recent examples are neural discriminate technique [25], Neurofuzzy [26,27], neural network ensemble [28,29] , evolving neural network [30], fuzzy SVM , that integrates the theory of fuzzy sets with SVM [31 ] and with LSSVM [32] classifiers to increase their sensitivity to outliers and generalization capabilities. Harris [33] utilized clustered SVM (CSVM) in order to reduce the computational complexity required to train the non linear SVM using large datasets.…”

Section: Introductionmentioning

confidence: 99%

Credit Risk Assessment Model Based Using Principal component Analysis And Artificial Neural Network

Hamdy

Hussein

2016

MATEC Web Conf.

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Abstract. Credit risk assessment for bank customers has gained increasing attention in recent years. Several models for credit scoring have been proposed in the literature for this purpose. The accuracy of the model is crucial for any financial institution's profitability. This paper provided a high accuracy credit scoring model that could be utilized with small and large datasets utilizing a principal component analysis (PCA) based breakdown to the significance of the attributes commonly used in the credit scoring models. The proposed credit scoring model applied PCA to acquire the main attributes of the credit scoring data then an ANN classifier to determine the credit worthiness of an individual applicant. The performance of the proposed model was compared to other models in terms of accuracy and training time. Results, based on German dataset showed that the proposed model is superior to others and computationally cheaper. Thus it can be a potential candidate for future credit scoring systems.

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Section: Introductionmentioning

confidence: 99%

Credit Risk Assessment Model Based Using Principal component Analysis And Artificial Neural Network

Hamdy

Hussein

2016

MATEC Web Conf.

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“…These businesses are concerned with the amount of risk they are taking by accepting someone or a certain corporate as a customer. Sustainability and profitability of these businesses particularly depends on their ability to distinguish faithful customers from bad ones [1][2]. In order to enable these businesses to take either preventive or correct immediate action, it is imperative to satisfy the need for efficient and reliable model that can accurately identify high-risk customers with potential default trend.…”

Section: Introductionmentioning

confidence: 99%

“…In such a CRM system that focusing on customer risk analysis, a generic approach is to apply a classification technique on similar data of previous customers -both faithful and delinquent customers -in order to find a relationship between the characteristics and potential default [1][2]. One important ingredient needed to accomplish this goal is to seek an accurate classifier in order to categorize new customers or existing customers as good or bad.…”

Section: Introductionmentioning

confidence: 99%

“…In the process of customer classification, data mining techniques, especially classification techniques, play a critical role. Some existing techniques about risk identification can be referred to [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

“…Recent studies [2][3] found that the unitary data mining technique did not produce consistently good performance because each data mining technique had their own shortcomings and different suitability. Therefore the ensemble data mining technique is an effective way to remedy this drawback.…”

Section: Introductionmentioning

confidence: 99%

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An Intelligent CRM System for Identifying High-Risk Customers: An Ensemble Data Mining Approach

Lai

Wang

et al. 2007

Lecture Notes in Computer Science

Self Cite

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Abstract. In this study, we propose an intelligent customer relationship management (CRM) system that uses support vector machine (SVM) ensembles to help enterprise managers effectively manage customer relationship from a risk avoidance perspective. Different from the classical CRM for retaining and targeting profitable customers, the main focus of our proposed CRM system is to identify high-risk customers for avoiding potential loss. Through experiment analysis, we find that the Bayesian-based SVM ensemble data mining model with diverse components and "choose from space" selection strategy show the best performance over the testing samples.

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Hybridized artificial neural network classifiers with a novel feature selection procedure based genetic algorithms and information complexity in credit scoring

Ilter

Deniz

Kocadağlı

2021

Appl Stoch Models Bus & Ind

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The credit scoring is a statistical analysis performed by financial institutions to represent the creditworthiness of an individual or small and medium‐sized enterprise. A credit score is a numerical quantity that can be qualified by a rating label showing the potential risk. In order to determine which customers are likely to bring in the most revenue at the exact interest rate and credit limits, the lenders take into accounts these credit scores. In this context, the robust statistical models are inevitable to reduce the number of wrong decisions in the credit evaluation process. Although the machine learning approaches provide superior performance to conventional statistical methods, they are mostly criticized due to the selection of model structure, model complexity, tuning parameters, time consumption in the high‐dimensional and excessive nonlinear cases. For this reason, this study introduces an efficient model estimation and feature selection procedure for artificial neural network (ANN) classifiers in the context of credit scoring. Essentially, this procedure hybridizes training of ANNs with a novel feature selection approach based on genetic algorithms and information complexity criterion. In the application, the proposed procedure was performed on a couple of benchmark credit scoring datasets. According to analysis results, the proposed approach not only estimates robust models from ANNs in terms of model complexity, feature selection, and time consumption, but also outperforms the traditional training procedure for the classification accuracies, false positive, and false negative errors overtraining and test datasets.

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Credit Risk Analysis Using a Reliability-Based Neural Network Ensemble Model

Cited by 57 publications

References 20 publications

Credit Risk Assessment Model Based Using Principal component Analysis And Artificial Neural Network

Credit Risk Assessment Model Based Using Principal component Analysis And Artificial Neural Network

An Intelligent CRM System for Identifying High-Risk Customers: An Ensemble Data Mining Approach

Hybridized artificial neural network classifiers with a novel feature selection procedure based genetic algorithms and information complexity in credit scoring

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