A hybrid neural network approach for credit scoring

Chun‐Ling Chuang · Szu-Teng Huang

54 citations0/0 files storedExpert Systems

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

Abstract

The development of an effective credit scoring model has become a very important issue as the credit industry is confronted with ever-intensifying competition and aggravating bad debt problems. During the past few years, a substantial number of studies in the field of statistics have been conducted to improve the accuracy of credit scoring models. In order to refine the classification and decrease misclassification, this paper presents a two-stage model. Focusing on classification, the first stage aims at constructing an artificial neural network (ANN)-based credit scoring model to categorize applicants into the group of accepted (good) credit and the group of rejected (bad) credit. Switching from classification to reassignment, the second stage proceeds to reduce the Type I error by retrieving the originally rejected good credit applicants to conditional acceptance using the Case-Based Reasoning (CBR) classification technique. The proposed model (RST–ANN–CBR) is applied to a credit card dataset to verify its effectiveness. As the results indicate, the proposed model is able to achieve more accurate credit scoring than four other methods; more importantly, it is validated to recover potentially lost customers and to increase business revenues.

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References

1998Forecasting with artificial neural networks:1992Managerial Applications of Neural Networks: The Case of Bank Failure Predictions 2000Neural network credit scoring models 2003Credit rating analysis with support vector machines and neural networks: a market comparative study 2000A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers 1999Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis 1996A comparison of neural networks and linear scoring models in the credit union environment 2007Using neural network ensembles for bankruptcy prediction and credit scoring

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2018Credit scoring for a microcredit data set using the synthetic minority oversampling technique and ensemble classifiers 2017A feature selection enabled hybrid‐bagging algorithm for credit risk evaluation 2021A graph-based semi-supervised reject inference framework considering imbalanced data distribution for consumer credit scoring 2016Features selection, data mining and finacial risk classification: a comparative study

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