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A Comparison Study of Credit Scoring Models

Defu Zhang · Hongyi Huang · Qingshan Chen · Yi Jiang

35 citations0/0 files storedInternational Conference on Natural Computation (ICNC)

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Financial Distress and Bankruptcy Prediction Data Mining and Machine Learning Applications Imbalanced Data Classification Techniques

Abstract

In this paper we consider a credit scoring problem. We compare three powerful credit scoring models: genetic programming (GP), backpropagation neural networks (BP) and support vector machines (SVM) when applied to this problem, then we give a combined model. The results show that the combined model produces good classification results.

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References

2000Neural network credit scoring models 2003Credit rating analysis with support vector machines and neural networks: a market comparative study 2003Benchmarking state-of-the-art classification algorithms for credit scoring 2000A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers 1996A comparison of neural networks and linear scoring models in the credit union environment 2005A new fuzzy support vector machine to evaluate credit risk 2005Building credit scoring models using genetic programming 2005Hybrid mining approach in the design of credit scoring models

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2008A Hybrid Credit Scoring Model Based on Genetic Programming and Support Vector Machines 2017An Improved Bank Credit Scoring Model: A Naïve Bayesian Approach 2008A Decision Tree Scoring Model Based on Genetic Algorithm and K-Means Algorithm 2016Credit scoring model: A combination of genetic programming and deep learning 2015Credit scoring using ensemble of various classifiers on reduced feature set

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