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- W4319065747 abstract "This study seeks to add to the existing literature on SME entity financial distress prediction by estimating models using Logistic Regression, Random Forests, Gradient Boosting (XGBoost and Catboost), and Artificial Neural Networks. Shapley’s Additive Explanations (SHAP) framework is used for modeling result interpretation and validation. Findings suggest that the most accurate way to estimate financial distress for SME entities is by employing the Catboost modeling technique. In addition, it was determined that historically overdue SMEs that are younger than ten years old and have a cash ratio below 20% are significantly more likely to experience financial distress. The study also finds that ‘hazardous’ feature combinations interact towards significantly higher (or lower) financial distress. Most notable are company age interactions, historic overdue accounts receivable, and cash ratios. Finally, applying explainable artificial intelligence techniques lowers the inherent opaqueness of SME entities, thus reducing information asymmetries between companies and potential lenders." @default.
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- W4319065747 date "2023-01-01" @default.
- W4319065747 modified "2023-09-30" @default.
- W4319065747 title "Interpretable Machine Learning for SME Financial Distress Prediction" @default.
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- W4319065747 doi "https://doi.org/10.1007/978-3-031-25344-7_42" @default.
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