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Small business credit scoring: a comparison of logistic regression, neural network, and decision tree models

Marijana Zekić–Sušac · Nataša Šarlija · Mirta Benšić

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Financial Distress and Bankruptcy Prediction Forecasting Techniques and Applications Imbalanced Data Classification Techniques

Abstract

The paper compares the models for small business credit scoring developed by logistic regression, neural networks, and CART decision trees on a Croatian bank dataset. The models obtained by all three methodologies were estimated; then validated on the same hold-out sample, and their performance is compared. There is an evident significant difference among the best neural network model, decision tree model, and logistic regression model. The most successful neural network model was obtained by the probabilistic algorithm. The best model extracted the most important features for small business credit scoring from the observed data

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References

1994Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)1997Statistical Classification Methods in Consumer Credit Scoring: A Review 2000Neural network credit scoring models 2001The Effect of Credit Scoring on Small-Business Lending 1994Stock performance modeling using neural networks: A comparative study with regression models 2000Credit Risk Assessment Using Statistical and Machine Learning: Basic Methodology and Risk Modeling Applications 2000Credit scoring using neural and evolutionary techniques

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