Lost in a black-box? Interpretable machine learning for assessing Italian SMEs default

DOI10.1002/asmb.2803MaRDI QIDQ6581548

Lisa Crosato, Unnamed Author, Caterina Liberati

Publication date: 30 July 2024

Published in: Applied Stochastic Models in Business and Industry (Search for Journal in Brave)

zbMATH Keywords

interpretabilitymachine learningsmall and medium sized enterprisesdefault predictionaccumulated local effects

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Greedy function approximation: A gradient boosting machine.
• Benchmarking state-of-the-art classification algorithms for credit scoring: an update of research
• Bounding sample size projections for the area under a ROC curve
• Evaluation of credit risk based on firm performance
• Multivariate adaptive regression splines
• Neural network credit scoring models
• Using genetic algorithms to select architecture of a feedforward artificial neural network
• RHSBoost: improving classification performance in imbalance data
• Predicting SME's default: are their websites informative?
• RobROSE: a robust approach for dealing with imbalanced data in fraud detection
• The value of text for small business default prediction: a deep learning approach
• Neural network ensemble strategies for financial decision applications
• Advances in credit scoring: combining performance and interpretation in kernel discriminant analysis
• Assessing the default risk by means of a discrete-time survival analysis approach
• Benchmarking state-of-the-art classification algorithms for credit scoring
• Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models
• Bankruptcy prediction by generalized additive models
• Default risk analysis via a discrete-time cure rate model
• Learning low-dimensional structure in house price indices

This page was built for publication: Lost in a black-box? Interpretable machine learning for assessing Italian SMEs default